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5114 руб.

The Wireless Sensor Network (WSN) composed of several nodes is used for different types of monitoring applications. The objective of deploying WSN is to observe a particular site for monitoring physical parameters like temperature, light, pressure, humidity or the occurrence of a phenomenon. The Sleep/Wake up scheduling for Wireless Sensor Networks has become an essential part of its working.In this book, the details of Low Energy Adaptive Clustering Hierarchy (LEACH) which introduces the concept of clustering in sensor networks, Energy-Efficient Clustering routing algorithm based on Distance and Residual Energy for Wireless Sensor Networks (DECSA) which describes scheduling based on distance and energy, and the Energy efficient clustering algorithm for data aggregation (EECA) are discussed. The LECSA (Load and Energy Consumption based Scheduling Algorithm) are also discussed.
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5364 руб.

Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service providers and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find go...
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6102 руб.

Master's Thesis from the year 2016 in the subject Engineering - Mechanical Engineering, grade: 5, Tallinn University (Department of Mechanical and Industrial Engineering - Chair of Production Engineering), course: Industrial Engineering and Management, language: English, abstract: Flow shop scheduling encompasses allocating a number of jobs in a previously ordered set of machines so that a determined objective function such as makespan is either minimized or maximized. Despite the apparent simplicity of the problem, there is no known non enumerative polynomial time algorithm capable of solving this type of optimization, except for in those cases that can be treated by the Johnson's algorithm or proportional flow shops.Indeed, understanding flow shop scheduling is proven to be mathematically intractable in the vast majority of cases. Considering this nature of flow shop scheduling, the primary objective of this dissertation was to develop algorithms capable of mitigating the computational burden associated with the problem.In this realm, three solutions were proposed. The first approach refers to a genetic algorithm that employed discrete event simulation and customized genetic operators as a means to eliminate the evaluation of unfeasible solutions and incorporate problem-specific knowledge. The second and third proposed solutions consisting of hybrid methods that have improved the aforementioned framework by including local search. Computational experiments that used...
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6439 руб.

A scheduling algorithm schedules a set of tasks in such way that the tasks are completed before their deadlines reached. There are varieties of algorithms for scheduling of periodic tasks on multiprocessor under partitioning scheme or global scheduling scheme. The most common scheduling algorithms are: Rate Monotonic (RM), Deadline Monotonic (DM), Earliest Deadline First (EDF) and Least Laxity First (LLF). In this book, we have proposed a new algorithm titled as D_EDF which is modified conventional EDF algorithm. The proposed algorithm along with EDF, LLF, RM, DM algorithms are simulated and tested for independent, preemptive, periodic tasks on tightly coupled real-time multiprocessor system under global scheduling. From experiments and result analysis it concludes that the proposed algorithm is very efficient in both underloaded and overloaded conditions. The algorithm proposed in this book; perform quite well during overloaded conditions. The algorithm is truly dynamic as it can work with available number of processors.
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16286.89 руб.

This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France. Jacques Teghem is Professor at the Universit
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15057.99 руб.

The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetic Problems provides a detailed and instructional overview of implementing MLFMA. The book: Presents a comprehensive treatment of the MLFMA algorithm, including basic linear algebra concepts, recent developments on the parallel computation, and a number of application examples Covers solutions of electromagnetic problems involving dielectric objects and perfectly-conducting objects Discusses applications including scattering from airborne targets, scattering from red blood cells, radiation from antennas and arrays, metamaterials etc. Is written by authors who have more than 25 years experience on the development and implementation of MLFMA The book will be useful for post-graduate students, researchers, and academics, studying in the areas of computational electromagnetics, numerical analysis, and computer science, and who would like to implement and develop rigorous simulation environments based on MLFMA.
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6602 руб.

Книга "Job Scheduling Strategies for Parallel Processing. 18th International Workshop, JSSPP 2014, Phoenix, AZ, USA, May 23, 2014. Revised Selected Papers".
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4402 руб.

Doctoral Thesis / Dissertation from the year 2009 in the subject Electrotechnology, grade: 1.0, University of Duisburg-Essen (Institute of Electrical Power Systems), course: Electrical Engineering, language: English, abstract: The primary objective of this dissertation is to develop a black box optimization tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous and mixed-integer power system optimization problems without any model reduction. Although there are many computational intelligence (CI) basedalgorithms which can handle these problems, they require intense human intervention in the form of parameter tuning, selection of a suitable algorithm for a given problem etc. The idea here is to develop an algorithm that works relatively well on a variety of problems with minimum human effort.The most significant optimization task in the power system operation is the scheduling of various generation resources (Unit Commitment, UC). The current practice used in UC modelling is the binary approach. This modelling results in a high dimension problem. This in turn leads to increased computational effort anddecreased efficiency of the algorithm. A duty cycle based modelling proposed in this thesis results in 80 percent reduction in the problem dimension. The stern uptime and downtime requirements are also included in the modelling. Therefore,the search process mostly starts in a feasible solution space. From the investigations on a benchmark problem...
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8514 руб.

