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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Efkarpidis, Nikolaos A. Christoforidis, Georgios C. Papagiannis, Grigoris K. University of Applied Sciences and Arts Northwestern Switzerland Institute of Electric Power Systems Windisch Switzerland University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Power Systems Laboratory School of Electrical & Computer Engineering Thessaloniki Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Buildings equipped with battery energy storages (BES), thermal energy storages (TES), Photovoltaics (PV), solar thermal collectors (STC) and modulating heat pumps (HP) can significantly enhance the energy management potential. The energy management systems are mainly based on self-consumption maximization (SCM) and time-of-use arbitrage (ToUA). In terms of electricity pricing, dynamic pricing policies with two, three tariff zones or real-time pricing (RTP) are mostly applied. In this study, different control algorithms based on the concepts of SCM and ToUA are evaluated for a complex energy system consisted of BES, TES, PV, STC and HPs with variable-speed compressors. The performance of the proposed energy management strategies is assessed under ToU and RTP pricing regimes. © 2022 IEEE.

关键词： Costs

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection systems 10

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Yusri, Muhammad Iqbal Habaebi, Mohamed Hadi Gunawan, Teddy Surya Mansor, Hasmah Kartiwi, Mira Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350365283

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models 38

Text-Guided Attention is All You Need for Zero-Shot Robustne...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yu, Lu Zhang, Haiyang Xu, Changsheng School of Computer Science and Engineering Tianjin University of Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation University of Chinese Academy of Sciences China

Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement module and the Attention-based Model Constraint module. Our goal is to maintain the generalization of the CLIP model and enhance its adversarial robustness: The Attention Refinement module aligns the text-guided attention obtained from the target model via adversarial examples with the text-guided attention acquired from the original model via clean examples. This alignment enhances the model's robustness. Additionally, the Attention-based Model Constraint module acquires text-guided attention from both the target and original models using clean examples. Its objective is to maintain model performance on clean samples while enhancing overall robustness. The experiments validate that our method yields a 9.58% enhancement in zero-shot robust accuracy over the current state-of-the-art techniques across 16 datasets. Our code is available at https://***/zhyblue424/TGA-ZSR. © 2024 Neural information processing systems foundation. All rights reserved.

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Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

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National Science Review 2023年第9期10卷 248-257页

作者： Yi Zhou Tianpeng Ding Yin Cheng Yi Huang Wu Wang Jianmin Yang Lin Xie Ghim Wei Ho Jiaqing He Shenzhen Key Laboratory of Thermoelectric Materials and Department of Physics Southern University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Department of Materials Science and Engineering National University of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research (A*STAR) GuangdongHong Kong-Macao Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices Southern University of Science and Technology

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to diele ctric structure irrationality and *** we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional *** delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mW m-2at 0.2 ***,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased *** enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy *** findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermo electrics and ferroelectrics.

关键词： non-planar dielectrics heat manipulation pyroelectrics weather-adaptive energy harvesting

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Mixture of Experts for Recognizing Depression from Interview and Reading Tasks

arXiv

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arXiv 2025年

作者： Ilias, Loukas Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an objective marker for the early recognition of depression. For this reason, many studies have been developed aiming to recognize depression through speech. However, existing methods rely on the usage of only the spontaneous speech neglecting information obtained via read speech, use transcripts which are often difficult to obtain (manual) or come with high word-error rates (automatic), and do not focus on input-conditional computation methods. To resolve these limitations, this is the first study in depression recognition task obtaining representations of both spontaneous and read speech, utilizing multimodal fusion methods, and employing Mixture of Experts (MoE) models in a single deep neural network. Specifically, we use audio files corresponding to both interview and reading tasks and convert each audio file into log-Mel spectrogram, delta, and delta-delta. Next, the image representations of the two tasks pass through shared AlexNet models. The outputs of the AlexNet models are given as input to a multimodal fusion method. The resulting vector is passed through a MoE module. In this study, we employ three variants of MoE, namely sparsely-gated MoE and multilinear MoE based on factorization. Findings suggest that our proposed approach yields an Accuracy and F1-score of 87.00% and 86.66% respectively on the Androids corpus. © 2025, CC BY-NC-ND.

