Article
Computer Science, Information Systems
Mohamed K. Metwaly, Jiashen Teh
Summary: The increased integration of intermittent renewable energy sources into power systems has caused more power grid congestions, leading system operators to require more advanced control methods to alleviate this pressure. The combination of dynamic thermal rating (DTR) and operational tripping scheme (OTS) can effectively improve power adequacy and system security by considering fuzzy numbers to avoid unnecessary generation tripping and blackouts. The novel Fuzzy-DTR-OTS delays generation tripping, enhances power supply adequacy, improves system security, and prevents cascading blackouts.
Article
Engineering, Electrical & Electronic
Aleksei Kirilenko, Masoud Esmaili, C. Y. Chung
Summary: The paper proposes models based on quantile regression and superquantile regression to predict the dynamic line rating of overhead conductors, aiming to enhance capacity usage and address overload risk through risk-based constraints. The efficiency of these models in terms of better utilization of conductor capacity, increased energy transfer, and reduced risk levels is confirmed through verification on the RTS test system.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Zhengnan Gao, Shubo Hu, Hui Sun, Zhonghui Wang, Songnan Liu, Fan Yang
Summary: With the increasing demand for cross-regional electricity trading, accurate evaluation of transmission capacity between regional power grids becomes important. This paper proposes a day-ahead Dynamic Thermal Rating (DTR) forecasting model based on ForecastNet, which utilizes numerical weather predictions to forecast DTR. The model uses time-variant weight coefficients to dynamically track the characteristics of meteorological factors and their effect on DTR. The performance of the model is verified using real data from Northeast China, and the day-ahead delivery capacity between two interconnected power grids in China is calculated. The results show that the proposed method achieves high accuracy in DTR forecasting and enables accurate evaluation of transmission capacity.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Computer Science, Information Systems
Omar Said
Summary: This paper proposes a bandwidth control scheme to address the challenge of bottlenecks in the IoT environment. The scheme includes a bottleneck detection methodology, bandwidth prediction approach, reduction of bandwidth usage mechanism, and bandwidth management model. Simulation results show that the proposed scheme outperforms traditional machine learning and deep learning techniques in improving IoT efficiency and reducing negative impact of bottlenecks.
INTERNET OF THINGS
(2023)
Review
Engineering, Electrical & Electronic
Li Yang, Jiashen Teh
Summary: On one hand, the integration of distributed energy resources and electric vehicles has increased the complexity and uncertainty of the distribution network. In response, researchers have proposed various evaluation methods and indicators to uncover the vulnerability and suggest improvement measures. On the other hand, this study summarizes the evaluation criteria, existing assessment methods, and indicators for vulnerability analysis. Additionally, the study explores the application of IoT technology and deep learning algorithms for vulnerability analysis.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Jonas A. Kersulis, Ian A. Hiskens
Summary: The paper presents an optimization method for assessing the vulnerability of transmission networks to small changes in generation. The method computes the smallest deviation from the nominal generation pattern that would cause a specific line to reach a specified temperature. The paper also develops an efficient algorithm to solve the problem.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Information Systems
Peiyun Zhang, YanHao Tao, QingLin Zhao, MengChu Zhou
Summary: This article proposes a rate-and-trust-based node selection model to address the low transmission rate and security risks faced by blockchain-enabled IoT. The model calculates the block transmission rate and trust value of a node based on latency, connectivity, and historical behaviors. A PageRank-based optimization algorithm is used to balance transmission rate and security risks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Qing Fan, Jianhua Chen, Lazarus Jegatha Deborah, Min Luo
Summary: Internet of Things (IoT) is a network convergence of multiple intelligent devices and advanced technologies aiming at connecting and exchanging data over the Internet. Security issues such as authentication and data privacy are critical concerns in IoT applications, and blockchain technology supports secure data sharing. An improved scheme has been proposed to overcome security flaws and achieve a well tradeoff between security and efficiency.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)
Article
Computer Science, Information Systems
Jaeseob Han, Gyeong Ho Lee, Sangdon Park, Joohyung Lee, Jun Kyun Choi
Summary: This article proposes an adaptive data transmission period control algorithm based on multivariate time series prediction for reducing unnecessary data transmissions from IoT sensors. By encoding and predicting time series data, it effectively reduces power consumption and data reconstruction errors.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Energy & Fuels
Anuj Kumar Rao, Pratim Kundu
Summary: This paper aims to minimize wind energy curtailment by adopting a system integrity protection scheme (SIPS) based method using real-time network data and meteorological data. By dynamically changing the thermal rating of the transmission line, the maximum amount of wind power injection into the system is achieved while fulfilling the objective of minimizing wind power curtailment.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Computer Science, Information Systems
Manzhu Yu, Fangcao Xu, Weiming Hu, Jian Sun, Guido Cervone
Summary: The research proposed a deep learning framework for accurate air temperature prediction at high temporal and spatial resolutions, using IoT data collected along major roads in New York City and historical weather observations. By leveraging both datasets, the LSTM model achieved increased accuracy and improved spatial resolution in temperature predictions.
