2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)最新文献_第5页

MLP Network Prediction for Blast Explosive based Training Algorithm 基于MLP网络爆炸预测的训练算法

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237100

P. Nagappan, M. H. Mat, Syahrull Hi-Fi Syam Ahmad Jamil, F.R. Hashim, Muhammed Alias Yusof, Mohd Sharil Salleh

引用次数: 0

Development of an IoT-based Fish Farm Monitoring System 基于物联网的养鱼场监测系统的开发

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237168

S. Loh, Peh Chiong Teh, Guang Yang Goay, J. Sim

引用次数: 1

An Optimized Hybrid Dragonfly Algorithm Applied for Solving the Optimal Reactive Power Dispatch Problem in Smart Grids 应用一种优化混合蜻蜓算法求解智能电网无功优化调度问题

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237155

Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Shamuhammet Rejepov, Hui Na Chua, Ahmad Sahban Rafsanjani, Ismail Ahmad Al-Qasem Al-Hadi

{"title":"An Optimized Hybrid Dragonfly Algorithm Applied for Solving the Optimal Reactive Power Dispatch Problem in Smart Grids","authors":"Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Shamuhammet Rejepov, Hui Na Chua, Ahmad Sahban Rafsanjani, Ismail Ahmad Al-Qasem Al-Hadi","doi":"10.1109/ICCSCE58721.2023.10237155","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237155","url":null,"abstract":"World’s growing population has resulted in an up-surged demand for electricity worldwide. The resulting pressure on the power systems has urged the search for measures to increase the performance of electricity distribution systems by minimizing loss, circumventing overload and reducing cost. The implementation of smart grid systems using artificial intelligence, and combinatorial optimization techniques is one of the ways to improve electricity distribution systems. Power grids including smart grids consist of a number of optimal power flow problems, one of which is the Optimal Reactive Power Dispatch (ORPD) problem. It involves determining the optimal configurations of the grid to curtail its cost. The ORPD problem may be solved by means of optimization algorithms including swarm intelligence algorithms. The Dragonfly Algorithm (DA), a high-performing swarm intelligence algorithm, has been successfully used for solving the ORPD problem. However, the performance of DA can still be amplified by overcoming its limitation of having a low exploitation phase. Previously, an optimized DA algorithm with an improved exploitation phase has been proposed. However, it has not been employed to solve the ORPD problem or to enhance the performance of energy distribution systems. In this paper, we propose a new algorithm by further intensifying the exploitation of the optimized DA. This is carried out by utilizing the steepest ascent hill climbing as a local search method instead of the stochastic hill climbing used in the optimized DA algorithm. The newly introduced algorithm is employed to solve the ORPD problem by making use of standard test cases and based on experimental results, it provides higher quality solutions in comparison to the original DA, the optimized DA, and a modified pathfinder algorithm.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130992646","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}

引用次数: 1

A Comparative Study on COCOMO II Model for Cost Estimation 成本估算COCOMO II模型的比较研究

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237162

Rahmi Rizkiana Putri, Daniel Siahaan, C. Fatichah

引用次数: 0

Explosive Blast Prediction using MLP Network based Training Algorithm 基于MLP网络的爆炸预测训练算法

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237166

M. H. Mat, P. Nagappan, Syahrull Hi-Fi Syam Ahmad Jamil, F.R. Hashim, Khairol Amali Bin Ahmad, Kamsani Kamal

引用次数: 0

Copyright Page 版权页

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/iccsce58721.2023.10237157

引用次数: 0

Human Action Recognition (HAR) using Image Processing on Deep Learning 基于深度学习的图像处理人类动作识别

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237158

A. P. Ismail, Muhammad Afiq Bin Azahar, N. Tahir, K. Daud, Nazirah Mohamat Kasim

{"title":"Human Action Recognition (HAR) using Image Processing on Deep Learning","authors":"A. P. Ismail, Muhammad Afiq Bin Azahar, N. Tahir, K. Daud, Nazirah Mohamat Kasim","doi":"10.1109/ICCSCE58721.2023.10237158","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237158","url":null,"abstract":"The advancement of artificial intelligence (AI) has bought many advances to human society as a whole. By using daily activities and integrating the technology from the fruits of AI, we can manage to gain further access to knowledge we can only begin to imagine. In identifying human action recognition (HAR); processing photos and videos to discern whether a human is present, then mapping the subject classified, which lastly determines the action being carried out is the objective. To achieve this, various steps are taken and careful approach is required, with the extensive amount of research, numerous troubleshooting and experimentation is required. The AI architecture has to learn from dataset collected for it to discern the identification of action properly. HAR is achieved by using Python code using real-time webcam feed. Human pose detection library known as MediaPipe Pose Detection detects human anatomy from input through joints key-points. MediaPipe algorithm that extract features in x-y-z axis with visibility (four variables) and the extracted data is trained using CNN-LSTM based on the trained and tested algorithm classifier model. The output obtained produced an RGB-skeleton and an action label on the detected subject as standing, waving, walking and sitting, has yielded good results.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976077","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}

