Pub Date : 2022-04-21DOI: 10.1109/sustech51236.2021.9467432
You are warmly welcome and highly appreciated to join us in the 2009 International Workshop on Chaos-Fractals Theories and Applications (IWCFTA 2009) to be held in Shenyang, China on 6-8 November 2009. We have already held a successful workshop in Zhangjiajie, Hunan, China last year. Now, we are gathering again to share our new scientific findings and research results, as well as teaching and learning experiences in the fields of nonlinear dynamics particularly chaos and fractals. The goal of this workshop series is to provide a dynamic platform for scientists, researchers and engineers to present their advances in the studies of chaos-fractals theories and applications, including complex networks and systems, multimedia technologies, cryptography, communications, biology, finance, and so on.
{"title":"Welcome Message from the Conference Chair","authors":"","doi":"10.1109/sustech51236.2021.9467432","DOIUrl":"https://doi.org/10.1109/sustech51236.2021.9467432","url":null,"abstract":"You are warmly welcome and highly appreciated to join us in the 2009 International Workshop on Chaos-Fractals Theories and Applications (IWCFTA 2009) to be held in Shenyang, China on 6-8 November 2009. We have already held a successful workshop in Zhangjiajie, Hunan, China last year. Now, we are gathering again to share our new scientific findings and research results, as well as teaching and learning experiences in the fields of nonlinear dynamics particularly chaos and fractals. The goal of this workshop series is to provide a dynamic platform for scientists, researchers and engineers to present their advances in the studies of chaos-fractals theories and applications, including complex networks and systems, multimedia technologies, cryptography, communications, biology, finance, and so on.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126229476","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467448
Qais Amarkhil, E. Elwakil, B. Hubbard
Construction project delay leads to significant time & cost overrun and results in revenue loss. Construction projects' complexity and poor planning have negatively impacted the construction industry in adopting new technology, improving productivity, and environmental sustainability. Researcher in the construction profession has extensively studied construction delays in various circumstances. Available literature demonstrates that identifying critical causes of project delay, delay compensability, and project delay claims are broadly studied topics. Few researchers have investigated how to measure and mitigate construction project delay causes proactively before actual construction time. On the other hand, the lack of a standardized delay approach in assessing the impact of critical causes of delays on project performance and the heterogeneity of identified causes of delay have aggravated the situation to comprehend the delay cause impact on a project and its environment. To this end, this study has focused on identifying critical causes of delay in a standardized delay format to measure and mitigate its impact on a project before the actual construction start. This paper has employed a mix-method to quantify the impact of the inherent causes of delay on the project schedule. A five-step framework is applied to achieve the study objective. The proposed approach and technique enable researchers and practitioners to actively measure the impact of inherent delay causes on a project to improve productivity and eliminate redundant processes. This approach is more feasible to be integrated with new technology to assess alternative options for a project schedule to have a tolerable level of sequencing criticality and inherent delay risk.
{"title":"Inherent Delay Risk Assessment in Construction: A proactive approach, mitigating the impact of causes of delay on schedule","authors":"Qais Amarkhil, E. Elwakil, B. Hubbard","doi":"10.1109/SusTech51236.2021.9467448","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467448","url":null,"abstract":"Construction project delay leads to significant time & cost overrun and results in revenue loss. Construction projects' complexity and poor planning have negatively impacted the construction industry in adopting new technology, improving productivity, and environmental sustainability. Researcher in the construction profession has extensively studied construction delays in various circumstances. Available literature demonstrates that identifying critical causes of project delay, delay compensability, and project delay claims are broadly studied topics. Few researchers have investigated how to measure and mitigate construction project delay causes proactively before actual construction time. On the other hand, the lack of a standardized delay approach in assessing the impact of critical causes of delays on project performance and the heterogeneity of identified causes of delay have aggravated the situation to comprehend the delay cause impact on a project and its environment. To this end, this study has focused on identifying critical causes of delay in a standardized delay format to measure and mitigate its impact on a project before the actual construction start. This paper has employed a mix-method to quantify the impact of the inherent causes of delay on the project schedule. A five-step framework is applied to achieve the study objective. The proposed approach and technique enable researchers and practitioners to actively measure the impact of inherent delay causes on a project to improve productivity and eliminate redundant processes. This approach is more feasible to be integrated with new technology to assess alternative options for a project schedule to have a tolerable level of sequencing criticality and inherent delay risk.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"22 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":"125120136","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467421
