Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31781
Wenwei Wang, Zhipeng Zhang, Yue Gao, Yiding Li
The efficient and accurate detection of lanes and the extraction of their key features are critical to autonomous driving. In this paper, a lane detection method that combines convolutional neural networks (CNN) and long-short-time memory neural networks (LSTM) is proposed to extract key features of lanes with great rapidity and accuracy. The main process is as follows: （ 1 ） The video is processed using a featurebased image processing method to extract key information of the lanes which is stored as a label. (2) The CNN model and the CNN-LSTM model are established respectively. （ 3 ） Training and testing are operated on above-mentioned models using the images and labels obtained in step(1). （ 4 ） Multi-platform verification of trained models is operated with entirely new videos. The results show that the detection rates of CNN model on training data and verification data are 94.9% and 91.2%, respectively, and the processing speed reaches up to 46.2 milliseconds per frame and its time consumption is only 5.59% of the traditional processing method; the detection rates of CNN-LSTM model are respectively 97.6% and 94.4%, and the processing speed achieves 54.7 milliseconds per frame which consumes only 6.61% time of the traditional method, and it also shows great performance on the micro platform.
{"title":"Lane Detection Using CNN-LSTM with Curve Fitting for Autonomous Driving","authors":"Wenwei Wang, Zhipeng Zhang, Yue Gao, Yiding Li","doi":"10.12783/dteees/iceee2019/31781","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31781","url":null,"abstract":"The efficient and accurate detection of lanes and the extraction of their key features are critical to autonomous driving. In this paper, a lane detection method that combines convolutional neural networks (CNN) and long-short-time memory neural networks (LSTM) is proposed to extract key features of lanes with great rapidity and accuracy. The main process is as follows: （ 1 ） The video is processed using a featurebased image processing method to extract key information of the lanes which is stored as a label. (2) The CNN model and the CNN-LSTM model are established respectively. （ 3 ） Training and testing are operated on above-mentioned models using the images and labels obtained in step(1). （ 4 ） Multi-platform verification of trained models is operated with entirely new videos. The results show that the detection rates of CNN model on training data and verification data are 94.9% and 91.2%, respectively, and the processing speed reaches up to 46.2 milliseconds per frame and its time consumption is only 5.59% of the traditional processing method; the detection rates of CNN-LSTM model are respectively 97.6% and 94.4%, and the processing speed achieves 54.7 milliseconds per frame which consumes only 6.61% time of the traditional method, and it also shows great performance on the micro platform.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83132090","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}
This paper proposes a state of health (SOH) estimation method with integration of grey relational analysis (GRA) with Elman neural network (NN). First, the experimental data of lithium-ion battery life attenuation are analyzed and the health factors (HFs) are extracted. Then, the correlation degree between HFs and SOH are analyzed by the GRA. Finally, the extracted HFs are considered as the model input, and the SOH as taken as a model target output for SOH prediction. The prediction results show that the proposed method has high prediction accuracy that it can be applied to the online SOH estimation.
{"title":"State of Health Estimation for Lithium-Ion Batteries Based on Elman Neural Network","authors":"Zheng Chen, Qiao Xue, Yonggang Liu, Jiangwei Shen, Renxin Xiao","doi":"10.12783/dteees/iceee2019/31814","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31814","url":null,"abstract":"This paper proposes a state of health (SOH) estimation method with integration of grey relational analysis (GRA) with Elman neural network (NN). First, the experimental data of lithium-ion battery life attenuation are analyzed and the health factors (HFs) are extracted. Then, the correlation degree between HFs and SOH are analyzed by the GRA. Finally, the extracted HFs are considered as the model input, and the SOH as taken as a model target output for SOH prediction. The prediction results show that the proposed method has high prediction accuracy that it can be applied to the online SOH estimation.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91522819","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31818
Jiabo Li, M. Ye, Shengjie Jiao, Dawei Shi, Xinxin Xu
In order to improve the accuracy of battery SOC, this paper presents a novel least squares support vector machine(LSSVM) framework based on machine learning. Put the current, voltage and temperature at the current moment and the SOC estimated at the previous time are used as input vectors of the model to estimate the SOC at the current time. The experimental results show that the proposed model can achieve better SOC estimation accuracy than the LSSVM model with limited data samples.
