International Journal of Engineering and Manufacturing @ijem
Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 550
Load test of induction motors based on PWM technique using genetic algorithm
Статья научная
Genetic algorithms(GA) is optimization technique used in the equivalent load test of the induction motors to select the values of the factors(modulation indicators) that effect on the performance properties in terms of the values of the currents and the total loss within the machine. One way to choose these parameters is by trial and error while this paper based on GA method to improve the parameters selection. A model is designed to simulate the loading of the induction motor and obtain its own results by the MATLAB program 2017a. There are different methods used to achieve this task such as PWM inverter with different modulation techniques, Constant Voltage Variable Frequency (CVVF) method, Variable Voltage Constant Frequency (VVCF) method and Variable Voltage Variable Frequency (VVVF) method.
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Local Reweighted Kernel Regression
Статья научная
Estimating the irregular function with multiscale structure is a hard problem. The results achieved by the traditional kernel learning are often unsatisfactory, since underfitting and overfitting cannot be simultaneously avoided, and the performance relative to boundary is often unsatisfactory. In this paper, we investigate the data-based localized reweighted regression model under kernel trick and propose an iterative method to solve the kernel regression problem. The new framework of kernel learning approach includes two parts. First, an improved Nadaraya-Watson estimator based on blockwised approach is constructed; second, an iterative kernel learning method is introduced in a series decreased active set to choose kernels. Experiments on simulated and real data sets demonstrate that the proposed method can avoid underfitting and overfitting simultaneously and improve the performance relative to the boundary effect.
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Статья научная
Vehicular adhoc networks (VANETs) are relegated as a subgroup of Mobile adhoc networks (MANETs), with the incorporation of its principles. In VANET the moving nodes are vehicles which are self-administrated, not bounded and are free to move and organize themselves in the network. VANET possess the potential of improving safety on roads by broadcasting information associated with the road conditions. This results in generation of the redundant information been disseminated by vehicles. Thus bandwidth issue becomes a major concern. In this paper, Location based data aggregation technique is been proposed for aggregating congestion related data from the road areas through which vehicles travelled. It also takes into account scheduling mechanism at the road side units (RSUs) for treating individual vehicles arriving in its range on the basis of first-cum-first order. The basic idea behind this work is to effectually disseminate the aggregation information related to congestion to RSUs as well as to the vehicles in the network. The Simulation results show that the proposed technique performs well with the network load evaluation parameters.
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Machine Learning-based Renewable Energy Adaptation: Case study Bangladesh
Статья научная
For environmental sustainability and energy security, renewable sources must be incorporated into sustainable energy solutions. Machine learning (ML) techniques are explored in this study to optimize the adoption of renewable energy sources in Bangladesh. Specifically, it proposes a three-phase methodology: (1) forecasting demand for nonrenewable energy, (2) predicting renewable energy availability and costs, and (3) analyzing potential savings and environmental benefits. Utilizing decision trees and random forests, this study presents a comparative analysis of energy demand and cost predictions, contributing to a data-driven framework for energy transition. The results indicate that strategic adoption of renewable energy can mitigate Bangladesh’s electricity shortages while reducing dependency on fossil fuels. Machine learning plays a crucial role in energy optimization by accurately forecasting energy demand and availability, allowing for better resource allocation. It helps identify patterns and trends in energy consumption, enabling more efficient integration of renewable sources. By using techniques like decision trees and random forests, machine learning models can optimize energy production and distribution, ultimately leading to more sustainable and cost-effective energy systems.The findings provide policymakers and energy planners with insights to enhance sustainability efforts.
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Machine learning approaches for cancer detection
Статья научная
Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.
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Man-made Object Detection Based on Texture Visual Perception
Статья научная
Based on human visual attention mechanism and texture visual perception, this paper proposes a method for man-made object detection by extracting texture and geometry structure features. Followed by clustering the texture feature, geometry structure feature is obtained to realize final detection. Then a man-made object detection scheme is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.
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Manufacturer's Pricing Strategy for Supply Chain with Service Level-Dependent Demand
Статья научная
This article considers the pricing strategies of a manufacturer in a two-echelon supply chain with service level-dependent demand. This chain consists of one manufacturer and two retailers. The manufacturer decides the wholesale prices as a Stackelberg leader, and the retailers determine their service levels as the Stackelberg followers. We discuss the segmented and unified pricing strategies of the manufacturer. We also compute the optimal service levels and profits of the retailers, as well as the optimal wholesale prices and profits of the manufacturer associated with different pricing strategies. We conclude that the segmented pricing strategy benefits the manufacturer, whereas it cannot benefit the two retailers simultaneously. Furthermore, it is disadvantageous to the profit of the entire supply chain. Moreover, the increase in service cost coefficient adversely affects the earnings of the customers, the retailers, the manufacturer, and the entire supply chain. However, an increase in diffusion intensity benefits the customers, the manufacturer, and the supply chain.
