Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Application of Neural Network on Burr Expert System in Micro-machining
Статья научная
The demands placed by designers on workpiece performance and functionality are increasing rapidly. Important aspects of manufacturing’s contribution to the fulfillment of these demands are the conditions at the work piece edges. However, Burrs are often created on the workpiece edges in micro-machining. In many cases, time consuming and expensive deburring processes have to be applied in order to ensure the desired part functionality. Burrs make troubles on production lines in terms of deburring cost, quality of products and cutting tool wear. To prevent problems caused by burrs in micro-machining, prediction and control of burr size is desirable. Experimental studies show that burr formation in micro-milling is a highly complex process depending on a number of parameters such as material properties, tool geometry and cutting parameters. It is very difficult to establish the relationship between burr sizes and cutting conditions. A web-based micro-machining burr expert system for burr sizes prediction and control was developed using ASP.NET platform. Burrs types and sizes prediction and cutting conditions optimization for burr controlling which based on the reasoning method of BP neural networks are realized. Operation results show that the system is reliable. It provides a new technology for burrs modelling and controlling.
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Статья научная
Neural network models are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went to step forward by optimizing neural network models using three intelligent optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), and ant colony (AC), to eliminate the risk of being exposed to local minima. This strategy was capable of significantly improving the accuracy of a neural network by optimizing network parameters such as weights and biases. Nuclear magnetic resonance (NMR) log measures some of the most useful characteristics of reservoir rock; the capabilities of the optimized models were used for prediction of nuclear magnetic resonance (NMR) log parameters in a carbonate reservoir rock of Iran. Conventional porosity logs, which are the easily accessible tools compared to NMR log’s parameters, were introduced to the models as inputs while free fluid porosity and permeability, which were measured by NMR log, are desire outputs. The performance of three optimized models was verified by some unseen test data. The results show that PSO-based network and ACO-based network is the best and poorest method, respectively, in terms of accuracy; however, the convergence time of GA-based model is considerably smaller than PSO-based and GA-based models.
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Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier
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Scour around the bridge pier is one of the major factors which affect the safety and stability of the bridge structure. Due to the presence of complexity in the scour mechanism, there is no common and simple method to estimate the scour depth. The present paper gives an idea of hybridizing two techniques such as an artificial neural network with swarm intelligence technique particle swarm optimization to estimate the scour depth around the bridge pier and abbreviated as PSO-ANN. The present discussion covers the estimation of scour depth for clear water and live bed scour condition around circular and rectangular pier shapes. The independent variables, Sediment size (d50), sediment quantity (Sq), velocity (u) and time (t) are used as input to develop the models to estimate or quantify a dependent variable scour depth (ds). The efficiency and accuracy of the model are measured using model performances indicators such as Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE), Nash Sutcliffe Error (NSE), and Normalized Mean Bias (NMB). The predicted results of both the models are compared with each other and also compared with measured scour depth. The study concludes that the proposed PSO-ANN model is suitable to estimate the scour depth in both the cases for circular and rectangular pier shapes.
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Статья научная
The stochastic character of asymmetrical loads in power networks emerged due to non-simultaneous activation of phases of various single-phase and poly-phase receivers, nonlinear characteristics of transformers and other reasons have caused the occurrence of currents and voltages of zero sequence. These electrical quantities with currents and voltages of direct sequence in a negative sense affect the asymmetry of phase voltages in networks on places where loads are connected. In this paper, the presented load is induction machine with coil connection in star connected to generic distribution system TN. We analyze the possibilities of simple CL structures of filter in the role of the device for correction of asymmetries to a network, which can be entered by zero sequence current occurred for some reason in induction machine (mostly non-simultaneous switching of phase coils of induction machine).
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Application of SQL RAT Translation
Статья научная
Since we have already designed a flexible form of representing the Relational Algebra Tree (RAT) translated by the SQL parser, the application of this kind of object-oriented representation should be explored. In this paper, we will show you how to apply this technique to complicated scenarios. The application of Reverse Query Processing and Reverse Manipulate Processing related to this issue will be discussed.