{"title":"State Estimation of Lithium Battery Based on Least Squares Support Vector Machine","authors":"Jiabo Li, M. Ye, Shengjie Jiao, Dawei Shi, Xinxin Xu","doi":"10.12783/dteees/iceee2019/31818","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31818","url":null,"abstract":"In order to improve the accuracy of battery SOC, this paper presents a novel least squares support vector machine(LSSVM) framework based on machine learning. Put the current, voltage and temperature at the current moment and the SOC estimated at the previous time are used as input vectors of the model to estimate the SOC at the current time. The experimental results show that the proposed model can achieve better SOC estimation accuracy than the LSSVM model with limited data samples.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82532574","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31811
Ya-Xiong Wang, Jinzhou Chen, Hongwen He
Proton exchange membrane (PEM) fuel cell engine has many advantages, including high energy density, high efficiency, low operation temperature, and zero emissions, which is a promising application to electric vehicles. In this paper, a multi-layer prediction control strategy for air supply system of a full-power PEM fuel cell electric vehicle is proposed, and a mathematical model of PEMF fuel cell engine air supply system is established in MATLAB/Simulink environment. The control scheme of the proposed multi-layer prediction strategy is that the top-layer model prediction is used for predicting the driving condition (speed of the vehicle) to obtain the desired air mass flow rate, and the bottom-layer air flow model prediction control (MPC) can adopt the top-layer airflow demand to regulate the oxygen excess ratio of PEM fuel cell engine. The proposed control strategy can meet the needs of the fuel cell stack reaction of oxygen as well as prevent air starvation that might occur in PEM fuel cell electric vehicle during driving conditions variation.
{"title":"Air Supply System Control of PEM Fuel Cell for Electric Vehicle Application Based on Multi-layer Prediction Strategy","authors":"Ya-Xiong Wang, Jinzhou Chen, Hongwen He","doi":"10.12783/dteees/iceee2019/31811","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31811","url":null,"abstract":"Proton exchange membrane (PEM) fuel cell engine has many advantages, including high energy density, high efficiency, low operation temperature, and zero emissions, which is a promising application to electric vehicles. In this paper, a multi-layer prediction control strategy for air supply system of a full-power PEM fuel cell electric vehicle is proposed, and a mathematical model of PEMF fuel cell engine air supply system is established in MATLAB/Simulink environment. The control scheme of the proposed multi-layer prediction strategy is that the top-layer model prediction is used for predicting the driving condition (speed of the vehicle) to obtain the desired air mass flow rate, and the bottom-layer air flow model prediction control (MPC) can adopt the top-layer airflow demand to regulate the oxygen excess ratio of PEM fuel cell engine. The proposed control strategy can meet the needs of the fuel cell stack reaction of oxygen as well as prevent air starvation that might occur in PEM fuel cell electric vehicle during driving conditions variation.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83752224","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31779
Yu Fu, Qie Sun, R. Wennersten
Energy storage system is an important unit to maintain stable output of the system, reduce energy cost, and realize peak-shaving in the integrated energy system. For the two commonly used energy storage methods, electrical energy storage and thermal energy storage, coordinating the optimal configuration of the two can make the system more efficient and energy-saving operation. At present, uncertainty brings great complexity to the optimal design of the system. At the same time, ignoring the influence of uncertainty on the system will lead to the optimization result being a suboptimal situation. Thus, in this paper, a multiobjective optimization under uncertainty is carried out to find the optimal configuration of electric and thermal energy storage system.
{"title":"Multi-objective Optimization for Electric and Thermal Energy Storage of Integrated Energy System under Uncertainty","authors":"Yu Fu, Qie Sun, R. Wennersten","doi":"10.12783/dteees/iceee2019/31779","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31779","url":null,"abstract":"Energy storage system is an important unit to maintain stable output of the system, reduce energy cost, and realize peak-shaving in the integrated energy system. For the two commonly used energy storage methods, electrical energy storage and thermal energy storage, coordinating the optimal configuration of the two can make the system more efficient and energy-saving operation. At present, uncertainty brings great complexity to the optimal design of the system. At the same time, ignoring the influence of uncertainty on the system will lead to the optimization result being a suboptimal situation. Thus, in this paper, a multiobjective optimization under uncertainty is carried out to find the optimal configuration of electric and thermal energy storage system.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80462192","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31786
Tiantian Lin, Zi-qiang Chen, Chang-wen Zheng
A diagnosis method of relay faults and sensor faults based on hybrid system for lithium-ion battery system is proposed in the paper. A battery system, which contains not only discrete dynamics but also continuous dynamics, can be classified into a hybrid system in which hybrid automaton is established to simultaneously handle these two dynamics. For battery fault diagnosis, besides the observation of discrete events, the distinguishability analysis of continuous dynamics is also needed. The recursive least squares and extended Kalman filter algorithm is used to track the continuous dynamics of the battery and generate voltage residual in this paper. The distinguishability analysis is performed based on the voltage residual and current. Federal Urban Driving Schedule test on a battery pack is conducted for evaluating the proposed method. The results indicate that the fault diagnosis method can perform well for the battery system.