Protocol complexity, node deployment, heterogeneity, requirement of GPS device, etc are major issue for a given system model. The CGDC protocol saves the extra cost by allowing homogenous network, with no GPS requirement and a simple model for the protocol implementation. The system also provides application of protocols in a wide range of domains. In this we discussed the lifecycle of the protocol in short and also the algorithm proposed for the group formation. In addition to this it also mentioned the data communication in steady phase towards the BS in a multi-hop manner. The CGDC Protocol includes three phases: Grouping, Scheduling and Protocol Implementation. This protocol eliminates many drawbacks of most of the present grouping protocols. Its one time setup nature saves energy and thus prolongs the network lifetime.
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9152 руб.

Книга "Job Scheduling Strategies for Parallel Processing. 19th and 20th International Workshops, JSSPP 2015, Hyderabad, India, May 26, 2015 and JSSPP 2016, Chicago, IL, USA, May 27, 2016, Revised Selected Papers".
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4358 руб.

Multiprocessors have become powerful computing means for running real-time applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NP-hard problem. To address this issue, this research presents two approaches. The first approach is Modified List Scheduling Heuristic (MLSH). The second approach is hybrid approach which is composed of Genetic Algorithm (GA) and MLSH for task scheduling in multiprocessor system.
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3944 руб.

The flowshop scheduling is one of the most well-studied production scheduling problems, that has gained wide attention in academic fields. Since a FSP with makespan criteria has been proved to be NP-hard in strong sense, producing good quality solutions by some heuristic techniques is very difficult due to large combinatorial search space. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In such situations it is pragmatic to find a near optimal solution which can be obtained rather quickly. To overcome this difficulty an artificial immune system (AIS) based algorithm is proposed to generate good solutions within considerable time span. The AIS is an intelligent stochastic problem-solving technique, which has been used in different optimization problems as reported in literature. It is a computational system inspired by theoretical immunology, observed immune functions, principles, and mechanisms in order to solve different engineering problems.
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6104 руб.

This study focuses on developing an approach to solve the complex problem of task allocation and motion coordination simultaneously for a large fleet of autonomous vehicles in highly constrained operational environments. A Simultaneous Task Allocation and Motion Coordination approach is developed to solve the problem in a concurrent manner. Furthermore, a novel algorithm: Simultaneous Path and Motion Planning is proposed for collision free motion coordination based on Dijkstra and A * algorithms. The appropriateness of heuristic and evolutionary algorithms to solve the problem is investigated. A distributed computational architecture is also developed to improve the efficiency of implementation of the approach. The proposed approach is tested with static and dynamic environments, and validated by using data from an automated container terminal with fleet of autonomous straddle carriers.
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7564 руб.

Scheduling is one of the main operations in the production industry and is studied in millions of studies by researchers. Unrelated parallel machines scheduling problem with job sequence, machine dependent setup times and also job splitting property are the main scope of this book. Splitting the jobs into sub jobs is a great requirement for some industries (e.g.: weaving process of textile manufacturing, drilling process of PCB manufacturing, dicing process of semiconductor wafer manufacturing). Additionally, the proposed algorithms are adapted on the problems that do not have job splitting property. It's shown that the developed hybrid algorithms outperformed most of the methods in literature. The proposed algorithms are verified by developed MIP models in the book.
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5214 руб.

The Space Vector Pulse Width Modulation technique generally we have algorithms based on the angle information. This requires more computational efforts. In this book focused on the simplified algorithm for the SVPWM Techniques without considering angle information. This contains four types of switching sequences, over these we can which one gives better performance. The comparison of the sequences defined based on the type samples (odd and even samples) per sector. The odd number of samples will gives better performance.
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6552 руб.

In this book, advanced methods and techniques of monitoring, fault diagnostics, and predictive maintenance for cryogenics are illustrated. In Part I on Background, mainstreams in the related research are reviewed. In Part II of Methods, for monitoring helium distribution and consumption in cryogenic systems for particle accelerators, a virtual flowmeter is presented. Then, for fault diagnostics, two methods, for fault detection on a compressor, and for distributed diagnostics based on a micro-genetic algorithm, are described. Finally, for predictive maintenance, a metaheuristic optimization scheduling algorithm is illustrated. In Part III of Application examples, several practical case studies are described for highlighting the application of the previous methods to cryogenics of particle accelerators at CERN.
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6359 руб.

The Cellular Manufacturing System (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families that is named cell formation. Cells are formed based on presuming fixed single route and parts demand (traditional cell formation) or fluctuation of parts demand (dynamic cell formation). This research work attempts to solve a developed comprehensive model which integrated cell formation and process planning problem meanwhile taking into consideration important cell design issues. In addition, one of the main challenges has been development of efficient algorithm for solving aforementioned model to find exact feasible optimal solution. Therefore, for this proposes good benchmarked algorithm, bacteria foraging algorithm is selected and developed to solve multi-objective cell formation model and traced constraints satisfaction handling to produce feasible optimal solution.
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9503.73 руб.

