2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)最新文献

{"title":"Performance Analysis of Artificial Neural Network Approach on Solar Radio Burst Detection","authors":"Mohd Rizman Sultan Mohd, J. Johari, F. Ruslan","doi":"10.1109/ICSET51301.2020.9265348","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265348","url":null,"abstract":"Solar radio burst is defined as a massive solar radio emission related to the solar flare event occurrences. It is related to space weather events and will triggered an interference in our radio waves signal and affected the electromagnetic spectrum on earth. The solar flare could strike and condemn entire communications line including satellite operation, navigation system, Global Positioning System (GPS), international electrical grid and many more. Solar radio burst is the early warning sign that can helps reducing the effect by taking a precaution action by shutting down system. Because the solar radio is in the low frequency range, the detector system consist of low-frequency receiver is used to detect the burst event. As for Malaysia, solar radio observations are currently carried out using Compact Astronomical Low-cost, Low Frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) which been placed at the Malaysia Space Agency (MYSA) Banting, Selangor. The application of Artificial Neural Network (ANN) helps in preparing the proper prediction on solar radio burst using solar radiation readings from the spectrometer. ANN is divided into two main group which are static and dynamic neural network. In static neural network, the data propagates in a single direction from input to the output whereas, in dynamic neural network, the data propagates regardless of its direction. In this paper, both static and dynamic neural network had been applied to the data obtained from CALLISTO to develop a solar radiation prediction model to detect the solar radio burst. Based from the results, it is shown that dynamic neural network given the best results compared to the static neural network.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115606594","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}

{"title":"Roll Rejection Control of 3-Axle Semi-Trailer Truck using Steerable-wheel Optimized with Particle Swarm Optimization (PSO)","authors":"Muhammad Nadwi Hakimi Adnan, Z. A. Kadir, N. H. Amer, K. Hudha, M. S. Rahmat, M. H. Harun","doi":"10.1109/ICSET51301.2020.9265368","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265368","url":null,"abstract":"This paper focuses on roll rejection control due to rollover accidents frequently occurred especially among the heavy vehicles. A roll control method has been proposed to counter the unwanted roll issue using a steerable-wheel at second axle on a 3-axle semi-trailer truck. PID and skyhook controller has been used in the control structure to reduce the unwanted roll, lateral and yaw motions of the truck vehicle. PID-skyhook parameters are then optimized using PSO to obtain the optimum responses of truck vehicle handling performance. The optimized controller finally implemented into controller showed a significant improvement in suppressing lateral acceleration, yaw rate and roll motion of truck vehicle.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123604595","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}

{"title":"Identification of Groundwater Potential Zones in Langkawi Through Remote Sensing and Geographic Information System (GIS) Techniques","authors":"Nur Mardhiah Mohamad Nor Sing, Maisarah Abdul Halim, N. Hashim, N. Hashim, N. Naharudin, Abdul Rauf Abdul Rasam","doi":"10.1109/ICSET51301.2020.9265145","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265145","url":null,"abstract":"Water demands in Langkawi Malaysia have risen due to an increase number of tourists each year which resulted in rapid developments of the island. These developments have severely affected the water resources in this small island. In this research, a weighted overlay analysis has been done for detecting groundwater potential zones (GWPZ). Weighted overlay analysis tool was chosen to find the potential of water beneath the earth through the combination of Geographic Information System (GIS) and remote sensing techniques as a solution for water issues in Langkawi Island. Seven factors namely elevation, lineament density, drainage density, geology, slope, soil type and land use land cover (LULC) were produced to create GWPZ map by rank. Firstly, a ranking based on diagram connection between potential groundwater zones influencing factors for GWPZ generation was being used to put a rank of all subclasses in the seven thematic maps. The thematic maps were then adjusted from rank one (1) as low value to five (5) as the highest value. Subsequently, GWPZ was generated by GIS software using weighted overlay analysis tool by overlaying all the seven thematic maps in GIS analysis tool. In the analysis, the output of GWPZ has been validated using existing wells presented as points in Langkawi to verify the relationship between generated GWPZ and existing data. It was found that existing wells satisfyingly overlapped on the moderate and high area of groundwaters in the GWPZ map. For second analysis using the depth of the existing well location, it was found that the depth varies in the same potential area from the generated GWPZ. This study has found that groundwater potential zones technique can interpret the availability of water under the ground area but may not interpret the depth of water below the earth correctly. All in all, using GIS and remote sensing, this research has proven to successfully detect groundwater in Langkawi and can be a reference for future projects for a sustainable water management in Langkawi Island.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"53 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132811078","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}

