2021 IEEE Conference on Technologies for Sustainability (SusTech)最新文献_第5页

Slope Stability Analysis as Applied to Rainfall- triggered Landslide in Itogon, Benguet Province, Philippines 菲律宾本盖特省伊托贡降雨引发滑坡的边坡稳定性分析

2021 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2021-04-22 DOI: 10.1109/SusTech51236.2021.9467461

Kim Gerard Oliver M. Alcala, Jhoyen Emmanuel Q. Angeles, Paul Kevin I. Lim, J. Buluran, F. J. Tan

{"title":"Slope Stability Analysis as Applied to Rainfall- triggered Landslide in Itogon, Benguet Province, Philippines","authors":"Kim Gerard Oliver M. Alcala, Jhoyen Emmanuel Q. Angeles, Paul Kevin I. Lim, J. Buluran, F. J. Tan","doi":"10.1109/SusTech51236.2021.9467461","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467461","url":null,"abstract":"Rainfall-triggered landslides are common in places where typhoons are frequently occurring such as the Philippines which experiences at least 20 typhoons per year. Heavy rainfalls saturate soil and affects the stability of the slopes causing landslides. A catastrophic rainfall-triggered landslide happened in Brgy. Ucab, Itogon, Benguet in Luzon Island which occurred during the Tropical Cyclone Mangkhut or locally known as Typhoon Ompong on September 15, 2018 having 94 casualties. The study focused on slope stability analysis of rainfall-triggered landslides to help improve the current understanding of the rainfall-triggered landslide in the area. The findings of the study can be used in modeling rainfall-triggered landslide hazard maps. The slope model of the landslide in Itogon was modelled using GEO5 2020. Both the analytical method of the Simplified Bishop Method and the numerical solution provided in the software GEO5 2020 were used in analyzing the slope stability of the landslide. The slope stability analysis showed that the slope was unstable upon considering factors such as its geological and hydrological characteristics agreeing with the disastrous event that transpired.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125579489","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 Deep Reinforcement Learning Approach to Traffic Signal Control 交通信号控制的深度强化学习方法

2021 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2021-04-22 DOI: 10.1109/SusTech51236.2021.9467450

Aquib Junaid Razack, Vysyakh Ajith, Rajesh K. Gupta

引用次数: 2

WeatherNet: Nowcasting Net Radiation at the Edge 天气网:边缘的临近预报净辐射

2021 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2021-04-22 DOI: 10.1109/SusTech51236.2021.9467444

Enrique Nueve, R. Jackson, R. Sankaran, N. Ferrier, S. Collis

{"title":"WeatherNet: Nowcasting Net Radiation at the Edge","authors":"Enrique Nueve, R. Jackson, R. Sankaran, N. Ferrier, S. Collis","doi":"10.1109/SusTech51236.2021.9467444","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467444","url":null,"abstract":"In addition to natural processes such as photosynthesis and evapotranspiration, net radiation affects industrial applications such as photovoltaic energy management and solar thermal collection. We propose a deep learning approach for nowcasting net radiation within subhourly and intrahour horizons to better understand and control processes influenced by net radiation. Specifically, we developed a deep-learning-based CNN-LSTM, named WeatherNet, that combines multiple local ground-based cameras and weather sensor data to predict net radiation. Unlike previous methodologies, our approach involves images from three different cameras: a sky-facing RGB camera, a horizon-facing RGB camera, and a horizon-facing forward-looking infrared camera. Further, WeatherNet was designed to run \"at the edge\" using the Waggle edge computing framework to reduce the data bandwidth, improve the latency of predictions, and eliminate centralized data collection. With our proposed dataset and model, WeatherNet, we present a novel methodology using relatively inexpensive equipment for nowcasting net radiation precisely between a 15- and 90-minute horizon.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124916883","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 GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking 考虑再生制动的基于遗传算法的电动汽车生态驾驶方法

2021 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2020-12-30 DOI: 10.1109/SusTech51236.2021.9467457

Mukesh Gautam, N. Bhusal, M. Benidris, P. Fajri

{"title":"A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking","authors":"Mukesh Gautam, N. Bhusal, M. Benidris, P. Fajri","doi":"10.1109/SusTech51236.2021.9467457","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467457","url":null,"abstract":"As the deployment of zero emission transportation technologies, specifically electric vehicles (EVs), is increasing, the concept of their eco-driving is gaining significant attention. Contrary to the eco-driving techniques used in conventional internal combustion engine vehicles that do not have the capability of regenerative braking, this paper proposes a genetic algorithm (GA)-based eco-driving technique for EVs considering regenerative braking. In the proposed approach, the optimal or near-optimal combination of variables in the driving cycle of EVs is searched using GA. The proposed approach starts by generating an initial population of chromosomes, where all variables under consideration are encoded in each chromosome. This population of chromosomes is passed through crossover, mutation, and elitist-based selection over a certain number of generations, which results in a driving cycle with the least energy consumption. The proposed method is verified using case studies consisting of two types of driving cycles. The results show the capability of the proposed method in computing the minimum energy driving cycle.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114685998","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

Program by Session 按时段安排节目

2021 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 1900-01-01 DOI: 10.1109/sustech51236.2021.9467429

Milou, Jansen

引用次数: 0