Introduction to Computational Contact Mechanics: A Geometrical Approach covers the fundamentals of computational contact mechanics and focuses on its practical implementation. Part one of this textbook focuses on the underlying theory and covers essential information about differential geometry and mathematical methods which are necessary to build the computational algorithm independently from other courses in mechanics. The geometrically exact theory for the computational contact mechanics is described in step-by-step manner, using examples of strict derivation from a mathematical point of view. The final goal of the theory is to construct in the independent approximation form /so-called covariant form, including application to high-order and isogeometric finite elements. The second part of a book is a practical guide for programming of contact elements and is written in such a way that makes it easy for a programmer to implement using any programming language. All programming examples are accompanied by a set of verification examples allowing the user to learn the research verification technique, essential for the computational contact analysis. Key features: Covers the fundamentals of computational contact mechanics Covers practical programming, verification and analysis of contact problems Presents the geometrically exact theory for computational contact mechanics Describes algorithms used in well-known finite element software packages Describes modeling of forces as an inverse contact algorithm Includes practical exercises Contains unique verification examples such as the generalized Euler formula for a rope on a surface, and the impact problem and verification of thå percussion center Accompanied by a website hosting software Introduction to Computational Contact Mechanics: A Geometrical Approach is an ideal textbook for graduates and senior undergraduates, and is also a useful reference for researchers and practitioners working in computational mechanics.
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8364 руб.

This work aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.
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3167 руб.

One challenge facing the utility is management of peak demands. Demand response management has proven to be a significant way of reducing these peaks. Scheduling of electrical appliances and proper design of the electricity tariffs are some of the mechanisms use in demand response management to reduce these peaks. It was therefore the aim of this study to come out with scheduling scheme to reduce peaking in the grid taking into account occupancy error detection factor. In this study a mixed integer linear programming based smart appliance scheduling scheme with real-time pricing and algorithm to service as motion sensor were proposed. Significant savings on electricity bill of the community was realized using the proposed schemes. Savings made on occupancy error detection alone was not significant as compared to the total power consumed by the community, but it must be realized that this is just a conceptual study and it does not represent reality. It was therefore recommended that the proposed scheduling scheme incorporating occupancy error detection mechanism should be deployed in a real community of residential housing and simulate in realistic conditions.
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5114 руб.

Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful.This monograph mostly focuses on the analyses of the partial update least-squares adaptive fi...
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5339 руб.

Doctoral Thesis / Dissertation from the year 2013 in the subject Computer Science - General, grade: 90, University of Mosul (College of Computer Sciences And Mathematics), language: English, abstract: Now a day completed real-time systems are distributed. One of the working area of real-time scheduling is distributed scheduling. Task scheduling in distributed systems is dealt with two levels: on the level of each processor (local scheduling), and on the level of the allocation of tasks to processors (global scheduling).In this thesis, a distributed real-time system with fault tolerance has been designed and called Fault Tolerance Distributed Real Time System FTDRTS. The system consists of heterogeneous processors act as servers and clients connected together via LAN communication network. This system has two types of scheduling schemes: (1) global model scheduling, (2) independent model scheduling for scheduling tasks in real time distributed manner. The time utility function TUF has been developed and called the DTUF (Developed TUF) function. This function gives another dimension and used to priorities' tasks, based on whether they are Urgent or Important, or both, or neither.A fault tolerance protocol called DRT-FTIP (Distributed Real Time - Fault Tolerance Integrity Protocol) has been developed. This protocol increases the integrity of the scheduling in distributed real time systems.The proposed Distributed Real-Time system with its scheduling algorithms and integrity ...
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3989 руб.

Research Paper (undergraduate) from the year 2009 in the subject Computer Science - Applied, , language: English, abstract: AbstractIn this thesis, we present an introduction about genetic algorithms (GAs). Genetic algorithms are not too hard to program or understand, since they are biologically based. This thesis also shows scheduling problems, expecially examination scheduling problems. We provide a way that can be easily used to apply the evolutionary principle to the problem solutions. Furthermore, these are the program's applications in reality as well as in science.Keyword: Genetic Algorithms, Scheduling Problems, ExaminationScheduling Problems.
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3944 руб.

Process history based approaches for fault diagnosis has been widely used recently. Principal Component Analysis (PCA) is one of these approaches, which is a linear approach; however most of the processes are nonlinear. Hence nonlinear extensions of the PCA have been developed. Nonlinear Principal Component Analysis (NLPCA) based on the neural networks is a common method which is used for process monitoring and fault diagnosis. NLPCA based neural networks are implemented using different methods, in this book we apply Auto-Associative Neural Networks (AANN) for implementing NLPCA. This work is aimed towards the development of an algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. Also an algorithm is developed for locating the source of the process fault.
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10216.51 руб.

Praise for the First Edition «…complete, up-to-date coverage of computational complexity theory…the book promises to become the standard reference on computational complexity.» -Zentralblatt MATH A thorough revision based on advances in the field of computational complexity and readers’ feedback, the Second Edition of Theory of Computational Complexity presents updates to the principles and applications essential to understanding modern computational complexity theory. The new edition continues to serve as a comprehensive resource on the use of software and computational approaches for solving algorithmic problems and the related difficulties that can be encountered. Maintaining extensive and detailed coverage, Theory of Computational Complexity, Second Edition, examines the theory and methods behind complexity theory, such as computational models, decision tree complexity, circuit complexity, and probabilistic complexity. The Second Edition also features recent developments on areas such as NP-completeness theory, as well as: A new combinatorial proof of the PCP theorem based on the notion of expander graphs, a research area in the field of computer science Additional exercises at varying levels of difficulty to further test comprehension of the presented material End-of-chapter literature reviews that summarize each topic and offer additional sources for further study Theory of Computational Complexity, Second Edition, is an excellent textbook for courses on computational theory and complexity at the graduate level. The book is also a useful reference for practitioners in the fields of computer science, engineering, and mathematics who utilize state-of-the-art software and computational methods to conduct research. A thorough revision based on advances in the field of computational complexity and readers’feedback, the Second Edition of Theory of Computational Complexity presents updates to theprinciples and applications essential to understanding modern computational complexitytheory. The new edition continues to serve as a comprehensive resource on the use of softwareand computational approaches for solving algorithmic problems and the related difficulties thatcan be encountered.Maintaining extensive and detailed coverage, Theory of Computational Complexity, SecondEdition, examines the theory and methods behind complexity theory, such as computationalmodels, decision tree complexity, circuit complexity, and probabilistic complexity. The SecondEdition also features recent dev
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11483.67 руб.

