2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)最新文献

{"title":"An image-guided mobile robotic system for underground pipe navigation and condition monitoring","authors":"Y. B. Juganaikloo, M. Gooroochum","doi":"10.1109/ICECCME52200.2021.9590854","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590854","url":null,"abstract":"This paper presents the design of a mobile robotic system for navigating inside the vertical and horizontal segments of underground pipes using visual data. The development of this system stems from the need to inspect underground pipes buried for various reasons, and for which it becomes a challenging task to inspect the interior conditions of the pipe. The specific application under study was a horizontal ground-coupled heat exchanger system for which a 160 mm diameter PVC pipework of approximately 60m length was buried at a depth of 3m below ground. The purpose of the mobile robotic system was to provide video recording of the interior of the PVC pipe network while also measuring the prevailing temperature and humidity level using on-board sensors. This paper presents the design process followed to develop the system, with emphasis on the image processing and analysis techniques for navigation along straight segments, both vertical and horizontal, of the pipe and around corners. The system design allows the robot to contract in diameter at a 90-degree elbow pipe and navigate through bends, which are detected using visual cues generated by using specific lighting arrangements.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143547","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":"Hierarchical Clustering Algorithm for Analyzing Risk of Earthquake on Sumatra Island","authors":"H. Pratiwi, S. Handajani, Irwan Susanto, S. Sangadji, Renny Meilawati, Indah S. Khairunnisa","doi":"10.1109/ICECCME52200.2021.9590890","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590890","url":null,"abstract":"Earthquakes are vibrations produced due to the sudden release of energy from beneath the earth's surface, creating seismic waves. As a part of Indonesia region, Sumatra Island is known for its high level of seismicity, and one of the major earthquakes that caused a tsunami in Aceh occurred in 2004. This study aims to explore the clustering analysis and the hierarchical algorithm of an earthquake on Sumatra Island. This incidence is unpredictable since it occurs in an unexpected location, time, and magnitude. Therefore, to reduce earthquake risk, clustering analysis was carried out in the suspected region. This method includes agglomerative nesting (Agnes) and divisive analysis (Diana) algorithms. They were used in this research due to their effectiveness in grouping objects based on the closest distance or similarities using Euclid's metric. The optimum number of clusters was determined by the silhouette coefficient. The comparison of the cophenetic correlation coefficients in agglomerative nesting gave the conclusion that Ward linkage is the best method with a value of 0.8042. This showed that the solution generated from the clustering process with Ward linkage is quite good. Based on the silhouette coefficient, the Diana algorithm gave better result than the Agnes algorithm for clustering Sumatra earthquake data. The objects of clusters 1 and 2 respectively indicated the occurrence of an earthquake with a high and small risk. The first cluster has larger member than the second, making it susceptible to high earthquake risk.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837720","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":"Optimal sizing of Hybrid energy Sources by Using Genetic Algorithm and Particle Swarm Optimization algorithms considering Life Cycle Cost","authors":"Amare A. Ashagire, K. Adjallah, Getachew Bekele","doi":"10.1109/ICECCME52200.2021.9590952","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590952","url":null,"abstract":"One of the critical challenges for power energy engineers and decision-makers is to select an optimum size for a renewable energy-based electrification system as a standalone microgrid with as minimum investment as possible. Several researchers have used genetic algorithm (GA) and Particle swarm optimization (PSO) tools for solving optimization problems in engineering. We used both algorithms here to select the optimum size of renewable energy (RE) generation units. In our system design, photovoltaic (PV) modules, wind turbines, and battery-banks are used as the primary power units whereas diesel generator serves as a backup. We have selected a village with 260 households in Ethiopia's eastern part, the Somali region, Darahtoleh village (7.31567N,45.52884E). Metrological data from Ethiopian Metrological Agency are used as a primary source to analyze the potential of solar and wind energy resources. We have run GA and PSO algorithms for twenty different numbers of runs (20, 30,40,…, 200) with 100 iterations for each considering life cycle cost (LCC) as an objective function and fulfilling the constraint function of peak energy demands. The results from both algorithms based on minimum LCC value are further analyzed. Results suggest an optimal combination of generation units with a minimum levelized cost of energy (LCOE) of 0.179$/kWhr and 65% of renewable energy (RE) penetration.