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Статья научная
Sabkha is an Arabic word for a salt-flat area found mainly along arid area coastlines and inlands within sand dunes areas. The sabkha that form within the sand are relatively flat and very saline areas of sand or silt that forms just above the water-table where the sand is cemented together by evaporite salts from seasonal ponds. Such shallow water is normally highly saline. Here the crust is rich in gypsum and halite veins where the underline thin layer is made of sand and silt. Such sabkha have an average thickness of a meter or slightly less. On the other hand, marine sabkha represent transitional environments between the land and the sea. The UAE is home to some of the largest concentrations of sabkha both coastal and inland. The coastal areas of Abu Dhabi include several small shoals, islands, protected lagoons, channels and deltas, an inner zone of intertidal flats with algal mats and broad areas of supratidal sabkha salt flats. Identifying sabkha habitats from remotely sensed data is a challenging process. Traditional classification techniques of multispectral data alone, usually fail to properly identify sabkha pixels or provide lower rates of mapping accuracy for sabkha habitats. The primary objective of this research is to develop a much more accurate methodology for properly mapping and identifying sabkha areas from remotely sensed data. Properly mapping sabkha habitats from remotely sensed data is the first steps towards studying the ecological changes within such habitats using earth observation techniques. Furthermore, sabkha habitats can in certain situations be a geotechnical hazard due to its highly salinity and with adverse effects on concrete, asphalt, steel and other structures, in addition to their sporadic heaves and collapses. As the UAE continue to build major infrastructure and development projects identifying the location of such habitats is vitally important. In this research a new technique that combines the multispectral information of Landsat 8, principal component analysis and spectral soil salinity detection is developed. The study area is located in the western part of the UAE along the border with the Kingdom of Saudi Arabia, an area known to include large tracks of inland and coastal sabkha. Landsat 8 data from path 161 and row 43 was acquired for the study. A multi-source classification approach was followed that utilizes the multispectral data of Landsat 8 along with components from the principal components analysis and the spectral salinity index maps. The preliminary results confirmed by field observations show that the combined data improved the classification accuracy to almost 90% in comparison to multispectral data alone of 78%.
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Medical Image Synthesis Using Variational Autoencoder and Generative Adversarial Networks
Статья научная
Nowadays, image synthesis has become essential in the medical field for lever- aging deep learning technique to improve decision- making. Our proposed research work combines Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to synthesize medical im- ages, enhancing diagnostics, medical training, and image analysis. The model presented combines a Discriminator, and a Variational Autoencoder to capitalize on the strengths of both VAEs and GANs. The Decoder is tasked with generating synthetic medical images, the Discriminator evaluates their distinguishing factor, and the VAE learns a probabilistic mapping from input to latent space, ensuring a structured representation of underlying medical features. The training process involves a decoder creating realistic medical images, a discriminator distinguishing real from synthetic ones, and a VAE capturing meaningful data variations in the latent space. Verified on the dataset sourced from the Kaggle. The model refines its parameters iteratively using a training loop, resulting in enhanced quality and variety of generated medical images. The proposed VAE- GAN model demonstrates its efficacy by generating diverse and realistic medical images. The structured latent space contributes to interpretability, making the images suitable for purposes like data augmentation, anomaly detection, and machine learning model training.
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Memory controller and its interface using AMBA 2.0
Статья научная
This paper elaborates the AMBA bus interface bridge between memory controller and other supporting peripheral. The work claims the integration with FIFO, RAM and ROM with slave interface and the master of AHB bus. The AHB master initiates the operation and generates the necessary control signal. Memory controller is implemented with finite state machine considering with all the peripheral works in synchronous mode. Despite these shortcomings of the work performed study and development that followed has led the development of a memory controller on AMBA-AHB bus at a very advanced stage and next to prototyping. VHDL code is utilized to develop the design and it is synthesized in Xilinx Virtex 6 device (XC6VCX75T). The design claims a minor area overhead with improvement in speed 185.134 MHz.
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Mining Associated Factors about Emotional Disease Bases on FP-Tree Growing Algorithm
Статья научная
The objective of this paper is to mine the useful information from anger and anger-in life events questionnaire, Eysenck Personality Questionnaire (EPQ) , State Trait Anger eXpression Inquiry (STAXI) Scale, Trait Coping Style Questionnair (TCSQ), Perceived Social Support Scale (PSSS) , anger and anger-in predisposition questionnaire, anger and anger-in Physiological State Questionnaire (PSQ) and a number of test indicator data, Look for associated factors, fumble rule, guide people to do early prevention and treatment. In this paper the forming process of FP-tree of the Emotional database is analyzed, the algorithm of structuring frequent model FP-tree and mining frequent itemsets are designed, the database information scanned is recorded by using FP-Tree growing algorithm through state-trees, frequent itemsets meet minimum support required are generated through reducing the search space of project sets and scanning database only one. The mine of all factors associated with emotional disease is actualized. The experiment shows strong factors associated with emotional disease can be mined from database system by the mining algorithm bases on FP-Tree frequent itemsets. The mining results can provide scientific basis for the analysis, prevention and treatment of symptoms.
