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Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D

Chinese Journal of electrical engineering 2022年第1期8卷 53-62页

作者： Shurong Peng Heng Zhang Yunhao Yang Bin Li Sheng Su Shijun Huang Guodong Zheng School of Electrical&Information Engineering Changsha University of Science&TechnologyChangsha 410114China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China

The charging load of electric vehicles(EVs)has a strong spatiotemporal *** the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale *** studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and ***,a spatial-temporal dynamic load forecasting model,dilated causal convolution-2D neural network(DCC-2D),is ***,a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension *** entire network is then formed by stacking the layers,ensuring that the network can accept long-term historical input,enabling the model to learn time dimension *** model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM *** results prove the validity of the proposed prediction model.

关键词： Time and space dynamic prediction dilated convolution charging load convolutional neural network

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Adaptive Dead Time Control Strategy for ClosedLoop TCM GaN-Based Buck Converters 6

Adaptive Dead Time Control Strategy for ClosedLoop TCM GaN-B...

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6th International Future Energy Electronics Conference, IFEEC 2023

作者： Nguyen, Dinh Phuc Liu, Yu-Chen Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronic and Computer Science Engineering Taiwan National Taipei University of Technology Department of Electrical Engineering Taiwan

ISBN: (纸本)9798350339888

Gallium nitride (GaN) offers the advantages of high switching speeds and low on-resistance, which makes it suitable for extensive use in high-frequency applications requiring high power density and high efficiency. GaN-based high-frequency DC-DC buck converters typically employ the triangular current mode (TCM) to realize zero-voltage switching (ZVS) and reduce the inductor size. However, as the switching frequency increases, the effect of the dead time becomes greater. In addition, the ZVS of a device depends on the output power, which makes the design of dead time complex. Traditionally, the design of dead time is based on the worst condition;thus, the dead time is too long in the normal condition;this increases the duty loss and reduces the overall performance dramatically. To reduce the dead time loss in GaNbased TCM buck converters, an adaptive dead time control strategy is proposed in this paper to optimize the dead time in the entire power range. The experimental results demonstrate that adaptive dead time control can improve the overall efficiency compared with fixed dead time control, and the total loss was reduced by 9.2% at 200 W. © 2023 IEEE.

关键词： Gallium nitride

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Enhancing Intrusion detection: Leveraging Federated Learning and Hybrid Machine Learning Algorithms On ToN_IoT Dataset 20

Enhancing Intrusion detection: Leveraging Federated Learning...

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20th International Conference on Frontiers of Information technology, FIT 2023

作者： Naeem, Faiza Malik, Asad Waqar Abbas Khan, Safdar Jabeen, Farzana National University of Science and Technology School of Electrical Engineering and Computer Sciences Islamabad Pakistan

ISBN: (纸本)9798350395785

An efficient and productive IoT application may ease some real-time tasks however, it is at risk of cyber-attack. Intrusion Detection Systems (IDS) are of significant importance for the security measures of IoT applications. IoT/IIoT devices that deal with large data volumes are at risk of malicious attacks and as a result, anomaly-based IDS are developed. Anomaly-based intrusion detection systems perform more efficaciously than other methods. But, the question that arises is whether the performance of models meets the required standards. The research intends to improve the efficiency of IDS with a focus on the Telemetry data of IoT/IIoT sensors data from the ToN_IoT dataset. It includes data about seven IoT/IIoT devices. Federated Learning based on Deep auto-encoder is adopted to design a model with efficiently identifies attacks while solving the issue of privacy concerns. Hybrid models use Machine Learning algorithms with increased detection rates. The algorithms used for the Hybrid model are Random Forest and XGBoost. The XGBoost algorithm improves the accuracy of the Hybrid model with better predictions. The hybrid model ensures efficient pre-processing and feature selection for intrusion detection. The accuracy percentage of the Federated model is 97% on the dataset of Garage Door and 88% on the Motion Light dataset while the Hybrid model outperforms on all IoT/IIoT devices with an average accuracy score of 99.99%. © 2023 IEEE.