M. Afkhamiaghda, E. Elwakil
The United States spends around 450 million dollars just for lodging the survivors and creating temporary shelters in the wake of hurricane Harvey, Irma, and Maria in 2017. Post-disaster temporary housing is a multi-objective process, thus reaching the optimized model relies on numerous objectives and their interaction with each other. The construction activities, especially in a post-disaster scenario, is considered challenging, leading to ineffective management in post-disaster housing reconstruction. Acknowledging and creating a balance between these issues by the policymakers who provide accommodations to post-disaster victims is one of the main challenges that need to be addressed.Post-disaster temporary housing is an integral part of the recovery process; however, not many research types have been done regarding how the factors and to what degree factors can affect the classification. One way to categorize the temporary housing units (THU) is based on how they get built, either made onsite or created offsite. However, the mechanism of selecting the THU types is mainly based on expert opinion and tacit knowledge, which can result in the insufficiency of the process.This model aims to study how and to what degree the factors that affect the post-disaster temporary housing process dictate the type of THU in terms of being built onsite or modeled offsite. The researchers designed a questionnaire to understand each main factor's importance compared to each other through a ranking process. It also asks each participant to rate the different THUs being used based on their importance. In this study, an ordinal classification framework is introduced using the K-Nearest Neighbor (KNN) model to help decision-makers choose the right type of temporary houses based on their needs. This model’s results show how supervised classification models can be an efficient tool and holistic approach to providing more robust, efficient decisions as an alternative to the current strategy, which relies on tacit knowledge.
{"title":"The Application of Using Supervised Classification Techniques in Selecting the Most Optimized Temporary House Type in Post-disaster Situations","authors":"M. Afkhamiaghda, E. Elwakil","doi":"10.1109/SusTech51236.2021.9467421","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467421","url":null,"abstract":"The United States spends around 450 million dollars just for lodging the survivors and creating temporary shelters in the wake of hurricane Harvey, Irma, and Maria in 2017. Post-disaster temporary housing is a multi-objective process, thus reaching the optimized model relies on numerous objectives and their interaction with each other. The construction activities, especially in a post-disaster scenario, is considered challenging, leading to ineffective management in post-disaster housing reconstruction. Acknowledging and creating a balance between these issues by the policymakers who provide accommodations to post-disaster victims is one of the main challenges that need to be addressed.Post-disaster temporary housing is an integral part of the recovery process; however, not many research types have been done regarding how the factors and to what degree factors can affect the classification. One way to categorize the temporary housing units (THU) is based on how they get built, either made onsite or created offsite. However, the mechanism of selecting the THU types is mainly based on expert opinion and tacit knowledge, which can result in the insufficiency of the process.This model aims to study how and to what degree the factors that affect the post-disaster temporary housing process dictate the type of THU in terms of being built onsite or modeled offsite. The researchers designed a questionnaire to understand each main factor's importance compared to each other through a ranking process. It also asks each participant to rate the different THUs being used based on their importance. In this study, an ordinal classification framework is introduced using the K-Nearest Neighbor (KNN) model to help decision-makers choose the right type of temporary houses based on their needs. This model’s results show how supervised classification models can be an efficient tool and holistic approach to providing more robust, efficient decisions as an alternative to the current strategy, which relies on tacit knowledge.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"23 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":"116575348","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467438
Michael Zhang, H. Atighechi, Mehran Zamani, A. Das
Rapid adoption of electric vehicles has introduced new challenges to power utilities by increasing demand on distribution systems. To avoid costly system reinforcements, power utilities can reduce adverse grid impacts and excessive costs for the customers using time-of-use pricing or smart charging programs. Proper infrastructure and data acquisition methodology are the necessities of such program. The data acquisition methodology should be accurate, secure, and economically viable. Different methodologies have been introduced and utilized during the past few years that may not be economically viable or may not provide acceptable accuracy and security levels. In this paper, OCPP has been used to transfer measured data from onboard meters of OCPP-enabled AC Level 2 chargers used in a BC Hydro TOU measurement trial for residential EV charging. The accuracy of the collected data from different chargers has been verified against a utility-grade smart meter. It is shown that the measured data is within a reasonable accuracy range, and it is transferred and stored securely without loss of accuracy. Finally, the collected EV load data is assessed over a period of 24 hours to demonstrate the impact of TOU on the total household load and the importance of proper scheduling.