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123988333","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":"Calculation and verification of ship magnetic field based on boundary integral-iterative method","authors":"Jun Gao, Peng-Zhong Wang, Xuemei Zhu","doi":"10.1109/ICECCME52200.2021.9590861","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590861","url":null,"abstract":"In view of the problem that the known magnetic field of the ship is not at the same horizontal plane, and the far-field magnetic field needs to be calculated by the known magnetic field. In this paper, an integral-iterative method is proposed to calculate the magnetic field to the same plane, make full use of the known magnetic field, and then calculate the far field of the ship. Firstly, the simulated ellipsoid is used to simulate the ship, and the feasibility of this method is verified by the simulation magnetic field. Then, the experimental ship model is further used for verification. The final simulation and experiment show that the method can greatly improve the calculation accuracy of ship magnetic field and meet the calculation requirements of ship magnetic field.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121290427","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":"Conversion of a Flash Power Plant to Organic Rankine System for Olkaria Geothermal Power Plants","authors":"Moses Jeremiah Barasa Kabeyi, O. Olanrewaju","doi":"10.1109/ICECCME52200.2021.9591048","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9591048","url":null,"abstract":"Organic Rankine cycles are the best thermodynamic cycles for application in low temperature heat energy sources. Most geothermal power plants globally use the single flash technology to produce electricity. Depending on the thermodynamic conditions, it is possible to install an organic Rankine plant as a bottoming plant to a conventional geothermal plant. In this study, is an organic Rankine plant to utilize waste heat in used geothermal fluid living flash stations SD2 and SD3 for Olkaria Power station in Kenya is proposed. The study showed that up to 73.9MW power can be generated from waste brine for Olkaria IV geothermal field before reinjection in. The main objective of this study was to develop an ORC plant running to generate electricity from brine leaving flash tanks to reduce energy wastage in brine and on n-pentane as the working fluid installed on this brine stream will have a gross capacity of 7.6 MW.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114193744","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 survey on privacy preservation in video big data","authors":"Bingwen Feng, Yuchun Lin, Tianhao Xu, Jiping Duan","doi":"10.1109/ICECCME52200.2021.9591105","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9591105","url":null,"abstract":"The explosive growth of video data application raises the risk of privacy leaks in the video big data environment. Compared with other data formats, video big data presents special characteristics including various representation, high dimension, complex content, and so on, making the privacy protection for video big data more difficult. This paper analyzes the life cycle and overall architecture of video big data, and then discusses the privacy preservation method recently developed for video applications. These methods are categorized into three types: privacy preservation method for video storage, for video analysis, and for video release. Via analyzing their application scenarios and technique concepts, we finally come to a conclusion about their advantages, disadvantages, and possible applications, and present several research issues for video big data privacy preservation.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114317253","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 Two-Stage Deep Learning Strategy for Pneumothorax Classification","authors":"Yuchi Tian, Xiaodong Yang","doi":"10.1109/ICECCME52200.2021.9590988","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590988","url":null,"abstract":"Due to pneumothorax lesions in chest X-ray images show wide and complicated variation in size, shape, and location within lung regions that overlap with many other anatomic structures such as ribs and vessels, it is a challenge to develop a reliable computer-aided diagnosis systems (CADs) for automatic pneumothorax screening. To address this challenge, we propose a new two-stage deep learning strategy: local feature learning (LFL) followed by global multiple instance learning (GMIL). The GMIL stage intends to train a model that regards the given image as a set of patches and determines whether or not the image contains pneumothorax based on the patches. More specifically, the GMIL model first extracts the hierarchical feature map of a given image by using convolution layer, and takes the feature map of the last layer as a set of depth instances. Each instance is then provided to additional layers to produce its contribution to the final image level prediction. However, the GMIL model trained directly using the original image may still fail to learn highly discriminative features when large areas of non-lesion regions are contained in the image space and thus adversely affect performance. To resolve this problem, prior to the GMIL stage, another model with identical convolutional layers is first trained in the LFL stage using normal patches and pneumothorax-infected patches so that it can better learn the key distinguishing features by reducing most of the non-lesion regions in the X-ray image. The pre-trained convolutional weights are then utilized via transfer learning to enhance training of the GMIL model. Experiments carried on the benchmark ChestX-ray14 data set demonstrate that the proposed learning strategy can achieve the most advanced performance on accuracy, area under receiver operating characteristic curve (AUC), recall, specificity, and F1 scores of 94.4±0. 