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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics 2

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sundaram, Aishwarya Subramaniam, Hema Ab Hamid, Siti Hafizah Nor, Azmawaty Mohamad Universiti Malaya Institute for Advanced Studies Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia Universiti Malaya Faculty of Education Department of Educational Psychology and Counselling Kuala Lumpur Malaysia

ISBN: (纸本)9798350384277

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics. © 2024 IEEE.

关键词： Data Analytics

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Optimizing Convolutional Neural Networks for Plant Disease Detection on ARM-M Microcontrollers

Optimizing Convolutional Neural Networks for Plant Disease D...

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2024 IEEE International Conference on Agrosystem engineering, Technology and Applications, AGRETA 2024

作者： Nugroho, Hermawan Xan, Chew Jing Siang, Tay Fei Eswaran, Sivaraman University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Sarawak Malaysia Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350373660

Rice production in Malaysia is facing significant challenges due to plant diseases and environmental hazards, leading to a decline in the rice self-sufficiency ratio. To address these issues, this study explores the deployment of AI-driven plant disease detection models on ARM-M based microcontrollers, specifically the STM32H747I-DISCO Discovery kit. Utilizing Convolutional Neural Networks (CNNs) for image recognition, we aim to achieve real-time disease monitoring without relying on continuous internet connectivity. Our research evaluates five state-of-the-art models-FD-MobileNet, MobileNet V1, MobileNet V2, ResNet V1, and SqueezeNet V1-focusing on their suitability for resource-constrained environments. The results indicate that FD-MobileN et is the most resource-efficient model with the lowest number of Multiply-Accumulate operations (MACCs) and the shortest inference time, achieving an accuracy rate of 98.44%. However, SqueezeNet, despite having the highest accuracy at 99.22%, faces deployment challenges due to its large model size on the STM32H747I-DISCO board. This work highlights the potential of embedded AI applications in enhancing agricultural resilience and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Slotted Substrate Integrated Waveguide Antenna for Ka-band Automotive Wireless Communication

Design of Slotted Substrate Integrated Waveguide Antenna for...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Ashish Singh, Gurprince Sahoo, Bikash Chandra Kumar, Ashwini Singh, Gurmeet Chitkara University Chitkara University Institute of Engineering and Technology Punjab India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Indira Gandhi Institute of Technology Department of ETC Engg. Odisha Sarang India Poornima University Faculty of Engineering and Technology Rajasthan Jaipur India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

In this paper, a slotted multi-input-multi-output (MIMO) substrate integrated waveguide (SIW) antenna is presented for mm wave frequency bands and 5G wireless applications. The proposed design resonates in n257 frequency band dedicated for 5G communication according to 3rd generation partnership project (3GPP). The presented antenna design exhibits excellent impedance matching of -29 dB with the gain of about 8.5 dB. Also, the antenna covers required impedance bandwidth of 500 MHz with mutual coupling of about -35 dB. Further, in the context of beamforming feature, the proposed design achieves the ±15° with the progressive phase shift of feeds of the antenna structure and also the design demonstrates the excellent performance in terms of diversity parameters. The proposed design is suitable for various 5G automotive use cases with the calculated data rate according to link budget calculations. © 2024 IEEE.

关键词： Budget control

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Predicting short-term traffic with random forest: a granular data approach

Predicting short-term traffic with random forest: a granular...

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International Conference on Energy, Power, Environment, control and Computing (ICEPECC 2025)

作者： Ahmad Mustafa Khurram Shehzad Khattak Zawar Hussain Khan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar Pakistan College of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia

ISBN: (数字)9781837243150

Accurate short-term traffic flow prediction is critical for improving the efficiency of Intelligent Transportation systems (ITS), particularly in heterogeneous traffic environments. This paper presents a data-driven model based on the Random Forest (RF) algorithm to predict traffic flow using high-resolution data collected over a week. The dataset encompasses individual vehicle speed, vehicle classification (car, bicycle, motorbike, bus), timestamp, flow rate, peak hour factor, traffic density, time and distance headway, and density range, recorded from 9 AM to 5 PM over seven consecutive days. Six days of data are used for training the RF model, with Thursday’s traffic flow serving as the test case. The dataset features high-dimensional, heterogeneous traffic attributes, enabling the model to capture non-linear and complex interactions effectively. Our results demonstrate that the RF model effectively captures complex, non-linear relationships in the data, delivering highly accurate predictions with an R-squared value of 0.997. This study contributes to advancing machine learning applications in traffic management, emphasizing the importance of feature selection in enhancing model performance. Future research will investigate the inclusion of additional factors such as weather conditions and external events to further refine prediction accuracy.

