International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 649
Memory controller and its interface using AMBA 2.0
Scientific article
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
Scientific article
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
Scientific article
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
Scientific article
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
Scientific article
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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Scientific article
This article presents a methodology for modeling and manufacturing three-dimensional (3D) geometric figures. The need for developing the methodology is substantiated, and its significance for the STEM training of future specialists in engineering specialties is demonstrated. An advantage of the methodology is the clear structuring of all its stages, levels, and steps, which simplifies the process of implementing STEM projects. Structurally, the methodology includes a preparatory stage and four main stages. Stage 1 involves designing the object model, which includes collecting information about its characteristics and developing a mathematical model. Based on the mathematical model, the main parameters of the investigated object (part) are determined and calculated. At Stage 2, the computer-aided construction of a three-dimensional model of the object is performed, taking into account the parameters determined at the first stage. The development of drawings and the 3D model is carried out in an appropriate software environment, in particular, in the AutoCAD computer-aided design (CAD) system. Based on the calculated parameters, different types of three-dimensional models of the object are created: wireframe, surface, and solid. Stage 3 involves the implementation of additive manufacturing, that is, the manufacture of a physical prototype of the investigated object using 3D printing. For models of significant overall dimensions or complex geometric shapes that complicate printing, preliminary decomposition of the model into separate parts is performed. After printing, the individual components are joined, and the resulting physical prototype undergoes appropriate post-processing. Stage 4 involves the validation of the manufactured physical 3D model by assessing its conformity with the digital model of the object. Comparison of the physical and software models can be carried out based on the analysis of their photographic images using digital image processing methods. In addition, a visual assessment of the prototype and measurements of its main geometric parameters are performed. An example of the implementation of a STEM project aimed at the creation, investigation, and physical reproduction of a three-dimensional model of a small stellated dodecahedron by means of 3D printing is presented. According to the developed methodology, at the first stage, a mathematical model of the dodecahedron was developed. At the second stage, based on this model, wireframe, surface (polygonal), and solid (volumetric) models were sequentially developed. An important advantage of the proposed methodology is the step-by-step construction of the wireframe model, which begins with the construction of simple geometric primitives. At the subsequent stages, individual primitives and their groups are sequentially combined into more complex structural elements. This approach simplifies the construction process and enables the step-by-step creation of complex three-dimensional models. At the third stage, the model was decomposed, prepared for printing, and its individual components were manufactured using an FDM 3D printer. After that, the printed parts were assembled and the resulting product was post-processed. As a result, a physical model of the small stellated dodecahedron was manufactured, whose geometric dimensions and surface quality meet the requirements and objectives of the STEM project. A physical model of the small stellated dodecahedron was fabricated, with a pentagon edge length of b = 25.3 mm and a circumscribed sphere diameter of DS = 127.2 mm. The maximum dimensional deviation of the dodecahedron was 0.3 mm.
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Modeling and Real-Time simulation of large hydropower plant
Scientific article
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
Scientific article
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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Scientific article
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 and Intellectual Analysis of Quantitative Characteristics of Air Pollution
Scientific article
The current state of environmental safety requires the introduction of the latest technologies for monitoring and analyzing environmental data. Air pollution with fine particles (PM2.5, PM10) from local emission sources creates significant computational challenges due to insufficient data and the dynamic nature of pollution propagation processes. Objective. The goal of the work is to solve these problems by developing and integrating modern methods and tools for modeling and intelligent analysis of air pollution characteristics. A prototype hybrid algorithmic pipeline is proposed that integrates an adapted Gaussian model with optimized machine learning and neural network models. The method utilizes a Mamdani-type fuzzy logic system to determine the Atmospheric Stability Class based on continuous meteorological inputs. Additionally, Bayesian inverse modeling using Markov Chain Monte Carlo (MCMC) methods is applied to estimate unknown source emission intensity. The approach is implemented using cloud technologies (Azure Data Lake) and edge computing systems (Nvidia Jetson Nano). A large-scale comparative analysis of deep neural network architectures (Bidirectional LSTM, CNN) and ensemble models (XGBoost, CatBoost) was conducted. The Bidirectional LSTM provided the best overall performance (MSE=0.521, R2=0.985). The integration of fuzzy stability inputs reduced the MSE by 16%. The experiments conducted and numerical modeling confirmed the effectiveness of the proposed methods and the operability of the developed neuro-controller system. The results allow recommending the integrated approach for real-time environmental monitoring and decision support in data-scarce environments.
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Modelling of Air Standard Thermodynamic Cycles Using CyclePad
Scientific article
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
Scientific article
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
Scientific article
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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Scientific article
The objective of the present paper is to optimize the machining parameters for turning of EN8steel on lathe machine using a combination of Taguchi and Grey Relational Analysis to yield minimum cutting forces and surface roughness. The process parameters such as rotational speed, feed, depth of cut and cutting fluid have been selected. In this study, the experiments were carried out as per Taguchi experimental design and L9 orthogonal array was used. Analysis of variance (ANOVA) was also used to find out the most influence of processing parameters on the responses. The regression equations were also established between the process parameters and responses. The results indicate that the depth of cut is the most significant factor affecting the cutting force and surface roughness followed by a feed, speed and cutting fluid.