{"title":"Fault Diagnosis of Lithium-ion Battery System Based on Hybrid System and Recursive Least Squares-Extended Kalman Filter","authors":"Tiantian Lin, Zi-qiang Chen, Chang-wen Zheng","doi":"10.12783/dteees/iceee2019/31786","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31786","url":null,"abstract":"A diagnosis method of relay faults and sensor faults based on hybrid system for lithium-ion battery system is proposed in the paper. A battery system, which contains not only discrete dynamics but also continuous dynamics, can be classified into a hybrid system in which hybrid automaton is established to simultaneously handle these two dynamics. For battery fault diagnosis, besides the observation of discrete events, the distinguishability analysis of continuous dynamics is also needed. The recursive least squares and extended Kalman filter algorithm is used to track the continuous dynamics of the battery and generate voltage residual in this paper. The distinguishability analysis is performed based on the voltage residual and current. Federal Urban Driving Schedule test on a battery pack is conducted for evaluating the proposed method. The results indicate that the fault diagnosis method can perform well for the battery system.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81024580","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31787
Haitao Wang, T. Tao, Jun Xu, Xiaoyan Liu, Piao Gou, X. Mei
Effective battery thermal management is significant for electric vehicle to maintain the performance and life cycle of battery packs. In this paper, a novel modular liquid-cooled system for batteries is designed, and the effect of cooling water flow rate and cooling mode (Serial cooling and parallel cooling) on the thermal behavior of the battery module is studied by CFD numerical simulation and experiment. The results show that there is a limit to improve the cooling effect by increasing the cooling water flow rate for the specific cooling structure. When the flow rate is relatively small, increasing the cooling water flow rate can significantly lower the maximum temperature and improve the temperature uniformity in the battery module; when the flow rate increases to a certain value, increasing the cooling water flow rate has no obvious effect on improving cooling effect. Compared with serial cooling, parallel cooling can significantly lower the temperature of the battery module, reduce the temperature difference between single cells, and improve the temperature uniformity of the battery module.
{"title":"A Novel Modular Liquid-cooled Battery Thermal Management for Cylindrical Lithium-ion Battery Module","authors":"Haitao Wang, T. Tao, Jun Xu, Xiaoyan Liu, Piao Gou, X. Mei","doi":"10.12783/dteees/iceee2019/31787","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31787","url":null,"abstract":"Effective battery thermal management is significant for electric vehicle to maintain the performance and life cycle of battery packs. In this paper, a novel modular liquid-cooled system for batteries is designed, and the effect of cooling water flow rate and cooling mode (Serial cooling and parallel cooling) on the thermal behavior of the battery module is studied by CFD numerical simulation and experiment. The results show that there is a limit to improve the cooling effect by increasing the cooling water flow rate for the specific cooling structure. When the flow rate is relatively small, increasing the cooling water flow rate can significantly lower the maximum temperature and improve the temperature uniformity in the battery module; when the flow rate increases to a certain value, increasing the cooling water flow rate has no obvious effect on improving cooling effect. Compared with serial cooling, parallel cooling can significantly lower the temperature of the battery module, reduce the temperature difference between single cells, and improve the temperature uniformity of the battery module.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86329206","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31823
Wu Yan, Wang Lifang, Li Fang
Due to the strong nonlinearity and the uncertainty of the vehicle, accurate path tracking control becomes a challenge. This paper proposes a path tracking control strategy that can simultaneously consider the dynamic characteristics of tracking performance and system constraints. The control strategy is based on moving horizon sliding mode control (MHSMC), in which the sliding mode surface is used to construct the prediction model, and the effects of parameter perturbation and external disturbance are eliminated through feedback correction and rolling optimization, then the prediction results of the model output are corrected so as to make sure optimal performance control under system constraints. The MHSMC combines the advantages of sliding mode control and moving horizon control, effectively improving the dynamic performance and robustness of the system. The simulation results of typical conditions show that the control strategy proposed in this paper can make the vehicle track the reference path well and has strong robustness.
