International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 649
Hand Arm Vibration Alleviation of Motorcycle Handlebar using Particle Damper
Scientific article
Vibration induced in the vehicle affects the performance of the driver or rider because of reduction in comfort & safety level. In case of motorcycle, poor suspensions system & uneven road condition make driving difficult, these also introduces vibration. The vibrations are directly transferred to the body through the seat & handlebar. It has been seen that handlebar vibrations are more serious & creates physical problem to the rider. Particle damping technology is a derivative of impact damping with several advantages. Particle damping is the use of particles moving freely in a cavity to produce a damping effect. In this paper a passive damper using particle damping technique is designed and developed to reduce the hand arm vibrations (HAV). The experiments are planned and conducted using DOE. Optimum configuration of particle damper has been derived through this research work. Experimental tests shows by employing a particle damper the vibration amplitude is minimized significantly.
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Scientific article
The World is moving toward Smart traffic management and monitoring technologies. Vehicle detection and classification are the two important features of intelligent transportation system. Several algorithms for detection of vehicles such as Sobel, Prewitt, and Robert etc. but due to their less accuracy and sensitivity to noise they could not detect vehicles clearly. In this paper, a simple and rapid prototyping approach for vehicle detection and classification using MATLAB Xilinx system generator and Zedboard is presented. The Simulink model of vehicle detection and classification is designed using a complex canny edge detection algorithm for vehicle detection. The canny edge detection algorithm offers 91% accuracy as compared to its counterpart Sobel and Perwitt algorithms that offer 79.4% and 76.1% accuracy. The feature vector approach is used for vehicle classification. The proposed model is simulated and validated in MATLAB. The Canny edge detection and feature vector algorithms for vehicle detection and classification are synthesized through the Xilinx system generator in Zedboard. The proposed design is validated with the existing works. The implementation results reveal that the proposed system for vehicle detection and classification takes only 8 ns of execution time with a 128MHz clock, which is the lowest and optimum calculation period for the smart city.
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Hash Function Construction Based on RBFNN and Chaotic Mapping
Scientific article
One-way Hash function is not only widely used in the aspects of the digital signature, identity authentication and integrity checking, etc. but also the research hotspot in the field of contemporary cryptography. In this paper, it firstly utilized neural network and practiced the chaotic sequences produced by one-dimensional nonlinear mapping. And then, it constructed Hash function with cipherkey by means of altering sequences. One of the advantages of this algorithm is that neural network hides the chaotic mapping relations and make it difficult to obtain mapping directly. Simulation experiment showed that the algorithm have good unidirectionality and weak collision, and stronger confidentiality than the tradition-based Hash function, as well as easy to achieve.
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Healthcare System Technology using Smart Phones and Web Apps (Case Study Iraqi Environment)
Scientific article
In the Past a Few Years, smart devices like smart phones and tablets have radically change in many aspect, started from Entertainment to Shopping services to transfer Money and Banking, the next is Health Services. With the development in information technology And the big development in cloud computing Here smart phones have entered heavily in all aspects of health care. Now with the revolution of the smart devices (smart phones or tablets) and it's applications, there is many applications and tools are available started from attachments that allow to diagnose an infections and Now remotely and continuously monitor each heartbeat , blood pressure readings, the rate and depth of breathing, body temperature, oxygen concentration in the blood, glucose, brain waves, activity, mood, so the end result will be can reduce using of doctor ,also reduce the cost , and give us speed up and give power to patients , so make it possible for Patient to use portable devices (smart phones or tablet) to access their medical information, and achieve the goal to put information technology to work in health care and make the integration of health information technology into primary care .So the using information technology give us the good solution that won’t replace physicians. Health Information technology give the providers of health care to give better manage patient care. By making the health information are available electronically anytime and anywhere is needed, Health Information technology can help us to improve the quality Of Health , so can decrease the cost. Now, after all these advantages should shed light on the side of personal privacy And Hacking side that must have been tested all application and tools, All of these tools must be accurate and needs to be tested. So that must provide the highest level of security and privacy for the patients. After that. The application does not arrive the goal with 100% percent, according to the limitation that mentioned later.
