Search results for: Evolutionary algorithms

Authors: Hesham Abdel-Khalek, Sherif M. Hafez, Abdel-Hamid M. el-Lakany, Yasser Abuel-Magd

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Investment in a constructed facility represents a cost in the short term that returns benefits only over the long term use of the facility. Thus, the costs occur earlier than the benefits, and the owners of facilities must obtain the capital resources to finance the costs of construction. A project cannot proceed without an adequate financing, and the cost of providing an adequate financing can be quite large. For these reasons, the attention to the project finance is an important aspect of project management. Finance is also a concern to the other organizations involved in a project such as the general contractor and material suppliers. Unless an owner immediately and completely covers the costs incurred by each participant, these organizations face financing problems of their own. At a more general level, the project finance is the only one aspect of the general problem of corporate finance. If numerous projects are considered and financed together, then the net cash flow requirements constitute the corporate financing problem for capital investment. Whether project finance is performed at the project or at the corporate level does not alter the basic financing problem .In this paper, we will first consider facility financing from the owner's perspective, with due consideration for its interaction with other organizations involved in a project. Later, we discuss the problems of construction financing which are crucial to the profitability and solvency of construction contractors. The objective of this paper is to present the steps utilized to determine the best combination of minimum project financing. The proposed model considers financing; schedule and maximum net area .The proposed model is called Project Financing and Schedule Integration using Genetic Algorithms "PFSIGA". This model intended to determine more steps (maximum net area) for any project with a subproject. An illustrative example will demonstrate the feature of this technique. The model verification and testing are put into consideration.

Keywords: Project Management, Large-scale ConstructionProjects, Cash flow, Interest, Investment, Loan, Optimization, Scheduling, Financing and Genetic Algorithms.

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Authors: A. A. Khan, M. Naeem, S. Bashir, S. I. Shah

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In this paper we propose a Particle Swarm heuristic optimized Multi-Antenna (MA) system. Efficient MA systems detection is performed using a robust stochastic evolutionary computation algorithm based on movement and intelligence of swarms. This iterative particle swarm optimized (PSO) detector significantly reduces the computational complexity of conventional Maximum Likelihood (ML) detection technique. The simulation results achieved with this proposed MA-PSO detection algorithm show near optimal performance when compared with ML-MA receiver. The performance of proposed detector is convincingly better for higher order modulation schemes and large number of antennas where conventional ML detector becomes non-practical.

Keywords: Multi Antenna (MA), Multi-input Multi-output(MIMO), Particle Swarm Optimization (PSO), ML detection.

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Authors: Cecilia Nugraheni, Luciana Abednego

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Sudoku is a kind of logic puzzles. Each puzzle consists of a board, which is a 9×9 cells, divided into nine 3×3 subblocks and a set of numbers from 1 to 9. The aim of this puzzle is to fill in every cell of the board with a number from 1 to 9 such that in every row, every column, and every subblock contains each number exactly one. Sudoku puzzles belong to combinatorial problem (NP complete). Sudoku puzzles can be solved by using a variety of techniques/algorithms such as genetic algorithms, heuristics, integer programming, and so on. In this paper, we propose a new approach for solving Sudoku which is by modelling them as block-world problems. In block-world problems, there are a number of boxes on the table with a particular order or arrangement. The objective of this problem is to change this arrangement into the targeted arrangement with the help of two types of robots. In this paper, we present three models for Sudoku. We modellized Sudoku as parameterized multi-agent systems. A parameterized multi-agent system is a multi-agent system which consists of several uniform/similar agents and the number of the agents in the system is stated as the parameter of this system. We use Temporal Logic of Actions (TLA) for formalizing our models.

Keywords: Sudoku puzzle, block world problem, parameterized multi agent systems modelling, Temporal Logic of Actions.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of new methods in the sphere of data mining and also the incessant interest in inventing new algorithms, makes obligatory to scrutinize a critical analysis of the existing techniques and the future novelty. This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this speculative and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.

Keywords: Clustering, Cluster Ensemble methods, Co-association matrix, Consensus function, Median partition.

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Authors: Aleksander Nawrat, Karol Jędrasiak, Artur Ryt, Dawid Sobel

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The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

Keywords: Firearms shot detection, geometric recalibration, photometric recalibration, IR tracking algorithm, thermography, ballistics.