{"title":"Using OCPP for Data Collection in BC Hydro Time-of-Use Measurement Trial for Residential EV Charging","authors":"Michael Zhang, H. Atighechi, Mehran Zamani, A. Das","doi":"10.1109/SusTech51236.2021.9467438","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467438","url":null,"abstract":"Rapid adoption of electric vehicles has introduced new challenges to power utilities by increasing demand on distribution systems. To avoid costly system reinforcements, power utilities can reduce adverse grid impacts and excessive costs for the customers using time-of-use pricing or smart charging programs. Proper infrastructure and data acquisition methodology are the necessities of such program. The data acquisition methodology should be accurate, secure, and economically viable. Different methodologies have been introduced and utilized during the past few years that may not be economically viable or may not provide acceptable accuracy and security levels. In this paper, OCPP has been used to transfer measured data from onboard meters of OCPP-enabled AC Level 2 chargers used in a BC Hydro TOU measurement trial for residential EV charging. The accuracy of the collected data from different chargers has been verified against a utility-grade smart meter. It is shown that the measured data is within a reasonable accuracy range, and it is transferred and stored securely without loss of accuracy. Finally, the collected EV load data is assessed over a period of 24 hours to demonstrate the impact of TOU on the total household load and the importance of proper scheduling.","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":"129517323","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467452
M. Mathews, R. T.
Increased stability issues due to a decline in system inertia have led to the need for additional frequency response services in renewable integrated power grids. In the event of an increase or decrease in system frequency, quick acting energy storage can replicate the response of the conventional synchronous generator. Battery Energy Storage System (BESS) is a promising option for retaining grid stability and reliability by delivering frequency support services. This paper utilizes a time-derivative frequency signal and an SoC droop setting based control technique for BESS to produce an active power response for a deviation in system frequency. A novel power management technique incorporating the droop setting and a frequency deadband to ensure controlled charging and discharging of BESS in the context of safe operating limits is proposed. The proposed battery management strategy shows significant improvement in the SoC profile, battery reference power and utilisation of battery capacity. Simulation studies is performed on a modified IEEE-13 bus test system modelled as a low inertia grid. The effectiveness of the control strategy is verified by evaluating the frequency nadir,BESS response time, the change in frequency and the rate of change of frequency after a system disturbance.
{"title":"A Novel Power Management Strategy for Frequency Regulation in Low Inertia Grid","authors":"M. Mathews, R. T.","doi":"10.1109/SusTech51236.2021.9467452","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467452","url":null,"abstract":"Increased stability issues due to a decline in system inertia have led to the need for additional frequency response services in renewable integrated power grids. In the event of an increase or decrease in system frequency, quick acting energy storage can replicate the response of the conventional synchronous generator. Battery Energy Storage System (BESS) is a promising option for retaining grid stability and reliability by delivering frequency support services. This paper utilizes a time-derivative frequency signal and an SoC droop setting based control technique for BESS to produce an active power response for a deviation in system frequency. A novel power management technique incorporating the droop setting and a frequency deadband to ensure controlled charging and discharging of BESS in the context of safe operating limits is proposed. The proposed battery management strategy shows significant improvement in the SoC profile, battery reference power and utilisation of battery capacity. Simulation studies is performed on a modified IEEE-13 bus test system modelled as a low inertia grid. The effectiveness of the control strategy is verified by evaluating the frequency nadir,BESS response time, the change in frequency and the rate of change of frequency after a system disturbance.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"1 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":"126145881","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467419
Jacquelynne Hernández, A. Etemadi, Samuel Roberts-Baca, Venkat Koushik Muthyapu
Logistic regression models can serve as important tools in developing a framework to establish the value of electrical energy storage systems (ESSs). This study provides models that aggregate use-case scenarios of five battery types, as well as pumped hydro-electric storage systems. The grid applications include: bulk energy at generation, auxiliary services at transmission and distribution, and end-use customer services at distributed generation. The data is derived from 1,261 real world systems. Five different models were developed for short, medium, and long-duration grid services. The models are designed to be technology agnostic and are not sensitive to either performance characteristics or operating conditions of the ESS. The results indicate the probability that an energy storage project will provide an individual service use case given that it may also yield another service, and how technology types and multiple selected applications influence those probabilities.