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Heterostructure Silicon and Germanium Alloy Based Thin Film Solar Cell Efficiency Analysis
Scientific article
Thin film solar cell along with enhanced absorption property will be the best, so combination of SiGe alloy is considered. The paper presented here consists of a numerical model of Si/Si1−xGex heterojunction solar cell. The addition of Ge content to Si layer will affect the property of material. The research has investigated characteristics such as short circuit current density (Jsc), generation rate G , absorption coefficient (α), and open circuit voltage (Voc), power, fill factor (FF) with optimal Ge concentration. The speculative determination of appropriate germanium mole fraction is done to get the maximized thin-film solar cell efficiency.
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High Accuracy Swin Transformers for Image-based Wafer Map Defect Detection
Scientific article
A wafer map depicts the location of each die on the wafer and indicates whether it is a Product, Secondary Silicon, or Reject. Detecting defects in Wafer Maps is crucial in order to ensure the integrity of the chips processed in the wafer, as any defect can cause anomalies thus decreasing the overall yield. With the current advances in anomaly detection using various Computer Vision Techniques, Transformer Architecture based Vision models are a prime candidate for identifying wafer defects. In this paper, the performance of Four such Transformer based models – BEiT (BERT Pre-Training of Image Transformers), FNet (Fourier Network), ViT (Vision Transformer) and Swin Transformer (Shifted Window based Transformer) in wafer map defect classification are discussed. Each of these models were individually trained, tested and evaluated with the “MixedWM38” dataset obtained from the online platform, Kaggle. During evaluation, it has been observed that the overall accuracy of the Swin Transformer Network algorithm is the highest, at 97.47%, followed closely by Vision Transformer at 96.77%. The average Recall of Swin Transformer is also 97.54%, which indicates an extremely low encounter of false negatives (24600 ppm) in contrast to true positives, making it less likely to expose defective products in the market.
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Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection
Scientific article
Monitoring systems for electrical appliances have gained massive popularity nowadays. These frameworks can provide consumers with helpful information for energy consumption. Non-intrusive load monitoring (NILM) is the most common method for monitoring a household’s energy profile. This research presents an optimized approach for identifying load needs and improving the identification of NILM occupancy surveillance. Our study suggested implementing a dimensionality reduction algorithm, popularly known as genetic algorithm (GA) along with XGBoost, for optimized occupancy monitoring. This exclusive model can masterly anticipate the usage of appliances with a significantly reduced number of voltage-current characteristics. The proposed NILM approach pre-processed the collected data and validated the anticipation performance by comparing the outcomes with the raw dataset’s performance metrics. While reducing dimensionality from 480 to 238 features, our GA-based NILM approach accomplished the same performance score in terms of accuracy (73%), recall (81%), ROC-AUC Score (0.81), and PR-AUC Score (0.81) like the original dataset. This study demonstrates that introducing GA in NILM techniques can contribute remarkably to reduce computational complexity without compromising performance.
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Hotspot sequence patterns with an improvement in spatial feature
Scientific article
Forest fires in Sumatra and Kalimantan resulted in degradation of peatlands significantly. The strong indicator of forest and land fires including in peatland can be identified using hotspots which occurred consecutively in 2 to 5 days. The previous studies have been conducted in mining sequence patterns on hotspot datasets in Sumatra and Kalimantan. However, those studies applied the sequential pattern algorithms on the datasets containing temporal and rough spatial features. This study aims to generate sequence pattern of hotspot datasets using the SPADE algorithm with the improvement of the spatial feature. The study results in 892 1-frequent sequences and 28 2-frequent sequence patterns at the minimum support of 0.02%. A total of 484 hotspots were found from the 28 2-frequents sequence patterns, most of which were occurred in September to November 2014 and 2015. Central Kalimantan, Riau, and South Sumatra are the area where hotspots mostly occurred in 2014 and 2015. The visualization module for hotspot sequences was successfully developed in two iterations using the JavaScript.
