Search results for: Artificial Bee Colony algorithm
4846 An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning
Authors: Yanwen Li, Shuguo Xie
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In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.Keywords: gradient image, segmentation and extract, mean-shift algorithm, dictionary iearning
Procedia PDF Downloads 2584845 Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives
Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Anamarija Mandic, Katarina Penov Gasi, Marija Sakac, Aleksandar Okljesa, Andrea Nikolic
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The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology).Keywords: artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences
Procedia PDF Downloads 3134844 Adolescent-Parent Relationship as the Most Important Factor in Preventing Mood Disorders in Adolescents: An Application of Artificial Intelligence to Social Studies
Authors: Elżbieta Turska
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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.Keywords: mood disorders, adolescents, family, artificial intelligence
Procedia PDF Downloads 994843 Seismic Retrofitting of Structures Using Steel Plate Slit Dampers Based on Genetic Algorithm
Authors: Mohamed Noureldin, Jinkoo Kim
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In this study, a genetic algorithm was used to find out the optimum locations of the slit dampers satisfying a target displacement. A seismic retrofit scheme for a building structure was presented using steel plate slit dampers. A cyclic loading test was used to verify the energy dissipation capacity of the slit damper. The seismic retrofit of the model structure using the slit dampers was compared with the retrofit with enlarging shear walls. The capacity spectrum method was used to propose a simple damper distribution scheme proportional to the inter-story drifts. The validity of the simple story-wise damper distribution procedure was verified by comparing the results of the genetic algorithm. It was observed that the proposed simple damper distribution pattern was in a good agreement with the optimum distribution obtained from the genetic algorithm. Acknowledgment: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032809).Keywords: slit dampers, seismic retrofit, genetic algorithm, optimum design
Procedia PDF Downloads 2164842 Sequential Covering Algorithm for Nondifferentiable Global Optimization Problem and Applications
Authors: Mohamed Rahal, Djaouida Guetta
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In this paper, the one-dimensional unconstrained global optimization problem of continuous functions satifying a Hölder condition is considered. We extend the algorithm of sequential covering SCA for Lipschitz functions to a large class of Hölder functions. The convergence of the method is studied and the algorithm can be applied to systems of nonlinear equations. Finally, some numerical examples are presented and illustrate the efficiency of the present approach.Keywords: global optimization, Hölder functions, sequential covering method, systems of nonlinear equations
Procedia PDF Downloads 3654841 Preparation of Papers – Inventorship Status For AI - A South African Perspective
Authors: Meshandren Naidoo
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An artificial intelligence (AI) system named DABUS 2021 made headlines when it became the very first AI system to be listed in a patent which was then granted by the South African patent office. This grant raised much criticism. The question that this research intends to answer is (1) whether, in South African patent law, an AI can be an inventor. This research finds that despite South African law not recognising an AI as a legal person and despite the legislation not explicitly allowing AI to be inventors, a legal interpretative exercise would allow AI inventorship.Keywords: artificial intelligence, intellectual property, inventorship, patents
Procedia PDF Downloads 984840 Study of Adaptive Filtering Algorithms and the Equalization of Radio Mobile Channel
Authors: Said Elkassimi, Said Safi, B. Manaut
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This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.Keywords: adaptive filtering second equalizer, LMS, RLS Bran A, Proakis (B) MMSE, ZF
Procedia PDF Downloads 3084839 Sync Consensus Algorithm: Trying to Reach an Agreement at Full Speed
Authors: Yuri Zinchenko
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Recently, distributed storage systems have been used more and more in various aspects of everyday life. They provide such necessary properties as Scalability, Fault Tolerance, Durability, and others. At the same time, not only reliable but also fast data storage remains one of the most pressing issues in this area. That brings us to the consensus algorithm as one of the most important components that has a great impact on the functionality of a distributed system. This paper is the result of an analysis of several well-known consensus algorithms, such as Paxos and Raft. The algorithm it offers, called Sync, promotes, but does not insist on simultaneous writing to the nodes (which positively affects the overall writing speed) and tries to minimize the system's inactive time. This allows nodes to reach agreement on the system state in a shorter period, which is a critical factor for distributed systems. Also when developing Sync, a lot of attention was paid to such criteria as simplicity and intuitiveness, the importance of which is difficult to overestimate.Keywords: sync, consensus algorithm, distributed system, leader-based, synchronization.