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Scientific article
Accurate classification of kidney abnormalities from computed tomography (CT) images is essential for early diagnosis and clinical decision-making. However, distinguishing kidney stones, cysts, tumours, and normal kidneys remains challenging because of variations in lesion size, appearance, and imaging conditions. This study proposes a multi-resolution attention-fused, calibration-aware deep learning framework for four-class kidney CT image classification. The framework integrates convolutional neural networks and a Vision Transformer to extract complementary local and global features, which are combined through an adaptive attention-based fusion mechanism. Calibration-aware learning, incorporating focal loss, multi-class Brier loss, and feature-level orthogonality regularization, is employed to improve prediction reliability and confidence estimation. The model was trained and evaluated using an image-level data split on a publicly available kidney CT dataset containing normal, cyst, stone, and tumour images. Experimental results demonstrate an overall classification accuracy of 99.3%, a Macro-F1 score of 99.1%, an AUROC of 0.999, and an AUPRC of 0.998 on the independent test set. Robustness analysis under synthetic image corruptions showed only a modest reduction in performance, with the AUROC remaining at 0.995 under severe motion blur conditions. External validation on two independent cohorts (External Site-A and External Site-B) achieved AUROC values of 0.993 and 0.988, respectively, while post-hoc temperature scaling improved calibration by reducing the Expected Calibration Error (ECE) from 0.028 to 0.012 and 0.034 to 0.015. These results demonstrate that the proposed framework provides accurate, robust, and well-calibrated predictions, supporting its potential application in computer-aided kidney CT image analysis.
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Multi-Sensor Approach for Monitoring Pipelines
Scientific article
Pipeline vandalization is one of Nigeria's economy killer, since Nigeria economy to a great extent relies on oil in that capacity. Therefore, a third party damage to petroleum pipelines can be cataclysmic if undetected and prevented. This act results in budgetary misfortunes, ecological contamination and incessant death and loss of properties worth millions as an aftereffect of vandalization. Consequently, it is very paramount to protect these pipelines from vandals through intelligent monitoring systems. Several efforts have been made towards providing a reliable monitoring system for oil pipeline, however, no practically implementable solution have been achieved. Therefore, a pipeline monitoring system using multi-sensors is presented herewith. The sensor array consists of a Passive Infrared (PIR), vibration and sound sensor. An uninvolved infrared (PIR) sensor was utilized to detect intruders before they get in contact with the pipeline and for affirmation of intruders, sound and vibration sensor were set up. As the PIR recognizes an on-coming human the sound and vibration sensors affirms if the human is an intruder(s). An intrusion message containing the location of the vandals is sent to the appropriate authority by the microcontroller via a connected GSM module. Results obtained proved the system as a viable solution for detecting pipeline vandals.
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Multilingual Fake News Detection Using Machine Learning with Contextual-Based Feature Extraction
Scientific article
Fake news has become a major challenge in today’s digital environment, particularly in languages where labeled data is limited. Most existing research has primarily focused on English due to the easy availability of annotated datasets, whereas low-resource languages such as Bengali remain underexplored. This study presents a multilingual approach for fake news detection using machine learning with contextual-based feature extraction. The proposed method integrates n-gram techniques with sentence-level contextual embeddings to capture both word-level patterns and semantic meaning. Since labeled data is not available for the Bengali dataset, a translation-based strategy is employed, followed by a pseudo-labeling process to assign labels automatically. The models are trained on English news titles and subsequently evaluated on both English and Bengali datasets to examine their cross-lingual effectiveness. The experimental findings indicate that ensemble-based classifiers such as Random Forest and Gradient Boosting achieve reliable performance across both languages. In some cases, the results for Bengali data are comparable or slightly better than those for English. The study demonstrates that effective fake news detection is possible in low-resource languages using short text data without relying on manually labeled datasets. The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments.
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Multiple Master Communication in AHB IP using Arbiter
Scientific article
The major disadvantage of a standard bus topology is the constraint of being able to realize only one communication at a time (the tasks may take place in parallel but the communications are only done in a sequential). As these communications are handled by the bus arbiter, a Bottleneck when the number of communications increases, but also when the bandwidth constraints of several communications become important.This arbitration plays a predominant role because it authorizes communications on the bus but it is also in charge of resolving the conflicts (several requests of communications at the same time). This arbitration implies therefore a limitation on the number of IP connected to the bus to a dozen elements. This work elaborates the AMBA bus interface with four masters interacting with single memory system, using Arbiter between memory controller and other supporting peripherals. Different module of i.e., AHB MSTER, AHB SLAVE INTERFACE AND AHB ARBITER(round robin algorithm)has been developed with VHDL. Further integration with FIFO, RAM and ROM with memory controller is done. The Four AHB master initiates the operations and generates the necessary control signals on single bus to memory controller with the help of arbiter. The proposed architecture shows the area efficient management as compared to previous researches of multiple data communication in AHB BUS system. The system model is synthesized with Xilinx XC6vx75t-2ff484 and simulated with MODELSIM.
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Scientific article
In this paper, we study the two-point boundary value problems for systems of nonlinear third-order dierential equations .Under some conditions, we show the existence and multiplicity of positive solutions of the above problem by applying the fixed point theorems in cones.
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Multiple-address Technique of Satellite Communication
Scientific article
In this paper, we have studied the operation modes of satellite communication system. They include that signal transmission mode, signal processing mode and signal exchanging mode. We have especially studied the multiplex mode, modulation mode, coding mode, multiple-address connection, and channel distribution and exchanging system. All we have studied are important to the satellite communication station’s changing and signal processing.
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