{"title":"Intelligent Vehicle Path Tracking Control Based on Moving Horizon Sliding Mode Control","authors":"Wu Yan, Wang Lifang, Li Fang","doi":"10.12783/dteees/iceee2019/31823","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31823","url":null,"abstract":"Due to the strong nonlinearity and the uncertainty of the vehicle, accurate path tracking control becomes a challenge. This paper proposes a path tracking control strategy that can simultaneously consider the dynamic characteristics of tracking performance and system constraints. The control strategy is based on moving horizon sliding mode control (MHSMC), in which the sliding mode surface is used to construct the prediction model, and the effects of parameter perturbation and external disturbance are eliminated through feedback correction and rolling optimization, then the prediction results of the model output are corrected so as to make sure optimal performance control under system constraints. The MHSMC combines the advantages of sliding mode control and moving horizon control, effectively improving the dynamic performance and robustness of the system. The simulation results of typical conditions show that the control strategy proposed in this paper can make the vehicle track the reference path well and has strong robustness.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75867631","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31793
Yu Wang, R. Wennersten, Qie Sun
Over the recent decades, there has been an explosive growth in China’s urban buildings and related energy consumption. Great efforts have been done to transform the urban buildings into more sustainable paradigms. The aim of this work is to better understand the ongoing energy transition in China. Through a research based on qualitative data analysis, this work provides insights into the transition of China’s urban buildings towards deep decarbonization, decentralization and digitalization. This work also furnishes a discussion of the energy system configuration of the transitioning urban buildings.
{"title":"Qualitative Data Analysis of the Energy Transition of Urban Buildings in China","authors":"Yu Wang, R. Wennersten, Qie Sun","doi":"10.12783/dteees/iceee2019/31793","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31793","url":null,"abstract":"Over the recent decades, there has been an explosive growth in China’s urban buildings and related energy consumption. Great efforts have been done to transform the urban buildings into more sustainable paradigms. The aim of this work is to better understand the ongoing energy transition in China. Through a research based on qualitative data analysis, this work provides insights into the transition of China’s urban buildings towards deep decarbonization, decentralization and digitalization. This work also furnishes a discussion of the energy system configuration of the transitioning urban buildings.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72999115","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}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31796
S. Zhou, Wc Zhang
In the past decades, the Amur River Basin has experienced complex regional fluctuations and strong spatial heterogeneity in vegetation. In this study, the spatial-temporal variations in vegetation and their driving factors in the basin were investigated using GIMMS NDVI data over 1982–2013, and MODIS NDVI/EVI and NPP product from 2002 to 2013. A Mann- Kendall trend test were applied to investigate the sustainability and trends of NDVI in this basin. In addition, linear regression trend was applied to analyze spatial distribution of the NDVI, EVI and NPP trends over the period 2002–2013. For further assessment of the driving factors of vegetation, the impacts of individual climatic factors or changes of each land use type on vegetation were analyzed using PLSR and RR based on long-term meteorological data and annual time-series of land use. The relationship between vegetation and hydrological components were also assessed using SWAT hydrological process simulation results.
{"title":"Spatial-temporal Variations in Vegetation and their Driving Factors in the Amur River Basin","authors":"S. Zhou, Wc Zhang","doi":"10.12783/dteees/iceee2019/31796","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31796","url":null,"abstract":"In the past decades, the Amur River Basin has experienced complex regional fluctuations and strong spatial heterogeneity in vegetation. In this study, the spatial-temporal variations in vegetation and their driving factors in the basin were investigated using GIMMS NDVI data over 1982–2013, and MODIS NDVI/EVI and NPP product from 2002 to 2013. A Mann- Kendall trend test were applied to investigate the sustainability and trends of NDVI in this basin. In addition, linear regression trend was applied to analyze spatial distribution of the NDVI, EVI and NPP trends over the period 2002–2013. For further assessment of the driving factors of vegetation, the impacts of individual climatic factors or changes of each land use type on vegetation were analyzed using PLSR and RR based on long-term meteorological data and annual time-series of land use. The relationship between vegetation and hydrological components were also assessed using SWAT hydrological process simulation results.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73755054","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}
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