Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Mobile Robot Path Planning by RRT* in Dynamic Environments
Статья научная
Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.
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Mobile Robot Path Planning with Randomly Moving Obstacles and Goal
Статья научная
This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.
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Mode Research on Space Weapons Systems Innovation Based Quality Function Deployment
Статья научная
In the aviation industry, experts are enthusiastic over the research of sophisticated weapons. Little specialist pays attention to the innovation modes and methods. Up to now little quantization method suitable for aviation weapon systems innovation is presented. Base on the deep analysis and study on features of aviation weapon systems innovation and different innovation mode from the mass production, we have designed process model and quality chain model of aviation weapon systems innovation. Compared with the process model of large-scale innovation, the process models are more complex including many feedbacks and adding five steps: task decomposition, analysis of knowledge gap, accumulation of key knowledge, outsourcing selection, system integration. Meanwhile manufacturing process and R&D process are preformed simultaneously, and are involved in the process of module development. Technology application and diffusion are preformed with delivering the final innovation product to user. Quality function deployment and quality house are adopted to deal with the quality transfer among nodes. Quality demands of one node are converted into the technique features of another node in the quality house. We designed the top-down technique features transfer model and bottom-up demands transfer model to solve the quality transfer problems among nodes. At last an example is given to illustrate that this approach can accelerate to blaze the aviation weapon systems trails more than the existing methods and effectively reach quality management of aviation weapon systems innovation.
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Model Driven Test Case Optimization of UML Combinational Diagrams Using Hybrid Bee Colony Algorithm
Статья научная
To detect faults or errors for designing the quality software, software testing tool is used. Testing manually is an expensive and time taking process. To overcome this problem automated testing is used. Test case generation is a vital concept used in software testing which can be derived from requirements specification. Automation of test cases is a method where it can generate the test cases and test data automatically by using search based optimization technique. Model-driven testing is an approach that represents the behavioral model and also encodes the system behavior with certain conditions. Generally, the model consists of a set of objects that defined through variables and object relationships. This piece of work is used to generate the automated optimized test cases or test data with the possible test paths from combinational system graph. A hybrid bee colony algorithm is proposed in this paper for generating and optimizing the test cases from combinational UML diagrams.
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Model Reference PID Control of an Electro-hydraulic Drive
Статья научная
Hydraulic cranes are inherently nonlinear and contain components exhibiting strong friction, saturation, variable inertia mechanical loads, etc. The characteristics of these non-linear components are usually not known exactly as structure or parameters. For these reasons, tuning of the traditional PID controller parameters to control this system for the required performance faces a strong challenge. In this paper a new approach to design an adaptive PID control has the ability to solve the control problem of highly nonlinear systems such as the hydraulic crane was proposed. The core of the design method depends on comparing the performance of the Model Reference (MR) response with the nonlinear model response and feeding an adaptation signal to the PID control system to eliminate the error in between. It is found that the proposed MR-PID control policy provided the most consistent performance in terms of rise time and settling time regardless of the nonlinearities.
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Статья научная
Taking into consideration the lack of circumstantial alertness, automated fault analysis and labor-saving switches, the present-day electrical power grid system has been deteriorating day by day. The backbone technology of this grid system is too ill-fitted to the on-going demand for electricity. Despite the fact that the government of Bangladesh has set a new target of reaching the total power generation to be 40,000 MW by 2030. Hence the infrastructure and corresponding technology of the electrical power sector are required to be modernized to cope with this gigantic target within a short time. Another challenging fact is that the rapid expansion of population and power-intensive industrialization trigger off the carbon emissions that lead to global climate change. Also, the constraints of electricity generation capacity, unidirectional way of communication, failure of power equipment and dropping off conventional sources of energy impose burden on the existing electric power grid. This paper articulates the needfulness of reflection on CO2 emissions or reduction in the electricity bill of the consumer in developing countries by employing a mathematical model and by proposing some fruitful methods to implement AMI for smart grid.
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Modeling Epileptic EEG Time Series by State Space Model and Kalman Filtering Algorithm
Статья научная
The human brain is one of the most complex physiological systems. Therefore, electroencephalogram (EEG) signal modeling is important to achieve a better understanding of the physical mechanisms generating these signals. The aim of this study is to investigate the application of Kalman filter and the state space model for estimation of electroencephalogram signals in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) were analyzed. The estimation performance of the proposed method on EEG signals is evaluated using the root mean square (RMS) measurement. The result of the present study shows that this model is appropriate for the analysis of EEG recordings. In fact, this model is capable of predicting changes in EEG time series with phenomena such as epileptic spikes and seizures.