Procedia PDF Downloads 544838 Kinoform Optimisation Using Gerchberg- Saxton Iterative Algorithm
Authors: M. Al-Shamery, R. Young, P. Birch, C. Chatwin
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Computer Generated Holography (CGH) is employed to create digitally defined coherent wavefronts. A CGH can be created by using different techniques such as by using a detour-phase technique or by direct phase modulation to create a kinoform. The detour-phase technique was one of the first techniques that was used to generate holograms digitally. The disadvantage of this technique is that the reconstructed image often has poor quality due to the limited dynamic range it is possible to record using a medium with reasonable spatial resolution.. The kinoform (phase-only hologram) is an alternative technique. In this method, the phase of the original wavefront is recorded but the amplitude is constrained to be constant. The original object does not need to exist physically and so the kinoform can be used to reconstruct an almost arbitrary wavefront. However, the image reconstructed by this technique contains high levels of noise and is not identical to the reference image. To improve the reconstruction quality of the kinoform, iterative techniques such as the Gerchberg-Saxton algorithm (GS) are employed. In this paper the GS algorithm is described for the optimisation of a kinoform used for the reconstruction of a complex wavefront. Iterations of the GS algorithm are applied to determine the phase at a plane (with known amplitude distribution which is often taken as uniform), that satisfies given phase and amplitude constraints in a corresponding Fourier plane. The GS algorithm can be used in this way to enhance the reconstruction quality of the kinoform. Different images are employed as the reference object and their kinoform is synthesised using the GS algorithm. The quality of the reconstructed images is quantified to demonstrate the enhanced reconstruction quality achieved by using this method.Keywords: computer generated holography, digital holography, Gerchberg-Saxton algorithm, kinoform
Procedia PDF Downloads 5264837 Automated Test Data Generation For some types of Algorithm
Authors: Hitesh Tahbildar
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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.Keywords: ongest path, saturation point, lmax, kL, kS
Procedia PDF Downloads 3984836 Exploring Artificial Intelligence as a Transformative Tool for Urban Management
Authors: R. R. Govind
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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality
Procedia PDF Downloads 534835 A Method to Compute Efficient 3D Helicopters Flight Trajectories Based On a Motion Polymorph-Primitives Algorithm
Authors: Konstanca Nikolajevic, Nicolas Belanger, David Duvivier, Rabie Ben Atitallah, Abdelhakim Artiba
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Finding the optimal 3D path of an aerial vehicle under flight mechanics constraints is a major challenge, especially when the algorithm has to produce real-time results in flight. Kinematics models and Pythagorian Hodograph curves have been widely used in mobile robotics to solve this problematic. The level of difficulty is mainly driven by the number of constraints to be saturated at the same time while minimizing the total length of the path. In this paper, we suggest a pragmatic algorithm capable of saturating at the same time most of dimensioning helicopter 3D trajectories’ constraints like: curvature, curvature derivative, torsion, torsion derivative, climb angle, climb angle derivative, positions. The trajectories generation algorithm is able to generate versatile complex 3D motion primitives feasible by a helicopter with parameterization of the curvature and the climb angle. An upper ”motion primitives’ concatenation” algorithm is presented based. In this article we introduce a new way of designing three-dimensional trajectories based on what we call the ”Dubins gliding symmetry conjecture”. This extremely performing algorithm will be soon integrated to a real-time decisional system dealing with inflight safety issues.Keywords: robotics, aerial robots, motion primitives, helicopter
Procedia PDF Downloads 6114834 Inverse Mapping of Weld Bead Geometry in Shielded Metal Arc-Welding: Genetic Algorithm Approach
Authors: D. S. Nagesh, G. L. Datta
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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping
Procedia PDF Downloads 4474833 Genetic Algorithm Approach for Inverse Mapping of Weld Bead Geometry in Shielded Metal Arc-Welding
Authors: D. S. Nagesh, G. L. Datta
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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping
Procedia PDF Downloads 4174832 AI Features in Netflix
Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji
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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.Keywords: easy accessibility, recommends, accuracy, privacy