{"title":"Towards Modelling Autonomous Mobile Robot Localization by Using Sensor Fusion Algorithms","authors":"Abdul Khaiyum Baharom, S. A. Rahman, Rafidah Jamali, S. Mutalib","doi":"10.1109/ICSET51301.2020.9265372","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265372","url":null,"abstract":"Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes an early experiment towards modelling a low-cost and robust centimetre-level localization for mobile robots in crowded indoor and outdoor environments. While a wide range of methods have been developed and tested on high-end hardware in autonomous vehicles, the work utilizes multiple sensor information to achieve robustness with different types of mobile robots. The application can be used by any group or organization, especially the frontliners, in managing the COVID-19 pandemic. Different Simultaneous Localization and Mapping (SLAM) algorithms, such as GMapping, Google Cartographer and Hector SLAM, are used to achieve better localization. Sensor fusion strategy is applied for these SLAM packages using Real-Time Kinematic (RTK) positioning, a precise Global Navigation Satellite System (GNSS)-based sensor, by applying both Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate position, velocity and attitude (PVA). The performance of the proposed algorithm will be compared against the benchmark algorithm using different sets of data in crowded places in various settings.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129028662","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}

{"title":"Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors","authors":"John Daniel C. Arevalo, Pauline C. Calica, Bernadette Andree D. R. Celestino, Katami A. Dimapunong, D. J. Lopez, Yolanda D. Austria","doi":"10.1109/ICSET51301.2020.9265375","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265375","url":null,"abstract":"Deforestation is exponentially depleting the planet's biodiversity and natural ecosystems at an alarming rate. This research aims to address illegal logging through realtime alerting and monitoring of suspected gas-fueled chainsaw sounds in the forest. Features were extracted from a collated nature sound dataset and trained on a supervised machine learning algorithm. The model is deployed through a microcomputer to process the chainsaw sounds through radio frequency transmission. The system has a desktop application that triggers an alarm and visualizes relevant information from the detected illegal logging activity location. The device prototype is easily-replaceable, modular, and portable and can be reconfigured to large-scale domains such as rainforests. The main contributions of this research are the improvement of alert and monitoring of illegal logging through (1) real-time and online audio analysis and detection of gas-powered chainsaws sounds through k-nearest neighbors; (2) a deployable prototype capable of listening to chainsaw sounds in the forest while buried, and (3) development of a graphical user interface for monitoring of module feedback and responses. The experimental results show that our system has an accuracy of 96.00% an F1-score of 94.34%.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836195","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}

{"title":"GIS and Mapping Mobile Application for Local Food Finder in Shah Alam, Selangor","authors":"Nurzalina Idayu Ismail, N. M. Saraf, Abdul Rauf Abdul Rasam","doi":"10.1109/ICSET51301.2020.9265397","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265397","url":null,"abstract":"Food tourism is one of the popular sectors that connecting people around the world. Nowadays, due to the emerging technology era, people tend to rely on using their smartphones as a guide to do something or go somewhere. Specifically, since existing Selangor tourism websites are limited choices in terms of useful information and interesting interfaces, this application is intended to be more accessible to the users in the future. The main aim of this study is to design and develop GIS mobile application on Food Finder in Shah Alam. This app can guide users to find the best rating of variety restaurant in Shah Alam. Three main objectives are set in the study are to conduct an assessment of user requirement analysis for proposed mobile apps, to design and develop the proposed application based on Geographical Information System (GIS) and Android system, to evaluate the application performance according to public respondents. By considering the System Development Life Cycle (SDLC) framework, the application is developed by involving the preliminary study, need assessment, system design and development, and system testing. Android Studio which using a Java as a language and integration of Firebase as a cloud-based server is used to develop this application. The outcome result is the prototype of food finder apps where this app can practically relevant in the searching restaurant in Shah Alam by viewing it information and users can go to any best restaurant.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"590 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134370075","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}

