Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Статья научная
In robotic applications and research, inverse kinematics is one of the most important problems in terms of robot kinematics and control. Consequently, finding the solution of Inverse Kinematics in now days is considered as one of the most important problems in robot kinematics and control. As the intricacy of robot manipulator increases, obtaining the mathematical, statistical solutions of inverse kinematics are difficult and computationally expensive. For that reason, now soft-computing based highly intelligent based model applications should be adopted to getting appropriate solution for inverse kinematics. In this paper, a novel application of artificial neural network is used for controlling a robotic manipulator. The proposed methods are based on the establishments of the non-linear mapping between Cartesian and joint coordinates using multi layer perceptron and functional link artificial neural network.
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Статья научная
Most of the models projected in the literature on Emotion Recognition aims at recognizing the emotions from the mobilized persons in noise free environment and is subjected to the emotion recognition of an individual using a single word for testing and training. Literature available to identify the emotions in case of immobilized persons is confined to the results available from the machines only. In this process brain-computer interaction is utilized using neuro-scan machines like Encephalography (EEG), to identify the emotions of immobilized individuals. It uses the physiological signals available from EEG data extracted from the brain signals of immobilized persons and tries to determine the emotions, but these results vary from machine to machine, and there exists no standardization process which can identify the feelings of the brain diseased persons accurately. In this paper a novel method is proposed, Doubly Truncated Gaussian Mixture Model (DT-GMM) to have a complete emotion recognition system which can identify emotions exactly in a noisy environment from both the healthy individuals and sick persons. The results of the proposed system surpassed the accuracy rates of traditional systems.
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A Novel DTC-SVM Method For Induction Motor Fed By Matrix Converter
Статья научная
This paper presents a novel DTC-SVM method for matrix converter (MC) fed induction motor. The advantages of DTC method are combined with the advantages of the matrix converter based on space vector modulation (SVM) technique. This proposed novel method provides a precious input power factor control capability beside the high control performances. Furthermore, Conventional principles of DTC and SVM of MC were described. The combination of the two was given in detail. The FFT spectrum analysis of the input current shows the better harmonic contents as compared to the conventional DTC method. Finally, the simulation and experiment research were carried out to identify the new method effectiveness. The results of induction motor control at both steady state and transient state are shown to improve the low-speed performance and strong adaptability of this novel control strategy.
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A Novel Distance Metric for Aligning Multiple Sequences Using DNA Hybridization Process
Статья научная
This paper elucidates a new approach for aligning multiple sequences using DNA operations. A new distance measure using DNA hybridization melting temperature that gives approximate solutions for the multiple sequence alignment (MSA) problem is proposed. This paper provides proof for the proposed distance measure using the distance function properties. With this distance measure, a distance matrix is constructed that generates a guide tree for the alignment. Providing an accurate solution in less computational time is considered to be a challenging task for the MSA problem. Developing an algorithm for the MSA problem is essentially a trade-off between finding an accurate solution and that can be completed in less computational time. In order to reduce the time complexity, the Bio-inspired technique called the DNA computing is applied in calculating the distance between the sequences. The main application of this multiple sequence alignment (MSA) is to identify the sub-sequences for the functional study of the whole genome sequences. The detailed theoretical study of this approach is explained in this paper.
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A Novel Fuzzy Approach for Determining Best Position of Soccer Players
Статья научная
A combination of various fuzzy systems on identification and evaluation of soccer players’ position is represented in this article. In other words, fuzzy logic, as an appropriate means, has been applied to study soccer player's personality traits and skills to figure out which position suits each individual best. Soccer experts are aware of the fact that a successful soccer player in any intended position must bear some specific characteristics. Using fuzzy logic, a model has been offered, called soccer player position identification. This model has some important features: First, the most appropriate position for each individual player can be identified. Second, this system offers some solutions to problems caused by the experimental and intuitive couches’ ideas about player positioning which decreases the need for experts and professional couches. Third, this model overcomes the problems in the analysis of player positioning without the need for quantitative scales, and last but not least, this easy and interpretable method, applied to 264 soccer players' assessments have revealed that the designed fuzzy system is able to identify the qualities among players.
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A Novel Genetic-based Optimization for Transmission Constrained Generation Expansion Planning
Статья научная
Transmission constrained generation expansion planning (TC-GEP) problem involves decisions on site, capacity, type of fuel, and etc. of new generation units, which should be installed over a planning horizon to meet the expectations of energy demand. This may lead to adding or lightening transmission lines congestion. This paper presents an application of genetic algorithm (GA) to TC-GEP problem for simultaneously determination of new generation site, capacity and fuel type for a multi-period generation expansion plan. The objective function in this paper is to minimize the total generation cost which is composed of generation capital investment costs, operation and maintenance (O&M) costs, outage cost, transmission losses costs and transmission enhancement costs. In this paper, also a new method is proposed for computing transmission enhancement costs. In addition a new approach is presented in this paper to determine site and number of combined cycle power plants regarding to candidate units. The GA is applied to solve TC-GEP problem for 4 bus test system from Grainger & Stevenson for a planning horizon of one year and the results are compared and validated against Enumeration Method (EM). Then GA is applied to solve TC-GEP problem for IEEE-RTS 24-bus test system for a planning horizon of three years and results are discussed.