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Model Based Approach for Identification of Relevant Images from Ancient Paintings
Статья научная
In this paper an attempt is made to retrieve the relevant paintings based on the approach of the artist using Generalized Bivariate Laplacian Mixture Model (GBLMM). This article helps in understanding the outline of assorted artists and help as a means to categorize a scrupulous painting based on the style or the text ingrained within the images. To profile the artist style GBLMM is used. The projected model helps to discriminate the strokes of the artists and lend a hand in the classification of paintings. The proposed model is implemented using high resolution Chinese painting images.
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Modeling Aspects with AODML: Extended UML approach for AOD
Статья научная
Aspect Oriented Software Development (AOSD) has been considered one of the most promising abstractions to make software structure more maintainable and configurable. It also helps to overcome two big issues of current object oriented programming principles, to reduce the problem of code tangling and code scattering. Aspect Oriented Programming (AOP) has been focused largely in the implementation/coding phase. But nowadays the AOP has been matured enough to turn into AOSD, as it the main objective of separation of concerns right through the process of software development. In this paper we deal with the impact of aspect in development of software especially in designing aspect with Unified Modelling Language (UML). We propose visual models to incorporate aspect and aspectual constructs as an UML metamodel approach and new extensions to UML. The proposed language aspect oriented design modelling language (AODML) is an extension for aspect modelling into existing UML specifications. This paper allows designers to specify and realize aspects in the design and implementation phase explicitly. The proposed visual models, supports Aspect, aspectual components and its association with base components i.e. classes to be incorporated into UML. AODML motivates designer to get benefited to develop the system using AOSD paradigm. It allows to model aspects in design diagrams so that it can be implemented in any AOP language effectively.
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Modeling Changing Graphical Structure
Статья научная
We introduce the graphical models to describe the changing dependency structure between multivariate time series and design the algorithm by the markov chain monte carlo method. The model is applied to the stock market of Shanghai in China to study the changing correlation of five segments of the market, empirical results show that there is stronger dependency structure in the bear market and weaker correlation in the bull market.
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Modeling and Real-Time simulation of large hydropower plant
Статья научная
In this paper, modeling and simulation of large hydropower plant in real-time platform named Real-Time Laboratory (RT-LAB) is carried out. First, a hydropower plant model consisting of nonlinear hydro turbine with PID governor and synchronous generator (SG) with DC1A excitation system and connected to grid is developed in MATLAB/Simulink environment. This model is then simulated in RT-LAB after the modification of MATLAB/Simulink model required for suitable operation in RT-LAB environment. Finally, the real-time simulation of hydropower plant when subjected to disturbances of load addition, reduction of load and short circuit fault analysis is presented and discussed.
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Modeling and Simulation of an Indirect Natural Convection Solar Dryer with Thermal Storage Bed
Статья научная
The intermittent nature of solar energy limits a 24 hour operation and the effectiveness of solar thermal devices. Affordable and environmentally friendly materials for storing solar energy are currently in search. A natural convection solar cabinet dryer coupled with thermal energy storage bed (gravels) is modeled and simulated for space heating application (tomatoes drying) using TRNSYS 16 software. Performance of the solar thermal system (solar cabinet dryer) with a thermal storage bed will serve as a guide in developing a gravel-pit (GP) and or water-gravel pit storage system (WGPS) on a medium to large scale to facilitate solar thermal storage of heat for space and water heating applications in homes, health care and educational facilities. Thermal storage volume and thickness of gravel bed were determined and an optimized solar collector area obtained using TRNSYS 16 software for drying 6kg of tomatoes slices. A computer program was written to predict the product drying temperature, mass of moisture removed, moisture content and drying rate at two different trays including solar collector efficiency, heat storage bed temperature profile using meteorological data input of dryer location, gravel properties, solar collector parameters and solar cabinet dryer chamber variables. The month of August was used as the design month bearing in mind that it has the least solar radiation in Bauchi and thus, predicted the least drying performance while, the month of March with the most solar radiation predicted the optimum drying performance. The maximum predicted gravel bed temperatures were 44 and 59.3°C for the months of August and March respectively. Predicted performance of the solar cabinet dryer was compared to a similar cabinet dryer without thermal storage bed. Predicted maximum product drying temperatures of 48 and 69°C were obtained for solar cabinet dryer with thermal storage bed as against 46 and 66°C for solar cabinet dryer without thermal storage bed in the month of August and March corresponding to solar intensity value of 575.4 and 1049.2W/m2 respectively. To attain 4.5% moisture content for 3kg