{"title":"Zigbee-based Energy Saving Control System for Centralized Air-Conditioning System","authors":"M. Daud, Asma’ Abu-Samah","doi":"10.1109/ICSET51301.2020.9265378","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265378","url":null,"abstract":"With more than a decade of intensive research and development, wireless sensor network has emerged as a workable solution towards multiple innovative applications. However, the usage of electricity, especially in old buildings and with a centralized system, is still not optimized. Therefore, to overcome this, an energy-saving system through human presence detection was being developed and tested using a PIR sensor and data communication using the wireless sensor network approach. Zigbee wireless technology was used to transmit and receive the motion data to indicate any human movement in individual rooms using a centralized air conditioning system. This system works with the automated system, but since the physical switching off system is not touchable in most cases, it is not the focus of this study. Instead, a system was built to only display when an area served by an AHU unit is without users, so the last user can switch off the system manually using the knowledge. With this technology, we can control the use of electricity in an area and reduce unused electricity. Based on a case study in Universiti Kebangsaan Malaysia, with two lecturers' room, it is estimated that the system can save up to RM 680.40 kWh in a year. However, the system still needs to be improved to reduce the cost and maximize the energy saving.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134399103","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}

{"title":"Object Detection for Autonomous Vehicle with LiDAR Using Deep Learning","authors":"M. Yahya, S. A. Rahman, S. Mutalib","doi":"10.1109/ICSET51301.2020.9265358","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265358","url":null,"abstract":"This paper presents an object detection for Autonomous Vehicle (AV) using deep learning algorithm. Currently, most AVs use the camera for visualization to detect surrounding objects. However, the performance of a sensor, such as a camera with visual perception, is diminished in dim light, for instance at night-time due to the less light environment. Thus, the study attempts to employ the Light Detection and Ranging (LiDAR) sensor that uses light in the form of a pulsed laser to calculate ranges and ultimately detect objects. The use of LiDAR with the recent deep learning algorithm, namely You Only Look Once (YOLO) v2, was simulated on the Robot Operating System (ROS) in the Linux environment. The collected data has undergone several filtering processes, which includes noise removal, downsampling, and transformation. The study then applies the model on real-time data from the LiDAR sensor to perform object detection. The results show that YOLOv2 can identify the objects better compared to Single Shot Detection (SSD) algorithm.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114354769","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}

{"title":"Motorcycle Detection using Deep Learning Convolution Neural Network","authors":"Fatin Natasha Ismail, A. Yassin, Adizul Ahmad, M. Ali, R. Baharom","doi":"10.1109/ICSET51301.2020.9265361","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265361","url":null,"abstract":"Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on Deep Learning Neural Network to detect motorcycles. Training involves various motorcycle models and poses with different resolutions and road conditions. The AlexNet network structure was chosen for implementation due to its proven performance in object detection tasks. Transfer learning was used to repurpose the AlexNet network for the described task. Training and classification were performed using the MATLAB Deep Learning Toolbox. Test results on our custom dataset demonstrates the effectiveness of the approach for the task.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551508","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}

{"title":"A Review of Solar Radio Burst Detection Using CALLISTO","authors":"Mohd Rizman Sultan Mohd, J. Johari, Fazlina Ahmat","doi":"10.1109/ICSET51301.2020.9265400","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265400","url":null,"abstract":"Solar radio burst is one of the effects caused by the solar flare, which is directly related to the solar activities. Using the special-purposes spectrometers, the detection of solar radio burst can be achieved before the solar flare event took place. Solar flare can cause disruptions to our radio frequency spectrum and affected our technologies related to it, such as telecommunications network, Global Positioning System, and satellites applications. Compact Astronomical Low-cost Low Frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) spectrometer had been widely used to detect the solar radio burst, thanks to the cooperative research around the globe. A brief review on the space weather, CALLISTO and solar radio burst detected using the spectrometers will be presented in this paper. This paper is constructed as a guideline for the further understanding on how CALLISTO could bring a significant contribution towards the solar radio burst detection in order to develop a reliable and a high-performance prediction system using the data gained from it.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127599580","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}