Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Статья научная
Surveillance tests are performed periodically on standby systems of a Nuclear Power Plant (NPP), as they improve the systems’ availability on demand. High availability of safety critical systems is very essential to NPP safety, hence, careful analysis is required to schedule the surveillance activities for such systems in a cost effective way without compromising the plant safety. This forms an optimization problem wherein, two different cases can be formulated for deciding the value of Surveillance Test Interval. In one case, cost is the objective function to be minimized while unavailability is constrained to be at a given level and in another case, unavailability is minimized for a given cost level. Here, optimization is done using Genetic Algorithm (GA) and real encoding has been employed as it caters well to the requirements of this problem. A detailed procedure for GA formulation is described in this paper. Two different crossover methods, arithmetical crossover and blend crossover are explored and compared in this study to arrive at the most suitable crossover method for such type of problems.
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Study on Optimization of Phase Offset at Adjacent Intersections
Статья научная
Optimization of the phase offset at adjacent intersections is the key parameter regarding coordinated control of traffic signal for adjacent intersections, which decides the effect of the coordinated control for adjacent intersections. According to characters of saturated traffic flow of Chinese urban road, this thesis establishes a model for optimization of phase offset for adjacent interactions and finds a solution from such model by adopting genetic algorithm. The model is verified by actual traffic flow datum of two adjacent signal intersections on Changan Avenue. Then a comparison is made between the optimization result of such model and that of the existing mathematical method and SYNCHRO model, which indicates that the model established by this thesis can reduce the delay suffered by vehicles at the intersections and increase the traffic efficiency of the intersections.
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Study on The Earthquake Disaster Reduction Information Management System and Its Application
Статья научная
It is significant to scientifically carry out the urban earthquake disaster reduction. According to the features of China urban earthquake disaster reduction, this paper designed the urban earthquake disaster reduction information management system, which proposed a system design idea, system composition and function structure. The system adopted the object-oriented language VB6.0 and the component set ArcGIS Engine provided by ESRI for development. We applied a variety of information techniques (GIS and database) for spatial information acquisition, analysis and computing, and drew up function modules corresponding to inquiry, spatial analysis, risk analysis and data management. By using this system we can achieve scientific management about the earthquake disaster information in storage and transportation engineering, draw up kinds of earthquake emergency decisions intellectually and make them visual, which improved the efficiency and velocity of earthquake emergency evidently, and assisted the decision-making system effectively for the earthquake emergency work.
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Study on The Novel Transient Bus Protection Based on Morphological Top-bottom-operator
Статья научная
A novel transient component bus protection based on mathematical morphology is presented in this paper, which takes the morphological max top-bottom-operator of current traveling wave to fast distinguish the bus internal fault from the external fault. The method is based on the principle that the high frequency component of transient traveling wave caused by bus external fault will be attenuated by the bus capacitance but the traveling wave caused by bus internal fault changes slightly. Simulation is carried out with the electromagnetic transient simulation software PSCAD/EMTDC, the result verifies the bus protection is reliable and accurate. The novel bus protection also can treat lightning failure or lightning disturbance happened on transmission lines as bus external fault, without malfunction.
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Sugarcane crop yield forecasting model using supervised machine learning
Статья научная
Agriculture is the most important sector in the Indian economy and contributes 18% of Gross Domestic Product (GDP). India is the second largest producer of sugarcane crop and produces about 20% of the world's sugarcane. In this paper, a novel approach to sugarcane yield forecasting in Karnataka(India) region using Long-Term-Time-Series (LTTS), Weather-and-soil attributes, Normalized Vegetation Index(NDVI) and Supervised machine learning(SML) algorithms have been proposed. Sugarcane Cultivation Life Cycle (SCLC) in Karnataka(India) region is about 12 months, with plantation beginning at three different seasons. Our approach divides yield forecasting into three stages, i)soil-and-weather attributes are predicted for the duration of SCLC, ii)NDVI is predicted using Support Vector Machine Regression (SVR) algorithm by considering soil-and-weather attributes as input, iii)sugarcane crop is predicted using SVR by considering NDVI as input. Our approach has been verified using historical dataset and results have shown that our approach has successfully modeled soil and weather attributes prediction as 24 steps LTTS with accuracy of 85.24% for Soil Temperature given by Lasso algorithm, 85.372% accuracy for Temperature given by Naive-Bayes algorithm, accuracy for Soil Moisture is 77.46% given by Naive-Bayes, NDVI prediction with accuracy of 89.97% given by SVR-RBF, crop prediction with accuracy of 83.49% given by SVR-RBF.