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Статья научная
Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.
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A Novel Method in Food Safety Management by Using Case Base Reasoning Method
Статья научная
Today’s Food Industry has responsibility to provide most consuming food for people. These foods are consumed by large area of society. So they are important source of causes of diseases and food poisoning. Monitoring system have been created to control these diseases and they are used in duration of production step of food supply chain. Hazard Assurance Critical Control Point (HACCP) is regarded as best method in safety system. Necessity to create integrated HACCP system forced factories to use intelligent methods to build HACCP for every production. This paper proposes Case-Based Reasoning (CBR) technique and use of paired comparisons tables and similarity equations to create HACCP for food system of Sabz Nam Company. Our system is an intelligent system has based on RFID and it works as consulter by generating five proper safety suggestion to food expert. Finally we assess accuracy and efficiency of proposed system on real data of Sabz Nam Company.
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Статья научная
The aim of this paper is solving optimal control problems governed by non-local diffusion equations via a mesh-less method. The diffusion equation and in particular, the heat conduction equation is essential in sciences. This equation appears in many fields, such as engineering, electrostatic, and mathematics. For solving the mentioned optimal control problems, the method is established upon expanding of variables by the basis of Bezier functions. We apply, for the first time, the Bernstein approximation in solving an optimal control problem governed by the diffusion equation. A direct algorithm is given for solving this problem. Bernstein polynomials expand the trajectories and control functions with unknown control points. Then the optimal control problem is converted to a mathematical programming problem. By solving the mathematical programming problem, the approximated solution of trajectories and control are driven. The convergence of the method in approximating of the optimal control problem is proved. Some numerical examples for demonstrating the effectiveness of the method are included.
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A Novel Particle Swarm Optimization Algorithm Model with Centroid and its Application
Статья научная
In order to enhance inter-particle cooperation and information sharing capabilities, an improved particle swarm algorithm optimization model is proposed by introducing the centroid of particle swarm in the standard PSO model to improve global optimum efficiency and accuracy of algorithm, then parameter selection guidelines are derived in the convergence of new algorithm. The results of Benchmark function simulation and the material balance computation (MBC) in alumina production show the new algorithm, with both a steady convergence and a better stability, not only enhance the local searching efficiency and global searching performance greatly, but also have faster higher precision and convergence speed, and can avoid the premature convergence problem effectively.
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A Novel and Improved Firefly Algorithm Based on Two-order Oscillation
Статья научная
Firefly algorithm is a new and efficient intelligent algorithm proposed by Dr. Xin-She Yang at Cambridge University. In this paper, the conventional firefly algorithm will be introduced and improved using two-order oscillation. And an improved firefly algorithm based on two-order oscillation is proposed. And this method will be deduced and analyzed for astringency. Six typical functions will be tested to prove performances. Global Max/Min values in six multimodal functions could be found accurately with proposed scheme. The experiment results show our proposed method has excellent performances than conventional one.
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A Partial Backlogging Inventory Model with Time-Varying Demand During Shortage Period
Статья научная
Harris’s classic square root economic order quantity (EOQ) model forms the basis for many other models that relax one or more of its assumptions. A key assumption of the basic EOQ model is that stockouts are not permitted. Due to the excess demands, stock-out situations may arise occasionally. Sometimes, shortages are permitted and they are backordered and satisfied in the very next replenishment. Therefore the objective of this paper is to develop a partial backlogging inventory model, and proposes a new algorithm to minimize the total cost, at the same time also propose the prediction method and algorithm of ordering period. Finally, a practical example of the numerical analysis is given.
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A Particle Swarm Optimization Approach for Energy Efficient Clustering in Wireless Sensor Networks
Статья научная
In the previous years, wireless sensor networks (WSNs) got lot of attraction from the scientific and industrial society. WSNs are composed of huge number of small resource constrained devices recognized as sensors. Energy is a vital issue in WSN. Energy efficient clustering is an eminent optimization problem which has been studied extensively to prolong the lifetime of the network. This paper demonstrates the programming formulation of this problem followed by a proposed algorithm with particle swarm optimization (PSO) approach. The clustering method is stated by taking into consideration of energy saving of nodes. The proposed algorithm is experimented widely and results are evaluated with existing methods to show their supremacy in term of alive nodes, energy expenditure, packet delivery ratio, and throughput of network. Simulation results shows that our proposed algorithm outperform the other existing algorithms of its category.
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A Performance Evaluation of Improved IPv6 Routing Protocol for Wireless Sensor Networks
Статья научная
In the near future, IP-based wireless sensor networks will play a key role in several application scenarios such as smart grid, smart home, healthcare, and building automation... An IPv6 routing protocol is expected to provide internet connectivity to any IP-based sensor node. In this paper, we propose IRPL protocol for IP-based wireless sensor networks. IRPL protocol uses a combination of two routing metrics that are the link quality and the remaining energy state of the preferred parent to select the optimal path. In IRPL protocol, we combine two metrics based on an alpha weight. IRPL protocol is implemented in ContikiOS and evaluated by using simulation and testbed experiments. The results show that IRPL protocol has achieved better network lifetime, data delivery ratio and energy balance compared to the traditional solution of RPL protocol.
