Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1126
Power Optimized Multiplier Using Shannon Based Multiplexing Logic
Статья научная
In Digital Image Processing, Median Filter is used to reduce the noise in an image. The median filter considers each pixel in the image and replaces the noisy pixel by the median of the neighbourhood pixels. The median value is calculated by sorting the pixels. Sorting in turn consists of comparator which includes adders and multiplier. Multiplication is a fundamental operation in arithmetic computing systems and is used in many DSP applications such as FIR Filters. The adder circuit is used as a main component in the multiplier circuits. The Carry Save Array (CSA) multiplier is designed by using the proposed adder cell based on multiplexing logic. The proposed adder circuit is designed by using Shannon theorem.The multiplier circuits are schematised and their layouts are generated by using VLSI CAD tools. The proposed adder based multiplier circuits are simulated and results are compared with CPL and other circuit designed using Shannon based adder cell in terms of power and area and the intermediate state involved in the circuit is eliminated.The proposed adder based multiplier circuits are simulated by using 90nm feature size and with various supply voltages. The Shannon full adder circuit based multiplier circuits gives better performance than other published results in terms of power dissipation and area due to less number of transistors used in Shannon adder circuit.
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Статья научная
Low frequency oscillations result due to heavy loading conditions line faults, sudden change of generator output and also due to poor damping of interconnected power systems. There are different types of disturbances in the power system like sudden change of load, generation, faults, switching of lines. This hampers the power transmission capacity of the lines and the stability of the system There are significant impacts on the system stability during the charging and discharging operation of Electric Vehicle (EV). In the present work the charging operation of EV is considered as a load disturbance. The introduction of these vehicles in the system creates the problem of low frequency oscillation and endanger the system stability and security. In the present work the Single machine infinite bus system (SMIB) is first developed using mathematical modelling with consideration of EV disturbance. The LQR approach from optimal control theory is then applied in the system to damp the system oscillations, improving the system eigenvalues and enhancing the stability. The stability is seen in the system after LQR from various figures. In the second work the plotting of variation of different state variables is done using three different methods which are the transfer function model method, using code and then using state space representation of the system. The work is further extended by adding Power system stabilizer (PSS) to the system, again considering the EV disturbance. The time domain simulation results showed the improvement in stability using PSS device. Thus, in the present work the oscillations problems created due to the introduction of electric vehicles are solved by two methods. The first is implementing LQR approach from optimal control theory in the system and the second method is by adding PSS device in the same system.
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Статья научная
In this paper, power quality at the distribution system has been examined by introducing an observer based control technique with fuzzy logic controller for wind energy conversion systems with constant wind velocity, for combination of linear, nonlinear loads and with load removal in one of the phases. The power quality improvement, including voltage regulation and reactive power management on the distribution side is achieved and the device used here is a distributed static compensator (DSTATCOM) a voltage source converter(VSC) based power electronic device. The performance is found to be satisfactory with the implementation of DSTATCOM for better voltage regulation with self-sustained DC link voltage at VSC of DSTATCOM. The fuzzy logic controller is used to generate gate pulses to VSC for power quality improvement and is simulated in MATLAB environment and results are studied.
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Статья научная
In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PMBBO) is presented. The approach uses several predators with new proposed prey’s movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (Mass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.
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Predication and Optimization of Maintenance Resources for Weapon System
Статья научная
Maintenance resources are important part of the maintenance support system. The whole efficiency of weapon system is directly affected by the allocation of maintenance resources. Joint support for weapon system of multi-kinds of equipments is the main fashion of maintenance support in the future. However, there is a lack of the efficiency tools and methods for predication and optimization of weapon system maintenance resources presently. For the prediction requirement of maintenance resources of weapon system, the primary infection factors for the requirement of maintenance resources were analyzed. According to the different characteristics of maintenance resources and the analysis for the traditional classification methods, a kind of classification for weapon system’s maintenance resources was given. A prediction flow for the maintenance resources requirement was designed. Four kinds of models for predicting the maintenance resources requirement in a weapon system were designed and described in detail. In this paper, approaches of the optimal selection from the simulation schemes and reverse simulation for the resources allocation optimization were analyzed; some optimization models for maintenance resources such as spare parts and personnel were constructed. Further more, an optimization and decision-making system was not only designed but also developed. At last, an example was presented, which proved the prediction and optimization methods were applicability and feasibility, the decision-making system for the optimization of maintenance resources was a supportable and efficient tool.
