Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 508
Interpolation Method for Identification of Brain Tumor from Magnetic Resonance Images
Статья научная
During the past years, it is observed from the literature that, identification of the brain tumor identification in human being is gaining popularity. Diagnosing any disease without manual interaction with great accuracy makes computer science research more demanding, therefore, the present work is related to identify the tumor clots in the affected patients. For this purpose, a well-known Safdarganj Hospital, New Delhi, India is consulted and 2165 Magnetic Resonance Images (MRI) of a single patient are collected through scanning, and interpolation technique of numerical method used to identify the accurate position of the brain tumor. A system model is developed and implemented by the use of Python programming language and MATLAB for the identification of affected areas in the form of a contour of a patient. The desired accuracy and specificity are evaluated using the computed results and also presented in the form of graphs.
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Inverse Kinematics of Redundant Manipulator using Interval Newton Method
Статья научная
The paper presents an application of Interval Newton method to solve the inverse kinematics and redundancy resolution of a serial redundant manipulator. Such inverse problems are often encountered when the manipulator link lengths, joint angles and end-effector uncertainty bounds are given, which occurs due to because of inaccuracies in joint angle measurements, manufacturing tolerances, link geometries approximations, etc. The inverse kinematics of three degree of freedom planar redundant positioning manipulator without end-effector has been evaluated using the manipulability of Jacobian matrix as performance metric. To solve the nonlinear equation of inverse kinematics, the multidimensional Newton method is used. The inverse kinematics is intended to produce solutions for joint variables in interval of tolerances for specified end effector accuracy range. As exemplar problem solving, a planar 3-degrees-of-freedom serial link redundant manipulators is considered.
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Investigation of Infrared Image Prediction for Subsonic Exhaust Plume
Статья научная
An infrared imaging prediction model of exhaust plume was developed to understanding the infrared characteristics of exhaust plume. The method is based on the irradiance calculation of all pixels on the focal plane array. In order to compute the irradiance incident on each pixel, the gas radiation transfer path in the plume for the instantaneous field of view (IFOV) corresponds to the pixel was solved by the simultaneous equation of a cylinder which covers the exhaust plume and the line of sight. Radiance for the transfer path was calculated by equation of radiation transfer for nonscattering gas. The radiative properties of combustion were computed by Malkmus model with EM2C narrow band database(25cm-1). The pressure, species concentration for the path was determination by CFD analysis. The relatively intensity of each pixel was transferred to color in the display according to gray map coding and hot map coding. Infrared image of the exhaust plumes from a subsonic axisymmetric nozzle was predicted with the model. By changing the parameters, such as FOV and space resolution , the image of different imaging system can be predicted for varying relatively position of camera and the plume.
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Статья научная
The organic compound used as corrosion inhibitor was benzaldehyde. The use of organic compounds as corrosion inhibitors is of potential interest due to their abundant availability, cost effectiveness, and environmental acceptability. However, this study investigates the inhibition effect of this organic compound (benzaldehyde) on the corrosion of aluminium in phosphoric acid solution. Experiments were performed by varying the temperature and concentration of the compound under study. The results obtained from the study shows that the organic compound (benzaldehyde) is a potential inhibitor for the corrosion of aluminium in phosphoric acid solution. The inhibition efficiency was found to increases progressively as the concentration of the inhibitor increases but decreases when the temperature rises. Activation energy values for the corrosion process was found to be 32.61 kJ/mol in uninhibited phosphoric acid solution which increased to 49.53 kJ/mol in the presence 0.1 M concentration of the inhibitor. The values of the rate constant and half live were 9.12 × 10-3 hr-1 and 75.94 hr in uninhibited phosphoric acid solution which changed to 4.56× 10-3 and 151.80 hr in the presence of 0.1 M inhibitor concentration. Kinetics of the reaction in uninhibited and in the presence of the inhibitor revealed that the process follows a first order reaction. The evaluated enthalpy (ΔH) gave positive values which indicate that the heat of adsorption process on the surface of aluminum was endothermic. The negative values of entropy (ΔS) signified that the activated complex at the rate determining step is an association, not dissociation. Evaluated values of free energy of adsorption (ΔGads) were all negative, implying spontaneity of the process and were around and less than -20 kJ/mol, indicative of physisorption of the adsorption process.
