Search results for: Exact String-matching Algorithms

Authors: Emre Akyürek, Anthony Huynh, Tatiana Kalganova

Abstract:

The Ambidextrous Robot Hand is a robotic device with the purpose to mimic either the gestures of a right or a left hand. The symmetrical behavior of its fingers allows them to bend in one way or another keeping a compliant and anthropomorphic shape. However, in addition to gestures they can reproduce on both sides, an asymmetrical mechanical design with a three tendons routing has been engineered to reduce the number of actuators. As a consequence, control algorithms must be adapted to drive efficiently the ambidextrous fingers from one position to another and to include grasping features. These movements are controlled by pneumatic muscles, which are nonlinear actuators. As their elasticity constantly varies when they are under actuation, the length of pneumatic muscles and the force they provide may differ for a same value of pressurized air. The control algorithms introduced in this paper take both the fingers asymmetrical design and the pneumatic muscles nonlinearity into account to permit an accurate control of the Ambidextrous Robot Hand. The finger motion is achieved by combining a classic PID controller with a phase plane switching control that turns the gain constants into dynamic values. The grasping ability is made possible because of a sliding mode control that makes the fingers adapt to the shape of an object before strengthening their positions.

Keywords: Ambidextrous hand, intelligent algorithms, nonlinear actuators, pneumatic muscles, robotics, sliding control.

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Authors: Irina Maria Artinescu, Costin Radu Boldea, Eduard-Ionut Matei

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The paper is a comparative study of two classical vari-ants of parallel projection methods for solving the convex feasibility problem with their equivalents that involve variable weights in the construction of the solutions. We used a graphical representation of these methods for inpainting a convex area of an image in order to investigate their effectiveness in image reconstruction applications. We also presented a numerical analysis of the convergence of these four algorithms in terms of the average number of steps and execution time, in classical CPU and, alternativaly, in parallel GPU implementation.

Keywords: convex feasibility problem, convergence analysis, ınpainting, parallel projection methods

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Authors: Rado Flajs, Miran Saje

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We present a simple nonconforming approximation of the linear two–point boundary value problem which violates patch test requirements. Nevertheless the solutions, obtained from these type of approximations, converge to the exact solution.

Keywords: Generalized patch test, Irons' patch test, nonconforming finite element, convergence.

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Authors: Zulhadi Zakaria, Nor Ashidi Mat Isa, Shahrel A. Suandi

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This paper reports the study results on neural network training algorithm of numerical optimization techniques multiface detection in static images. The training algorithms involved are scale gradient conjugate backpropagation, conjugate gradient backpropagation with Polak-Riebre updates, conjugate gradient backpropagation with Fletcher-Reeves updates, one secant backpropagation and resilent backpropagation. The final result of each training algorithms for multiface detection application will also be discussed and compared.

Keywords: training algorithm, multiface, static image, neural network

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Authors: Huang, Wei-Hsiu Chang, Pei-Chann, Wang, Lien-Chun

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The problems with high complexity had been the challenge in combinatorial problems. Due to the none-determined and polynomial characteristics, these problems usually face to unreasonable searching budget. Hence combinatorial optimizations attracted numerous researchers to develop better algorithms. In recent academic researches, most focus on developing to enhance the conventional evolutional algorithms and facilitate the local heuristics, such as VNS, 2-opt and 3-opt. Despite the performances of the introduction of the local strategies are significant, however, these improvement cannot improve the performance for solving the different problems. Therefore, this research proposes a meta-heuristic evolutional algorithm which can be applied to solve several types of problems. The performance validates BBEA has the ability to solve the problems even without the design of local strategies.

Keywords: Combinatorial problems, Artificial Chromosomes, Blocks Mining, Block Recombination

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Authors: Vijay Kumar, Jagdev Singh, Yaduvir Singh, Sanjay Sood

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In a practical power system, the power plants are not located at the same distance from the center of loads and their fuel costs are different. Also, under normal operating conditions, the generation capacity is more than the total load demand and losses. Thus, there are many options for scheduling generation. In an interconnected power system, the objective is to find the real and reactive power scheduling of each power plant in such a way as to minimize the operating cost. This means that the generator’s real and reactive powers are allowed to vary within certain limits so as to meet a particular load demand with minimum fuel cost. This is called optimal power flow problem. In this paper, Economic Load Dispatch (ELD) of real power generation is considered. Economic Load Dispatch (ELD) is the scheduling of generators to minimize total operating cost of generator units subjected to equality constraint of power balance within the minimum and maximum operating limits of the generating units. In this paper, genetic algorithms are considered. ELD solutions are found by solving the conventional load flow equations while at the same time minimizing the fuel costs.