A unified, systematic approach to applying mixed integer programming solutions to integrated scheduling in customer-driven supply chains Supply chain management is a rapidly developing field, and the recent improvements in modeling, preprocessing, solution algorithms, and mixed integer programming (MIP) software have made it possible to solve large-scale MIP models of scheduling problems, especially integrated scheduling in supply chains. Featuring a unified and systematic presentation, Scheduling in Supply Chains Using Mixed Integer Programming provides state-of-the-art MIP modeling and solutions approaches, equipping readers with the knowledge and tools to model and solve real-world supply chain scheduling problems in make-to-order manufacturing. Drawing upon the author's own research, the book explores MIP approaches and examples-which are modeled on actual supply chain scheduling problems in high-tech industries-in three comprehensive sections: Short-Term Scheduling in Supply Chains presents various MIP models and provides heuristic algorithms for scheduling flexible flow shops and surface mount technology lines, balancing and scheduling of Flexible Assembly Lines, and loading and scheduling of Flexible Assembly Systems Medium-Term Scheduling in Supply Chains outlines MIP models and MIP-based heuristic algorithms for supplier selection and order allocation, customer order acceptance and due date setting, material supply scheduling, and medium-term scheduling and rescheduling of customer orders in a make-to-order discrete manufacturing environment Coordinated Scheduling in Supply Chains explores coordinated scheduling of manufacturing and supply of parts as well as the assembly of products in supply chains with a single producer and single or multiple suppliers; MIP models for a single- or multiple-objective decision making are also provided Two main decision-making approaches are discussed and compared throughout. The integrated (simultaneous) approach, in which all required decisions are made simultaneously using complex, monolithic MIP models; and the hierarchical (sequential) approach, in which the required decisions are made successively using hierarchies of simpler and smaller-sized MIP models. Throughout the book, the author provides insight on the presented modeling tools using AMPL® modeling language and CPLEX solver. Scheduling in Supply Chains Using Mixed Integer Programming is a comprehensive resource for practitioners and researchers working in supply chain planning, scheduling, and management. The book is also appropriate for graduate- and PhD-level courses on supply chains for students majoring in management science, industrial engineering, operations research, applied mathematics, and computer science.
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4749 руб.

This book is not only meant for the people having the knowledge of Scheduling but is also beneficial for Operations Research fraternity, Industrial units like Repair Workshops, semiconductors manufacturing, quality Control Centres etc. It is designed to offer the basic ideas of two stage Open Shop Scheduling problems. It covers not only Scheduling models with different concepts such as transportation time, break down interval, weights of job but also put emphasis to their relevance to Practical situation. Hence all the examples throughout the book help the readers to formulate the real world problems easily.
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5214 руб.

Recently,sparse signal approximation has become an increasingly important research area in signal processing. It attracts a lot of interest due to its wide range of practical applications. In this work, a novel adaptive filtering algorithm with relative low computational complexity that is capable of exploiting the sparsity of systems is proposed. The basic idea here is, we adopt a p-norm constraint in the cost function of the variable step-size least mean square (VSSLMS) algorithm. This constrain imposes a zero attraction at each filter coefficient based on their respective relative value. Also, the convergence analysis of the proposed algorithm is presented and the stability condition is derived. The performance of the proposed algorithm has been compared to those of the Zero Attraction Least Mean Square(ZA-LMS), windowing ZA-LMS(wZA-LMS), Non-uniform Norm Constraint LMS(NNCLMS) in a system identification setting for different additive Gaussian noise(AGN), additive correlated noise(ACN)and additive impulsive noise(AIN) environments. The proposed algorithm has always shown superior performance to the others with less or comparable number of computations.
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9866.16 руб.

Contains the latest research advances in computational nanomechanics in one comprehensive volume Covers computational tools used to simulate and analyse nanostructures Includes contributions from leading researchers Covers of new methodologies/tools applied to computational nanomechanics whilst also giving readers the new findings on carbon-based aggregates (graphene, carbon-nanotubes, nanocomposites) Evaluates the impact of nanoscale phenomena in materials
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8089 руб.

This work explains various tools to minimize the processing time inthe production industry . the step by step adaptation of these various tools are useful in minimizing the processing time in the production activities which is useful for maximizing the productivity so as to enhance the productivity performance .The other major criteria in this work is the optimization of the layout design using the ALDEP algorithm ,which provides the help to the production sector to remodify their layout design,this will be analysed by comparing the scores of the present and modified layout.After the layout it comes the scheduling the processing activities this work is handled by the methedology called "branch and bound algorithm "and "hodgson algorithm" paves the way to number of tardy jobs in the schedule.
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16563.14 руб.