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Investigation on Extended Number Field
Статья научная
This paper expands the field of complex number to general plural. Discussion on the forth power generalplural is emphasized, then creates the four fundamental operations of arithmetic and calculus which can be applied to the project.
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IoT Based Smart Shower to Minimize Home Water Usage
Статья научная
Water waste, particularly in home shower systems, remains a significant global concern, with showers accounting for substantial water consumption. This research proposes an IoT-based solution to mitigate water wastage using smart technology. According to surveys, the average shower duration is approximately 8 minutes, with a flow rate averaging 8 Liters per minute, resulting in significant water use per shower. Our approach integrates IoT technology, utilizing Arduino as a gateway device for data management. Water usage data is collected and stored in a cloud-based platform, Thing Speak, enabling users to monitor consumption patterns daily, monthly, and annually. The system employs hardware components including a solenoid valve, Arduino microcontroller, ESP 8266 WiFi module, LCD display, and sound player, complemented by software components like the Arduino IDE and ThinkSpeak database. Operationally, upon activation, the system controls water flow for the initial four minutes before alerting users to conserve water through visual and auditory cues. This study’s methodology involves the design and implementation of an IoT-enabled smart shower system, demonstrating its efficacy in reducing water consumption through real-time monitoring and user feedback. Results indicate a significant reduction in water usage compared to conventional shower systems, thereby highlighting the potential of IoT technology in promoting sustainable water management practices at the household level.
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IoT leak detection system for building hydronic pipes
Статья научная
Building’s Air Conditioning systems require moving liquids for dweller comfort. Clogged pipes, system degradation can cause pressure buildups, leaks and other faults which leads to damage to the building. Most of the leaks in the commercial building occur due to poor maintenance and/or material degradation. Visual inspection is most predominantly used to solve this problem in the industry. This paper introduces the Internet of Things technology to detect leakage in building’s hydronic pipes with the support of sensors, fault detection method and mechanical control. The system consists of: Microcontroller, Windows application and website application. Internet of Things technology was used to monitor and control the hydronics using microcontroller’s capability of connecting to main server which is used to transmit the data to the cloud. The prototype was successfully built and tested. Promising results show that leaks above 2ml/s could be detected after 4 seconds specifically for the built small-scale system while control and monitor feature could be implemented with Internet of Things technology.
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Light-Fidelity (Li-Fi) Based Patient Monitoring System
Статья научная
The healthcare landscape is rapidly evolving with the integration of advanced technologies to enhance patient care, monitoring, and overall medical practices. In this era of innovation, Light-Fidelity (Li-Fi) has emerged as a promising solution with the potential to revolutionize patient monitoring systems. This research aims to address current limitations in Li-Fi-based patient monitoring systems, such as data security concerns and the inability to provide continuous monitoring without on-site medical personnel. It is driven by the urgent need to tackle critical healthcare challenges arising from a significant shortage of medical personnel, particularly in certain regions and countries. The objective is to develop a Li-Fi-based patient monitoring system that can remotely and continuously monitor patient vital signs and medical data. The methodology involves a comprehensive approach that integrates advanced technology, data collection, data processing, and web application development. Results indicate that the developed system prioritizes performance and security, with evaluations based on latency, security vulnerabilities, and data throughput. This research advances Li-Fi's potential in healthcare, paving the way for innovative applications that can enhance patient care, improve healthcare outcomes, and potentially transform the entire healthcare industry.
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Статья научная
The depletion of fossil fuel is driving the world towards the application of renewable energy sources. However, their intermittent nature, in addition to load variation and transmission line sag-tension change due to temperature, is a great deal of problems for reliable power delivery. Without a reliable power delivery, power generation is just a waste of resources. A reliable power delivery can be achieved when the best and the worst case steady state information of a power system network is known to plan and control accordingly. If a system is affected by the presence of uncertainty, a deterministic load flow analysis fails to provide the worst-case load flow result in a single analysis. As a result, a load flow analysis considering the presence of uncertainty is mandatory. On this paper, a novel complex affine arithmetic (AA) based load flow analysis in the presence of generation and load uncertainties is proposed. The proposed approach is tested on an IEEE bus systems and compared with Monte Carlo approach. The proposed approach convergence faster than the Monte Carlo based method and it is slightly conservative.
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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 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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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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