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Super-resolution Image Created from a Sequence of Images with Application of Character Recognition
Статья научная
Super-resolution techniques allow combine multiple images of the same scene to obtain an image with increased geometric and radiometric resolution, called super-resolution image. In this image are enhanced features allowing to recover important details and information. The objective of this work is to develop efficient algorithm, robust and automated fusion image frames to obtain a super-resolution image. Image registration is a fundamental step in combining several images that make up the scene. Our research is based on the determination and extraction of characteristics defined by the SIFT and RANSAC algorithms for automatic image registration. We use images containing characters and perform recognition of these characters to validate and show the effectiveness of our proposed method. The distinction of this work is the way to get the matching and merging of images because it occurs dynamically between elements common images that are stored in a dynamic matrix.
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Статья научная
Generalized Adaptive Linear Element (GADALINE) Artificial Neural Network (ANN) as an Artificial Intelligence (AI) technique is used in this paper to online adaptive control of a Non-linear Inverted Pendulum (IP) system. The ANN controller is designed with specifications as: network type is three (Input, Hidden and Output) layered Feed-Forward Network (FFN), training is done by Widrow-Hoffs delta rule or Least Mean Square algorithm (LMS), that updates weight and bias states to minimize the error function. The research is focused on how to adapt the control actions to solve the problem of “parameter variations”. The method is applied to the Nonlinear IP model with the application of some uncertainties, and the experimental results show that the system responds very well to handle those uncertainties.
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Supervised support vector machine in predicting foreign exchange trading
Статья научная
Trends of currency rates can be predicted with supporting from supervised machine learning in the transaction systems such as support vector machine (SVM). By assumption of binary classification problems, the SVM can predict foreign exchange transaction as uptrend or downtrend. The prediction is performed basing on collected historical data. Alternative SVM models have been used to vote the best one, which is deployed detail in Expert Advisor (Robotics). This is to show that support vector machine models might help investors to automatically make transaction decisions of Bid/Ask in Foreign Exchange Market. For comparison, the transactions without using SVM model also are performed. The results of experimental transactions show the advantages of using SVM model compared to the transactions without using SVM model.
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Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering
Статья научная
In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa) based Analytic Hierarchy Process (AHP) is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.
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Support-Intuitionistic Fuzzy Set: A New Concept for Soft Computing
Статья научная
Today, soft computing is a field that is used a lot in solving real-world problems, such as problems in economics, finance, banking... With the aim to serve for solving the real problem, many new theories and/or tools which were proposed, improved to help soft computing used more efficiently. We can mention some theories as fuzzy sets theory (L. Zadeh, 1965), intuitionistic fuzzy set (K Atanasov, 1986). In this paper, we introduce a new notion of support-intuitionistic fuzzy (SIF) set, which is the combination a intuitionistic fuzzy set with a fuzzy set. So, SIF set is a directly extension of fuzzy set and intuitionistic fuzzy sets (Atanassov). Then, we define some operators on support-intuitionistic fuzzy sets, and investigate some properties of these operators.
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Survey on answer validation for Indonesian question answering system (IQAS)
Статья научная
Research on Question Answering System (QAS) has been done mainly in English. Unfortunately, for Indonesian, it is still rarely explored whereas Indonesian is the official language used more than 250 million people. Research in the area of Indonesian Question Answering System (IQAS) began in 2005s, and since then only few number of IQAS have been developed. One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS. To identify the future scope of research in this area, the need of comprehensive survey on IQAS and AV arises naturally. The goals of this survey are to find the cutting-edge method used in AV and to prove that AV has not been implemented on IQAS. Based on the results, we suggest new opportunities and research challenges for IQAS community.
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Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study
Статья научная
This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer’s perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers’ preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.