关键词： Internet of things

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A Relative-Error-Dependent TOD Protocol for Cyber-Physical Systems under an Event-Triggered Communication

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 318-328页

作者： Yang, Hongchenyu Peng, Chen Tian, Engang Shanghai University School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

To deal with different data magnitudes or measurement units of different system nodes, a novel relative-error-dependent try-once-discard (RED-TOD) communication protocol with event-triggered detectors is proposed to improve the accuracy and reliability of node selection for band-limited cyber-physical systems. With the combined effect of the absolute error and the relative error, a novel node activation condition is proposed to comprehensively reflect and fairly compare node transmission requirements. Under such a stricter scheduling rule, the node selection is more accurate as required and the available communication bandwidth is utilized with higher efficiency. Moreover, periodic sampling module and event-triggered detectors are placed before RED-TOD scheduling scheme to deal with redundancy and inaccurate transmissions of the existing variable sampling scheme. Taking the proposed scheduling scheme and non-small network-induced delay into account, an impulsive closed-loop system model is well established. Sufficient criteria for input-to-state stabilization are derived by employing the Lyapunov-Krasovskii functional approach. A searching algorithm is also presented to co-design optimized scheduler parameters and controller gains. Furthermore, to avoid continuous calculations, the next transmission instant and the dormant period of the event-triggered detector are predicted according to the present information carried by each node. Finally, an illustrative example is employed to demonstrate the effectiveness and superiority of the proposed scheduling method. © 2023 IEEE.

关键词： Feedback control

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Study on the Perception of Mouse Weight Based on Moment of Inertia and Center of Mass

Study on the Perception of Mouse Weight Based on Moment of I...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Baek, Minhyeok Ryu, Yeonsoo Heo, Daehyun Kim, Sunjun Department of Electrical Engineering and Computer Science Smart Input Device Lab. Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science & Technology Daegu South Korea

ISBN: (纸本)9798400713958

Video games have become a cultural phenomenon that captivate millions of people worldwide. Alongside the growth of gaming culture, the demand for better gaming equipment has led to diverse research on mice. However, there is a lack of research on how mass distribution affects user perception and performance. In this study, we explored the effects of varying mass distributions on user perception of mouse weight. We fabricated mice with variable centers of mass (CoM) and moments of inertia while keeping the actual weight constant. The results showed that changes in CoM can alter perceived weight, but changes in moments of inertia have only a limited effect. The results of this study could provide a preliminary understanding of how weight perception varies with different mass distributions of a mouse. © 2025 Copyright held by the owner/author(s).

关键词： center of mass computer mouse e-sports gaming mass distribution moment of inertia weight perception

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iDANSE: Iterative Data-driven Nonlinear State Estimation of Model-free Hidden Sequences

iDANSE: Iterative Data-driven Nonlinear State Estimation of ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Hang Ghosh, Anubhab Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350368741

We introduce a model-free hidden sequence (MHS) estimation problem where the task is to estimate a long sequence of 'model-free' process hidden under additive Gaussian noise. To estimate the posterior of the hidden sequence from the noisy observation sequence, we have three main challenges: (a) the process is 'model-free', that means there is no underlying state-space-model (SSM), limiting the use of traditional SSM-informed model-based methods like Kalman filter (KF) and particle filter (PF);(b) only an unlabelled dataset comprised of noisy observation sequences is available as training data, and hence no supervised learning possible;and, (c) the use of sequential ancestral sampling based methods, like dynamical variational autoencoders (DVAEs), results in prohibitive complexity. To address the challenges we adopt a recently established recurrent neural network (RNN)-based method called DANSE (data-driven nonlinear state estimation). We develop an iterative DANSE (iDANSE) in unsupervised learning setup where a set of neural networks (NNs) are used iteratively. The set of NNs refines MHS estimation over the iterations. Using simulations, the performance of iDANSE is demonstrated for a benchmark Lorenz process (a chaotic attractor) and compared with SSM-informed extended KF (EKF) and unscented KF (UKF). © 2025 IEEE.

关键词： deep neural networks Kalman filter sequential modelling State estimation unsupervised learning

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A Study on the Forensic Analysis of Airlines Applications on Android Operating System 6

A Study on the Forensic Analysis of Airlines Applications on...