{"title":"Developing a Logistic Regression Method for Valuation of Grid-Level Energy Storage Systems","authors":"Jacquelynne Hernández, A. Etemadi, Samuel Roberts-Baca, Venkat Koushik Muthyapu","doi":"10.1109/SusTech51236.2021.9467419","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467419","url":null,"abstract":"Logistic regression models can serve as important tools in developing a framework to establish the value of electrical energy storage systems (ESSs). This study provides models that aggregate use-case scenarios of five battery types, as well as pumped hydro-electric storage systems. The grid applications include: bulk energy at generation, auxiliary services at transmission and distribution, and end-use customer services at distributed generation. The data is derived from 1,261 real world systems. Five different models were developed for short, medium, and long-duration grid services. The models are designed to be technology agnostic and are not sensitive to either performance characteristics or operating conditions of the ESS. The results indicate the probability that an energy storage project will provide an individual service use case given that it may also yield another service, and how technology types and multiple selected applications influence those probabilities.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"23 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":"130765410","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467439
Julien Walzberg, Fu Zhao, Kali Frost, A. Carpenter, G. Heath
By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.
{"title":"Exploring Social Dynamics of Hard-Disk Drives Circularity with an Agent-Based Approach","authors":"Julien Walzberg, Fu Zhao, Kali Frost, A. Carpenter, G. Heath","doi":"10.1109/SusTech51236.2021.9467439","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467439","url":null,"abstract":"By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"27 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":"126750898","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467416
Tylor E. Slay, R. Bass
Renewable energy resources, particularly wind and solar photovoltaic, are becoming significant contributors to electric power generation. These re-sources will contribute towards achieving sustainable electric power systems. However, renewable resources will dramatically increase the demand for flexible power system operations. This paper proposes an energy service interface that will allow aggregated distributed energy resources, such as residential loads and inverter-based systems, to participate in NERC-defined smart energy reliability services. Such cyber-physical systems will increase system flexibility by ensuring match between energy supply and energy demand.Aggregation and coordinated dispatch of millions of distributed energy resources will require development of large-scale computing networks. Several smart grid interface-enabling technologies, including IEEE 2030.5, Common Smart Inverter Profile, SunSpec Modbus, and CTA 2045, are discussed. Residential loads are categorized by their static and dynamic energy characteristics to identify services in which they can participate. The business model for the energy services interface as well as probabilistic modeling for resource estimation are highlighted as future considerations.
{"title":"An Energy Service Interface for Distributed Energy Resources","authors":"Tylor E. Slay, R. Bass","doi":"10.1109/SusTech51236.2021.9467416","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467416","url":null,"abstract":"Renewable energy resources, particularly wind and solar photovoltaic, are becoming significant contributors to electric power generation. These re-sources will contribute towards achieving sustainable electric power systems. However, renewable resources will dramatically increase the demand for flexible power system operations. This paper proposes an energy service interface that will allow aggregated distributed energy resources, such as residential loads and inverter-based systems, to participate in NERC-defined smart energy reliability services. Such cyber-physical systems will increase system flexibility by ensuring match between energy supply and energy demand.Aggregation and coordinated dispatch of millions of distributed energy resources will require development of large-scale computing networks. Several smart grid interface-enabling technologies, including IEEE 2030.5, Common Smart Inverter Profile, SunSpec Modbus, and CTA 2045, are discussed. Residential loads are categorized by their static and dynamic energy characteristics to identify services in which they can participate. The business model for the energy services interface as well as probabilistic modeling for resource estimation are highlighted as future considerations.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"11 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":"124952349","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467453
M. Jorat, V. Manousiouthakis
The dynamic coexistence of trees and grass is a vital component for a sustainable ecological system. This dynamical coexistence could be affected through continuous and alternate natural perturbations such as herbivore grazing, wildfires, droughts, and human activities. To fully understand the dynamics and sustainability of such ecological systems, first rigorous mathematical models need to be developed, capturing the systems' behavior, and subsequently, tools are created to analyze their sustainability. With this goal in mind, two concepts of Sustainability over Sets (SOS) and Sustainizability over Sets (SIZOS) have been recently introduced as sustainable system analysis and synthesis tools, respectively, with broad flexibility in incorporating human input. In this work, the SOS concept is first briefly introduced and afterward applied to a dynamical system consisting of tree grass components under different wildfire frequencies. The obtained results show that an increase in wildfire frequency will result in a system only consisting of grassland and thus the ecosystem is deemed unsustainable.