Article
Chemistry, Analytical
Sikandar Zulqarnain Khan, Yannick Le Moullec, Muhammad Mahtab Alam
Summary: The study utilizes machine learning algorithms in a smart gateway to optimize data transmission efficiency in NB-IoT networks. Experimental results show significant reductions in the number of NB-IoT radio transmissions, energy consumption, and data transmission time.
Article
Public, Environmental & Occupational Health
Gaige Hunter Kerr, Hamada S. Badr, Lauren M. Gardner, Javier Perez-Saez, Benjamin F. Zaitchik
Summary: Meteorological variables like temperature and humidity have been widely recognized to influence the seasonal transmission of respiratory viruses and influenza in temperate climates. However, studies on the sensitivity of COVID-19 to these factors lack consensus in their conclusions. Addressing methodological challenges and choosing appropriate scales of analysis will be crucial for future research in this area.
Article
Computer Science, Information Systems
Peng-Yong Kong, Yujae Song
Summary: This article proposes the application of dynamic thermal rating (DTR) as an Internet of Things (IoT) application in the smart grid, which requires real-time measurement of conductor temperature from field sensors. To achieve robust communication for IoT-based transmission line monitoring, the article suggests grouping sensors with similar measurement characteristics into a cluster and establishing multiple routes to the control center. The article also introduces the usage of artificial neural networks (ANNs) to transfer knowledge from a global decision maker to local decision makers.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Mohammad Reza Nosouhi, Keshav Sood, Neeraj Kumar, Tricia Wevill, Chandra Thapa
Summary: This article presents a machine learning-based approach for early detection of bushfires by detecting anomalies in spatiotemporal measurements of environmental parameters. The proposed method trains a model to learn the normal behavior of environmental data and uses a classification model to verify detected anomalies. The effectiveness of the approach is confirmed through experiments.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Multidisciplinary Sciences
Yu-Fang Hsieh, Chih-Kuo Lee, Weichung Wang, Yu-Cheng Huang, Wen-Jeng Lee, Tzung-Dau Wang, Cheng-Ying Chou
Summary: This study focuses on utilizing a territory-based patient-specific approach derived from CCTA to estimate FFR and WSS for coronary arteries. The non-invasive simulation aids in diagnosing coronary stenosis significance and myocardial ischemia probability. By simulating fluid dynamics, mass flow rate estimation and FFR/WSS derivation can be achieved. The territory-based method shows high accuracy, sensitivity, and specificity in distinguishing between significant and non-significant stenosis.
SCIENTIFIC REPORTS
(2021)
Article
Agriculture, Multidisciplinary
Joe-Air Jiang, Chih-Hao Syue, Chien-Hao Wang, Min-Sheng Liao, Jiann-Shing Shieh, Jen-Cheng Wang
Summary: Traditional pest control methods may not be effective due to the lack of scientific information. This study proposes a fuzzy logic system with short-term data to forecast the population dynamics of the oriental fruit fly and the tobacco cutworm, and develops corresponding forecasting models.
