2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)最新文献_第4页

Real Time Voronoi-like Path Planning Using Flow Field and A* 使用流场和A*的实时类voronoi路径规划

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322832

Mark Sabbagh, M. H. Tanveer, Antony Thomas, J. Faile, Muhammad Salman

{"title":"Real Time Voronoi-like Path Planning Using Flow Field and A*","authors":"Mark Sabbagh, M. H. Tanveer, Antony Thomas, J. Faile, Muhammad Salman","doi":"10.1109/HONET50430.2020.9322832","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322832","url":null,"abstract":"In this paper we introduce a path planning approach for robotic and related applications to reach a target within a known environment. We improve the flow fields based path planning approach by avoiding typical obstacle hugging behavior. Flow field path planning defines the potential at each node of an environment which allows a large number of agents to traverse the environment while avoiding the task of calculating paths for each individual agent. This approach maintains the same benefit of typical flow field path planning with the addition of avoiding paths that are too close to obstacles. This method can be referred to as Valley Field Path Planning. In the general method, this algorithm takes two major steps. Firstly, the obstacle potential field that results in a low potential in regions farthest from the obstacles. The second step of the algorithm is to find potential provided that the goal node corresponds to a higher potential at nodes farther. The weighted sum of these two potential fields results in a flow field that avoids obstacle hugging. To avoid the local minima problem with this approach, $A^{ast}$ path planning can been introduced with the benefit of a configurable deterministic path at the cost of calculating the path on a per agent basis. The results show that this combination of an obstacle potential field and the A* search algorithm results in an efficient and inexpensive way to generate a configurable path.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596687","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: 2

Design and Modification of multiband M-slot patch antenna for wireless applications 无线应用中多波段m槽贴片天线的设计与改进

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322656

Ali I. Abdalla, I. H. Ali

引用次数: 3

Multiscale Dilated UNet for Segmentation of Multi-Organ Nuclei in Digital Histology Images 数字组织学图像中多器官核的多尺度扩展UNet分割

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322833

S. Rashid, M. Fraz, S. Javed

引用次数: 9

An Evolution of CNN Object Classifiers on Low-Resolution Images CNN对象分类器在低分辨率图像上的进化

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322661

Muhammad Mohsin Kabir, Abu Quwsar Ohi, Md. Saifur Rahman, M. Mridha

{"title":"An Evolution of CNN Object Classifiers on Low-Resolution Images","authors":"Muhammad Mohsin Kabir, Abu Quwsar Ohi, Md. Saifur Rahman, M. Mridha","doi":"10.1109/HONET50430.2020.9322661","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322661","url":null,"abstract":"Object classification is a significant task in computer vision. It has become an effective research area as an important aspect of image processing and the building block of image localization, detection, and scene parsing. Object classification from low-quality images is difficult for the variance of object colors, aspect ratios, and cluttered backgrounds. The field of object classification has seen remarkable advancements, with the development of deep convolutional neural networks (DCNNs). Deep neural networks have been demonstrated as very powerful systems for facing the challenge of object classification from high-resolution images, but deploying such object classification networks on the embedded device remains challenging due to the high computational and memory requirements. Using high-quality images often causes high computational and memory complexity, whereas low-quality images can solve this issue. Hence, in this paper, we investigate an optimal architecture that accurately classifies low-quality images using DCNNs architectures. To validate different baselines on low-quality images, we perform experiments using webcam captured image datasets of 10 different objects. In this research work, we evaluate the proposed architecture by implementing popular CNN architectures. The experimental results validate that the MobileNet architecture delivers better than most of the available CNN architectures for low-resolution webcam image datasets.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"5 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116675435","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: 4

Efficient and Secure Energy Trading in Internet of Electric Vehicles Using IOTA Blockchain 利用IOTA区块链实现电动汽车互联网高效安全的能源交易

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322826

Mudassir Ali, A. Anjum, Adnan Anjum, M. Khan

引用次数: 3

Securing ZigBee IoT Network Against HULK Distributed Denial of Service Attack 保护ZigBee物联网网络免受HULK分布式拒绝服务攻击

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322808

Ekele A. Asonye, Ifeoma Anwuna, S. Musa

引用次数: 3

Level Control of Coupled Conical Tank System using Adaptive Model Predictive Controller 基于自适应模型预测控制器的耦合锥形水箱液位控制

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322842

M. Gulzar, Muhammad Munawar, Zarak Dewan, Muhammad Salman, S. Iqbal

引用次数: 2

DAI: Dynamic ACL Policy Implementation for Software-Defined Networking 软件定义网络的动态ACL策略实现

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322835

Mujahid Ali, Nadir Shah, Muazzam A. Khan Khattak

引用次数: 4

Network Intrusion Detection Leveraging Machine Learning and Feature Selection 利用机器学习和特征选择的网络入侵检测

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322813

Arshid Ali, S. Shaukat, M. Tayyab, M. Khan, J. Khan, Sher Arshad, Jawad Ahmad, Amreen Batool, Jawad Arshad k

引用次数: 7

Dynamic Line Rating (DLR) by Weather-Based Calculation for Power Grid Optimization in Tenaga Nasional Berhad (TNB) 基于天气计算的Tenaga国家电网优化动态线路额定值(DLR)

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322810

M. S. M. Shokri, F. Kasran, Muhammad Hanif Abdul Aziz, N. Miswan, Mohd Noorfazly Noran, Azlan Abd Rahim

{"title":"Dynamic Line Rating (DLR) by Weather-Based Calculation for Power Grid Optimization in Tenaga Nasional Berhad (TNB)","authors":"M. S. M. Shokri, F. Kasran, Muhammad Hanif Abdul Aziz, N. Miswan, Mohd Noorfazly Noran, Azlan Abd Rahim","doi":"10.1109/HONET50430.2020.9322810","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322810","url":null,"abstract":"Dynamic line rating (DLR) is a practical solution for line optimization in power transmission by dynamically increase and decrease line rating. Instead of limiting the line rating to the fixed value in Static Line Rating (SLR) based on worst conditions, DLR uses the line monitoring sensor and weather monitoring system to provide the actual line operating condition. TNB, as the power utility operator in Malaysia, always aims to find the best solution to improve overhead line performance. To date, two DLR sensors have been installed at selected line spans and the DLR data for these lines is accessible via a web portal of the system. However, the current DLR data is limited only to the line span with the installed sensors. Therefore, this paper attempts to provide the DLR data based on weather parameters that will be useful for line rating indication without line sensor installation. A detailed comparison of DLR data is analyzed between the weather-based calculation and the existing system at the line with sensor and it shows a consistent pattern between both DLR data. This work will then serve as a base for future research in the weather-based calculation to obtain DLR value for TNB grid optimization.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128721451","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