At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today’s competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control Written by an author with more than 20 years’ experience in studying and formulating a complete theoretical system in production planning technologies Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.
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8239 руб.

This book is designed for specially structured flow shop scheduling problems which are more close to real world industry. In the modern era of globalization a cut throat competition an edge in the business, industries depends upon multitude of factors with the sole purpose of maximization of profit and minimization of cost. With the resources being limited the emphasis lies on finding out a best way for effecient utilization of resources. Scheduling is a key factor to solve the problem. Scheduling is the allocation of resources over time to perform a collection of task. It is an important subject of production and operations management area. In flow-shop scheduling, the objective is to obtain a sequence of jobs which when processed in a fixed order of machines, will optimize some well defined criteria. We consider the important concepts transportation time, weightage of jobs which shows relative importance of a job over another job.
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8377 руб.

This unique book provides a comprehensive introduction to computational mathematics, which forms an essential part of contemporary numerical algorithms, scientific computing and optimization. It uses a theorem-free approach with just the right balance between mathematics and numerical algorithms. This edition covers all major topics in computational mathematics with a wide range of carefully selected numerical algorithms, ranging from the root-finding algorithm, numerical integration, numerical methods of partial differential equations, finite element methods, optimization algorithms, stochastic models, nonlinear curve-fitting to data modelling, bio-inspired algorithms and swarm intelligence. This book is especially suitable for both undergraduates and graduates in computational mathematics, numerical algorithms, scientific computing, mathematical programming, artificial intelligence and engineering optimization. Thus, it can be used as a textbook and/or reference book.
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9464 руб.

Книга "Quality-aware Scheduling for Key-value Data Stores".
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3944 руб.

This book has concentrated on fingerprint-based biometric identification systems. Further, I have focused only on the core technology of fingerprint classification and identification. In this work I concentrate only fingerprint identification speed. I my research work I firstly calculate the identification speed based on traditional fingerprint identification systems and then, I calculate the identification speed based on the proposed algorithm. Identification based on the proposed algorithm takes less time than the traditional scheme. After the experimental results I find out that the proposed scheme is several times faster than the previous scheme.
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3944 руб.

A wireless adhoc network is a collection of wireless mobile nodes forming a temporary network without the aid of any established infrastructure or centralized administration. The wireless networks face challenges to form an optimal routing protocol. A set is dominating if all the nodes in the system are either in the set or neighbors of nodes in the set. Routing based on a connected dominating set is a Efficient approach, where the searching space for a route is reduced to nodes in the set, The proposed algorithm is an enhancement of the distributed algorithm proposed by Wu and Li. In this book, we propose a simple and efficient distributed algorithm for calculating connected dominating set in adhoc wireless networks, where connections of nodes are determined by their geographical distances. We also propose an update/recalculation algorithm for the connected dominating set when the topology of the adhoc wireless network changes dynamically. The simulation results show that the average dominating set of nodes decreased considerable after applying the new algorithm. Our approach can be potentially used in designing efficient routing algorithms based on a connected dominating set.
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4749 руб.

In wireless multimedia systems, the medium access control (MAC) protocol should play a central role in maximizing the utilization of limited wireless resources while guaranteeing the various QoS requirements for all multimedia traffic classes, especially for the stringent real-time constraint of video service. In addition, the fairness performance becomes one of the main challenges for MAC protocol design, especially for contention-based MAC protocol. This book, therefore, provides firstly a comprehensive overview of the most efficient wireless MAC protocols. Then three novel bandwidth allocation algorithms for video traffic over wireless networks are presented. The first algorithm is based on providing fair packet delay, and the second algorithm is based on providing fair queuing for video packets. The third is based on simple bit rate estimation algorithm for efficient tracking the variable bit rate of video source. Finally, four novel contention-based MAC protocols for wireless networks are presented to maximize the channel throughput and improve the fairness of random access channels while maintaining the simplicity of implementation using fuzzy controller.
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3944 руб.

Virtual guide is an android application based on the concept of information retrieval using images. The application makes use of a back-end database which is used to store technical information about images to be displayed once an image has been matched. In order to come up with an algorithm for matching images, a comparative study of image matching/recognition algorithms is carried out and a hybrid algorithm is proposed. The algorithm utilizes more than one recognition techniques for getting more accuracy in results and we maintain priorities by defining a scoring mechanism for each technique according to the results they produce. The final product is able to provide information/guidance to people at run-time and can be further extended to provide location-based services.
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9364 руб.

In the first part of this book, three Pre-FUFP maintenance algorithms are thus proposed to efficiently maintain and update the FUFP-tree structures regardless of whether records are inserted, deleted or modified in dynamic databases. In the second part of this book, a novel HUP-tree algorithm is proposed to efficiently mine the high utility itemsets based on the downward closure property. A HUP tree is first designed to keep the related information for later mining process. A HUP-growth mining algorithm is then presented to efficiently mine high utility itemsets from it. In the third part of this book, we attempt to extend the FP-tree algorithm for handling quantitative data from the global values of fuzzy regions. Thus, the fuzzy FP-tree algorithm, the compressed fuzzy frequent pattern tree (CFFP-tree) algorithm, and the upper-bound fuzzy frequent pattern tree (UBFFP-tree) algorithm are then proposed to efficiently mine the fuzzy frequent itemsets.
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5314 руб.