PRECISION AGRICULTURE
(2022)
Article
Computer Science, Information Systems
Joe-Air Jiang, Huan-Chieh Chiu, Yu-Cheng Yang, Jen-Cheng Wang, Chien-Hsing Lee, Cheng-Ying Chou
Summary: An IoT-based sag-monitoring system is proposed in this study, which is capable of real-time detection of line sags. Long-term field testing has verified the effectiveness of the system. Compared to other sag measuring methods, the proposed system offers high measurement accuracy and wide field implementation.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Chien-Hsing Lee, Hsiang-Wen Cheng, Bo-Wei Liao, Joe-Air Jiang
Summary: This article demonstrates the feasibility of recovering energy from discarded primary batteries using the self-adaptive pulse discharge method. The energy recovery efficiency may depend on the remaining capacity of the discarded batteries.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Mythra Varun Nemallapudi, Atiq Rahman, Augustine Ei-Fong Chen, Shih-Chang Lee, Chih-Hsun Lin, Ming-Lee Chu, Chen-Ying Chou
Summary: Proton therapy is widely used for cancer treatment due to its precise dose deposition capability, but the proton range uncertainty remains a challenge. This study measured the depth distribution of positron emitters from a PMMA sample irradiated with protons and assessed the timing capabilities of the system.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Huanyi Zhou, Stanley Reeves, Cheng-Ying Chou, Andrew Brannen, Peter Panizzi
Summary: In this study, a constrained two-step online geometry calibration algorithm was developed to calibrate an integrated X-ray imaging system for small animals. The algorithm was validated through experiments on phantom and mouse imaging data.
Article
Engineering, Biomedical
Atiq Ur Rahman, Mythra Varun Nemallapudi, Cheng-Ying Chou, Chih-Hsun Lin, Shih-Chang Lee
Summary: The study utilized deep learning methods to map detector data directly to intrinsic dose distributions, with relative errors and uncertainties within acceptable ranges. This approach has the potential for broad application in particle therapy and may be extended to other areas of medical imaging in the future.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Agriculture, Multidisciplinary
Cheng-Ying Chou, Shan-Cheng Chang, Zi-Ping Zhong, Ming- Chi Guo, Ming-Hsien Hsieh, Jui- Chu Peng, Ling- Chieh Tai, Ping-Liang Chung, Jen- Cheng Wang, Joe-Air Jiang
Summary: This study developed an AIoT system for asparagus growth and pest monitoring, providing guidelines for farmers and preventing outbreaks of pests and diseases. The system uses IoT and AI sensors to track environmental changes and pest populations, and a deep learning model for accurate detection and counting of pests.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Aerospace
Chien-Hsing Lee, Xin-Jie Wang, Kuei-You Lin, Joe-Air Jiang
Summary: This study experimentally tested a multistage constant-current (CC) charging method for lithium-ion batteries controlled by the state-of-charge (SoC) discretization. The results show that the three-stage CC (3SCC) protocol with unequally discretized SoC intervals reduces the energy loss, decreases the charge time, increases the charge efficiency, and lowers the average cell surface temperature rise compared to the protocol with equally discretized SoC intervals.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yiuwai Ng, Min-Tsun Liao, Ting-Li Chen, Chih-Kuo Lee, Cheng-Ying Chou, Weichung Wang
Summary: The proliferation of wearable devices has made it easier to collect electrocardiogram recordings and accurately identify atrial fibrillation using deep learning models. However, training personalized models with sufficient labeled data for individual patients is impractical. In this study, the researchers proposed a Siamese-network-based transfer learning approach to address this issue and achieved high accuracy in detecting atrial fibrillation.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Environmental Sciences
Yi-Shin Chou, Cheng-Ying Chou
Summary: This study proposes an efficient and accurate method for detecting and mapping paddy fields in Taiwan using aerial images and remote sensing technology. By determining the optimal band combinations and vegetation index and employing an instance segmentation model, the study successfully detects paddy fields at different phenological stages. The results of this study are significant for agricultural production management and land use survey.