7%, 97.3±0.5%, 94.6±1.5%, 94.2±0.4% and 94.4±0.7%, separately. We demonstrate the importance and effectiveness of reducing most of the non-lesion regions in the images for learning more discriminative features. The results show that our proposed CAD system is an effective auxiliary tool for screening pneumothorax.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121703089","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":"Automatic Flash Tool for Mobile Devices","authors":"Luiz Correia, Thales Silva, Gabriel Villacrez","doi":"10.1109/ICECCME52200.2021.9591121","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9591121","url":null,"abstract":"Operational Systems (OS) can be roughly seen as a bunch of code that is usually integrated to become a system image, vendors must be careful with the integration process to ensure quality control. To achieve that, they also need to provide an efficient management system, one of the most popular tools available on the market is the JIRA. Besides management, the size of an OS must be taken into consideration, in particular, OS images from mobile devices are significantly large to compile from a local machine, one way to overcome this matter would be investing in remote build systems such as QuickBuild(QB) which provides a fast and distributed build platform. With the built final OS image is necessary to perform tests on the target device. This paper focuses on Android Mobile Devices OS images and proposes an automated flash tool that will acquire a task, search for related task OS image, download the target OS image and finally flash it to the mobile device. This tool will handle the mentioned management tool and also the QuickBuild remote build system, to automate several manual tasks done by a user. We successfully made this tool work in the proposed scenario which reduced time spent on manual tasks and also contributes to team task's time effectiveness.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"431 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114831469","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 Deep Transfer Learning Model for the Identification of Bird Songs: A Case Study for Mauritius","authors":"Evans Jason Henri, Zahra Mungloo-Dilmohamud","doi":"10.1109/ICECCME52200.2021.9590917","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9590917","url":null,"abstract":"Birds communicate with their colonies through sound and inform them of potential problems like forest fires. The identification of bird sounds is therefore very important and has the potential to solve some global problems. Convolutional neural networks (CNNs) are sophisticated deep learning algorithms that have proven to be effective in image processing and in sound classification. This paper describes the work done to develop a tool using a deep learning model for classifying Mauritius bird sounds from audio recordings. A dataset obtained from the Xeno-canto bird song sharing site, which hosts a vast collection of labeled and classified recordings, is used to fine-tune three pre-trained CNN models, namely InceptionV3, MobileNetV2 and RestNet50 and a custom model. The neural network's input is represented by spectrograms created from downloaded mp3 files. Time shifting and pitch stretching have been used for data augmentation. The best performing model has been integrated into a website to identify birds sounds recordings. In this work, transfer learning has been used successfully to produce a model with a weighted accuracy of 84%. Although a custom CNN was trained, better accuracy was achieved through the use of transfer learning.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124324538","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":"High-Precision Precharge Control Circuit for SRAM in Convolutional Neural Network Processor","authors":"Xiaowei Chen","doi":"10.1109/ICECCME52200.2021.9591078","DOIUrl":"https://doi.org/10.1109/ICECCME52200.2021.9591078","url":null,"abstract":"This paper proposals a high precision pre-charge control circuit for SRAM used in convolutional neural network (CNN) processors. The control circuit has a precision of 1 ps and can be easily adjusted by changing the number of buffers in the delay cell and the tristate buffer sizes. A 1 KB CNN processor SRAM is designed and simulated to further verify our design concepts. Layout area overhead are also analyzed for the proposed control circuit.","PeriodicalId":102785,"journal":{"name":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124391708","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}