2018 IEEE Conference on Technologies for Sustainability (SusTech)最新文献

SusTech 2018 Committees

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/sustech.2018.8671368

引用次数: 0

Effects of Environmental Influences on Active Thermography to Detect the Inner Structures of Wind Turbine Rotor Blades 环境影响对风电转子叶片内部结构主动热成像检测的影响

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671329

Daniel Schwahlen, U. Handmann

引用次数: 5

Minimizing Cost of Load Matching in Multiple Micro-Grids Using MESS 基于MESS的多微电网负荷匹配成本最小化

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671382

Athanasios Rompokos, S. Kuppannagari, R. Kannan, V. Prasanna

引用次数: 0

EcoPrinting: Investigation of Solar Powered Plastic Recycling and Additive Manufacturing for Enhanced Waste Management and Sustainable Manufacturing 生态印刷:太阳能塑料回收和增材制造的研究，以加强废物管理和可持续制造

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671370

M. Mohammed, Daniel Wilson, Eli Gomez-Kervin, Lucas Rosson, Johannes Long

{"title":"EcoPrinting: Investigation of Solar Powered Plastic Recycling and Additive Manufacturing for Enhanced Waste Management and Sustainable Manufacturing","authors":"M. Mohammed, Daniel Wilson, Eli Gomez-Kervin, Lucas Rosson, Johannes Long","doi":"10.1109/SUSTECH.2018.8671370","DOIUrl":"https://doi.org/10.1109/SUSTECH.2018.8671370","url":null,"abstract":"In this article we propose the EcoPrinting technology, which aims at a near zero carbon foot print means of recycling waste polymers into functional, working products. To achieve this goal, we demonstrate a nanogrid device by which solar energy can be stored in a modest sized battery system and use this to power instrumentation for melt extrusion of waste polymers into 3D printer filaments. We then use this filament in a modified 3D printer system to manufacture functional humanitarian aid components such as water seals and pipe connectors. We investigate the feasibility of the EcoPrinting principal using ABS and HDPE plastics, while evaluating and optimizing enabling device energy consumption and manufacturing performance. We conclude that the EcoPrinting principal is possible and functional devices can be manufactured with mechanical integrity equivalent to commercially available components. We finally demonstrate that EcoPrinting can be used as a tool for humanitarian use, realizing a manufacturing paradigm that is self-sufficient and potentially capable of addressing challenges of plastic proliferation in developing nations.","PeriodicalId":127111,"journal":{"name":"2018 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"80 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000649","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: 19

Advancing Systematic and Fundamental Changes in Agricultural Water Resources Management 推进农业水资源管理的系统性和根本性变革

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671352

Amir Kordijazi, Marcia Silva

引用次数: 1

Improved Range Prediction for Electric Vehicles by a Smart Tire Pressure Monitoring System 基于智能胎压监测系统的电动汽车里程预测改进

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671339

H. Fechtner, B. Schmuelling

{"title":"Improved Range Prediction for Electric Vehicles by a Smart Tire Pressure Monitoring System","authors":"H. Fechtner, B. Schmuelling","doi":"10.1109/SUSTECH.2018.8671339","DOIUrl":"https://doi.org/10.1109/SUSTECH.2018.8671339","url":null,"abstract":"In recent years, the topic vehicle mass estimation has become more and more popular. One of the reasons for this development is the growing spread of electric vehicles. The advantages of a precise vehicle mass estimation for owners of electric vehicles are e.g., a reliable range prediction or the selection of energy efficient routes depending on the current payload and state of charge. This paper presents a novel approach to estimate the vehicle mass by monitoring the tire pressure in combination with a two-stage step detection and a modified Kalman filter. The so-called Smart Tire Pressure Monitoring System offers many chances to enhance energy efficient driving strategies or advanced driver assistance systems. The presented paper shows the results of a large-scale test series of the Smart Tire Pressure Monitoring System. Based on these results, the second part of the paper clarifies the gained improvement of the range prediction by the detected vehicle masses. The analysis of the energy consumption of a Mitsubishi i-MiEV with four own driving cycles highlights the potential for improvement for the range prediction in detail.","PeriodicalId":127111,"journal":{"name":"2018 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116950546","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

Design of an Inflight Power Generation and Storage System for Use in UAVs 一种用于无人机的机载发电和存储系统设计

