Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1123.2023
Faiz Rofi Hencya, Satria Mandala, T. Tang, Mohd Soperi, Mohd Zahid
Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.
{"title":"A Transfer Learning-Based Model for Brain Tumor Detection in MRI Images","authors":"Faiz Rofi Hencya, Satria Mandala, T. Tang, Mohd Soperi, Mohd Zahid","doi":"10.25077/jnte.v12n2.1123.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1123.2023","url":null,"abstract":"Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48394276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1094.2023
F. Effah, Daniel Kwegyir, D. Opoku, Peter Asigri, E. Frimpong
The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.
{"title":"Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network","authors":"F. Effah, Daniel Kwegyir, D. Opoku, Peter Asigri, E. Frimpong","doi":"10.25077/jnte.v12n2.1094.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1094.2023","url":null,"abstract":"The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47299152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1008.2023
A. Yulianto, Ni'matul Ma'muriyah, Lina Lina
To be able to do their daily activities, a visually impaired person needs a guidance device to help him/her walk including to avoid obstacles on their way to the destination. The quick and clear instruction is given to the user is the most challenging problem to be solved. The visually impaired person should have simple guidance about the obstruction in front of him/her. Most guidance devices use simple sounds to give the warning without information about which direction the user should go. In this paper, an obstacle warning system based on image processing methods was developed. A guidance device for visually impaired persons using a single-board computer based on an image-processing algorithm has been designed. The main sensor of the guidance device is a NoIR camera. The distance measurement approximation model was developed with errors up to 4.3%. The test found that the proposed system can detect obstruction in the form of a person, the device also detects the stairs. The best detection obtains when the object position is less than 300 cm in front of the user. The stair detection was carried out by using the Hough line transform method. The output of the system is the sound of direction that can be heard through the headset.
{"title":"Audible Obstacle Warning System for Visually Impaired Person Based on Image Processing","authors":"A. Yulianto, Ni'matul Ma'muriyah, Lina Lina","doi":"10.25077/jnte.v12n2.1008.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1008.2023","url":null,"abstract":"To be able to do their daily activities, a visually impaired person needs a guidance device to help him/her walk including to avoid obstacles on their way to the destination. The quick and clear instruction is given to the user is the most challenging problem to be solved. The visually impaired person should have simple guidance about the obstruction in front of him/her. Most guidance devices use simple sounds to give the warning without information about which direction the user should go. In this paper, an obstacle warning system based on image processing methods was developed. A guidance device for visually impaired persons using a single-board computer based on an image-processing algorithm has been designed. The main sensor of the guidance device is a NoIR camera. The distance measurement approximation model was developed with errors up to 4.3%. The test found that the proposed system can detect obstruction in the form of a person, the device also detects the stairs. The best detection obtains when the object position is less than 300 cm in front of the user. The stair detection was carried out by using the Hough line transform method. The output of the system is the sound of direction that can be heard through the headset.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42992385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1098.2023
Franky Surya Parulian, M. Riyadi, I. Z. Pane, Muhamad Muflih
In the application of the Indonesian Low-Speed Tunnel (ILST), the control of wind tunnel operations can determine the validity of the data and the number of tests achieved daily. The current operation control mechanism is still done manually and separately with one series of measurements for one test model configuration, inefficient human resources, acquisition of data that can be different, and the cost of using electric power is quite expensive. Therefore, this research and development activity proposes a wind tunnel automatic operation control system that integrates several plant facilities and ILST data acquisition based on Human Machine Interface (HMI) with the Waterfall method, using SCADA software and PLC. This aims to improve wind tunnel operation in one measurement series for multiple test model configurations with high data acquisition accuracy, faster and easier operation to reduce operating costs. This automatic operation control can increase operation time two times faster and 61% cheaper than manual operation. The design results will be used at the implementation stage in aerodynamic model testing.
{"title":"The Design of Improved Automatic Operation Control of Indonesian Low- Speed Wind Tunnel Based on Programmable Logic Controller and Human Machine Interface","authors":"Franky Surya Parulian, M. Riyadi, I. Z. Pane, Muhamad Muflih","doi":"10.25077/jnte.v12n2.1098.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1098.2023","url":null,"abstract":"In the application of the Indonesian Low-Speed Tunnel (ILST), the control of wind tunnel operations can determine the validity of the data and the number of tests achieved daily. The current operation control mechanism is still done manually and separately with one series of measurements for one test model configuration, inefficient human resources, acquisition of data that can be different, and the cost of using electric power is quite expensive. Therefore, this research and development activity proposes a wind tunnel automatic operation control system that integrates several plant facilities and ILST data acquisition based on Human Machine Interface (HMI) with the Waterfall method, using SCADA software and PLC. This aims to improve wind tunnel operation in one measurement series for multiple test model configurations with high data acquisition accuracy, faster and easier operation to reduce operating costs. This automatic operation control can increase operation time two times faster and 61% cheaper than manual operation. The design results will be used at the implementation stage in aerodynamic\u0000model testing.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46365731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1059.2023
N. Santiyadnya, Kadek Reda Setiawan Suda
A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.