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Human Identification Using Foot Features
Scientific article
The goal of this paper is to investigate a new technique for human identification using foot features. This work can be mainly decomposed into image preprocessing, feature extraction and pattern recognition by Artificial Neural Network (ANN). Foot images are rarely of perfect quality. To obtain good minutiae extraction in foot with varying quality, we conducted preprocessing in form of image enhancement and binarization .To extract features from human foot based on shape geometry of foot boundaries by extracting 16 geometric features from a human foot image. The foot center has been determined, and then the distances between the center point and outer points are measured with different angles .The angles are from 30˚ to 360˚ by increment with 30˚ gradual. The 13th feature that can be extracted is the length of a foot which is defined as the distance between the top point of the foot and the bottom point. The 14th, 15th and 16th are three major features the width of the foot. The first width is passing through center point, therefore, the second widths of foot is measured from the upper part above the center point and third width from the region the center point under the center point at the bottom of the foot. Euclidean distance is used in the proposed system. Artificial Neural network used for recognition. MATLAB version 8.1(R2013a) and windows 7 with 32 bit is used to build the application and performed on pc of core i3 processor, and our test system on 40 persons, results were satisfactory up to more 92.5%.
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Hybrid Deep Learning-Based Automated Genre Classification of Assamese Regional Songs
Scientific article
This work aims to preserve and promote the rich musical heritage of Assam by developing an automated classification system for Assamese regional songs using a hybrid deep learning approach. This method not only modernizes the preservation of traditional music but also enhances its accessibility to a global audience for integrating AI with cultural conservation. Five genres of Assamese songs—Bihu, Kamrupiya Lokageet, Goalporiya Lokageet, Borgeet, and Naam—are considered in this study. By leveraging Convolutional Neural Networks (CNNs) and advanced audio feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms, a hybrid model combining VGG16 and ResNet50 is developed. This fusion utilizes the strengths of both architectures, enhancing the model’s performance and accuracy. Following the process, it is observed that two distinctly different genres, Bihu and Borgeet, are accurately categorized by the proposed model, while the remaining three show slight labeling inconsistencies.
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Hybrid Feature Fusion and Bayesian-Optimized Ensemble Learning for Robust Citrus Disease Detection
Scientific article
Detecting citrus diseases at an early stage is very important for ensuring fruit quality and minimizing production losses, as well as for raising awareness about sustainable agriculture. As a solution to this problem, the authors of this paper propose a hybrid feature-based citrus disease classification system that integrates deep learning representations, handcrafted descriptors, feature selection, and ensemble learning, all of which are tuned via Bayesian optimization. We perform tests on two real-world citrus disease datasets that differ greatly in nature: a four-class lemon dataset and a two-class orange dataset. Both datasets were collected under quite different environmental conditions, so they show diverse disease symptoms and varying background complexity. Deep semantic features were obtained by running a pretrained ResNet50 network. In addition to those, other complementary handcrafted features, such as color, texture, spatial, and statistical features, were extracted from the segmented infected areas. Together, the hybrid feature vector of 2082 dimensions was subjected to an optimization method known as Neighborhood Component Analysis (NCA). This technique selects 300 features that are most effective for discrimination while at the same time ensuring the preservation of class separability and the minimization of redundancy. For the classification task, two classifiers, namely Random Forest (RF) and Bayesian-Optimized Random Forest (BORF), were employed. The latter is based on Bayesian optimization to locate the model hyperparameters. To measure the model's performance in an unbiased manner, five-fold cross-validation was performed. Based on the experimental results, BORF can improve classification accuracy on the lemon dataset from 90.42% to 93.75% and on the orange dataset from 95.42% to 95.92% compared to the baseline RF classifier. Cross-validation mean accuracies of the proposed system were 93.75 ± 0.82% and 95.92 ± 0.47% for the lemon and orange datasets, respectively. Receiver Operating Characteristic (ROC) analysis provided class-specific area under the curve (AUC) values of 0.975 and 0.972 for the orange dataset, with a macro-averaged AUC of about 0.94 for the multi-class lemon dataset. The combination of hybrid feature fusion, NCA-based feature optimization, and Bayesian-optimized ensemble classification leads to enhanced discriminative power, greater robustness, and better generalization performance for the system in citrus disease identification in a real-world agricultural setting, as demonstrated by the results.