Книга "Ability Grouping in Education".
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7402 руб.

Cryptographic systems are divided into two kinds. One is symmetric and other is asymmetric. In symmetric we require only a single key to encrypt and decrypt the data while asymmetric or public key cryptography requires two separate keys-one to lock and other one to unlock the text. In this project a public key crypto-scheme has been studied and implemented. The project is aimed to implement the methods for solving Elliptic curve Discrete Logarithm Problem (ECDLP) on which the elliptic curve schemes are based. For this, Pollard's Rho method has been chosen for implemen- tation. This way, cryptanalysis of a public key elliptic curve algorithm can be performed. The project also explains what Elliptic Curve Discrete Log- arithm Problem is and implements the Diffie Hellman algorithm based on ECDLP. Thus, this project implements cryptographic algorithm as well as cryptanalytic method.
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10755.4 руб.

This book discusses HVDC grids based on multi-terminal voltage-source converters (VSC), which is suitable for the connection of offshore wind farms and a possible solution for a continent wide overlay grid. HVDC Grids: For Offshore and Supergrid of the Future begins by introducing and analyzing the motivations and energy policy drives for developing offshore grids and the European Supergrid. HVDC transmission technology and offshore equipment are described in the second part of the book. The third part of the book discusses how HVDC grids can be developed and integrated in the existing power system. The fourth part of the book focuses on HVDC grid integration, in studies, for different time domains of electric power systems. The book concludes by discussing developments of advanced control methods and control devices for enabling DC grids. Presents the technology of the future offshore and HVDC grid Explains how offshore and HVDC grids can be integrated in the existing power system Provides the required models to analyse the different time domains of power system studies: from steady-state to electromagnetic transients This book is intended for power system engineers and academics with an interest in HVDC or power systems, and policy makers. The book also provides a solid background for researchers working with VSC-HVDC technologies, power electronic devices, offshore wind farm integration, and DC grid protection.
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5707 руб.

Information fusion is becoming a major need in Data Mining. Typical applications of these techniques include data modeling (ensemble methods). The behavior of various classification algorithms differs based on accuracy and computational complexity. For some algorithms there may be a significant variation in the performance when some parameters are varied. In this research the behavior of the modified AdaBoost algorithm with NN as a base classifier and as a preprocessing step feature selection combined with the evaluation schemas (like subset evaluation, consistency based, correlation based, filter approach, wrapper approach etc.) are applied by varying the number of parameters. Predictive accuracy is substantially improved when combining multiple predictors. A novel idea of an Ensemble System applying Boosting to Neural Networks for High Dimensional Datasets. The method uses Genetic Algorithms (to select relevant features) for essential feature selection with various Evaluation Schemes. As Genetic Algorithms deal well with large solution spaces, tuning it to adjust as per the requirements of the ensemble, we can get optimum feature selection. Finally Boosting algorithm that finishe
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10612.49 руб.

Helps you choose the right computational tools and techniques to meet your drug design goals Computational Drug Design covers all of the major computational drug design techniques in use today, focusing on the process that pharmaceutical chemists employ to design a new drug molecule. The discussions of which computational tools to use and when and how to use them are all based on typical pharmaceutical industry drug design processes. Following an introduction, the book is divided into three parts: Part One, The Drug Design Process, sets forth a variety of design processes suitable for a number of different drug development scenarios and drug targets. The author demonstrates how computational techniques are typically used during the design process, helping readers choose the best computational tools to meet their goals. Part Two, Computational Tools and Techniques, offers a series of chapters, each one dedicated to a single computational technique. Readers discover the strengths and weaknesses of each technique. Moreover, the book tabulates comparative accuracy studies, giving readers an unbiased comparison of all the available techniques. Part Three, Related Topics, addresses new, emerging, and complementary technologies, including bioinformatics, simulations at the cellular and organ level, synthesis route prediction, proteomics, and prodrug approaches. The book's accompanying CD-ROM, a special feature, offers graphics of the molecular structures and dynamic reactions discussed in the book as well as demos from computational drug design software companies. Computational Drug Design is ideal for both students and professionals in drug design, helping them choose and take full advantage of the best computational tools available. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
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5608.03 руб.

This book presents an algorithm for the detection of an orthogonal frequency division multiplexing (OFDM) signal in a cognitive radio context by means of a joint and iterative channel and noise estimation technique. Based on the minimum mean square criterion, it performs an accurate detection of a user in a frequency band, by achieving a quasi-optimal channel and noise variance estimation if the signal is present, and by estimating the noise level in the band if the signal is absent. Organized into three chapters, the first chapter provides the background against which the system model is presented, as well as some basics concerning the channel statistics and the transmission of an OFDM signal over a multipath channel. In Chapter 2, the proposed iterative algorithm for the noise variance and the channel estimation is detailed, and in Chapter 3, an application of the algorithm for the free-band detection is proposed. In both Chapters 2 and 3, the principle of the algorithm is presented in a simple way, and more elaborate developments are also provided. The different assumptions and assertions in the developments and the performance of the proposed method are validated through simulations, and compared to methods of the scientific literature
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16286.89 руб.