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A Practical Application of ARM Cortex-M3 Processor Core in Embedded System Engineering
Статья научная
Embedded Systems Engineering has grown in recent years to become an integral part of our daily living as it finds striking applications in various spheres of our lives. These range from Manufacturing, Electronic Health, Telecommunications, Construction and Robotics to numerous other fields. Primarily, Embedded Systems are usually a combination of selected electrical and electronic components functioning together under the direct control of a programmed controller. They serve fundamentally as additional units incorporated within already existing infrastructures with the sole aim of providing dedicated services to the larger infrastructure. Many of the controllers used operate on uniquely designed processor cores, instruction sets, and architecture profiles. This paper seeks to elucidate the application of the ARM Cortext-M3 processor based NXP LPC 1768 Microcontroller unit in the design and development of a Temperature Monitoring and Logging System. The write-up starts off with an overview of the principal ARM processor core families, architecture profiles, instruction sets and subsequently, demonstrates its utilization in the design of a Temperature Monitoring and Logging System. The paper shows how the NXP LPC 1768 Microcontroller Unit successfully serves as the brain of the temperature logger device through its standardized interfacing with a TMP102 temperature sensor using the Inter- Integrated Circuit (I2C) protocol. The Microcontroller is programmed using Embedded C while other unique functionalities of the ARM Cortex-M3 core such as Interrupt Handling and System Tick Timer efficiency are also explored.
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A Proposal to Improve Communication between Distributed Development Teams
Статья научная
Distributed system development (DSD) is implemented by distributed development teams that are separated by long distances and different time-zones. Communication between distributed development teams in distributed software development applies a major and critical role in the success of process. Conflicts between distributed teams bring high risks to fail a development project due to poor communication. Therefore, it is important for distributed teams to communicate effectively to complete a successful project. In this paper, the authors propose an improved solution for effective communication among distributed development team by integrating administrative and technical procedures to successfully complete a project. Survey is used as a research design to validate the proposed solution. The results show that the respondents support the proposed solution that it will solve the industry problem by providing an effective means of communication in a DSD environment.
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Статья научная
The Technological evolution which occurred in digital systems favored the appearance of new services to be applied in railways. Radio communications played an important role in the management, exploration, and maintenance of railway transports. Due to the vital importance of security, the track-to-train communications system is studied in depth, with significant changes through the integration in its operation of the global system for mobile communication and global positioning systems. This paper has proposed the implementation of a global positioning system (GPS) based train monitoring system that could locate a train at every instant. Here a GPS–GPRS module transmits the location information to a web server. The paper also presents a low-budget system to track the trains inside tunnels. The paper presents a solution implemented at Egyptian National railways, to provide an intelligent train tracking and management system to improve the existing railway transport service.
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A Proposed Stacked Machine Learning Model to Predict the Survival of a Patient with Heart Failure
Статья научная
Now a days heart failure is one of the most common chronic diseases that cause death. As it possesses high risk of death, it is important to predict patient’s survival and optimize treatment strategies. Machine learning techniques have come to light as useful tools for evaluating enormous quantities of patient data and deriving important patterns and insights in recent years. The purpose of the study is to investigate the feasibility of using the machine learning methods for predicting heart failure patient’s chances of survival. We have worked on a dataset with 2029 heart failure patients and the dataset comprises 13 features. To conduct this research, we suggested a model (Stacked machine learning model using scikit-learn using Decision Tree, Naive Bias, Random Forest, Linear Regression, SVM, XGBoost, ANN) using which we got better results than previously existed researches. We believe the suggested model will help advance our understanding of heart attack prediction.
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A Prototype Automatic Solar Panel Controller (ASPC) with Night-time Hibernation
Статья научная
Solar cells, as an alternate means of electricity supply, is rapidly advancing. Generally, output of solar cells depends largely on intensity of sun and angle of incidence on the cells. This means that to get maximum efficiency from these cells, they must remain directly pointed at the sun from sun rise to sun set. However, the position of sun's highest intensity with respect to a given spot changes with time of the day. It is therefore necessary to automatically control position of solar cells to always align with the highest intensity of sun. In this paper, we present a prototype automatic solar panel controller, with night time hibernation. The proposed system consists of both software and hardware parts, and it automatically provides best alignment of solar panel with sun to get maximum intensity. The solar panel controller system detects the presence of sun rays using light dependent resistors (LDR). At the heart of the control mechanism is an AT89C52 microcontroller. It is programmed to constantly monitor the output of an LDR, actuate a stepper motor to reposition the solar panel to a direction with the highest intensity. The proposed system also has an option of manual control of the panel via a computer interface or a keypad unit for easy of user interactivity during maintenance. Testing the proposed system, results shows that it can successfully track the sun and enter idle mode in the absence of sun rays, hence, conserving over 50% of energy required to operate the system.
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A Review of Methods of Instance-based Automatic Image Annotation
Статья научная
Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retrieving process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most important instance-based image annotation methods are analyzed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and compared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.
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