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Authors: Arvind Kumar, Manoj Kumar, Dattatraya H. Nagaraj, Amanpreet Singh, Jayanthi Prattapati

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The purpose of this paper is to study and compare two maximum power point tracking (MPPT) algorithms in a photovoltaic simulation system and also show a simulation study of maximum power point tracking (MPPT) for photovoltaic systems using perturb and observe algorithm and Incremental conductance algorithm. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency and minimize the overall system cost. Since the maximum power point (MPP) varies, based on the irradiation and cell temperature, appropriate algorithms must be utilized to track the (MPP) and maintain the operation of the system in it. MATLAB/Simulink is used to establish a model of photovoltaic system with (MPPT) function. This system is developed by combining the models established of solar PV module and DC-DC Boost converter. The system is simulated under different climate conditions. Simulation results show that the photovoltaic simulation system can track the maximum power point accurately.

Keywords: Incremental conductance Algorithm, Perturb and Observe Algorithm, Photovoltaic System and Simulation Results.

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Authors: Maria E. Eggink

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Innovation plays an important role in economic growth and development. Evolutionary economics has entrepreneurs at the centre of the innovation system, but includes all other participants as contributors to the performance of the innovation system. Education and training institutions, one of the participants in the innovation system, contributes in different ways to human capital. The gap in literature on the competence building as part of human capital in the analysis of innovation systems is addressed in this paper. The Mpumalanga Province of South Africa is used as a case study. It was found that the absence of a university, the level of education, the quality and performance in the education sector and the condition of the education infrastructure have not been conducive to learning.

Keywords: Education institutions, human capital, innovation systems, Mpumalanga Province.

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

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This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: Biomass concentration, Extended Kalman Filter, Particle Filter, State estimation, Specific growth rate.

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Authors: Stefan Wagenpfeil, Felix Engel, Paul McKevitt, Matthias Hemmje

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Multimedia Indexing and Retrieval is generally de-signed and implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results but also leads to more complex graph structures. However, graph-traversal-based algorithms for similarity are quite inefficient and computation intensive, espe-cially for large data structures. To deliver fast and effective retrieval, an efficient similarity algorithm, particularly for large graphs, is mandatory. Hence, in this paper, we define a graph-projection into a 2D space (Graph Code) as well as the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph-traversals due to a simpler processing model and a high level of parallelisation. In consequence, we prove that the effectiveness of retrieval also increases substantially, as Graph Codes facilitate more levels of detail in feature fusion. Thus, Graph Codes provide a significant increase in efficiency and effectiveness (especially for Multimedia indexing and retrieval) and can be applied to images, videos, audio, and text information.

Keywords: indexing, retrieval, multimedia, graph code, graph algorithm

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

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Authors: Ahmad M. Abdel Fattah, Ayman M. Bahaa El-Din, Hossam M.A. Fahmy

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Modular multiplication is the basic operation in most public key cryptosystems, such as RSA, DSA, ECC, and DH key exchange. Unfortunately, very large operands (in order of 1024 or 2048 bits) must be used to provide sufficient security strength. The use of such big numbers dramatically slows down the whole cipher system, especially when running on embedded processors. So far, customized hardware accelerators - developed on FPGAs or ASICs - were the best choice for accelerating modular multiplication in embedded environments. On the other hand, many algorithms have been developed to speed up such operations. Examples are the Montgomery modular multiplication and the interleaved modular multiplication algorithms. Combining both customized hardware with an efficient algorithm is expected to provide a much faster cipher system. This paper introduces an enhanced architecture for computing the modular multiplication of two large numbers X and Y modulo a given modulus M. The proposed design is compared with three previous architectures depending on carry save adders and look up tables. Look up tables should be loaded with a set of pre-computed values. Our proposed architecture uses the same carry save addition, but replaces both look up tables and pre-computations with an enhanced version of sign detection techniques. The proposed architecture supports higher frequencies than other architectures. It also has a better overall absolute time for a single operation.

Keywords: Montgomery multiplication, modular multiplication, efficient architecture, FPGA, RSA

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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Modelling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve more dense and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D Models, Environment, Matching, Pleiades.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Vipin Pal, Girdhari Singh, R P Yadav

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As wireless sensor networks are energy constraint networks so energy efficiency of sensor nodes is the main design issue. Clustering of nodes is an energy efficient approach. It prolongs the lifetime of wireless sensor networks by avoiding long distance communication. Clustering algorithms operate in rounds. Performance of clustering algorithm depends upon the round time. A large round time consumes more energy of cluster heads while a small round time causes frequent re-clustering. So existing clustering algorithms apply a trade off to round time and calculate it from the initial parameters of networks. But it is not appropriate to use initial parameters based round time value throughout the network lifetime because wireless sensor networks are dynamic in nature (nodes can be added to the network or some nodes go out of energy). In this paper a variable round time approach is proposed that calculates round time depending upon the number of active nodes remaining in the field. The proposed approach makes the clustering algorithm adaptive to network dynamics. For simulation the approach is implemented with LEACH in NS-2 and the results show that there is 6% increase in network lifetime, 7% increase in 50% node death time and 5% improvement over the data units gathered at the base station.