With its discussion of strategies for modeling complex materials using new numerical techniques, mainly those based on the finite element method, this monograph covers a range of topics including computational plasticity, multi-scale formulations, optimization and parameter identification, damage mechanics and nonlinear finite elements.
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8777 руб.

Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. Task scheduling is heart of any grid application which guides resource allocation in grid. Heuristic task scheduling strategies have been used for optimal task scheduling. Hence, this book mainly devoted to task scheduling strategies in grid.
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7277 руб.

This book proposes a new optimization algorithm for solving short term load forecasting problem. Globalized Nelder Mead is used for training of Artificial Neural Networks. Nelder Mead is fast optimization algorithm with no gradient calculation. The weights of Neural Networks are tuned with the help of Nelder Mead algorithm. To find proficiency of this algorithm, Australian Energy Market Operator (AEMO) data and California data are taken for testing. Results show that proposed algorithm outclasses other techniques in literature.
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5114 руб.

The adiabatic quantum computation (AQC) is based on the adiabatic theorem to approximate solutions of the Schrödinger equation. The design of an AQC algorithm involves the construction of a Hamiltonian that describes the behavior of the quantum system. This Hamiltonian is expressed as a linear interpolation of an initial Hamiltonian whose ground state is easy to compute, and a final Hamiltonian whose ground state corresponds to the solution of a given combinatorial optimization problem. The adiabatic theorem asserts that if the time evolution of a quantum system described by a Hamiltonian is large enough, then the system remains close to its ground state. An AQC algorithm uses the adiabatic theorem to approximate the ground state of the final Hamiltonian that corresponds to the solution of the given optimization problem. In this book, we investigate the computational simulation of AQC algorithms applied to the MAX-SAT problem. A symbolic analysis of the AQC solution is given in order to understand the involved computational complexity of AQC algorithms. This approach can be extended to other combinatorial optimization problems and can be used for the classical simulation of an AQC algorithm where a Hamiltonian problem is constructed. This construction requires the computation of a sparse matrix of dimension 2ⁿ × 2ⁿ, by means of tensor products, where n is the dimension of the quantum system. Also, a general scheme to design AQC algorithms is proposed, based on a natural corre...
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5608.03 руб.

The authoritative industry guide on good practice for planning and scheduling in construction This handbook acts as a guide to good practice, a text to accompany learning and a reference document for those needing information on background, best practice, and methods for practical application. A Handbook for Construction Planning & Scheduling presents the key issues of planning and programming in scheduling in a clear, concise and practical way. The book divides into four main sections: Planning and Scheduling within the Construction Context; Planning and Scheduling Techniques and Practices; Planning and Scheduling Methods; Delay and Forensic Analysis. The authors include both basic concepts and updates on current topics demanding close attention from the construction industry, including planning for sustainability, waste, health and safety and Building Information Modelling (BIM). The book is especially useful for early career practitioners – engineers, quantity surveyors, construction managers, project managers – who may already have a basic grounding in civil engineering, building and general construction but lack extensive planning and scheduling experience. Students will find the website helpful with worked examples of the methods and calculations for typical construction projects plus other directed learning material. This authoritative industry guide on good practice for planning and scheduling in construction is written in a direct, informative style with a clear presentation enabling easy access of the relevant information with a companion website providing additional resources and learning support material. the authoritative industry guide on construction planning and scheduling direct informative writing style and clear presentation enables easy access of the relevant information companion website provides additional learning material.
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11642.06 руб.

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution The distributions from the one-dimensional exponential family Markov chains and their long-run behavior The Metropolis-Hastings algorithm Gibbs sampling algorithm and methods for speeding up convergence Markov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.
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11984.29 руб.

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: • Brings together the perspectives of researchers in areas of inverse problems and data assimilation. • Assesses the current state-of-the-art and identify needs and opportunities for future research. • Focuses on the computational methods used to analyze and simulate inverse problems. • Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
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7952 руб.

Книга "Supply Chain Scheduling".
Новинка

9439 руб.

Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to improve the performance of IDS in terms of detection accuracy and reduce False Alarm Rate (FAR). Clustering is an important task in data mining that is used in IDS applications to detect novel attacks. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-Means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The Gravitational Search Algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this study, a hybrid approach based on GSA and k-Means (GSA-kMeans), which uses the advantages of both algorithms, is presented.
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8639 руб.

In this book a model formulation technique uses the swarm intelligence population based ABC algorithm in its procedure to obtain stable and approximate low order model for the Original higher order discrete systems. The quality of a formulated low order model is judged by designing the PID Controller. PID controller of the formulated low order model efficiently controls the original higher order system. This approach minimizes the complexity involved in direct design of PID controller for higher order discrete systems. The algorithm is simple to implement and computer oriented.
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14673.14 руб.

