2022 International Conference on Future Trends in Smart Communities (ICFTSC)最新文献_第5页

Low Frequency Underwater Acoustic Modelling Based on Deep Learning 基于深度学习的低频水声建模

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040062

R. Azhagumurugan, C. Sai Ganesh, K. Porkumaran, Sr Y Aouthithiye Barathwaj, C. Nayanatara, N. C. Haariharan

{"title":"Low Frequency Underwater Acoustic Modelling Based on Deep Learning","authors":"R. Azhagumurugan, C. Sai Ganesh, K. Porkumaran, Sr Y Aouthithiye Barathwaj, C. Nayanatara, N. C. Haariharan","doi":"10.1109/ICFTSC57269.2022.10040062","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040062","url":null,"abstract":"In ocean environments, acoustic communication is the most efficient method and the study of the behaviour of sound and its nature in the ocean is called ocean acoustics. Modelling of ocean acoustic propagation is an essential step for designing acoustic devices as the environmental behaviour of the ocean varies with climate, seasons, aquatic life and other form of chemical reactions. Modelling acoustic propagation based on parabolic equations is one of the most efficient ways, especially with low-frequency applications. The computational complexity and the time for modelling the parabolic equations are resolved by the predictive modelling presented in this paper. Modelling of several environments with different acoustic parameters generates the necessary data for the predictive modelling system. Deep learning is the process of learning data inspired by biological systems based on weights and biases. A custom deep learning model is developed for the understanding of transmission loss data of different ocean environments. In acoustic communication, transmission loss is the decrease in the sound signal by the ocean environmental parameters. A user application is developed that resembles the traditional modelling but is backed by a deep learning system that predicts the transmission loss for the entire environment.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820445","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

Disassociation of Visual-proprioception Feedback to Enhance Endotracheal Intubation 视觉-本体感觉反馈分离增强气管插管

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039795

Mahdiyeh Sadat Moosavi, Jacob Williams, Christophe Guillet, F. Mérienne, J. Cecil, Michael Pickett

{"title":"Disassociation of Visual-proprioception Feedback to Enhance Endotracheal Intubation","authors":"Mahdiyeh Sadat Moosavi, Jacob Williams, Christophe Guillet, F. Mérienne, J. Cecil, Michael Pickett","doi":"10.1109/ICFTSC57269.2022.10039795","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039795","url":null,"abstract":"This paper discusses the key elements of a research study that focused on training an important procedure called “Endotracheal intubation” to novice students. Such a procedure is a virtual part of treating patients who are infected with the covid-19 virus. A virtual reality environment was created to facilitate the training of novice nurses (or nurse trainees) using the HTC Vive platform. The primary interaction with the virtual objects inside this simulation-based training environment was using the hand controller. However, the small mouth of the virtual patient and the necessity of utilizing both hands to pick up the laryngoscope and endotracheal tube at the same time (during training), led to collisions involving the hand controllers and hampered the immersive experience of the participants. A multi-sensory conflict notion-based approach was proposed to address this problem. We used “Haptic retargeting” method to solve this issue. And we compared the result of the haptic retargeting method with reference condtion. Initial Results (through a questionnaire) suggest that this Haptic retargeting approach increases the participants’ sense of presence in the virtual environment.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128353189","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

Photovoltaics Performance Evaluation Using IoT Technology 基于物联网技术的光伏性能评估

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040053

Melvinder Singh a-l Balbir Singh, N. M. Nor, T. Ibrahim, M. Z. Rahman

{"title":"Photovoltaics Performance Evaluation Using IoT Technology","authors":"Melvinder Singh a-l Balbir Singh, N. M. Nor, T. Ibrahim, M. Z. Rahman","doi":"10.1109/ICFTSC57269.2022.10040053","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040053","url":null,"abstract":"The excessive utilization of non-renewable energy sources to fulfill the energy demands of the world, particularly in the electricity generation sector, has brought upon dire effects to the Earth’s ecosystems. To reduce the dependence on non-renewable energy sources, the Malaysian government has pushed for the utilization of solar photovoltaics (PV) to produce electricity for the nation. This initiative is present in both utility scale, via the Large-Scale Solar (LSS) scheme, and domestic scale via the Net Metering (NEM) scheme. Contained in this paper is the elaboration on the utilization of Internet-of-Things (IoT) technology to evaluate the performance of PV on the domestic scale. A prototype is designed to collect data, which will then feed the data into an algorithm which will provide an evaluation of the health of a PV system. Two experiments were conducted to determine the if the integration of the mathematical model, algorithm, software, and hardware would produce a prototype can provide accurate results. The experiments conducted provided conclusive results that the data collected by the prototype is accurate as it is able to detect PV performance issues, and the results were verified via manual measurements.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128464051","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 and Development of An Estimate Controller for a Fusion Tank System 核聚变罐系统估计控制器的设计与开发

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040027

A. Gandhi, K. Porkumaran, C. Jeeva, R. Kothai, R. Kannan

引用次数: 0

General Framework of Geometric Simplification for Mitigating Cybersickness 缓解晕动症的几何简化的一般框架

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040068

R. Lou, F. Mérienne, D. Bechmann

引用次数: 0

Explainable Artificial Intelligence Applied to Deep Reinforcement Learning Controllers for Photovoltaic Maximum Power Point Tracking 可解释人工智能应用于光伏最大功率点跟踪的深度强化学习控制器

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040061

Pei Seng Tan, T. Tang, E. Ho

{"title":"Explainable Artificial Intelligence Applied to Deep Reinforcement Learning Controllers for Photovoltaic Maximum Power Point Tracking","authors":"Pei Seng Tan, T. Tang, E. Ho","doi":"10.1109/ICFTSC57269.2022.10040061","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040061","url":null,"abstract":"Deep Reinforcement Learning (DRL) algorithms have been applied to extract maximum power from photovoltaic (PV) modules under a variety of environmental conditions. However, it is difficult for a human to explain how a DRL-based maximum power point tracking (MPPT) controller works as it consists of Neural Networks (NNs) that are generally complex and non-linear. Various Explainable Artificial Intelligence (XAI) techniques have been proposed to interpret NNs in power system applications, but MPPT controllers have yet to be analyzed. This paper presents the application of XAI techniques to the DRL agents for MPPT. Two distinct DRL agents were developed, one with and one without the information of the converter's duty cycle, using the Deep Deterministic Policy Gradient (DDPG) algorithm and analyzed using XAI techniques, namely Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). The results reveal that the converter's input power is the most crucial information for the DRL agents when the converter is operating away from the maximum power point. When the converter approaches operation at the maximum power point, the DRL agents are significantly dependent on the power differential of the converter across time. If the information about the converter's duty cycle is available, the DRL agents are significantly reliant on the converter's duty cycle and disregard other observations for decision-making.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122403346","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

Development of Gas Flow Characteristic Prediction for Industrial Flow Meter using Long Short-Term Memory (LSTM) 基于长短期记忆(LSTM)的工业流量计气体流动特性预测研究进展

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039824

Mohd Faizal Mustafa, Ahmad Muizuddin Talib, Rahimi Zaman Jusoh A. Rashid, I. Ismail, A. Awang, Mohamad Naufal Mohamad Saad, Muhd. Safwan Zahari

引用次数: 1