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671363

S. Dobbs, Zhen Yu, K. Anderson, Jonathan A. Franco, Alexander E. Deravanessian, A. Lin, Andrew Ahn

{"title":"Design of an Inflight Power Generation and Storage System for Use in UAVs","authors":"S. Dobbs, Zhen Yu, K. Anderson, Jonathan A. Franco, Alexander E. Deravanessian, A. Lin, Andrew Ahn","doi":"10.1109/SUSTECH.2018.8671363","DOIUrl":"https://doi.org/10.1109/SUSTECH.2018.8671363","url":null,"abstract":"This paper describes the design of an inflight power generation, management and storage system applicable to Unmanned Air Vehicles (UAV). Emerging UAV, drones and other aircraft can use electrical propulsion systems. To extend battery charge and aircraft range, power can be generated from multiple sources during flight including aero-elastic vibrations from gusts and flutter, and bending movements and sunlight. These sources of \"free\" energy can be summed and used during flight operation. This research will employ the aero-elastic vibrations of the wing that will be captured using motor generator created devices that uses vibrations to generate electricity. Stress flexing piezoelectric devices will be attached at the root of the wing, where the most bending strain occurs. Flexible solar panels are collocated to the top of UAV wing to enhance energy harvesting. These three sources will be summed together to power the propeller of an aircraft. A Maximum Power Point Tracker (MPPT) board is utilized to adjust the input voltage to harvest power from the wing vibration phenomenon and transform this power to supply the voltage requirements of the battery or graphene super-capacitors under load.","PeriodicalId":127111,"journal":{"name":"2018 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122126477","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: 6

Robotic Sorting of Used Button Cell Batteries: Utilizing Deep Learning 废旧纽扣电池的机器人分类:利用深度学习

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671351

H. Karbasi, Adam Sanderson, A. Sharifi, C. Pop

引用次数: 6

Predictive Analytics to Estimate Level of Residential Participation in Residential Demand Response Program 预测分析估计居民参与住宅需求响应计划的水平

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671335

Saurav M. S. Basnet, W. Jewell

引用次数: 2

Genetic Programming and Gaussian Process Regression Models for Groundwater Salinity Prediction: Machine Learning for Sustainable Water Resources Management 地下水盐度预测的遗传规划和高斯过程回归模型:可持续水资源管理的机器学习

2018 IEEE Conference on Technologies for Sustainability (SusTech) Pub Date : 2018-11-01 DOI: 10.1109/SUSTECH.2018.8671343

A. Lal, B. Datta

{"title":"Genetic Programming and Gaussian Process Regression Models for Groundwater Salinity Prediction: Machine Learning for Sustainable Water Resources Management","authors":"A. Lal, B. Datta","doi":"10.1109/SUSTECH.2018.8671343","DOIUrl":"https://doi.org/10.1109/SUSTECH.2018.8671343","url":null,"abstract":"Degradation of the quality of groundwater due to saltwater intrusion is considered as a major constraint limiting the use of water resources in coastal areas. Groundwater salinity prediction models can be used as surrogate models in a linked simulation-optimization methodology needed for developing and solving computationally feasible sustainable coastal aquifer management models. The present study utilizes two machine learning algorithms, namely, Genetic Programming (GP) and Gaussian Process Regression (GPR) models to approximate density dependent saltwater intrusion processes and predict salinity concentrations in an illustrative coastal aquifer system. Specifically, the GP and GPR models are trained and validated using pumping and resulting salinity concentration datasets obtained by solving a numerical 3D transient density dependent finite element based coastal aquifer flow and transport model. Prediction capabilities of the developed GP and GPR models are quantified using standard statistical parameters such as root mean squared error, coefficient of correlation and the Nash-Sutcliffe coefficient calculations. The results suggest that once trained and tested, both the GP and GPR models can be used to predict salinity concentration at selected monitoring locations in the modelled aquifer under variable groundwater pumping conditions. The performance evaluation results for the illustrative aquifer study area also show that the predictive capability of the GPR models are superior to those of the GP models. Therefore, GPR prediction models can be used as a substitute for the complex numerical simulation model in a linked simulation-optimization approach requiring numerous solutions of the simulation model to develop computationally feasible regional scale sustainable coastal aquifer management strategies.","PeriodicalId":127111,"journal":{"name":"2018 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258283","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: 10