The development of materials for clean and efficient energy generation and storage is one of the most rapidly developing, multi-disciplinary areas of contemporary science, driven primarily by concerns over global warming, diminishing fossil-fuel reserves, the need for energy security, and increasing consumer demand for portable electronics. Computational methods are now an integral and indispensable part of the materials characterisation and development process. Computational Approaches to Energy Materials presents a detailed survey of current computational techniques for the development and optimization of energy materials, outlining their strengths, limitations, and future applications. The review of techniques includes current methodologies based on electronic structure, interatomic potential and hybrid methods. The methodological components are integrated into a comprehensive survey of applications, addressing the major themes in energy research. Topics covered include: • Introduction to computational methods and approaches • Modelling materials for energy generation applications: solar energy and nuclear energy • Modelling materials for storage applications: batteries and hydrogen • Modelling materials for energy conversion applications: fuel cells, heterogeneous catalysis and solid-state lighting • Nanostructures for energy applications This full colour text is an accessible introduction for newcomers to the field, and a valuable reference source for experienced researchers working on computational techniques and their application to energy materials.
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9987.88 руб.

Bad scheduling can doom a construction project from the start Construction Project Scheduling and Control provides a comprehensive examination of the analytical methods used to devise a reasonable, efficient, and successful schedule for construction projects of all sizes. This updated third edition contains new information on building image modeling (BIM) and its relationship to project scheduling and control, as well as thorough coverage of the latest developments in the field. Written by a career construction professional, this informative text introduces students to new concepts in CPM scheduling, including the author's own Dynamic Minimum Lag technique. The expanded glossary and acronym list facilitate complete understanding, and the numerous solved and unsolved problems help students test their knowledge and apply critical thinking to issues in construction scheduling. A complete instructor's manual provides solutions to all problems in the book, test questions for each chapter, and additional exam questions for more comprehensive testing. The entire success of a construction process hinges on an efficient, well-thought out schedule, which is strictly defined while allowing for inevitable delays and changes. This book helps students learn the processes, tools, and techniques used to make projects run smoothly, with expert guidance toward the realities of this complex function. Discover realistic scheduling solutions and cutting edge methods Learn the duties, responsibilities, and techniques of project control Get up to date on the latest in sustainability, BIM, and lean construction Explore the software tools that help coordinate scheduling Scheduling encompasses everything from staff requirements and equipment needs to materials delivery and inspections, requiring a deep understanding of the process. For the student interested in construction management, Construction Project Scheduling and Control is an informative text on the field's current best practices.
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10294.74 руб.

Real-time systems are used in a wide range of applications, including control, sensing, multimedia, etc. Scheduling is a central problem for these computing/communication systems since it is responsible for software execution in a timely manner. This book, the second of two volumes on the subject, brings together knowledge on specific topics and discusses the recent advances for some of them. It addresses foundations as well as the latest advances and findings in real-time scheduling, giving comprehensive references to important papers, but the chapters are short and not overloaded with confusing details. Coverage includes scheduling approaches for networks and for energy autonomous systems. Other sophisticated issues, such as feedback control scheduling and probabilistic scheduling, are also addressed. This book can serve as a textbook for courses on the topic in bachelor’s degrees and in more advanced master’s degree programs. It also provides a reference for computer scientists and engineers involved in the design or the development of Cyber-Physical Systems which require up-to-date real-time scheduling solutions.
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12292.61 руб.

Molecular modeling techniques have been widely used in drug discovery fields for rational drug design and compound screening. Now these techniques are used to model or mimic the behavior of molecules, and help us study formulation at the molecular level. Computational pharmaceutics enables us to understand the mechanism of drug delivery, and to develop new drug delivery systems. The book discusses the modeling of different drug delivery systems, including cyclodextrins, solid dispersions, polymorphism prediction, dendrimer-based delivery systems, surfactant-based micelle, polymeric drug delivery systems, liposome, protein/peptide formulations, non-viral gene delivery systems, drug-protein binding, silica nanoparticles, carbon nanotube-based drug delivery systems, diamond nanoparticles and layered double hydroxides (LDHs) drug delivery systems. Although there are a number of existing books about rational drug design with molecular modeling techniques, these techniques still look mysterious and daunting for pharmaceutical scientists. This book fills the gap between pharmaceutics and molecular modeling, and presents a systematic and overall introduction to computational pharmaceutics. It covers all introductory, advanced and specialist levels. It provides a totally different perspective to pharmaceutical scientists, and will greatly facilitate the development of pharmaceutics. It also helps computational chemists to look for the important questions in the drug delivery field. This book is included in the Advances in Pharmaceutical Technology book series.
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2614 руб.

Seminar paper from the year 2012 in the subject Computer Science - Miscellaneous, grade: none, Indian Institute of Technology, Delhi, course: Computer Science and Engineering, language: English, abstract: In this project we explore the various algorithms for the Robot Localization Problem and build a simulator to visualize the results on various 2D maps. Robot localization is an important problem in robotics.Simply put, the robot localization problem is as follows. A robot isplaced at an unknown point inside a simple polygon P . The robothas a map of P and can compute visibility polygon from its currentlocation. The robot must determine its correct location inside thepolygon P at a minimum cost of travel distance. We implement anapproximation algorithm as given by Apurva Mudgal [2006]. The paper gives an O(log3 n) factor approximation algorithm however ourmain emphasis is to show the practicality of the algorithm. In thisproject we are simulating it on different maps without taking timecomplexity in consideration. Computational Geometry AlgorithmsLibrary CGAL has been used for the various computational geometry algorithms. This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France. Jacques Teghem is Professor at the Universit