Keywords: ELD, Equality constraints, Genetic algorithms, Strings.

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Authors: Irwin Ma, Tony Wong, Thiagas Sankar

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By systematically applying different engineering methods, difficult financial problems become approachable. Using a combination of theory and techniques such as wavelet transform, time series data mining, Markov chain based discrete stochastic optimization, and evolutionary algorithms, this work formulated a strategy to characterize and forecast non-linear time series. It attempted to extract typical features from the volatility data sets of S&P100 and S&P500 indices that include abrupt drops, jumps and other non-linearity. As a result, accuracy of forecasting has reached an average of over 75% surpassing any other publicly available results on the forecast of any financial index.

Keywords: Discrete stochastic optimization, genetic algorithms, genetic programming, volatility forecast

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Authors: G. Settanni, A. Panarese, R. Vaira, A. Galiano

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Nowadays, artificial intelligence is used successfully in the field of e-commerce for its ability to learn from a large amount of data. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them the most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Also, Long Short-Term Memory algorithms have been implemented and trained on historical data in order to predict customer scores of the different items. Items with the highest scores are recommended to customers.

Keywords: Deep Learning, Long Short-Term Memory, Machine Learning, Recommender Systems, Support Vector Machine.

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Authors: Vahid R. Asghari, Mehrdad Ardebilipour

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In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter-s spreading sequence. In our previous paper, we used Genetic algorithm (GA), to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. To solve this problem we introduce Particle Swarm Optimization (PSO) into code estimation in spread spectrum communication system. In searching process for code estimation, the PSO algorithm has the merits of rapid convergence to the global optimum, without being trapped in local suboptimum, and good robustness to noise. In this paper we describe how to implement PSO as a component of a searching algorithm in code estimation. Swarm intelligence boasts a number of advantages due to the use of mobile agents. Some of them are: Scalability, Fault tolerance, Adaptation, Speed, Modularity, Autonomy, and Parallelism. These properties make swarm intelligence very attractive for spread spectrum code estimation. They also make swarm intelligence suitable for a variety of other kinds of channels. Our results compare between swarm-based algorithms and Genetic algorithms, and also show PSO algorithm performance in code estimation process.

Keywords: Code estimation, Particle Swarm Optimization(PSO), Spread spectrum.

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Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

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Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the dataset of 19 patients consisting of a total of 917 images. We obtained datasets of 11 patients from Ackron University, USA and of 8 patients from AGA Khan Medical University, Pakistan. After testing the algorithms against datasets, the deficiencies of each algorithm have been highlighted.

Keywords: Computer Aided Diagnosis (CAD), MathematicalMorphology, Medical Image Analysis, Region Growing, Segmentation, Thresholding,

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Authors: Jae Moon Lee

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In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments.

Keywords: Flocking behavior, heterogeneous agents, similarity, simulation.

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Authors: H. Sagha, S. Kasaei, E. Enayati, M. Dehghani

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Although Face detection is not a recent activity in the field of image processing, it is still an open area for research. The greatest step in this field is the work reported by Viola and its recent analogous is Huang et al. Both of them use similar features and also similar training process. The former is just for detecting upright faces, but the latter can detect multi-view faces in still grayscale images using new features called 'sparse feature'. Finding these features is very time consuming and inefficient by proposed methods. Here, we propose a new approach for finding sparse features using a genetic algorithm system. This method requires less computational cost and gets more effective features in learning process for face detection that causes more accuracy.

Keywords: Face Detection, Genetic Algorithms, Sparse Feature.

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Authors: Mohsen Soori, Fooad Karimi Ghaleh Jough

Abstract:

The integration of Artificial Intelligence (AI) techniques in the optimization of steel moment frame structures represents a transformative approach to enhance the design, analysis, and performance of these critical engineering systems. The review encompasses a wide spectrum of AI methods, including machine learning algorithms, evolutionary algorithms, neural networks, and optimization techniques, applied to address various challenges in the field. The synthesis of research findings highlights the interdisciplinary nature of AI applications in structural engineering, emphasizing the synergy between domain expertise and advanced computational methodologies. This synthesis aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking a comprehensive understanding of the state-of-the-art in AI-driven optimization for steel moment frame structures. The paper commences with an overview of the fundamental principles governing steel moment frame structures and identifies the key optimization objectives, such as efficiency of structures. Subsequently, it delves into the application of AI in the conceptual design phase, where algorithms aid in generating innovative structural configurations and optimizing material utilization. The review also explores the use of AI for real-time structural health monitoring and predictive maintenance, contributing to the long-term sustainability and reliability of steel moment frame structures. Furthermore, the paper investigates how AI-driven algorithms facilitate the calibration of structural models, enabling accurate prediction of dynamic responses and seismic performance. Thus, by reviewing and analyzing the recent achievements in applications artificial intelligent in optimization of steel moment frame structures, the process of designing, analysis, and performance of the structures can be analyzed and modified.