Keywords: Wireless Sensor Network, Clustering, Energy Efficiency, Round Time.

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Authors: Chih-Hao Chen, Hsing-Chung Lee, Qingdong Ling, Hsiao-Jung Chen, Sun-Chong Wang, Li-Ching Wu, H.C. Lee

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Segmentation, filtering out of measurement errors and identification of breakpoints are integral parts of any analysis of microarray data for the detection of copy number variation (CNV). Existing algorithms designed for these tasks have had some successes in the past, but they tend to be O(N2) in either computation time or memory requirement, or both, and the rapid advance of microarray resolution has practically rendered such algorithms useless. Here we propose an algorithm, SAD, that is much faster and much less thirsty for memory – O(N) in both computation time and memory requirement -- and offers higher accuracy. The two key ingredients of SAD are the fundamental assumption in statistics that measurement errors are normally distributed and the mathematical relation that the product of two Gaussians is another Gaussian (function). We have produced a computer program for analyzing CNV based on SAD. In addition to being fast and small it offers two important features: quantitative statistics for predictions and, with only two user-decided parameters, ease of use. Its speed shows little dependence on genomic profile. Running on an average modern computer, it completes CNV analyses for a 262 thousand-probe array in ~1 second and a 1.8 million-probe array in 9 seconds

Keywords: Cancer, pathogenesis, chromosomal aberration, copy number variation, segmentation analysis.

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Authors: G. T. Balakayeva, K. K. Nurlybayeva, M. B. Zhanuzakov

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In the proposed work, we have developed software and designed a software architecture for the implementation of enterprise business processes. The proposed software has a multi-level architecture using a domain-specific tool. The developed architecture is a guarantor of the availability, reliability and security of the system and the implementation of business processes, which are the basis for effective enterprise management. Automating business processes, automating the algorithmic stages of an enterprise, developing optimal algorithms for managing activities, controlling and monitoring, reducing risks and improving results help organizations achieve strategic goals quickly and efficiently. The software described in this article can connect to the corporate information system via two methods: a desktop client and a web client. With an appeal to the application server, the desktop client program connects to the information system on the company's work PCs over a local network. Outside the organization, the user can interact with the information system via a web browser, which acts as a web client and connects to a web server. The developed software consists of several integrated modules that share resources and interact with each other through an API. The following technology stack was used during development: Node js, React js, MongoDB, Ngnix, Cloud Technologies, Python.

Keywords: Algorithms, document processing, automation, integrated modules, software architecture, software design, information system.

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Authors: Refaat H. Abd El Razek, Ahmed M. Diab, Sherif M. Hafez, Remon F. Aziz

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Time-Cost Optimization "TCO" is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Since there is a hidden trade-off relationship between project and cost, it might be difficult to predict whether the total cost would increase or decrease as a result of the schedule compression. Recently third dimension in trade-off analysis is taken into consideration that is quality of the projects. Few of the existing algorithms are applied in a case of construction project with threedimensional trade-off analysis, Time-Cost-Quality relationships. The objective of this paper is to presents the development of a practical software system; that named Automatic Multi-objective Typical Construction Resource Optimization System "AMTCROS". This system incorporates the basic concepts of Line Of Balance "LOB" and Critical Path Method "CPM" in a multi-objective Genetic Algorithms "GAs" model. The main objective of this system is to provide a practical support for typical construction planners who need to optimize resource utilization in order to minimize project cost and duration while maximizing its quality simultaneously. The application of these research developments in planning the typical construction projects holds a strong promise to: 1) Increase the efficiency of resource use in typical construction projects; 2) Reduce construction duration period; 3) Minimize construction cost (direct cost plus indirect cost); and 4) Improve the quality of newly construction projects. A general description of the proposed software for the Time-Cost-Quality Trade-Off "TCQTO" is presented. The main inputs and outputs of the proposed software are outlined. The main subroutines and the inference engine of this software are detailed. The complexity analysis of the software is discussed. In addition, the verification, and complexity of the proposed software are proved and tested using a real case study.

Keywords: Project management, typical (repetitive) large scale projects, line of balance, multi-objective optimization, genetic algorithms, time-cost-quality trade-offs.