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Application of Weighted Additive Fuzzy Goal Programming Approach to Quality Control System Design
Статья научная
The problem of decision-making in designing a quality control system (QCS), is one of the most difficult problems decisions facing the manager in the industrial firms , this problem of decision requires of fixing the levels of inputs and variables that meet the required output specifications. in the context of the problem a QCS, the parameters can be imprecise and expressed through intervals or fuzzy. The aim of this study is to presents the formulation for designing a QCS based on Weighted fuzzy goal programming (WAFGP) developed by Yaghoobi and Tamiz [12] and Yaghoobi et al [13], the advantage of the proposed formulation as a linear , use all types of membership functions and integrate explicitly the decision-maker’s preference . Finally, we compare the results of our model with the major important mathematical models used in the QCS It has been shown that the best model.
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Application of bat algorithm for texture image classification
Статья научная
Textural feature extraction of image is a basic work for image analysis. A number of approaches have been put forward to describe texture features quantitatively, such as gray level co-occurrence matrix, fractal wavelet, Gabor wavelet and local binary pattern etc, among them texture feature extracted based on “tuned” mask will not suffer from rotation and scale of images. However, it needs to take a lot of time to learn the tuned mask with the traditional methods and could not acquire the satisfying mask sometimes. In essence, it is a very hard combinational optimization problem and easy to fall into the local optimum with mountain climbing method. Bat algorithm is a newly proposed meta-heuristic optimization, which is employed to tune the optimal mask in the paper. The procedure of bat algorithm to learn the tuned mask is detailed. Experiments results testifies that the proposed method is propitious to draw texture features, its performance is better than the simple particle swarm optimization and genetic algorithm based mask tuning scheme, which is a robust approach for texture image analysis.
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Статья научная
This article addresses an optimal allocation of multi Distributed Generation (DG) units in an Indian practical radial distribution network (RDN) for minimization of network loss and voltage deviation. For this work, combined sensitivity index (CSI) is utilized to identify the appropriate positions/locations of DG units. However, the appropriate size of DG is determined through a nature-inspired; population-based Bird Swarm Algorithm (BSA). Secondly, the influence of DG penetration level on network loss and voltage profile is investigated and presented. In this regard, two types of DG technologies (solar and biomass) are considered for loss reduction and voltage deviation reduction. The performance of CSI and BSA methodology is successfully evaluated on an Indian practical 52-bus RDN.
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Application of the Rise Feedback Control in Chaotic Systems
Статья научная
In this paper a new RISE controller is gained to control chaos in a tracking task. The technique copes with the chattering phenomenon whilst works for different classes of nonlinear systems incorporating different relative degrees. This control strategy will be primarily implemented on a Duffing chaotic system. In order to assess performance of the controller, the technique will be implemented on a more complex system, so called Genesio-Tesi dynamic. The result will be finally compared with an optimal controller. The capability of the proposed feedback technique to control the chaos is verified through simulation study with respect to similar classic approaches.
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Approximate Reasoning through Multigranular Approximate Rough Equalities
Статья научная
The notion of rough set was introduced by Pawlak as an uncertainty based model, which basically depends upon single equivalence relations defined over a universe or a set of equivalence relations, which are not considered simultaneously. Hence, from the granular computing point of view it is unigranular by nature. Qian et al in 2006 and in 2010 introduced two types of multigranular rough sets (MGRS) called the optimistic and pessimistic MGRS respectively. The stringent notion of mathematical equality of sets was extended by introducing a kind of approximate equality, called rough equality by Novotny and Pawlak, which uses basic rough sets. Later three more related types of such approximate equalities have been introduced by Tripathy et al. He has also provided a comparative analysis of these four types of approximate equalities of sets leading to approximate reasoning in real life situations. Two of these four types of approximate equalities; namely the rough equality and rough equivalence have been extended to the context of multigranulations by Tripathy et al very recently. In this paper we carry out this study further by introducing the notion of approximate rough equalities for multigranulations and establish their properties. We use a real life example to illustrate the results in the paper and also to construct examples in support of some parts of the properties.
