International Journal of Engineering and Manufacturing @ijem
Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 544
Indonesian Sign Language: Detection with Applying Convolutional Neural Network in a Song Lyric
Статья научная
Indonesian Sign Language (BISINDO) is one of the visual-based alternative languages used by people with hearing impairments. There are hundreds of thousands of Indonesian vocabularies that sign language gestures can represent. However, because the number of deaf people in Indonesia is only seven million or 3% of the population, sign language has become unfamiliar and challenging for some normal or laypeople to understand. This study aims to classify and detect gestures in sign language vocabulary directly based on mobile. Classification learning techniques are needed to recognize variations in gestures, such as machine learning with supervised learning techniques. The development of this research uses the convolutional neural network method with the help of techniques from the single shot detector architecture as the object of detection and the MobileNet architecture for classification. The object is 32 gestural vocabularies from the lyrics of the song 'Bidadari Tak Bersayap' with a dataset of 17,600 images. Then the images are divided into two parts of the model based on the nature of the biased and non-biased data, amounting to 8 and 24 classes, respectively. The research results in a biased model prediction of 15 out of 16, while a non-biased model of 36 out of 48 correct predictions with a total accuracy of real-time based testing on mobile of 93.75% and 75%, respectively.
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Industrial Monitoring System with Real-time Alerts and Automated Protection Mechanisms
Статья научная
This work presents the design and prototyping of an Industrial Monitoring and Protection System aimed at enhancing safety and operational efficiency in industrial environments. The system integrates multiple sensors with a GSM module to monitor and respond to critical environmental parameters, such as ambient light levels, temperature, and smoke detection. A Light Dependent Resistor (LDR) is configured to detect excessive lighting levels, interfacing with a microcontroller to activate the GSM module and send alert messages when thresholds are exceeded. The temperature sensor continuously monitors ambient temperature, and upon detecting overheating, the microcontroller triggers the GSM module to notify operators. Similarly, a smoke sensor detects the presence of harmful smoke and initiates an alert through the GSM module for early fire hazard detection. These sensors are connected to the microcontroller via analog and digital input pins, with their outputs processed to enable condition-based responses. A relay switch, controlled by the microcontroller, automatically disconnects connected loads when safety thresholds are breached, preventing equipment damage and ensuring personnel safety. Real-time sensor readings and system status are displayed on an OLED screen, providing operators with comprehensive, up-to-date information on the monitored environment. The system dynamically responds to environmental conditions by triggering alerts and actions based on customizable safety thresholds for light intensity, temperature, and smoke levels. This integrated architecture ensures seamless communication between sensors, the microcontroller, and the GSM module, delivering real-time monitoring, automated protective mechanisms, and early warning capabilities. The proposed system demonstrates the feasibility of affordable and scalable solutions for industrial safety, offering immediate responses to hazardous conditions while minimizing downtime. Furthermore, its adaptable design allows for customization across different industrial environments, making it suitable for a wide range of applications.
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Industry 4.0: The Oil and Gas Sector Security and Personal Data Protection
Статья научная
Industry 4.0 is directly connected to the Internet of things, cyber-physical systems, artificial intelligence, robotics and other advanced technologies. It is these technologies that made fully automated digital production, controlled by intelligent systems in real-time with constant interaction via the Internet, a reality. They turned the production system into a “smart network factory”, where all activities are digitally controlled, and the use of financial and material resources becomes more efficient. Along with this, in Industry 4.0, a significant increase in data volumes brought to the forefront data protection issues, including in such a sensitive area as personal data. Illegitimate methods of using personal data to obtain additional preferences have become the goal of some communities of people. Video surveillance data are an integral part of personal data, therefore protection of personal data processed in video surveillance systems has been given increased attention. The video surveillance system includes video cameras, information and communication channels for data transmission, processing devices, analytics and personal data storage. The proposed model is a subsystem of smart video surveillance in the oil and gas sector, that consisting of such subsystems as a smart field, smart grid, smart maintenance, smart transportation, smart security, etc. Conceptual tasks are considered and recommendations for their solution are given.
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Infusion of Warm Fluid During Abdominal Surgery Prevents Hypothermia and Postanaesthetic Shivering
Статья научная
BACKGROUND: Perioperative hypothermia is a frequent occurrence and can lead to several complications. The aim of this study is to evaluate the efficacy of warm fluid in maintaining normal core temperature during the intraoperative period. METHODS: We studied 30 American Society of Anesthesiologists (ASA) physical status I or II adult patients who required general anesthesia for abdominal surgery. In control group (n=15), fluids were infused at room temperature; in test group (n=15), fluids were infused at 37°C. Core temperature was measured at the tympanic site. During recovery, shivering was evaluated by an independent observer. RESULTS: The two groups did not differ significantly in patient characteristics. In control group, core temperature decreased to 35.5±0.3°C during the first 3 hours, and then stabilized at the end of anesthesia. In test group, core temperature decreased during the first 60 min, but increased to 36.9±0.3°C at the end of anesthesia. In control group, 8 patients shivered at grade ≥2. In test group, none of the patients reached grade ≥ 2 (P < 0.01). CONCLUSIONS: Infusion of warm fluid is effective to keep patients nearly normothermic and prevent postanaesthetic shivering. It may provide an easy and effective method for perioperative hypothermia prevention.
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Internet of things based system for smart kitchen
Статья научная
This paper provides insight to the dynamics that come with the emergence of IoT in the furniture and kitchen manufacturing industry. By implementing the concept of IoT companies are currently evaluating how internal knowledge and skillsets correspond to the new technical requirements that the emerging digital setting outlines and by directing internal research they are learning more about IoT and connected products as they proceed. One current major problem is that there are no open protocols that can connect all products regardless of supplier. Nevertheless, implementation of IoT does not solely involve technical aspects and companies are also faced with the dilemma on how to design and develop corresponding commercial processes. To this point early product implementations have arrived on the consumer markets and the future vision is to achieve full integration that imbeds connectivity and interaction among all products in the home.
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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
This paper presents a comprehensive study encompassing both liquefaction susceptibility evaluation and slope stability analysis of embankment soil adjacent to road. The paper focuses on the vulnerability of embankment soil to liquefaction-related failures. It examines the liquefaction vulnerability of embankments ML-CL soil, previously considered non-liquefiable but raising concerns post-1999 Kocaeli Earthquake. The paper evaluates liquefaction susceptibility using Chinese Criteria and Modified Chinese Criteria based on index test results of embankments soil samples. The soil at various depths was found to be not susceptible to liquefaction as per Chinese criteria, whereas the second evaluation as per Modified Chinese criteria gave different and more specific results taking into the % clay-sized particles. Based on Modified Chinese criteria, the soils ranging from 10-20 ft, 25-30 ft and 30-35 ft were found explicitly non-susceptible, whereas soil ranging from 0-10 ft and 20-25 ft requires further study on non-plastic clay-sized grains as per the criteria. The paper delves into slope stability analysis using PLAXIS2D and GEOSLOPE software to determine the optimal earthworks layout for a embankment excavation based on Eurocode 7. Upon numerical modelling, various trials were carried out considering various factors of safety across different earthworks layouts and the one with satisfying factor of safety is considered safest, ensuring safety and cost-effectiveness of embankment cut besides the road.
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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-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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