{"title":"Sustainability analysis of ecological systems in fire prone areas using the concept of Sustainability over Sets (SOS)","authors":"M. Jorat, V. Manousiouthakis","doi":"10.1109/SusTech51236.2021.9467453","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467453","url":null,"abstract":"The dynamic coexistence of trees and grass is a vital component for a sustainable ecological system. This dynamical coexistence could be affected through continuous and alternate natural perturbations such as herbivore grazing, wildfires, droughts, and human activities. To fully understand the dynamics and sustainability of such ecological systems, first rigorous mathematical models need to be developed, capturing the systems' behavior, and subsequently, tools are created to analyze their sustainability. With this goal in mind, two concepts of Sustainability over Sets (SOS) and Sustainizability over Sets (SIZOS) have been recently introduced as sustainable system analysis and synthesis tools, respectively, with broad flexibility in incorporating human input. In this work, the SOS concept is first briefly introduced and afterward applied to a dynamical system consisting of tree grass components under different wildfire frequencies. The obtained results show that an increase in wildfire frequency will result in a system only consisting of grassland and thus the ecosystem is deemed unsustainable.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"8 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":"131183822","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467440
A. Mukherjee, S. Kesavan, Soumyprakash Das
As per FAO estimates, annually around one-third of food produced worldwide is lost or wasted. Plant pests, pathogens and weeds account for a large proportion of global crop production losses in the pre-harvest stages. Bacterial blight, caused by Xanthomonas oryzae, is one of the most devastating diseases in rice that has the potential to destroy up to 70% of a smallholder farmer's seasonal yield. In this paper, we describe "AnnaData" which employs a robust multi-sensor and multilevel fusion model that combines advanced computer vision techniques along with hyperspectral and thermal data processing, to recognize crop abnormalities in the incipient stages and alert farmers regarding potential onset of bacterial blight disease in rice crop. The efficacy of the AnnaData model has been validated in a lab setting by artificially inoculating the pathogen on a research farm in partnership with India's National Rice Research Institute (NRRI) in Odisha state. Compared to standard computer vision models based on visual and near-infrared image markers that delivered 40%-80% detection rates in asymptomatic stages of the disease, AnnaData's multi-sensor model achieved greater than 95% detection accuracy with less than 5% false positive rates. The AnnaData model is currently being pilot-tested on 5 farm sites in disease-endemic districts of Odisha before being productized for wider distribution among rice farmers in the state.
{"title":"AnnaData: Design and Development of a Robust Multi-sensor Early Warning System for Bacterial Blight Detection in Rice Crop using Deep Learning Techniques","authors":"A. Mukherjee, S. Kesavan, Soumyprakash Das","doi":"10.1109/SusTech51236.2021.9467440","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467440","url":null,"abstract":"As per FAO estimates, annually around one-third of food produced worldwide is lost or wasted. Plant pests, pathogens and weeds account for a large proportion of global crop production losses in the pre-harvest stages. Bacterial blight, caused by Xanthomonas oryzae, is one of the most devastating diseases in rice that has the potential to destroy up to 70% of a smallholder farmer's seasonal yield. In this paper, we describe \"AnnaData\" which employs a robust multi-sensor and multilevel fusion model that combines advanced computer vision techniques along with hyperspectral and thermal data processing, to recognize crop abnormalities in the incipient stages and alert farmers regarding potential onset of bacterial blight disease in rice crop. The efficacy of the AnnaData model has been validated in a lab setting by artificially inoculating the pathogen on a research farm in partnership with India's National Rice Research Institute (NRRI) in Odisha state. Compared to standard computer vision models based on visual and near-infrared image markers that delivered 40%-80% detection rates in asymptomatic stages of the disease, AnnaData's multi-sensor model achieved greater than 95% detection accuracy with less than 5% false positive rates. The AnnaData model is currently being pilot-tested on 5 farm sites in disease-endemic districts of Odisha before being productized for wider distribution among rice farmers in the state.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"430 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133940505","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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