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Arabic Opinion Mining Using Combined CNN - LSTM Models
Статья научная
In the last few years, Sentiment Analysis regarding customers' reviews in order to comprehend the opinion polarity on social media has received considerable attention. However, the improvement of deep learning for sentiment analysis relating to customer reviews in Arabic language has received less attention. In fact, many users post and jot down their reviews in Arabic daily, so we ought to shed more light on Arabic sentiment analysis. Most likely all previous work depends on conventional classification techniques, such as KNN, Naïve Bayes (NB), etc. But in this work, we implement two deep learning models: Long Short Term Memory (LSTM) and Convolution Neural Networks (CNN), in addition to three traditional techniques: Naïve Bayes, K-Nearest Neighbor (KNN), Decision trees for sentiment analysis and compared the experimental results. Also, we offer a combined model from CNN and Recurrent Neural Network (RNN) architecture where this model collects local features through CNN as the input for RNN for Arabic sentiment analysis of short texts. An appropriate data preparation has been conducted for each utilized dataset. Our Conducted experiments for each dataset against traditional machine learning classifier; KNN, NB, and decision trees and regular deep learning models; CNN and LSTM, has resulted in impressive performance using our proposed combined (CNN-LSTM) model with an average accuracy of 85,83%, 86,88% for HTL and LABR datasets respectively.
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Arabic Text Categorization Using Logistic Regression
Статья научная
Several Text Categorization (TC) techniques and algorithms have been investigated in the limited research literature of Arabic TC. In this research, Logistic Regression (LR) is investigated in Arabic TC. To the best of our knowledge, LR was never used for Arabic TC before. Experiments are conducted on Aljazeera Arabic News (Alj-News) dataset. Arabic text-preprocessing takes place on this dataset to handle the special nature of Arabic text. Experimental results of this research prove that the LR classifier is a competitive Arabic TC algorithm to the state of the art ones in this field; it has recorded a precision of 96.5% on one category and above 90% for 3 categories out of the five categories of Alj-News dataset. Regarding the overall performance, LR has recorded a macroaverage precision of 87%, recall of 86.33% and F-measure of 86.5%.
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Статья научная
Modern Integrated circuits (ICs) suffer from excessive power and temperature issues because of embedding a large number of applications on small silicon real estate. Low power technique is introduced to reduce the power. With the reduction of power, area of circuit increases and vice versa. It shows a trade-off nature between them. Increase of area is against the trend of technology scaling which demands small area. Due to small area and high power dissipation, power-density increases. As power-density is directly converging into temperature, it emerges as a challenge in front of the VLSI design engineer to minimize the effect of temperature by reducing power-density. In this work, an attempt has been made to reduce the effect of power-density along with area and power so that AND-XOR based circuit is balanced in terms of area, power, and temperature. AND-XOR based reed-muller (RM) mixed polarity circuit forms are considered in this work. Polarity conversions are made in such a way that possibility of maximum sharing among the sub-function is increased. Genetic algorithm is (a non-exhaustive heuristic algorithm) used to select the polarity of the input variable for maximum sharing. The proposed synthesis approach shows 27.11%, 20.69%, and 32.30% savings in area, power, and power-density respectively than that of reported results. For the validation of the proposed approach, the best solutions are implemented in Cadence digital domain to obtain actual silicon area and power consumption. HotSpot tool is used to get the absolute temperature of the circuit.
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Статья научная
This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D) fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load.
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Artificial Fish School Algorithm Applied in a Combinatorial Optimization Problem
Статья научная
An improved artificial fish swarm algorithm (AFSA) for solving a combinatorial optimization problem—a berth allocation problem (BAP), which was formulated. Its objective is to minimize the turnaround time of vessels at container terminals so as to improve operation efficiency customer satisfaction. An adaptive artificial fish swarm algorithm was proposed to solve it. Firstly, the basic principle and the algorithm design of the AFSA were introduced. Then, for a test case, computational experiments explored the effect of algorithm parameters on the convergence of the algorithm. Experimental results verified the validity and feasibility of the proposed algorithm with rational parameters, and show that the algorithm has better convergence performance than genetic algorithm (GA) and ant colony optimization (ACO).