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Authors: Jehad Al Dallal

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Program slicing is the task of finding all statements in a program that directly or indirectly influence the value of a variable occurrence. The set of statements that can affect the value of a variable at some point in a program is called a program slice. In several software engineering applications, such as program debugging and measuring program cohesion and parallelism, several slices are computed at different program points. In this paper, algorithms are introduced to compute all backward and forward static slices of a computer program by traversing the program representation graph once. The program representation graph used in this paper is called Program Dependence Graph (PDG). We have conducted an experimental comparison study using 25 software modules to show the effectiveness of the introduced algorithm for computing all backward static slices over single-point slicing approaches in computing the parallelism and functional cohesion of program modules. The effectiveness of the algorithm is measured in terms of time execution and number of traversed PDG edges. The comparison study results indicate that using the introduced algorithm considerably saves the slicing time and effort required to measure module parallelism and functional cohesion.

Keywords: Backward slicing, cohesion measure, forward slicing, parallelism measure, program dependence graph, program slicing, static slicing.

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Authors: Dávid Csercsik, Sándor Imre

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The performance of state of the art worldwide telecommunication networks strongly depends on the efficiency of the applied routing mechanism. Game theoretical approaches to this problem offer new solutions. In this paper a new continuous network routing model is defined to describe data transfer in fixed telecommunication networks of multiple hosts. The nodes of the network correspond to routers whose latency is assumed to be traffic dependent. We propose that the whole traffic of the network can be decomposed to a finite number of tasks, which belong to various hosts. To describe the different latency-sensitivity, utility functions are defined for each task. The model is used to compare router and host intelligent types of routing methods, corresponding to various data transfer protocols. We analyze host intelligent routing as a transferable utility cooperative game with externalities. The main aim of the paper is to provide a framework in which the efficiency of various routing algorithms can be compared and the transferable utility game arising in the cooperative case can be analyzed.

Keywords: Routing, Telecommunication networks, Performance evaluation, Cooperative game theory, Partition function form games

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Authors: M. R. AlRashidi, M. F. AlHajri, M. E. El-Hawary

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An enhanced particle swarm optimization algorithm (PSO) is presented in this work to solve the non-convex OPF problem that has both discrete and continuous optimization variables. The objective functions considered are the conventional quadratic function and the augmented quadratic function. The latter model presents non-differentiable and non-convex regions that challenge most gradient-based optimization algorithms. The optimization variables to be optimized are the generator real power outputs and voltage magnitudes, discrete transformer tap settings, and discrete reactive power injections due to capacitor banks. The set of equality constraints taken into account are the power flow equations while the inequality ones are the limits of the real and reactive power of the generators, voltage magnitude at each bus, transformer tap settings, and capacitor banks reactive power injections. The proposed algorithm combines PSO with Newton-Raphson algorithm to minimize the fuel cost function. The IEEE 30-bus system with six generating units is used to test the proposed algorithm. Several cases were investigated to test and validate the consistency of detecting optimal or near optimal solution for each objective. Results are compared to solutions obtained using sequential quadratic programming and Genetic Algorithms.

Keywords: Particle Swarm Optimization, Optimal Power Flow, Economic Dispatch.

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Authors: Min Kyung An

Abstract:

In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: Data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional.

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Authors: A.K.M. Khaled Ahsan Talukder, Taibun Nessa, Kaushik Roy

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The weight constrained shortest path problem (WCSPP) is one of most several known basic problems in combinatorial optimization. Because of its importance in many areas of applications such as computer science, engineering and operations research, many researchers have extensively studied the WCSPP. This paper mainly concentrates on the reduction of total search space for finding WCSP using some existing Genetic Algorithm (GA). For this purpose, some controlled schemes of genetic operators are adopted on list chromosome representation. This approach gives a near optimum solution with smaller elapsed generation than classical GA technique. From further analysis on the matter, a new generalized schema theorem is also developed from the philosophy of Holland-s theorem.

Keywords: Genetic Algorithm, Evolutionary Optimization, Multi Objective Optimization, Non-linear Schema Theorem, WCSPP.

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Authors: Wayan F. Mahmudy, Romeo M. Marian, Lee H. S. Luong

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This paper presents modeling and optimization of two NP-hard problems in flexible manufacturing system (FMS), part type selection problem and loading problem. Due to the complexity and extent of the problems, the paper was split into two parts. The first part of the papers has discussed the modeling of the problems and showed how the real coded genetic algorithms (RCGA) can be applied to solve the problems. This second part discusses the effectiveness of the RCGA which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

Keywords: Flexible manufacturing system, production planning, part type selection problem, loading problem, real-coded genetic algorithm

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Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.

Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.

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Authors: Jin Zhang, Wing Lon Ng

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In recent years, copulas have become very popular in financial research and actuarial science as they are more flexible in modelling the co-movements and relationships of risk factors as compared to the conventional linear correlation coefficient by Pearson. However, a precise estimation of the copula parameters is vital in order to correctly capture the (possibly nonlinear) dependence structure and joint tail events. In this study, we employ two optimization heuristics, namely Differential Evolution and Threshold Accepting to tackle the parameter estimation of multivariate t distribution models in the EML approach. Since the evolutionary optimizer does not rely on gradient search, the EML approach can be applied to estimation of more complicated copula models such as high-dimensional copulas. Our experimental study shows that the proposed method provides more robust and more accurate estimates as compared to the IFM approach.

Keywords: Copula Models, Student t Copula, Parameter Inference, Differential Evolution, Threshold Accepting.

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Authors: Maria E. Eggink

Abstract:

It is the aim of this paper to place the role of innovation in economic development in its theoretical context through a literature review. The review compares classical economic theory and the neoclassical theories of “equilibrium in the markets” and “perfectly competitive markets” with the Schumpeterian theory. It was found that Schumpeter’s role in contributing towards economic development theories, and by creating awareness of the role of innovation in these theories is of immeasurable importance. His contribution led to a change in economic thinking, although this was only realized much later than when his theories were first published. The neo-Schumpeterian thinking expanded on the Schumpeterian theory by studying innovation within a system of interaction among different role players. Studies on innovation should be founded in the neo-Schumpeterian school of thought in order to accommodate the complexity of the innovation system concept.

Keywords: Economic development, evolutionary economics, innovation, Schumpeter.

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Authors: Wayan F. Mahmudy, Romeo M. Marian, Lee H. S. Luong

Abstract:

 This paper deals with modeling and optimization of two NP-hard problems in production planning of flexible manufacturing system (FMS), part type selection problem and loading problem. The part type selection problem and the loading problem are strongly related and heavily influence the system’s efficiency and productivity. These problems have been modeled and solved simultaneously by using real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

Keywords: Flexible manufacturing system, production planning, part type selection problem, loading problem, real-coded genetic algorithm.

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Authors: Y. Harold Robinson, M. Rajaram, Honey Raju

Abstract:

Image segmentation and color identification is an important process used in various emerging fields like intelligent robotics. A method is proposed for the manipulator to grasp and place the color object into correct location. The existing methods such as PSO, has problems like accelerating the convergence speed and converging to a local minimum leading to sub optimal performance. To improve the performance, we are using watershed algorithm and for color identification, we are using EPSO. EPSO method is used to reduce the probability of being stuck in the local minimum. The proposed method offers the particles a more powerful global exploration capability. EPSO methods can determine the particles stuck in the local minimum and can also enhance learning speed as the particle movement will be faster.

Keywords: Color information, EPSO, hue, saturation, value (HSV), image segmentation, particle swarm optimization (PSO). Active Contour, GMM.

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Authors: Rafal Michalski, Jakub Zygadlo

Abstract:

We present the concept and scientific methods and algorithms of our computation system called ATOMIC MATTERS. This is the first presentation of the new computer package, that allows its user to describe physical properties of atomic localized electron systems subject to electromagnetic interactions. Our solution applies to situations where an unclosed electron 2p/3p/3d/4d/5d/4f/5f subshell interacts with an electrostatic potential of definable symmetry and external magnetic field. Our methods are based on Crystal Electric Field (CEF) approach, which takes into consideration the electrostatic ligands field as well as the magnetic Zeeman effect. The application allowed us to predict macroscopic properties of materials such as: Magnetic, spectral and calorimetric as a result of physical properties of their fine electronic structure. We emphasize the importance of symmetry of charge surroundings of atom/ion, spin-orbit interactions (spin-orbit coupling) and the use of complex number matrices in the definition of the Hamiltonian. Calculation methods, algorithms and convention recalculation tools collected in ATOMIC MATTERS were chosen to permit the prediction of magnetic and spectral properties of materials in isostructural series.

Keywords: Atomic matters, crystal electric field, spin-orbit coupling, localized states, electron subshell, fine electronic structure.

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Authors: Vinay Chandwani, Vinay Agrawal, Ravindra Nagar

Abstract:

Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.

Keywords: Artificial neural networks, Genetic algorithms, Back-propagation algorithm, Ready Mix Concrete, Slump value.

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