关键词： Deep neural networks

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Event-Triggered Adaptive Consensus Tracking Control of Uncertain Nonlinear Multi-agent systems with Uniformly Positive Inter-Event Times 1

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Wu, Xiangkai Liu, Yuxiao Long, Jiang Wang, Wei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710072 China Zhongguancun Laboratory Beijing100191 China

ISBN: (数字)9789819733408

ISBN: (纸本)9789819733392

In this paper, asymptotical consensus tracking control problem is considered for a class of uncertain nonlinear multi-agent systems under directed graph with event-triggered communication. In this work, the main challenge is to handle cross-coupling term associated with unknown parameters of all agents and asymmetric Laplacian matrix when the Lyapunov function candidate is selected in terms of local consensus errors. This leads to the difficulty in designing distributed asymptotical control laws which can guarantee a positive minimum inter-event time for each agent with event-triggered communication. To handle this issue, an event-triggered based control scheme is proposed by applying adaptive immersion & invariance control and concurrent learning adaptive control. We prove that all closed-loop signals are uniformly bounded, and all agents’ states can exponentially track the desired trajectory. Moreover, the inter-event times of all agents are uniformly lower bounded by a positive constant. Finally, an illustrative example is given to validate the effectiveness of the proposed control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Stochastic Unit Commitment: Model Reduction via Learning

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Current Sustainable/Renewable Energy Reports 2023年第2期10卷 36-44页

作者： Liu, Xuan Conejo, Antonio J. Constante Flores, Gonzalo E. Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Department of Integrated Systems Engineering The Ohio State University ColumbusOH43210 United States Davidson School of Chemical Engineering Purdue University West LafayetteIN47907 United States

Purpose of Review: As weather-dependent renewable generation increases its share in the generation mix of most electric energy systems, a stochastic unit commitment becomes the natural day-ahead scheduling tool. However, such a tool is generally computationally intractable if a detailed uncertainty description is considered. Taking this into account, we proposed a learning method to make the stochastic unit commitment problem tractable. Recent Findings: Recent advances in statistical learning and machine learning to address optimization problems can be advantageously applied to the rather intractable stochastic unit commitment problem. Considering these advances, we explore simple learning techniques to drastically reduce the size of a stochastic unit commitment problem without significantly altering its optimal solution. Summary: The considered stochastic unit commitment problem is formulated as a two-stage stochastic programming problem. The first stage represents commitment decisions, while the second one represents the operation conditions under different scenarios. Taking into account historical solved instances (or proxies for them), we reduce the size (measured by numbers of constraints and variables) of the stochastic unit commitment problem by (i) fixing unchanged binary variables and by (ii) eliminating inactive inequality constraints. Our numerical results show that the reduced problem generally requires significantly less time to solve while obtaining high-quality solutions, which are very close to or indistinguishable from the one obtained by solving the original problem. We use an Illinois 200-bus system to illustrate and characterize the performance of the proposed problem-reduction method. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Stochastic programming

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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DT4I4-Secure: Digital Twin Framework for Industry 4.0 systems Security 14

DT4I4-Secure: Digital Twin Framework for Industry 4.0 System...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Lin, Yu-Zheng Shao, Sicong Rahman, Md Habibor Shafae, Mohammed Satam, Pratik University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND58202 United States University of Arizona Department of Systems and Industrial Engineering TucsonAZ85721 United States

ISBN: (纸本)9798350304138

The rapid adoption of automation in the Cyber-Physical systems (CPS) is triggering Industry 4.0 (I4.0), integrating cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity into traditionally isolated systems. These I4.0 changes are optimizing the performance of Smart Manufacturing (SM) systems at the cost of increased complexity, exposing I4.0 systems to more cyberattacks than ever before. To address these challenges, this work presents DT4I4-Secure: A Digital Twin Framework for Industry 4.0 Security. The DT4I4-Secure presents a framework to create Digital Twins (DT) for I4.0 systems using a combination of models (including physics and data-based models). This paper showcases the use of the DT framework to detect attacks on I4.0 systems by comparing observations with future predictions from the DT. This paper evaluates the performance of the DT4I4-Secure for a computer Numerical Control (CNC) turning process manufacturing a metallic spool, wherein the experimental results show the model can predict normal operations with a mean absolute error (MAE) of 0.005081. This work also explores using an Exponentially Weighted Moving Average (EWMA) based dynamic threshold instead of a traditional static threshold for attack detection when the CNC turning process is under three separate attack scenarios. The DT4I4-Secure combined with the dynamic threshold shows a 3.46 times improvement in F1-Scores over all three attack scenarios for instantaneous attack detection while having 100% accuracy during the manufacturing cycle. © 2023 IEEE.

关键词： Cyber Physical System

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