of tomatoes slices placed on each tray containing 94% of moisture, requires 37 (20 hours of sunshine and 7 hours of supplementary heat stored) and 53 (26 hours of sunshine and 6 hours of supplementary heat stored) hours of drying for solar cabinet dryer with thermal storage bed and, 52 (25 hours of sunshine) and 75 (34 hours of sunshine) hours under same weather condition for similar solar cabinet dryer without thermal storage bed for the month of March and August respectively. The average moisture extraction rate is 0.0759 and 0.0531kg per hour in the month of March for solar cabinet dryer with and without thermal storage bed and, 0.0540 and 0.0374kg per hour the month of August respectively. Predicted maximum solar collector efficiency for cabinet dryer with thermal storage bed is 50.12 and 43.85% for the month of March and August whereas, it was 45.83 and 37.66% for cabinet dryer without thermal storage bed respectively. The performance prediction of the solar cabinet dryer with thermal storage bed indicates clearly good potential for storing solar thermal heat collected during the day and effectively utilizing the stored heat during off-sunshine hours for heating applications. It is recommended that a gravel-pit (GP) and or water-gravel pit storage system (WGPS) should be developed and adequately studied for a range of operating parameters based on temperature distribution, thermal energy stored, available energy stored in the bed, energy consumption by blower (for active bed), and thermal efficiency of the collector to give clear guidelines for using the gravels for large scale solar thermal energy storage for space and water heating applications.
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Статья научная
In this paper, a novel method based on online monitoring of Instantaneous Exciting Current Space Phasor is presented in order to detect inter-turn faults on the transformer windings. This approach detects winding faults by comparison between presentation of the Instantaneous Exciting Current Space Phasor under healthy and faulty condition. In this work, the angular speed of Instantaneous Exciting Current Space Phasor has been introduced as one of the fault detection tools that has good sensitivity for detection of minor inter-turn faults. Firstly, a typical transformer is simulated based on Finite Element Analysis (FEA) to investigate the transformer behavior under different conditions. Then, the accuracy and performance of proposed diagnosis technique are studied by applying it to the simulated transformer.
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Modelling of Air Standard Thermodynamic Cycles Using CyclePad
Статья научная
The paper aims to explore the application of CyclePad in modelling air standard thermodynamic cycles. CyclePad is a powerful software tool designed for the simulation and analysis of various thermodynamic cycles. This paper provides an in-depth investigation into its capabilities and effectiveness in modelling air standard cycles, including the analysis of performance parameters such as efficiency, work output, and heat transfer. To explore the potential of CyclePad, Carnot, Otto, Stirling, Ericsson, Diesel, and Dual cycles were explored first thermodynamically and then modelled using the software. These cycles were tested against practical numerical problems, and it has been observed that the results obtained from the CyclePad are in agreement with the existing literature. Moreover, to understand the impact of input parameters on the performance of cycle output and efficiency sensitivity analysis was performed and reported. The results obtained are very encouraging and stem from the fact the CyclePad can be used effectively to understand and analysis any thermodynamic cycle (both open and close) having any level of complexity.
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Modular Approach based Backbone Construction Using STP with CDS
Статья научная
In a dense environment, wireless sensor network (WSN) requires more energy to work in an effective and efficient manner. Hence, energy conservation is the main objective. In the paper, we have proposed a methodology to construct a backbone using modular antennas in combination with spanning tree protocol (STP), graph sampling, and Connecting Dominating Set (CDS) strategy. The backbone construction is based upon the modular antenna based WSNs, where the dominating sets can avoid the intermediate connection in order to reduce the hop count and energy consumption. The dominating sets have been connected using the modular transmission range of the wireless sensor networks to construct the backbone. The dominating set selection procedure to construct the WSN backbone is based upon the degree of connections of the nodes, which enables the locally centralized behavior of the connected dominating sets. The proposed methodology has been proved effective resulting in the construction of an energy efficient backbone.
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Monkeypox Detection Using Support Vector Machine with a Quadratic Polynomial Kernel
Статья научная
This study looks at how well a Support Vector Machine (SVM) with a quadratic polynomial kernel works for detecting Monkeypox. The SVM method is compared to other machine learning models like Neural Networks, KNN, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes. By using features from medical images called Local Binary Patterns (LBP), the SVM model showed the best results, with 93.33% accuracy, 95.24% recall, 91.67% true negative rate, and 90.91% precision. The LBP features are used because they exhibit unique textural patterns that can distinguish Monkeypox and normal cases. The results show that the SVM with this kernel is good at telling the difference between Monkeypox and normal cases, making it a helpful tool for early detection in healthcare.
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