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Modeling Uncertainty in Ontologies using Rough Set
Статья научная
Modeling the uncertain aspect of the world in ontologies is attracting a lot of interests to ontologies builders especially in the World Wide Web community. This paper defines a way of handling uncertainty in description logic ontologies without remodeling existing ontologies or altering the syntax of existing ontologies modeling languages. We show that the source of vagueness in an ontology is from vague attributes and vague roles. Therefore, to have a clear separation between crisp concepts and vague concepts, the set of roles R is split into two distinct sets Rcand Rvrepresenting the set of crisp roles and the set of vague roles respectively. Similarly, the set of attributes A was split into two distinct sets Acand Avrepresenting the set of crisp attributes and the set of vague attributes respectively. Concepts are therefore clearly classified as crisp concepts or vague concepts depending on whether vague attributes or vague roles are used in its conceptualization or not. The concept of rough set introduced by Pawlak is used to measure the degree of satisfiability of vague concepts as well as vague roles. In this approach, the cost of reengineering existing ontologies in order to cope with reasoning over the uncertain aspects of the world is minimal.
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Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters
Статья научная
The precise modeling of average air temperature is a significant and much essential parameter in frame of reference for decision-making in agriculture field, drought detection and environmental related issues. The aim of this research is to construct an accurate model to modeling average air temperature using hybrid Wavelet-ANFIS techniques. Being cognizant of the fact, uncertainty handling capability is achieved with ANFIS technique; a cognitive approach to integrate ANFIS technique along with pre-processed data by using Wavelet transformation. Detailing on approach, in this work utilized Discrete Wavelet transform under Daubechies mother Wavelet up to 3rd level of decomposition. This study extends up to seven station's meteorological data records. The following developed hybrid model's performance is compared with single ANFIS models for all seven stations. The obtained results were evaluated using correlation coefficient, root mean square error and scatter index These results confirmed that the proposed hybridized Wavelet- ANFIS model has estimable potential in terms of modeling temperature than ANFIS model alone.
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Modeling of Multi-Agent Oriented learning System for Impaired Students with JADE
Статья научная
In this research paper we presented a model of multi-agent system based learning environment for physically impaired students. The learning system is modeled on the basis of both centralized as well as distributed multi-agent planning. The entire learning system keeps track of the type of impairment the student has and mode of interaction of the environment is set depending on the type of impairment(s). The system consists of agents which are developed using JADE agent technology that helps the students with disabilities to continue studies from their own places.
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Статья научная
The optimal resources allocation to tasks was the primary objective of the research dealing with scheduling problems. These problems are characterized by their complexity, known as NP-hard in most cases. Currently with the evolution of technology, classical methods are inadequate because they degrade system performance (inflexibility, inefficient resources using policy, etc.). In the context of parallel and distributed systems, several computing units process multitasking applications in concurrent way. Main goal of such process is to schedule tasks and map them on the appropriate machines to achieve the optimal overall system performance (Minimize the Make-span and balance the load among the machines). In this paper we present a Time Petri Net (TPN) based approach to solve the scheduling problem by mapping each entity (tasks, resources and constraints) to correspondent one in the TPN. In this case, the scheduling problem can be reduced to finding an optimal sequence of transitions leading from an initial marking to a final one. Our approach improves the classical mapping algorithms by introducing a control over resources allocation and by taking into consideration the resource balancing aspect leading to an acceptable state of the system. The approach is applied to a specific class of problems where the machines are parallel and identical. This class is analyzed by using the TiNA (Time Net Analyzer) tool software developed in the LAAS laboratory (Toulouse, France).
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Modeling, Simulation and Control Issues for a Robot ARM; Education and Research (III)
Статья научная
This paper extends writer's previous work and proposes design, modeling and control issues of a simple robot arm design. Mathematical, Simulink models and MATLAB program are developed to return maximum numerical visual and graphical data to select, design, control and analyze arm system. Testing the proposed models and program for different input values, when different control strategies are applied, show the accuracy and applicability of derived models. The proposed are intended for education and research purposes.
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Статья научная
This paper presents some considerations regarding design, modeling and control solutions for Photovoltaic Panel-Converter (PVPC) system. Different control approaches and corresponding models are derived, developed and tested, to control output characteristics and performance of both overall PVPC system and each subsystem to meet desired output characteristics, performance and both and/or either voltages and currents requirements. The proposed approaches and models allow designer have the maximum output numerical visual and graphical data to select, evaluate and control the PVPC system output characteristics for a given PVPC system parameters, under given working conditions of PV panel. The proposed models and approaches were implemented and tested in MATLAB/Simulink.
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Статья научная
The accurate control of motion is a fundamental concern in mechatronics applications, where placing an object in the exact desired location with the exact possible amount of force and torque at the correct exact time is essential for efficient system operation. An accurate modeling, simulation and dynamics analysis of actuators for mechatronics motion control applications is of big concern. The ultimate goal of this paper addresses different approaches used to derive mathematical models, building corresponding simulink models and dynamic analysis of the basic open loop electric DC motor system, used in mechatronics motion control applications, particularly, to design, construct and control of a mechatronics robot arm with single degree of freedom, and verification by MATLAB/Simulink. To simplify and accelerate the process of DC motors sizing, selection, dynamic analysis and evaluation for different motion applications, different mathematical models in terms of output position, speed, current, acceleration and torque, as well as corresponding simulink models, supporting MATLAB m.file and general function block models are to be introduced. The introduced models were verified using MATLAB/ Simulink. These models are intended for research purposes as well as for the application in educational process. This paper is part I of writers' research about mechatronics motion control, the ultimate goal of this research addresses design, modeling, simulation, dynamics analysis and controller selection and design issues, of mechatronics single joint robot arm. where a electric DC motor is used and a control system is selected and designed to move a Robot arm to a desired output position, θ corresponding to applied input voltage, Vin and satisfying all required design specifications.