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Hybrid Quantum-Classical Framework for Computational Mental Energy from Multichannel EEG Streams
Scientific article
This paper presents a hybrid quantum-classical framework for real-time estimation of cognitive engagement from multichannel electroencephalography (EEG) using a new operational indicator called Computational Mental Energy (CME). The proposed approach integrates signal preprocessing (windowing, filtering, spectral feature extraction), spectral feature extraction, a 4-qubit variational quantum classifier (VQC) for flow-state probability estimation, and a metaheuristic optimization loop for balancing predictive quality and quantum resource cost. CME is defined as a window-level function of aggregated spectral energy, task complexity, and estimated flow probability, measured in a dedicated signal-energy unit called Vernik (Vn), with session-level aggregation rules. The system supports quantum-only, classical-only, and hybrid inference modes and is designed for streaming deployment with wearable EEG devices and server-side inference services. A single-subject pilot study involving eight cognitive activities and EEG recordings from a Muse Athena headband demonstrates that the hybrid mode (μ = 0.6) achieves 0.914 AUROC for flow-state detection, compared to 0.548 for the standalone quantum model, while reducing prediction variance by 40.9%. Validation on the IBM Marrakesh 156-qubit Heron r2 quantum processor shows strong agreement between simulator and hardware results (r = 0.869, MAE = 0.045), confirming the practical feasibility of execution on current quantum hardware. Across activities, CME rates differed significantly, with approximately a nine fold gap between coding and resting states, illustrating the framework’s ability to capture activity-dependent cognitive demand. The proposed architecture provides a reproducible pipeline for EEG-based cognitive-state analytics, resource-aware quantum inference, and future adaptive human-computer interaction systems.
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Hybrid Solar Power Plant in Saint Martin's Island can Enlarge Tourist Attraction in Bangladesh
Scientific article
Saint Martin's Island is the best tourist spot in Bangladesh and one of the most beautiful tourist places in the world. But the accommodation facilities are not suitable for tourists. The supply of electricity in the hotels is only 4-5 hours from the generator. For the geographical position, the electricity cannot supply from the mainland grid and the cost of electricity is so high and is not favourable to the environment. In this paper, Hybrid system of photovoltaic (PV), diesel generator, battery for generating electricity in the Saint Martin's Island is analyzed for 18 hotels. The main objective of the present study is to determine the optimum size of Hybrid system which can fulfil the requirements of 528 kWh/day primary load with 125 kW peak for 18 hotels in this island. By using HOMER (Hybrid Optimization Model for Electric Renewables) software an optimum model is established for the renewable system. The aim is to configure a renewable system with low interest and low energy cost. The diagrams and tables which show prices and performances of the types of equipment on the optimum model are also presented. The result shows that PV (185 kW), diesel generator (105 kW), converter (96 kW) and 615 piece batteries of the Hybrid system is most commercially reliable and least cost of energy is about 19.48Tk per kWh or $ 0.253 per kWh ($1=77Tk) with total net present cost $ 624,391 or 48,078,107TK. The emission of CO2 is very low in this Hybrid system.
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Scientific article
Precise mapping of water bodies is crucial for flood monitoring, disaster risk and response reduction, as well as sustainable water resource management. In this paper, we introduce a deep learning model for effective segmentation of rivers, lakes, and reservoirs from high-resolution Gaofen-2 satellite images. Leveraging the Five-Billion-Pixels dataset-more than 5 billion annotated pixels for 24 land cover classes—our approach solves the problem of segmenting water bodies on various terrains and environmental conditions. The proposed U-Net and ViT-UNet models, with the former employing Vision Transformers to enhance global context perception. For enhancing generalization, the dataset is augmented using Albumentations and flipping, rotation, and scaling transformations. Hybrid loss functions of Dice Loss, Binary Cross-Entropy, and Focal Loss are employed to handle class imbalance, especially for slender river segments. The ViT-UNet model attained 98.8% pixel accuracy, which mirrors its ability to preserve fine detail and large-scale spatial pattern. Mixed-precision training and the AdamW optimizer has enhanced the computational efficiency. Further, demonstrates the potential of transformer-based segmentation models for remote sensing achieved accuracy of 98% for environmental risk management and decision support in disaster-prone areas.