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Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models
Статья научная
This paper analyses the performance of machine learning models in forecasting the Tehran Stock Exchange's automobile index. Historical daily data from 2018-2022 was pre-processed and used to train Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) models. The models were evaluated on mean absolute error, mean squared error, root mean squared error and R2 score metrics. The results indicate that LR and SVR outperformed RF in predicting automobile stock prices, with LR achieving the lowest error scores. This demonstrates the capability of machine learning techniques to model complex, nonlinear relationships in financial time series data. This pioneering study on a previously unexplored dataset provides empirical evidence that LR and SVR can reliably forecast automobile stock market prices, holding promise for investing applications.
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Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks
Статья научная
Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.
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Predicting Future Products Rate using Machine Learning Algorithms
Статья научная
Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.
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Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis
Статья научная
Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.
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Predicting Student Academic Performance at Degree Level: A Case Study
Статья научная
Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.
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Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique
Статья научная
Clustering is partitioning of data set into subsets (clusters), so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.
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Prediction of Drought Resistance Gene with Clustered Amino Acid Features
Статья научная
Drought resistant gene plays important role in molecular breeding while little is known for its genetic mechanism. By extracting the clustered amino acids features, crucial numerical features are inferred for the resistance property of the given gene. Support vector machine algorithm is used to testify the reliability of feature extraction method. After carefully parameters choosing, the accuracy of the predictor achieves 79.36% in Jack-knife test, and the Mathews correlation coefficient achieves 0.5636.
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Prediction of Missing Associations Using Rough Computing and Bayesian Classification
Статья научная
Information technology revolution has brought a radical change in the way data are collected or generated for ease of decision making. It is generally observed that the data has not been consistently collected. The huge amount of data has no relevance unless it provides certain useful information. Only by unlocking the hidden data we can not use it to gain insight into customers, markets, and even to setup a new business. Therefore, the absence of associations in the attribute values may have information to predict the decision for our own business or to setup a new business. Based on decision theory, in the past many mathematical models such as naïve Bayes structure, human composed network structure, Bayesian network modeling etc. were developed. But, many such models have failed to include important aspects of classification. Therefore, an effort has been made to process inconsistencies in data being considered by Pawlak with the introduction of rough set theory. In this paper, we use two processes such as pre process and post process to predict the output values for the missing associations in the attribute values. In pre process we use rough computing, whereas in post process we use Bayesian classification to explore the output value for the missing associations and to get better knowledge affecting the decision making.
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Prediction of Operating Loads Contribution to Assembly Relation and Product Behavior
Статья научная
In the process of product manufacturing, control of assembly error will directly affect product operating behavior. When product running, operating loads will lead to change of assembly relation of product parts, affecting product behavior. Based on Jacobian-Torsor method, the Jacobian Torsor tolerance model, considering contribution of operating loads, was extended and corrected, the assembly error (assembly relation change) resulted from operating loads can be calculated. Variation of running behavior with assembly error was divided to three phases: compensation phase, rapid loss phase and total loss phase. Based on changing curve of product behavior, function of behavior loss was constructed to describe behavior loss resulting from assembly error of a part of product. The conception and calculating method of behavior loss index (BLI) are given to reflect behavior changing status of whole product under certain assembly accuracy. Combined with extended Jacobia -Torsor method, the calculated results can be used to predict product behavior change led by operating loads. The prediction can help to know next measurement adopted in product design phase. An example is given to demonstrate calculating procedure of given method.
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Prediction of Possible Business of a Newly Launched Film using Ordinal Values of Film-genres
Статья научная
Film industry is the most important component of entertainment industry. Both profit and loss are very high for this business. Like every other business, business prediction system plays a vital role for this industry. Before release of a particular movie, if the Production Houses or distributors get any type of prediction that how the film will do business, then it will be very useful to reduce the risk of the investors. In this paper we have proposed a method using back propagation neural network for prediction about a given movie’s profitability. Initially the entire range of profit-loss has been divided into a number of groups. The proposed algorithm can assign a given movie to it’s appropriate profit-loss group. Note that, a similar such method has been successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.