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Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection
Статья научная
Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.
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Modelling Electronic Sharing (E-Sharing) Behaviour: A Perspective on Location-aware Technology
Статья научная
The purpose of this paper is to investigate the factors affecting electronic sharing (E-sharing) behaviour, with a particular focus on location-aware technology. Based on an extensive literature review, a structural model consisting of seven factors was proposed to model the E-sharing behaviour of location-based knowledge (LBK). The main constructs were: reward expectancy (WE), reputation expectancy (RE), perceived benefits (PB), perceived trust (PT), attitudes towards LBK, attitudes toward knowledge sharing incentives (KSI) and intention to share knowledge (ISK). The model was examined by empirical data gathered from four hundred and ninety (n=490) respondents. Results herein indicate that attitude toward KSI can be determined by the RE and WE, while attitude toward LBK E-sharing can be predicted by the PB and PT. The two attitude constructs (KSI and LBK) can determine the behavioural ISK. All of the proposed relationships within the model were statistically significant.
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Modelling and Simulation of Hydro Power Plant using MATLAB & WatPro 3.0
Статья научная
Increase in demand of electricity and clean drinking water has produced a chronic need of a promising and reliable technology for the supply of both commodities, which should be entirely based on renewable sources of energy. The authors, in their previous work, had proposed a design of a hybrid power plant which used graphene membrane for power generation using reverse osmosis process. The proposal included removal of arsenic, poorly biodegradable pollutants using TiO2 nanoparticles. Chlorine production using the process of electrolysis. The plant was also electronically implemented and included pump control, fouling detection modules and decision module for the volume of effluents to be discharged. The performance of a power system is essential to be analyzed for control, stabilization and efficient modelling. In the present research paper, simulation model of the hybrid plant is analyzed. The chemical behavior is analyzed with 'Watpro 3.0' industrial software and turbine governance system is studied via MATLAB. This plant is a potential replacement of chemical purification techniques with high overhead and excess cost. It is a better, efficient, safe and reliable system to produce clean and safe drinking water and electricity simultaneously.
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Modelling oil pipelines grid: neuro-fuzzy supervision system
Статья научная
One of the major challenges for researchers and governments across the world is reducing resources-waste or loss. Resources loss can happen if there is not a capable control system that contributes to environmental change. The specific aim is to create user-friendly control and monitoring system to reduce the waste in resources. New Artificial intelligence techniques have been introduced to play an important part in developing such systems. In oilfields, the oil is extracted then distributed via oil pipes until it reaches the end consumer. This operation will occur without a full and complete monitoring for the oil in the pipeline’s journey to the provider. Although, the existing oilfield monitoring systems can communicate locally but they will not send information back to the main provider. That means the provider is not aware of the whole circumstances happened in the transportation process. That gives the provider no control on the process. For example, a sudden decision from the main provider to stop transporting to a specific destination or knowing where the leakage is and which pipe is leaking in the pipelines grid. This paper, introduces for the first-time oilfield pipeline Neuro-fuzzy (NF) supervision system using Simscape simulation software package. This system can be the first step solution to keep real time communication between the main provider and the oil transportation process in the oilfields and enables the provider to have full supervision on the oil pipes grid. The simulation supervision system illustrates a clear real-time oilfield pipeline grid that gives the provider the ability to control and monitor pipeline grid and prioritise the recovering process. The two parameters selected for control and monitoring were volume and pressure. The results in this paper show full control for the NF supervision system on the transportation process.
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Статья научная
In this paper THD (Total Harmonic Distortion) is analysed and compared by using UPFC in a multi-line transmission system of 500 KV having 5-buses in two different arrangements. The UPFC converters are arranged as a Diode Clamped multilevel Converter (DCMLC) that leads to the cost reduction as compared with other multi-level converters. The comparison has been done by both series zig-zag/2Y-2Δ and series zig-zag/4Y transformer configuration for 48-pulses GTO based diode clamped converter. The THD is reduced to 42.59% and 58.82% of input waveform at bus B2 by using series zig-zag/4Y transformer configuration. This transformer converter configuration also reduces the difficulty of designing the transformer winding ratio. For calculation of THD, FFT analysis is carried out using MATLAB.
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Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding
Статья научная
Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipment are producing images of different organs, which are used during various stages of analysis. Examples of such equipment include MRI, CT, ultrasound and X-Ray. In medical image processing, image denoising has become a very essential exercise all through the diagnosis as Ultrasound images are normally affected by speckle noise. The noise in the image has two negative outcomes, the first being the degradation of the image quality and the second and more important, obscures important information required for accurate diagnosis.Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can suppress the information which is valuable for the general practitioner. Consequently medical images can be very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in Ultrasound images and provides the knowledge about adaptive and anisotropic diffusion techniques for speckle noise removal from different types of images, like Ultrasound.
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