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Artificial Intelligence Based Domotics Using Multimodal Security
Статья научная
All electronic devices in our cutting-edge technology world must be networked together via the Internet if users want to have remote access to them. As a result, it may raise a variety of serious security issues. This study suggests a remote access home automation security system that incorporates utilizing the Internet of Things (IoT), and Artificial Intelligence (AI) for ensuring the security of the house. For a highly efficient security system, Face recognition has been used to maneuver the door access. In case of power outage or for any technical issues, an alternative security PIN has been added which is only accessible by the owner. Moreover, individuals are able to monitor and control the door access along with other attributes of the house using an application. In this work, Face detection is performed using the Haar Cascade classifier, while face recognition is performed using the Local Binary Pattern Histogram (LBPH). 95.7% accuracy in recognizing faces has been achieved after evaluating the proposed system.
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Статья научная
Tourist attraction was used to achieve a stable waterfront, while the fuzzy-logic added intelligence to the select strategy through an automatic selection of the tourist attraction coefficient. This paper examines the relationship between heritage sites, waterfronts, and relationships between people in present-day in urban culture. Urban waterfronts are important and special assets and that, when redeveloped, they often contribute to healthy traditional communities. Waterfronts can serve as a unifying force in a city or town and can be, and often are, a force for community enrichment. Further, vibrant communities are essential for environmental, economic and social advancement. There are several strategy specific principles as a way to improve of usage from particular places and waterfront city. Select and design the best strategy play important role to tourist attraction. Fuzzy logic inference system is used to select the best methodology and based on this research and fuzzy logic method, striped methodology is used to have a best performance.
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Artificial Intelligence SVC Based Control of Two Machine Transmission System
Статья научная
The main target in this paper is to present, design fuzzy logic controller (FLC) applied to static var compensator (SVC) on two machine transmission system to improve transient stability and rapid damping oscillations of synchronous generators, when power generators sudden changes occur.stability that also played important role in power systems. static var compensator with fuzzy logic controller (SVCFLC) is a new control strategy can help improve transient stability.The effect of three phase fault causes instability on power system. By and large, it is very difficult to control machine speeds ,rotor angle and voltage during three-phase fault.SVCFLC is a voltage stablizer using three static var compensator which are controlled by SVC with fuzzy logic controller(FLC).The FLC is an effective device for transient stability of two-mashine transmission system. The nonlinear model dynamic formulation problem in unstable system can be solved by using artificial intelligence theorem. Fuzzy logic theory is used to improve the system stability . simulation results of three-phase fault in power system show that SVCFLC caused to increase the stability and damp out the oscillation of machine, compared with effective of SVC in the presence of power system stabilizer(PSS).
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Artificial Neural Network based Design of Modified Shaped Patch Antenna
Статья научная
Artificial neural network based model is estimated for modified shaped circular patch antenna. The Levenberg Marquardt (LM) algorithm is used to train the network, different antenna parameters in the X and Ku band are taken as input and delivers antenna dimensions as output. The dimensions obtained from estimated neural network model closely agrees the simulated results over the X and Ku band for FR4 epoxy substrate with 1.5 mm thickness. The simulation of microstrip patch antenna is carried out using Ansoft HFSS simulation software and the analysis of neural network model results are carried out using MATLAB. Thus, the estimated model can be used to obtain the antenna dimensions for circular patch antenna.
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Статья научная
The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angle), the generated data was subjected to training, validation, and testing. The ANN was further trained using the Levenberg-Marquardt algorithm to fit the inputs and the targets to create a dynamic model for RIA forecasting. Further validation was tested using actual data of rain attenuation from a Ku-band beacon at the site. Subsequently, the RIA model created by the ANN was compared to those generated using the synthetic storm technique, ITU, and the actual rain attenuation obtained from a beacon measurement. The highest rain rate observed was about 225.8 mm/hr with a corresponding rain attenuation of about 61 dB as estimated by the SST model and about 68 dB by the ITU model, while the predicted attenuation by the ANN is 55 dB. This implies that an extra power of 6 dB and 13 dB is added by the SST model and ITU model, respectively, for the downlink signal, to compensate for the rain attenuation link. The results also reveal that during 0.01 percent of an average year that signal may be attenuated, a relatively tiny margin of error between anticipated rain attenuation using ANN and the SST model is exceeded. In general, the new ANN-generated RIA model had the lowest root mean square error, average relative error, and standard deviation at the selected time percentages, according to the model validation. Hence, the new ANN model can predict more effective RIA in the region when compared with the global ITU-R model.
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