引用次数: 0

Prediction the Number of Lamps Required for the Lighting System According to the JKR Lux Standards by Using the Artificial Neural Network Method 基于人工神经网络的JKR Lux照明系统所需灯数预测

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237163

Nuzul Lokmanhakim Zulkifli, M. A. Idin, Samshul Munir Muhamad, A. Samat, K. A. Ahmad, N. Napiah

{"title":"Prediction the Number of Lamps Required for the Lighting System According to the JKR Lux Standards by Using the Artificial Neural Network Method","authors":"Nuzul Lokmanhakim Zulkifli, M. A. Idin, Samshul Munir Muhamad, A. Samat, K. A. Ahmad, N. Napiah","doi":"10.1109/ICCSCE58721.2023.10237163","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237163","url":null,"abstract":"This paper aims to develop an advanced neural network-based model for accurately predicting the number of lamps required in lighting system designs, with a focus on cost-effectiveness and compliance with JKR Standards. The scope of the research encompasses the design and evaluation of the model using a comprehensive dataset and a range of parameters. The methodology involves leveraging machine learning techniques, specifically neural networks, to analyze various factors and optimize the lamp prediction process. Extensive testing and validation are conducted to assess the model’s performance and efficiency. The findings demonstrate the superiority of the proposed model in terms of accuracy, efficiency, and cost-effectiveness compared to traditional methods. The study contributes to the field of lighting design by providing a reliable and automated solution that reduces human error and improves energy efficiency, occupant satisfaction, and safety. The findings of this research indicate that the ‘trainlm’ algorithm is the most effective for predicting the number of lamps needed. It achieved a regression value of 1.00 and a low error percentage of only 0.499%. These results demonstrate the algorithm’s accuracy and suitability for the task at hand. The conclusion highlights the significance of the developed model in streamlining the design process and offers recommendations for future work, such as exploring additional factors and evaluating the model on diverse datasets. Overall, this research enhances the understanding and application of neural networks in lamp prediction for optimal lighting system designs.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128454639","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}

引用次数: 0

Disturbance Forces Compensation in Machine Tools using Robust Controller 基于鲁棒控制器的机床扰动力补偿

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237098

Lee Yit Chang, T. H. Chiew, Y. Lee, Kai Ming Chang, Jia Jan Ong, Y. Goh

{"title":"Disturbance Forces Compensation in Machine Tools using Robust Controller","authors":"Lee Yit Chang, T. H. Chiew, Y. Lee, Kai Ming Chang, Jia Jan Ong, Y. Goh","doi":"10.1109/ICCSCE58721.2023.10237098","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237098","url":null,"abstract":"Milling machine is one of the most vital machinery applied in manufacturing industries worldwide. However, the typical control system of the machines is no longer able to cope with different disturbance forces, especially the unavoidable cutting forces during the milling process. This paper proposes the super twisting sliding mode controller to counter the cutting forces and compares the control performances of the proposed controller against the existing controller. Three controllers, namely; proportional-integral-derivative controller, pseudo-sliding mode controller, and super twisting sliding mode controller were designed and simulated using a direct drive positioning system. Three types of cutting forces produced from end-milling process with different spindle speed ranged from 1000 rpm to 2000 rpm were applied as disturbances. Evaluation and comparison of the designed controllers were performed in terms of tracking error reduction, and cutting forces rejection via spectral analysis. The simulated results revealed that the proposed controller outperformed the traditional linear controller in tracking error reduction with an average improvement of 99.8%. It also performed three folds better than the pseudo-sliding mode controller in terms of cutting forces compensation. The pronounced robustness in the proposed controller compared to standard controller would promote its application in real-time direct drive system.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129308927","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}

引用次数: 0

Learning Rate Optimization for Enhanced Hand Gesture Recognition using Google Teachable Machine 使用谷歌可教机器增强手势识别的学习率优化

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237148

Safyzan Salim, M. M. A. Jamil, R. Ambar, Wan Suhaimizan Wan Zaki, Suraya Mohammad

{"title":"Learning Rate Optimization for Enhanced Hand Gesture Recognition using Google Teachable Machine","authors":"Safyzan Salim, M. M. A. Jamil, R. Ambar, Wan Suhaimizan Wan Zaki, Suraya Mohammad","doi":"10.1109/ICCSCE58721.2023.10237148","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237148","url":null,"abstract":"Developing efficient sign language recognition systems using wearable devices is a major challenge in Machine Learning. One obstacle is effectively translating gestures based on sensor data. Traditional methods involve complex programming using data fusion and mapping techniques. To address this, we need emerging technologies that simplify gesture data processing while maintaining accuracy. This study explores an artificial intelligence approach for detecting Bahasa Melayu using a ready-to-use machine learning framework-Google Teachable Machine. By experimenting with these tools, the research aims to improve the simplicity and accuracy of hand gesture detection. The study also investigates the impact of the learning rate, an important parameter in machine learning algorithms, on system performance, providing insights for optimizing gesture detection. The results of our study emphasize the significance of thoughtfully choosing the learning rate for successful model training. This underscores the importance of finding the optimal learning rate to ensure effective training, regardless of the specific machine learning framework employed.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604367","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}

引用次数: 0