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IBSMFO-Optimized UPQC-DG Control for Harmonic Mitigation in Hybrid Renewable Microgrids
Scientific article
Distributed generation (DG) systems are increasingly integrated into modern power networks through power-electronic interfaces. However, the presence of nonlinear loads and power-electronic converters introduces harmonic distortion, which can significantly degrade power quality, particularly in three-wire systems. This study investigates harmonic mitigation in a hybrid renewable DG system incorporating solar, wind, fuel-cell-based generation, and grid-connected operation using a Unified Power Quality Conditioner (UPQC). An Improved Bat Search–Moth Flame Optimization (IBSMFO) based control strategy is proposed for optimizing the proportional-integral (PI) controller parameters of the UPQC. The IBSMFO algorithm combines the search characteristics of the Improved Bat Search Algorithm (IBSA) with the Moth Flame Optimization Algorithm (MFOA) to minimize the selected error-based objective function and enhance the dynamic performance of the compensation system. The proposed UPQC is evaluated under ideal and non-ideal source-voltage conditions with nonlinear loads. Simulation results demonstrate effective mitigation of voltage and current harmonics and improved power quality. In particular, the source-current THD is reduced to 4.03% under the operating conditions considered, while a minimum THD of 1.01% is achieved under the corresponding compensation condition. The results confirm that the proposed IBSMFO-optimized UPQC provides effective harmonic compensation and improved power quality performance in hybrid renewable DG systems.
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IIoT-RoboAuth: PUF-Based Command-Bound Authentication for Industrial Robotic Control Networks
Scientific article
Industrial robotic controllers require identity assurance and command-level integrity within bounded control intervals. This study redesigns a fog-assisted three-factor scheme as IIoT-RoboAuth, a PUF-rooted protocol in which the session key is derived from the three participant nonces, an ephemeral P-256 secret, the policy epoch, and the current RPUF epoch; each command is then authenticated over its canonical payload, sequence, timestamp, role scope, and safety-profile hash. The protocol uses four handshake messages (two end-to-end round trips), 483 application-layer bytes, and a 224-byte command envelope. Evaluation used a Python 3.12 message-driven simulator rather than robot hardware or NS-3. Thirty fixed seeds generated 1,000 sessions and 1,000 trials for each of four attack classes per seed. The modeled mean authentication latency was 7.903 ms (95% CI, 7.896-7.909 ms), compared with 22.735 ms (95% CI, 22.698-22.773 ms) for the centralized baseline under the stated delay assumptions. The complete validator rejected 30,000 of 30,000 command modifications, replays, stale commands, and out-of-range commands in each class; removing the command MAC, sequence chain, 5-ms freshness check, or kinematic check caused the corresponding attack class to pass. The result establishes reproducible protocol-level command binding and an explicit deployment boundary; hardware timing, PUF reliability, physical tamper resistance, and safety certification remain outside the evidence provided here.
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IOT Based Burglar Detection and Alarming System Using Raspberry Pi
Scientific article
In today’s world, security has become the most difficult task. With increasing urbanization and the growth of big cities, the crime graph is also on the rise. In order to ensure the security and safety of our home while we are away, we propose the use of Raspberry Pi to implement an IOT-based burglar detection and alert system. IoT involves the improvement of networks to efficiently acquire and inspect statistics from different sensors and actuators, then send the statistics via Wi-Fi connection to a personal smartphone or laptop. The concept of antitheft devices has been around for decades, but most are only CCTVs, IP cameras, or magnetic doorbells. There is a limited amount of work devoted to face recognition and weapon detection. The design of anti-theft protection devices relies primarily on face recognition and remote tracking. Here, our objective is to improve this system by incorporating weapon detection feature by image processing. The system uses Raspberry Pi, in which a person is only permitted access to the house if his/her face is recognized by the proposed system, and if he/she does not carry any weapons. From the standpoint of security, this system is more reliable and efficient. The proposed system is intended to develop a secure access control application based on face recognition along with weapon detection. By using the Telegram app, the proprietor can monitor the digital camera mounted on the door frame. As a means of improving the accuracy and efficiency of our system, we use the Python language and the Open CV library.