Procedia PDF Downloads 594831 Employing Artificial Intelligence Tools in Making Clothing Designs Inspired by the Najdi Art of Sadu
Authors: Basma Abdel Mohsen Al-Sheikh
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This study aimed to create textile designs inspired by Najdi Al-Sadu art, with the objective of highlighting Saudi identity and heritage. The research proposed clothing designs for women and children, utilizing textiles inspired by Najdi Al-Sadu art, and incorporated artificial intelligence techniques in the design process. The study employed a descriptive-analytical approach to describe Najdi Al-Sadu, and an experimental method involving the creation of textile designs inspired by Al-Sadu. The study sample consisted of 33 participants, including experts in the fashion and textile industry, fashion designers, lecturers, professors, and postgraduate students from King Abdulaziz University. A questionnaire was used as a tool to gather opinions regarding the proposed designs. The results demonstrated a clear acceptance of the designs inspired by Najdi Al-Sadu and incorporating artificial intelligence, with approval rates ranging from 22% to 81% across different designs. The study concluded that artificial intelligence applications have a significant impact on fashion design, particularly in the integration of Al-Sadu art. The findings also indicated a positive reception of the designs in terms of their aesthetic and functional aspects, although individual preferences led to some variations in opinions. The results highlighted a demand for designs that combine heritage and modern fashion, striking a balance between authenticity and contemporary style. The study recommended that designers continue to explore ways to integrate cultural heritage, such as Al-Sadu art, with contemporary design elements to achieve this balance. Furthermore, it emphasized the importance of enhancing the aesthetic and functional aspects of designs, taking into consideration the preferences of the target market and customer expectations. The effective utilization of artificial intelligence was also emphasized to improve design processes, expand creative possibilities, and foster innovation and authenticity.Keywords: Najdi Al-Sadu art, artificial intelligence, women's and children's fashion, clothing designs
Procedia PDF Downloads 664830 Maximum Efficiency of the Photovoltaic Cells Using a Genetic Algorithm
Authors: Latifa Sabri, Mohammed Benzirar, Mimoun Zazoui
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The installation of photovoltaic systems is one of future sources to generate electricity without emitting pollutants. The photovoltaic cells used in these systems have demonstrated enormous efficiencies and advantages. Several researches have discussed the maximum efficiency of these technologies, but only a few experiences have succeeded to right weather conditions to get these results. In this paper, two types of cells were selected: crystalline and amorphous silicon. Using the method of genetic algorithm, the results show that for an ambient temperature of 25°C and direct irradiation of 625 W/m², the efficiency of crystalline silicon is 12% and 5% for amorphous silicon.Keywords: PV, maximum efficiency, solar cell, genetic algorithm
Procedia PDF Downloads 4184829 A Comparative Analysis on QRS Peak Detection Using BIOPAC and MATLAB Software
Authors: Chandra Mukherjee
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The present paper is a representation of the work done in the field of ECG signal analysis using MATLAB 7.1 Platform. An accurate and simple ECG feature extraction algorithm is presented in this paper and developed algorithm is validated using BIOPAC software. To detect the QRS peak, ECG signal is processed by following mentioned stages- First Derivative, Second Derivative and then squaring of that second derivative. Efficiency of developed algorithm is tested on ECG samples from different database and real time ECG signals acquired using BIOPAC system. Firstly we have lead wise specified threshold value the samples above that value is marked and in the original signal, where these marked samples face change of slope are spotted as R-peak. On the left and right side of the R-peak, faces change of slope identified as Q and S peak, respectively. Now the inbuilt Detection algorithm of BIOPAC software is performed on same output sample and both outputs are compared. ECG baseline modulation correction is done after detecting characteristics points. The efficiency of the algorithm is tested using some validation parameters like Sensitivity, Positive Predictivity and we got satisfied value of these parameters.Keywords: first derivative, variable threshold, slope reversal, baseline modulation correction
Procedia PDF Downloads 4044828 Graph-Based Semantical Extractive Text Analysis
Authors: Mina Samizadeh
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In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems.Keywords: keyword extraction, n-gram extraction, text summarization, topic clustering, semantic analysis