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Prediction of Rainfall in India using Artificial Neural Network (ANN) Models
Статья научная
In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.
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Prediction of Stock Market in Nigeria Using Artificial Neural Network
Статья научная
Prediction of Nigerian stock market is almost not done by any researcher and is an important factor which can be used to determine the viability of Nigerian stock market. In this paper, the prediction models were developed using Artificial Neural Network. The result of the prediction of Nigerian Stock Exchange (NSE) market index value of selected banks using Artificial Neural Network was presented. The multi-layer feed forward neural network was used, so that each output unit is told what its desired response to input signals ought to be. This work has confirmed the fact that artificial neural network can be used to predict future stock prices. The data collection period is from 2003 to 2006.
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Prediction of performance point of semi-rigid steel frames using artificial neural networks
Статья научная
One of the main steps in the performance based seismic analysis and design of structures is determination of performance point where the nonlinear static analysis approach is used. The aim of this paper is to predict the performance point of semi-rigid steel frames using Artificial Neural Networks. As such, to generate data required for the prediction, several semi-rigid steel frames were modeled and their performance point was determined then. Ten input variables including number of bays, number of stories, bays width, moment of inertia of beams, cross sectional area of columns, cross sectional area of braces, rigidity degree of connections and soft story (existence or nonexistence) were considered in the prediction. In addition, the actual results were obtained at the presence of different earthquake intensity levels and soil types. Back Propagation with eleven different algorithms and Radial Basis Function Artificial Neural Networks were used in the prediction. The prediction process was carried out in two steps. In the first step, all samples were used for the prediction and the performance metrics were computed. In the second step, three of the best networks were selected, and the optimum number of samples was found considering a very slight reduction in the accuracy of the networks used. Finally, it was shown that, despite using rather limited number of samples, the generated Artificial Neural Networks accurately predict the performance point of semi-rigid steel frames.
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Prediction of water demand using artificial neural networks models and statistical model
Статья научная
The prediction of future water demand will help water distribution companies and government to plan the distribution process of water, which impacts on sustainable development planning. In this paper, we use a linear and nonlinear models to predict water demand, for this purpose, we will use different types of Artificial Neural Networks (ANNs) with different learning approaches to predict the water demand, compared with a known type of statistical methods. The dataset depends on sets of collected data (extracted from municipalities databases) during a specific period of time and hence we proposing a nonlinear model for predicting the monthly water demand and finally provide the more accurate prediction model compared with other linear and nonlinear methods. The applied models capable of making an accurate prediction for water demand in the future for the Jenin city at the north of Palestine. This prediction is made with a time horizon month, depending on the extracted data, this data will be used to feed the neural network model to implement mechanisms and system that can be employed to predicts a short-term for water demands. Two applied models of artificial neural networks are used; Multilayer Perceptron NNs (MLPNNs) and Radial Basis Function NNs (RBFNNs) with different learning and optimization algorithms Levenberg Marquardt (LM) and Genetic Algorithms (GAs), and one type of linear statistical method called Autoregressive integrated moving average ARIMA are applied to the water demand data collected from Jenin city to predict the water demand in the future. The execution results appear that the MLPNNs-LM type is outperformed the RBFNN-GAs and ARIMA models in the prediction the water demand values.
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Process Modeling and Simulation of Feedwater Heaters Drains and Vents System of PFBR
Статья научная
Nuclear Power Plants are a complex system and need to be controlled very meticulously to avoid any catastrophe from occurring. The safety and availability of the power plant relies on the human operators both through their ability and reliability to ensure smooth and trouble-free plant operations. Training the operators on normal plant operation, maintenance, fault diagnosis and unforeseen emergencies in the plant helps reduce the latency period of the plant and thus increase the efficiency. Operator Training Simulator has become an indispensable entity in imparting hands on training to these operators. Development of process simulators calls for the process to be designed, modeled and implemented to replicate the real plant in steady state and transient conditions.
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