{"title":"“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD","authors":"N. Santiyadnya, Kadek Reda Setiawan Suda","doi":"10.25077/jnte.v12n2.1059.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1059.2023","url":null,"abstract":"A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45044888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1124.2023
Abdul-Fatawu Seini Yussif, Elvis Twumasi, E. Frimpong
This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.
{"title":"Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators","authors":"Abdul-Fatawu Seini Yussif, Elvis Twumasi, E. Frimpong","doi":"10.25077/jnte.v12n2.1124.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1124.2023","url":null,"abstract":"This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47225092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1091.2023
Gunawan Dewantoro, Dinar Rahmat Hadiyanto, A. A. Febrianto
Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. The robot is able to transverse along the maze omnidirectionally measured depth. However, this approach only perceives the presence of the objects without recognizing the type of these objects. Therefore, computer vision has become more popular for classification purpose in robot applications. In this study, a maze solving robot is equipped with a camera to recognize the types of obstacles in a maze. The types of obstacles are classified as: intersection, dead end, T junction, finish zone, start zone, straight path, T–junction, left turn, and right turn. Convolutional neural network, consisting of four convolution layers, three pooling layers, and three fully-connected layers, is employed to train the robot using a total of 24,000 images to recognize the obstacles. Jetson Nano development kit is used to implement the trained model and navigate the robot. The results show an average training accuracy of 82% with a training time of 30 minutes 15 seconds. As for the testing, the lowest accuracy is 90% for the T-junction with the computational time being 500 milliseconds for each frame. Therefore, the convolutional neural network is adequate to serve as classifier and navigate a maze solving robot.
{"title":"An Embedded Convolutional Neural Network for Maze Classification and Navigation","authors":"Gunawan Dewantoro, Dinar Rahmat Hadiyanto, A. A. Febrianto","doi":"10.25077/jnte.v12n2.1091.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1091.2023","url":null,"abstract":"Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. The robot is able to transverse along the maze omnidirectionally measured depth. However, this approach only perceives the presence of the objects without recognizing the type of these objects. Therefore, computer vision has become more popular for classification purpose in robot applications. In this study, a maze solving robot is equipped with a camera to recognize the types of obstacles in a maze. The types of obstacles are classified as: intersection, dead end, T junction, finish zone, start zone, straight path, T–junction, left turn, and right turn. Convolutional neural network, consisting of four convolution layers, three pooling layers, and three fully-connected layers, is employed to train the robot using a total of 24,000 images to recognize the obstacles. Jetson Nano development kit is used to implement the trained model and navigate the robot. The results show an average training accuracy of 82% with a training time of 30 minutes 15 seconds. As for the testing, the lowest accuracy is 90% for the T-junction with the computational time being 500 milliseconds for each frame. Therefore, the convolutional neural network is adequate to serve as classifier and navigate a maze solving robot.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45016658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1106.2023
Toshiharu Yasui, Minoru Sasaki, K. Matsushita, Joseph K. Muguro, Waweru Njeri, T. Mulembo
Japan has continued to experience population decline which adversely affect working-age group (15-64 years). As a remedy to this social issue, advancements in robotics and human-machine cooperation is proposed to make up for the declining labor force. To this end, design of robots which can work in constrained (indoor) workspace is desirable. A coaxial two-wheeled robot with an appended robot arm aimed at transporting objects is proposed in this paper. The robot is designed with center of gravity below the axle to make it statically stable at rest. It is combined with a robot arm with two links, two degrees of freedom. The goal is to maintain equilibrium of the arm tip during motion with the robot-arm is inclined at 0-, 3-, and 120-degree. In this study, simulations to combine a stable coaxial two-wheel robot with the robot arm is performed to confirm the effectiveness of the designed LQ, and LQI controller. From the results, all the controllers are able to maintain the robot-arm tip at 0-degrees. For 120-degrees, LQI performs better than LQ controller in stabilizing the rotation speed of the wheels by 1.7 seconds. In the future, the proposed controller model will be incorporated in the actual robot to confirm the performance for object transportation.