Procedia PDF Downloads 614827 Photovoltaic Maximum Power-Point Tracking Using Artificial Neural Network
Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani
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Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking
Procedia PDF Downloads 804826 Knowledge-Driven Decision Support System Based on Knowledge Warehouse and Data Mining by Improving Apriori Algorithm with Fuzzy Logic
Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam
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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic
Procedia PDF Downloads 3264825 Blind Watermarking Using Discrete Wavelet Transform Algorithm with Patchwork
Authors: Toni Maristela C. Estabillo, Michaela V. Matienzo, Mikaela L. Sabangan, Rosette M. Tienzo, Justine L. Bahinting
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This study is about blind watermarking on images with different categories and properties using two algorithms namely, Discrete Wavelet Transform and Patchwork Algorithm. A program is created to perform watermark embedding, extraction and evaluation. The evaluation is based on three watermarking criteria namely: image quality degradation, perceptual transparency and security. Image quality is measured by comparing the original properties with the processed one. Perceptual transparency is measured by a visual inspection on a survey. Security is measured by implementing geometrical and non-geometrical attacks through a pass or fail testing. Values used to measure the following criteria are mostly based on Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The results are based on statistical methods used to interpret and collect data such as averaging, z Test and survey. The study concluded that the combined DWT and Patchwork algorithms were less efficient and less capable of watermarking than DWT algorithm only.Keywords: blind watermarking, discrete wavelet transform algorithm, patchwork algorithm, digital watermark
Procedia PDF Downloads 2644824 Novel Inference Algorithm for Gaussian Process Classification Model with Multiclass and Its Application to Human Action Classification
Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park
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In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm
Procedia PDF Downloads 3294823 Modeling and Optimization of Micro-Grid Using Genetic Algorithm
Authors: Mehrdad Rezaei, Reza Haghmaram, Nima Amjadi
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This paper proposes an operating and cost optimization model for micro-grid (MG). This model takes into account emission costs of NOx, SO2, and CO2, together with the operation and maintenance costs. Wind turbines (WT), photovoltaic (PV) arrays, micro turbines (MT), fuel cells (FC), diesel engine generators (DEG) with different capacities are considered in this model. The aim of the optimization is minimizing operation cost according to constraints, supply demand and safety of the system. The proposed genetic algorithm (GA), with the ability to fine-tune its own settings, is used to optimize the micro-grid operation.Keywords: micro-grid, optimization, genetic algorithm, MG
Procedia PDF Downloads 5004822 Haemocompatibility of Surface Modified AISI 316L Austenitic Stainless Steel Tested in Artificial Plasma
Authors: W. Walke, J. Przondziono, K. Nowińska
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The study comprises evaluation of suitability of passive layer created on the surface of AISI 316L stainless steel for products that are intended to have contact with blood. For that purpose, prior to and after chemical passivation, samples were subject to 7 day exposure in artificial plasma at the temperature of T=37°C. Next, tests of metallic ions infiltration from the surface to the solution were performed. The tests were performed with application of spectrometer JY 2000, by Yobin – Yvon, employing Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). In order to characterize physical and chemical features of electrochemical processes taking place during exposure of samples to artificial plasma, tests with application of electrochemical impedance spectroscopy were suggested. The tests were performed with application of measuring unit equipped with potentiostat PGSTAT 302n with an attachment for impedance tests FRA2. Measurements were made in the environment simulating human blood at the temperature of T=37°C. Performed tests proved that application of chemical passivation process for AISI 316L stainless steel used for production of goods intended to have contact with blood is well-grounded and useful in order to improve safety of their usage.Keywords: AISI 316L stainless steel, chemical passivation, artificial plasma, ions infiltration, EIS
Procedia PDF Downloads 2584821 Fast and Scale-Adaptive Target Tracking via PCA-SIFT
Authors: Yawen Wang, Hongchang Chen, Shaomei Li, Chao Gao, Jiangpeng Zhang
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As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm.Keywords: target tracking, PCA-SIFT, mean-shift, scale-adaptive