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6th IEEE Conference on Dependable and Secure Computing, DSC 2023

作者： Maryam, Urva Hussain, Mehdi National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350382112

Airline mobile applications store a lot of sensitive data in mobile devices such as users' Personal Identifiable Information (PII) and credit/debit card details. In this paper, we have forensically acquired and analyzed the device-stored data remnants from five airline applications running on the Android operating system. For all five applications, we designed and conducted a set of activities. From the acquired physical image of a rooted Android testing device, we were able to extract and examine data artifacts left in the root folder. This study has revealed that sensitive information can still be extracted from mobile devices even after the applications have been deleted which reflects poor implementation of security and privacy measures employed in these applications. This major technical flaw poses a serious vulnerability and a risk to user privacy. On the other hand, this data may be used for evidence-collection purposes by digital forensics practitioners. This study can be used by mobile application developers to mitigate these vulnerabilities to make the applications risk-Averse and secure. © 2023 IEEE.

关键词： Android (operating system)

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Online Weight Factor Tuning for MPC Regulated Power Quality Compensators 4

Online Weight Factor Tuning for MPC Regulated Power Quality ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Metry, Morcos Rohouma, Wesam Balog, Robert S. Texas A&M University of Qatar Department of Electrical and Computer Engineering Doha Qatar University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350306262

Literature on power quality compensators (PQC) was shown to increase the reliability of the power system. While finite control set model predictive control (MPC) achieves high fidelity tracking for multi-objective cost functions, it results in high switching frequency, which increases losses and device stresses and reduces the overall converter reliability. Additionally, cost function weight factors assignment is not an entirely solved issue, as it is mostly based on trial and error and little expertise in the system. This paper develops a case for both high-fidelity harmonic compensation performance and reliability-oriented performance of a distribution power quality compensator (PQC). A lookup table for the weight factor tuning strategy, adaptable to diverse grid requirements was developed. The significance of the developed approach is in allowing the converter to deliver high-fidelity harmonic compensation performance during periods of elevated renewable energy penetration, and then transition to reliability-oriented operation during other periods, thereby extending the service life of the converter. © 2024 IEEE.

关键词： Model predictive control

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Design and Analysis of DC-DC Two-Phase Interleaved Buck Converter for EV Charging Applications 18

Design and Analysis of DC-DC Two-Phase Interleaved Buck Conv...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Yousuf, Muhammad Zeb, Kamran School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9798350328172

While the world is booming with Electric Vehicles (EVs), countries where bikes are a common mode of transport still require adequate charging infrastructure to cope up with the slow EV growth. Efficient power conversion is necessary for EV chargers to be feasible. This paper presents an efficient solution for the DC-DC stage of the EV chargers for two wheelers by designing a two-phase Interleaved Buck Converter (IBC). The converter has been equipped with a PI controller for corrective measures against the disturbances, reducing the steady state error and the settling time of the system. The designed PI controller eliminates the need for an additional controller for current balancing between the two branches of the IBC. With significant advantages of reduced ripple, mitigated stress on the components and efficient conversion of power, IBC looks out as a great breakthrough to shift the dynamics of two-wheeler chargers. IBC in this paper has been designed using MATLAB/Simulink and is evaluated in Proteus with the non-ideal components that can be used in the prototype along with a small-scale hardware validation of 12W prototype. © 2023 IEEE.

关键词： Controllers

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Movie Sense—Hybrid Movie Recommendation System with Sentimental Analysis 3rd

Movie Sense—Hybrid Movie Recommendation System with Sentime...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sudhane, G. Sai Rao, G. Venkateswara Vani, V. Karthik, N. Department of Electrical and Electronics Engineering Puducherry India Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609609 India

ISBN: (纸本)9789819783540

Post-pandemic, with the advent of many OTT platforms, there are a number of different movies and web series available which makes it difficult for the users to find a suitable movie to watch. So the movie recommendation system helps the users by recommending some movies based on their past search history, their interests and also considering the movie genre, actors, director and movie ratings. Our system uses a hybrid filtering technique which makes the system suitable for new users as well by considering the interests of various other users using SVD. Sentimental Analysis is done using a Sentiment Intensity Analyzer (SIA). Since it uses the most accurate methods, it makes the system a robust one. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recommender systems

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