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Identifying Cross-Site Scripting Attacks Based on URL Analysis
Scientific article
Cross-site scripting (XSS) is one of the major threats to the security of web applications. Many techniques have been taken to prevent XSS. This paper presents an approach to identify Cross-Site Scripting attacks based on URL analysis. The fundamental assumption of our method is that the URL contains a part that can produce a valid JavaScript syntax tree. First, we extract the parameters of the URL to produce a valid JavaScript syntax tree and weight its parsing depth. If its depth exceeds a user-defined threshold, the URL is considered suspicious. Second, to the exception URLs, a second level of defense is formed by analyzing its structure. The experimental results demonstrate that our approach can effectively distinguish most of the malicious URLs from the benign ones.
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Scientific article
This research presents a systematic analysis framework for wind farm integration in scaled IEEE 33-bus agricultural distribution systems (×26.38, representing 98 MW peak agricultural load), focusing on comprehensive power quality, reliability, and economic assessment. The scaled system exhibits baseline voltage violations (24 buses) characteristic of extended radial distribution topologies serving concentrated agricultural loads, consistent with real utility system characteristics in Indonesian agricultural regions. Four wind penetration scenarios (0%, 15%, 30%, 45%) are evaluated using an integrated backward-forward sweep methodology coupling fundamental power flow with empirical harmonic calculations validated against full harmonic load flow analysis (±0.3% accuracy). The analysis employs realistic harmonic injection models for VFD-dominated agricultural loads and full-converter wind turbines, time-domain operational profiles capturing diurnal variations, and revised reliability modeling incorporating protection coordination constraints and battery energy storage system limitations. Results demonstrate that 30% penetration achieves optimal multi-objective performance validated through systematic sensitivity analysis across 10-45% penetration levels: active power loss reduction of 35.5% (210.9 kW to 136.1 kW), harmonic distortion mitigation up to 46.1% THD_V reduction at the wind connection point (1.52% to 0.82%), and realistic reliability improvements of 5.0% SAIDI reduction (8.52 to 8.09 hours/year) accounting for islanding effectiveness constraints (19% successful islanding events with proper battery energy storage and protection infrastructure). The 45% penetration scenario introduces voltage regulation challenges (V_max = 1.052 p.u.) that offset incremental benefits. Economic analysis reveals that system-wide benefits (loss reduction: $670,000/year) substantially exceed agricultural productivity gains ($4,800/year with realistic nonlinear yield models), positioning wind integration as primarily a power quality and efficiency enhancement with supplementary agricultural reliability benefits. Practical implementation requires comprehensive voltage regulation equipment ($2.1-2.5M investment), harmonic filters for VFD-dominated buses ($240,000), and advanced protection schemes for islanding operation ($450,000-$700,000). The integrated analysis framework provides utilities and agricultural operators with quantitative guidance for optimal distributed generation deployment, balancing technical performance, economic viability, and operational constraints.
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Image Denoising by Nonlinear Diffusing on Mixed Curvature
Scientific article
A basic problem in the image denoising is noise pressing and edge preserving, while it is difficult to do well in the two aspects at the same time. The Partial Differential Equation (PDE) based methods, such as nonlinear diffusing method, energy minimal method and active contour method, provide a new choice. Here, focus is put on the classic Total Variation and hypersurface minimal problems, which consider regularizing term of isolevel smoothing and mean curvature. In fact, Total Variation smoothing term works well for preserving clear edges and inefficiently in plain areas, while hypersurface minimal smoothing term does well on denoising in plain areas and excessively on edges causing blurring. A projected isolevel curvature is proposed here just as the Beltrami-Laplace operator to mean curvature, considering the gradient while smoothing and keeping edge sharp effectively. And a mixed curvature of mean curvature and projected isolevel curvature forms by a weighting variable. The new denoising method based on the mixed curvature, smoothing in plain areas of image like hypersurface minimal and on edges like a projected isolevel curvature diffusing. Results of relative experiments indicate the proposed mixed curvature denoising method possesses the merits of the two original.
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