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Future Security Issues in Internet of Energy 4

Future Security Issues in Internet of Energy

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4th International Conference on Advanced science and engineering, ICOASE 2022

作者： Benrebbouh, Chahrazed Cherbal, Sarra Mansouri, Houssem Pathan, Al-Sakib Khan Univ Ferhat Abbas Setif 1 Lrsd Laboratory Faculty of Sciences Department of Computer Science Setif Algeria United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665472227

One of the most popular new technologies today is the Internet of Energy (IoE), which utilizes the Internet for collecting, organizing, optimizing and managing network energy information from various edge devices. In this way, a distributed smart energy infrastructure is developed. As IoE is essentially linked with the Internet, the cyber security concerns for this environment are also significant. Like any other technology connected with the Internet, IoE also is vulnerable to various kinds of attacks and threats. In this paper, we investigate the security issues in Internet of Energy. We study various security techniques proposed or developed for this environment during the recent years and discuss what to expect in the future. © 2022 IEEE.

关键词： Internet of Energy

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Improving Motor Imagery Classification in BCI systems Using EMD and Multi-Layer CNNs

Improving Motor Imagery Classification in BCI Systems Using ...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Reza Arghand Ali Chaibakhsh Moein Radman Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering University of Guilan Rasht Guilan Iran Brain-Computer Interfacing and Neural Engineering Laboratory University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional Neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. The features extracted from these networks are combined and used to train an SVM classifier. The proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches.

关键词： Support vector machines Knowledge engineering Accuracy Image recognition Empirical mode decomposition Neuromuscular Motors Feature extraction Brain-computer interfaces Convolutional neural networks

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The Use of Smart Devices for Assessing the User’s Physiological Status 1

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8th Future Technologies Conference, FTC 2023

作者： Fodor, Kristián Balogh, Zoltán Molnár, György Department of Computer Science Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary

ISBN: (数字)9783031474576

ISBN: (纸本)9783031474569

Currently, there are various technical solutions as well as methods by which the user’s physiological functions can be obtained. From the point of view of the technical solutions, approaches based on the application of sensors (galvanic skin response, heart rate, temperature, etc.) or the use of a camera for capturing the face and its parts are most often used. The article is devoted to the creation of a system for identifying and classifying the emotional state of the user while maintaining non-invasiveness, which includes an IoT-based (Internet of Things) user interface. It deals with the capture of data about the user and the subsequent evaluation of this data with the help of a web application for visualizing and evaluating the obtained data. The research uses a pulse sensor and a galvanic skin response sensor, which are connected to an Arduino and a computer, for data evaluation. The results of the paper include the design of a portable and affordable device that can be non-invasively attached to the human body and monitor the measured values for a long time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Strong Stabilization of the Acrobot at the Down–Up Equilibrium Point ⋆

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IFAC-PapersOnLine 2023年第2期56卷 9167-9172页

作者： Ziyu Wang Xin Xin Yannian Liu School of Automation Southeast University and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan

In this paper, the strong stabilization of the Acrobot at the Down-Up equilibrium point is addressed, with the first link being downward and the second link being upright. By converting the strong stabilization of the Acrobot at the Down–Up equilibrium point equivalently to the existence and design of a stable stabilizing controller for a fourth-order single-input single-output linear plant with adjustable zeros, a pair of poles on the imaginary axis, and a pair of real poles located symmetrically with respect to the origin, this paper has three main contributions. Firstly, the existence of a stable stabilizing controller for any Acrobot which is linearly controllable at the Down–Up equilibrium point is proved by showing the range of the adjustable zero via adjusting a parameter of the output signal. Secondly, a necessary and sufficient condition on the mechanical parameters of the Acrobot is provided to guarantee the existence of a second-order stable stabilizing controller for the Acrobot around its Down– Up equilibrium point. Thirdly, a direct method is presented to design a second-order stable controller, whose transfer function is preset with three parameters. By utilizing the Liénard– Chipart criterion for a fourth-order polynomial, the necessary and sufficient conditions on these parameters for achieving the strong stabilization are obtained, which are expressed in a cascade form for obtaining these parameters conveniently. A numerical example is presented to validate the effectiveness of the proposed method.

关键词： Robust control strong stabilization underactuated mechanical systems reduced-order controller linear systems

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