{"title":"Equilibrium Control of Robot Arm Tip Mounted on a Transfer Coaxial Two-Wheel Robot","authors":"Toshiharu Yasui, Minoru Sasaki, K. Matsushita, Joseph K. Muguro, Waweru Njeri, T. Mulembo","doi":"10.25077/jnte.v12n2.1106.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1106.2023","url":null,"abstract":"Japan has continued to experience population decline which adversely affect working-age group (15-64 years). As a remedy to this social issue, advancements in robotics and human-machine cooperation is proposed to make up for the declining labor force. To this end, design of robots which can work in constrained (indoor) workspace is desirable. A coaxial two-wheeled robot with an appended robot arm aimed at transporting objects is proposed in this paper. The robot is designed with center of gravity below the axle to make it statically stable at rest. It is combined with a robot arm with two links, two degrees of freedom. The goal is to maintain equilibrium of the arm tip during motion with the robot-arm is inclined at 0-, 3-, and 120-degree. In this study, simulations to combine a stable coaxial two-wheel robot with the robot arm is performed to confirm the effectiveness of the designed LQ, and LQI controller. From the results, all the controllers are able to maintain the robot-arm tip at 0-degrees. For 120-degrees, LQI performs better than LQ controller in stabilizing the rotation speed of the wheels by 1.7 seconds. In the future, the proposed controller model will be incorporated in the actual robot to confirm the performance for object transportation.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43732671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.25077/jnte.v12n2.1105.2023
Michelle Valerie, I. Salamah, Lindawati
This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.
本文介绍了一个具有高级语音识别和自然语言处理(NLP)能力的个人助理机器人原型的开发和评估。它由树莓派微处理器驱动，是机器人硬件的核心组件。它的设计目的是接收命令，并通过使用集成的语音识别和NLP技术，执行请求的动作，迅速做出反应。该样机旨在通过音频到文本的转换和高质量的图像捕获来提高会议效率和生产力。结果显示，该方法在印尼语和英语中的准确率分别为100%和99%。高效的处理速度，印尼语平均每分钟9.07秒，英语平均每分钟15.3秒，进一步增强了机器人的功能。此外，集成了一个高分辨率的网络摄像头，使1280 x 720像素的高质量图像捕获。实时集成谷歌驱动器，确保安全存储和无缝的数据管理。研究结果强调了原型在促进顺利互动和有效沟通方面的有效性，并利用NLP进行智能语言理解。集成基于nlp的语音识别、可视化文档和数据传输提供了一个管理音频、文本和图像数据的综合平台。本研究中提出的个人助理机器人原型代表了人机交互的重大进步，特别是在会议和协作工作环境中。NLP的进一步改进可以提高效率，促进无缝的人机交互体验。
{"title":"Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype","authors":"Michelle Valerie, I. Salamah, Lindawati","doi":"10.25077/jnte.v12n2.1105.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1105.2023","url":null,"abstract":"This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46256822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leakage current measurements can be used to determine the aging condition of the ZnO arrester. The leakage current that occurs in the arrester is divided into two, namely external and internal leakage currents. The external leakage current is affected by contamination and the internal leakage current is affected by the aging of the varistor in the arrester. The external and internal leakage currents are measured separately to determine their contribution to the arrester condition. In this study, the effect of salt contamination on the arrester was studied further. The level of contamination used consisted of low, medium and heavy. The obtained leakage current is analyzed using wavelet energy. The results of this study indicate that the wavelet energy of each leakage current is different and can be used as an indicator in further analysis. The conclusion obtained is that the external leakage current is affected by contamination and has a different energy with the internal leakage current due to aging of the varistor arrester components.
{"title":"External Leakage Current Separation to Determine Arrester Condition Due to Contamination","authors":"N. Novizon, Mondrizal Mondrizal, Darwison Darwison, Aulia Aulia, Tesya Uldira Septiyeni","doi":"10.25077/jnte.v12n2.1026.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n2.1026.2023","url":null,"abstract":"Leakage current measurements can be used to determine the aging condition of the ZnO arrester. The leakage current that occurs in the arrester is divided into two, namely external and internal leakage currents. The external leakage current is affected by contamination and the internal leakage current is affected by the aging of the varistor in the arrester. The external and internal leakage currents are measured separately to determine their contribution to the arrester condition. In this study, the effect of salt contamination on the arrester was studied further. The level of contamination used consisted of low, medium and heavy. The obtained leakage current is analyzed using wavelet energy. The results of this study indicate that the wavelet energy of each leakage current is different and can be used as an indicator in further analysis. The conclusion obtained is that the external leakage current is affected by contamination and has a different energy with the internal leakage current due to aging of the varistor arrester components.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48004013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：