Keywords: Artificial Intelligent, optimization process, steel moment frame, structural engineering.

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Authors: K. Benamrane, T. Abdelkrim, T. Benslimane, Aeh. Benkhelifa, B. Bezza

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This paper presents a comparison between two Pulse Width Modulation (PWM) algorithms applied to a three-level Neutral Point Clamped (NPC) Voltage Source Inverter (VSI). The first algorithm applied is the triangular-sinusoidal strategy; the second is the Space Vector Pulse Width Modulation (SVPWM) strategy. In the first part, we present a topology of three-level NCP VSI. After that, we develop the two PWM strategies to control this converter. At the end the experimental results are presented.

Keywords: Multilevel inverter, Space vector pulse width modulation (SVPWM), triangular-sinusoidal strategy.

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Authors: Binu Thomas, Raju G., Sonam Wangmo

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In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program.

Keywords: Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.

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Authors: H. E. Michail, V. N. Thanasoulis, G. A. Panagiotakopoulos, A. P. Kakarountas, C. E. Goutis

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In this paper an efficient implementation of Ripemd- 160 hash function is presented. Hash functions are a special family of cryptographic algorithms, which is used in technological applications with requirements for security, confidentiality and validity. Applications like PKI, IPSec, DSA, MAC-s incorporate hash functions and are used widely today. The Ripemd-160 is emanated from the necessity for existence of very strong algorithms in cryptanalysis. The proposed hardware implementation can be synthesized easily for a variety of FPGA and ASIC technologies. Simulation results, using commercial tools, verified the efficiency of the implementation in terms of performance and throughput. Special care has been taken so that the proposed implementation doesn-t introduce extra design complexity; while in parallel functionality was kept to the required levels.

Keywords: Hardware implementation, hash functions, Ripemd-160, security.

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Authors: Leili Nosrati, Alireza Nemaney Pour

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Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.

Keywords: Concept drift, Content-based filtering, E-mail, Spammail.

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Authors: Leehter Yao, Kuei-Song Weng, Cherng-Dir Huang

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A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA) with an adaptive distance norm based on covariance matrices of prototype data points is adopted to learn the ellipsoids. GKA is able toadapt the distance norm to the underlying distribution of the prototypedata points except that the sizes of ellipsoids need to be determined a priori. To overcome GKA's inability to determine appropriate size ofellipsoid, the genetic algorithm (GA) is applied to learn the size ofellipsoid. With GA combined with GKA, it will be shown in this paper that the proposed method outperforms the benchmark algorithms as well as algorithms in the field.

Keywords: Ellipsoids, genetic algorithm, classification, fuzzyc-means (FCM)

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Authors: B. Q. Huang, Tarik Rashid, M-T. Kechadi

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this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.

Keywords: Gradient descent method, recurrent neural network, learning algorithms, time series, BP

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Authors: Andrew Lock, Alastair Allen

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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance ofinvestigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.

Keywords: Biometrics, iris recognition, reversible watermarking.

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Authors: H. Ozbasaran

Abstract:

Lateral torsional buckling is a global buckling mode which should be considered in design of slender structural members under flexure about their strong axis. It is possible to compute the load which causes lateral torsional buckling of a beam by finite element analysis, however, closed form equations are needed in engineering practice for calculation ease which can be obtained by using energy method. In lateral torsional buckling applications of energy method, a proper function for the critical lateral torsional buckling mode should be chosen which can be thought as the variation of twisting angle along the buckled beam. Accuracy of the results depends on how close is the chosen function to the exact mode. Since critical lateral torsional buckling mode of the cantilever I-beams varies due to material properties, section properties and loading case, the hardest step is to determine a proper mode function in application of energy method. This paper presents an approximate function for critical lateral torsional buckling mode of doubly symmetric cantilever I-beams. Coefficient matrices are calculated for concentrated load at free end, uniformly distributed load and constant moment along the beam cases. Critical lateral torsional buckling modes obtained by presented function and exact solutions are compared. It is found that the modes obtained by presented function coincide with differential equation solutions for considered loading cases.