Procedia PDF Downloads 4254820 Identification of the Parameters of a AC Servomotor Using Genetic Algorithm
Authors: J. G. Batista, K. N. Sousa, ¬J. L. Nunes, R. L. S. Sousa, G. A. P. Thé
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This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values.Keywords: modeling, AC servomotor, permanent magnet synchronous motor-PMSM, genetic algorithm, vector control, robotic manipulator, control
Procedia PDF Downloads 4634819 The Cultural Persona of Artificial Intelligence: An Analysis of Anthropological Challenges to Public Communication
Authors: Abhivardhan, Ritu Agarwal
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The role of entrepreneurial ethics is connected with materializing the core components of human life, and the flexible and gullible attributions dominate the materialization of human lifestyle and outreach in the age of the internet and globalization. One of the key bi-products of the age of information – Artificial Intelligence has become a relevant mechanism to materialize and understand human empathy and originality via various algorithmic policing methodologies with specific intricacies. Since it has a special connection with ethnocentrism – it has the potential to influence the approach of international law and politics owed to the rise of and approach towards perception and communication via populism in progressive and third world countries. The paper argues about the cultural persona of artificial intelligence, and its ontological resemblance in human life is connected with the ethnocentric treatment of cyberspace, with an analysis of the influence of the ethics of entrepreneurship in international politics. The paper further provides an analysis of fake news and misinformation as the sub-strata of communication strategies involving populism determined as a communication strategy and about the legal case of constitutional redemption in recent legislative developments in Europe, the U.S, and Asia with reference to certain important strategies, policy documentation, declarations, and legal instruments. The paper concludes that the capillaries of the anthropomorphic developments of cultural perception via towards artificial intelligence have a hidden and unstable connection with the common approach of entrepreneurial ethics, which influences populism to disrupt the peaceful order of international politics via some minor backlashes in the technological, legal and social realm of human life. Suggestions with the conclusion are hereby provided.Keywords: ethnocentrism, perception politics, populism, international law, slacktivism, artificial intelligence ethics, enculturation
Procedia PDF Downloads 1214818 An Efficient Algorithm for Global Alignment of Protein-Protein Interaction Networks
Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang
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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks
Procedia PDF Downloads 5974817 On the convergence of the Mixed Integer Randomized Pattern Search Algorithm
Authors: Ebert Brea
Abstract:
We propose a novel direct search algorithm for identifying at least a local minimum of mixed integer nonlinear unconstrained optimization problems. The Mixed Integer Randomized Pattern Search Algorithm (MIRPSA), so-called by the author, is based on a randomized pattern search, which is modified by the MIRPSA for finding at least a local minimum of our problem. The MIRPSA has two main operations over the randomized pattern search: moving operation and shrinking operation. Each operation is carried out by the algorithm when a set of conditions is held. The convergence properties of the MIRPSA is analyzed using a Markov chain approach, which is represented by an infinite countable set of state space λ, where each state d(q) is defined by a measure of the qth randomized pattern search Hq, for all q in N. According to the algorithm, when a moving operation is carried out on the qth randomized pattern search Hq, the MIRPSA holds its state. Meanwhile, if the MIRPSA carries out a shrinking operation over the qth randomized pattern search Hq, the algorithm will visit the next state, this is, a shrinking operation at the qth state causes a changing of the qth state into (q+1)th state. It is worthwhile pointing out that the MIRPSA never goes back to any visited states because the MIRPSA only visits any qth by shrinking operations. In this article, we describe the MIRPSA for mixed integer nonlinear unconstrained optimization problems for doing a deep study of its convergence properties using Markov chain viewpoint. We herein include a low dimension case for showing more details of the MIRPSA, when the algorithm is used for identifying the minimum of a mixed integer quadratic function. Besides, numerical examples are also shown in order to measure the performance of the MIRPSA.Keywords: direct search, mixed integer optimization, random search, convergence, Markov chain
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