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System with Distributed Lag: Adaptive Identification and Prediction
Статья научная
Adaptive algorithms of parametric identifica-tion of discrete systems with lag variables are proposed. Adaptive algorithms (AA) in the presence of lag input variables are developed. The convergence of the AA and the boundedness of the trajectories the adaptive system is proved. Convergence domain АА depends on operating disturbance. Models with multiplicative parameters (MPM) for the decrease of a number estimated parameters are offered. The process for selection of the vector of base parameters MPM was developed. The performance of adaptive system identification for this case is proved. It is shown that parameters of system estimation at the application of multiplicative identification must be chosen from a condition of minimization of the criterion of the prediction error. Transformation of interdependence be-tween the lagged variables is offered, allowing eliminating their effect on system work. In the second part of work, the method of synthesis АА identification of the systems containing lagged output variables is offered. We consider a case of linear correlation between an output of the system and operating disturbance. For a solution of a problem, we suggest fulfilling an estimation of operating disturbance. Corresponding procedures are described and proved their efficiency. Simulation results are presented that confirm the efficiency of the adaptive methods.
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Technology of gene expression profiles filtering based on wavelet analysis
Статья научная
The paper presents the technology of gene expression profiles filtering based on the wavelet analysis methods. A structural block-chart of the wavelet-filtering process, which involves concurrent calculation of Shannon entropy for both the filtered data and allocated noise component is proposed. Simulation of the wavelet-filtering process was performed with the use of orthogonal and biorthogonal wavelets on different levels of wavelet decomposition and with the use of various values of the thresholding coefficient. Result of the simulation has allowed us to propose the technology to determine the optimal parameters of the wavelet filter based on complex analysis of the filtered data and allocated noise component.
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Temperament and Mood Detection Using Case-Based Reasoning
Статья научная
Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our system is to help individuals who are out of reach of a professional psychologist to manage their personality and moods because as humans, moods affect our perceptions, personal health, the way we view the world around us and the way we react to it.
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Статья научная
This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.
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Temporal Weather Prediction using Back Propagation based Genetic Algorithm Technique
Статья научная
Hybrid back propagation based genetic algorithm approach is a popular way to train neural networks for weather prediction. The major drawback of this method is that weather parameters were assumed to be independent of each other and their temporal relation with one another was not considered. So in the present research a modified time series based weather prediction model is proposed to eliminate the problems incurred in hybrid BP/GA technique. The results are very encouraging; the proposed temporal weather prediction model outperforms the previous models while performing for dynamic and chaotic weather conditions.
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Temporal community-based collaborative filtering to relieve from cold-start and sparsity problems
Статья научная
Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the temporal changes in the community structure to enhance the existing system. Our framework also provides solutions to common inherent issues of collaborative filtering approach such as cold-start, sparsity and compared against static and traditional collaborative systems. The outcomes indicate that the proposed system yields higher values in quality standards and minimizes the drawbacks of the traditional recommender system.
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Text Classification based on Discriminative-Semantic Features and Variance of Fuzzy Similarity
Статья научная
Due to the rapid growth of the Internet, large amounts of unlabelled textual data are producing daily. Clearly, finding the subject of a text document is a primary source of information in the text processing applications. In this paper, a text classification method is presented and evaluated for Persian and English. The proposed technique utilizes variance of fuzzy similarity besides discriminative and semantic feature selection methods. Discriminative features are those that distinguish categories with higher power and the concept of semantic feature takes into the calculations the similarity between features and documents by using only available documents. In the proposed method, incorporating fuzzy weighting as a measure of similarity is presented. The fuzzy weights are derived from the concept of fuzzy similarity which is defined as the variance of membership values of a document to all categories in the way that with some membership value at the same time, the sum of these membership values should be equal to 1. The proposed document classification method is evaluated on three datasets (one Persian and two English datasets) and two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Comparing the results with other text classification methods, demonstrate the consistent superiority of the proposed technique in all cases. The weighted average F-measure of our method are %82 and %97.8 in the classification of Persian and English documents, respectively.
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Статья научная
In this article an investigation into search operations for the multiplicative inverse in the ring of integers modulo m for Error Control Coding tasks and for data security is shown. The classification of the searching operation of the multiplicative inverse in the ring of integers modulo m is provided. The best values of parameters for Joye-Paillier method and Lehmer algorithm were also found. The improved Bradley modification for the extended Euclidean algorithm is also offered, which gives the operating speed improvement for 10-15%. The integrated experimental research of basic classes of searching methods for multiplicative inverse in the ring of integers modulo m is conducted for the first time and the analytical formulas for these calculations of random access memory necessary space when operated at k-ary RS-algorithms and their modifications are shown.
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