Keywords: Buckling mode, cantilever, lateral-torsional buckling, I-beam.

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Authors: Ahmad Rabanimotlagh

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In this paper an ant colony optimization algorithm is developed to solve the permutation flow shop scheduling problem. In the permutation flow shop scheduling problem which has been vastly studied in the literature, there are a set of m machines and a set of n jobs. All the jobs are processed on all the machines and the sequence of jobs being processed is the same on all the machines. Here this problem is optimized considering two criteria, makespan and total flow time. Then the results are compared with the ones obtained by previously developed algorithms. Finally it is visible that our proposed approach performs best among all other algorithms in the literature.

Keywords: Scheduling, Flow shop, Ant colony optimization, Makespan, Flow time

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Authors: A. Tellaeche, R. Arana

Abstract:

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in necessary algorithms for plastic injection process and monitoring, and also a flexible data acquisition system that allows rapid implementation of complex algorithms to assess their correct performance and can be integrated in the quality control process. This is the main topic of this paper. Finally, to demonstrate the performance achieved by this combination, a real case of use is presented.

Keywords: Plastic injection, machine learning, rapid complex algorithm prototyping.

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Authors: Nadia Belu, Laurentiu M. Ionescu, Agnieszka Misztal

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In this paper we propose a computer-aided solution with Genetic Algorithms in order to reduce the drafting of reports: FMEA analysis and Control Plan required in the manufacture of the product launch and improved knowledge development teams for future projects. The solution allows to the design team to introduce data entry required to FMEA. The actual analysis is performed using Genetic Algorithms to find optimum between RPN risk factor and cost of production. A feature of Genetic Algorithms is that they are used as a means of finding solutions for multi criteria optimization problems. In our case, along with three specific FMEA risk factors is considered and reduce production cost. Analysis tool will generate final reports for all FMEA processes. The data obtained in FMEA reports are automatically integrated with other entered parameters in Control Plan. Implementation of the solution is in the form of an application running in an intranet on two servers: one containing analysis and plan generation engine and the other containing the database where the initial parameters and results are stored. The results can then be used as starting solutions in the synthesis of other projects. The solution was applied to welding processes, laser cutting and bending to manufacture chassis for buses. Advantages of the solution are efficient elaboration of documents in the current project by automatically generating reports FMEA and Control Plan using multiple criteria optimization of production and build a solid knowledge base for future projects. The solution which we propose is a cheap alternative to other solutions on the market using Open Source tools in implementation.

Keywords: Automotive industry, control plan, FMEA.

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Authors: M. Anandhavalli Gauthaman

Abstract:

DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.

Keywords: DNA microarray, gene expression, association rule mining.

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Authors: Su Xiaohan, Jin Chicheng, Liu Yijing, Burra Venkata Durga Kumar

Abstract:

Energy-aware scheduling in real-time systems aims to minimize energy consumption, but issues related to resource reservation and timing constraints remain challenges. This study focuses on analyzing two scheduling algorithms, Fair-Share Scheduling (FFS) and Priority-Driven Preemptive Scheduling (PDPS), for solving these issues and energy-aware scheduling in real-time systems. Based on research on both algorithms and the processes of solving two problems, it can be found that FFS ensures fair allocation of resources but needs to improve with an imbalanced system load. And PDPS prioritizes tasks based on criticality to meet timing constraints through preemption but relies heavily on task prioritization and may not be energy efficient. Therefore, improvements to both algorithms with energy-aware features will be proposed. Future work should focus on developing hybrid scheduling techniques that minimize energy consumption through intelligent task prioritization, resource allocation, and meeting time constraints.

Keywords: Energy-aware scheduling, fair-share scheduling, priority-driven preemptive scheduling, real-time systems, optimization, resource reservation, timing constraints.

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Authors: Z. Zainuddin, W. K. Lye

Abstract:

In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well. In KHM, the problem can be avoided. This leads to improvement in the classification performance when compared to other clustering algorithms. A comparison of the classification accuracy was performed between KM, FCM and KHM. The classification performance is based on the benchmark data sets: Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM algorithm shows better accuracy in classification problem.

Keywords: Neural networks, Radial basis functions, Clusteringmethod, K-harmonic means.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

Abstract:

Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results.

Keywords: Random Forests [RF], ensembles, UCI.

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Authors: I. Oloyede

Abstract:

The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.

Keywords: BCNN, CNN, Images, COVID-19, Deep Learning.

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Authors: Katrin Zaiß, Tim Schlüter, Stefan Conrad

Abstract:

Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web

Keywords: Instances, Ontology Matching, Semantic Web

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