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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Ul Ain Mushtaq, Noor Narejo, Sanam Memon, Madeha Korai, Fahad Ahmed Abbas, Azhar Jatoi, Sahil Mehran University of Engineering and Technology Department of Computer System Engineering Jamshoro Pakistan Tampere University Department of Biomedical Sciences and Engineering Tampere Finland Mehran University of Engineering and Technology Department of Biomedical Engineering Jamshoro Pakistan

ISBN: (纸本)9798331516680

Industrial robots have significantly transformed manufacturing processes and are increasingly revolutionizing sectors such as logistics, healthcare, agriculture, and construction by enhancing efficiency, quality, and safety, making them indispensable tools for modern industries. Industrial pick-and-place robotics, while significantly advancing, still faces several challenges like Object variability, Environmental factors, Task complexity, Precision and speed. Reinforcement learning (RL) offers a transformative approach to industrial pick-and-place robotics, addressing the limitations of traditional, pre-programmed methods. In order to overcome these challenges, the present research explores how reinforcement learning (RL) may assist robots to determine optimal picking and placing techniques without requiring explicit programming. We developed a framework that employs deep reinforcement learning (DRL) to create an autonomous pick-and-place system. Using Gazebo and ROS, we designed a simulated environment where the robotic arm is trained with Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms. © 2024 IEEE.

关键词： Reinforcement learning

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A MECHANISM TO ENHANCE PROPOSAL MERGING IN AN E-DELIBERATION SYSTEM 21

A MECHANISM TO ENHANCE PROPOSAL MERGING IN AN E-DELIBERATION...

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21st International Conference on Applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024

作者： Rokkou, Georgia Triantafyllou, Vassilis Electrical & Computer Engineering Department University of Peloponnese Greece

ISBN: (纸本)9789898704627

This paper introduces an innovative method for automating the proposal merging process in e-deliberation systems, utilizing advanced natural language processing (NLP) and machine learning algorithms. The primary goal is to efficiently manage and consolidate lexically and conceptually similar proposals, thereby minimizing human intervention. Our approach not only enhances the precision of proposal merging but also significantly reduces the time required for this task. By automating this process, we improve the overall quality of proposals and streamline the deliberation process, resulting in more organized and representative outcomes. Our findings demonstrate the potential of this methodology to advance e-deliberation practices by ensuring a more accurate and efficient handling of public proposals. © 2024 Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024. All rights reserved.

关键词： Adversarial machine learning

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Trajectory Prediction for Autonomous Driving Using Agent-Interaction Graph Embedding 27

Trajectory Prediction for Autonomous Driving Using Agent-Int...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Samiuddin, Jilan Boulet, Benoit Wu, Di McGill University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331505929

Trajectory prediction module in an autonomous driving system is crucial for the decision-making and safety of the autonomous agent car and its surroundings. This work presents a novel scheme called AiGem (Agent-Interaction Graph Embedding) to predict traffic vehicle trajectories around the autonomous car. AiGem tackles this problem in four steps. First, AiGem formulates the historical traffic interaction with the autonomous agent as a graph in two steps: (1) at each time step of the history frames, agent-interactions are captured using spatial edges between the agents (nodes of the graph), and then, (2) connects the spatial graphs in chronological order using temporal edges. Then, AiGem applies a depthwise graph encoder network on the spatial-temporal graph to generate graph embedding, i.e., embedding of all the nodes in the graph. Next, a sequential Gated Recurrent Unit decoder network uses the embedding of the current timestamp to get the decoded states. Finally, an output network comprising a Multilayer Perceptron is used to predict the trajectories utilizing the decoded states as its inputs. Results show that AiGem outperforms the state-of-the-art deep learning algorithms for longer prediction horizons. © 2024 IEEE.

关键词： Graph embeddings

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KNOWLEDGE DISTILLATION BASED ON TRANSFORMED TEACHER MATCHING 12

KNOWLEDGE DISTILLATION BASED ON TRANSFORMED TEACHER MATCHING

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12th International Conference on Learning Representations, ICLR 2024

作者： Zheng, Kaixiang Yang, En-Hui Department of Electrical and Computer Engineering University of Waterloo Canada

As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature scaling is applied to both teacher's logits and student's logits in KD. Motivated by some recent works, in this paper, we drop instead temperature scaling on the student side, and systematically study the resulting variant of KD, dubbed transformed teacher matching (TTM). By reinterpreting temperature scaling as a power transform of probability distribution, we show that in comparison with the original KD, TTM has an inherent Rényi entropy term in its objective function, which serves as an extra regularization term. Extensive experiment results demonstrate that thanks to this inherent regularization, TTM leads to trained students with better generalization than the original KD. To further enhance student's capability to match teacher's power transformed probability distribution, we introduce a sample-adaptive weighting coefficient into TTM, yielding a novel distillation approach dubbed weighted TTM (WTTM). It is shown, by comprehensive experiments, that although WTTM is simple, it is effective, improves upon TTM, and achieves state-of-the-art accuracy performance. Our source code is available at https://***/zkxufo/TTM. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Distillation

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Manifold Clustering Based Nonlinear Model Reduction with Application to Nonlinear Convection 63

Manifold Clustering Based Nonlinear Model Reduction with App...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wu, T. Wilson, D. Djouadi, S.M. Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350316339

This paper proposes a new cluster method combined with Dynamic Mode Decomposition with Control (DMDc), and the Proper Orthogonal Decomposition (POD) to construct more accurate reduced order models. DMDc and POD are popular data-driven techniques that extract loworder models from high-dimensional complex dynamic systems. However, these methods are inherently linear, i.e., the data is assumed to belong to linear manifolds. However, this may lead to inaccuracies in the reduced models commensurate with the presence of nonlinearities. To capture the nonlinear behavior, manifold clustering is introduced to group the snapshots obtained by experiments or numerical simulation into several sub-regions based on the underlying non-linear structure. Manifold clustering is a powerful approach for exploratory data analysis, allowing the discovery of patterns and structures that are not apparent in raw high-dimensional data. It does not require knowing the number of clusters and the intrinsic manifold dimensions in advance. Manifold clustering is combined with DMDc and POD to construct the local reduced-order models. Time clustering is applied to the snapshots generated by a nonlinear convective flow governed by the 2D Burgers' equations with boundary actuation. The manifold cluster reduced order model outperforms standard and other cluster-based (K-means) reduced order models. © 2024 IEEE.

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Highly Accurate Anomaly Detection for the Combustion System of a Marine Engine Using Multivariate Transformers 22

Highly Accurate Anomaly Detection for the Combustion System ...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Sharifirad, Iman Boudjadar, Jalil Larsen, Peter Gorm Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9798350385601

This paper proposes a novel anomaly detection system to identify combustion process irregularities, in real-time, within individual cylinders of marine engines. The system monitors the instantaneous speed of the crankshaft to detect deviations that might indicate abnormal power output from specific cylinders. This information is then communicated to the engine control system, enabling timely corrective actions. Due to cost, privacy, and availability constraints in acquiring real-world training data, we address this by generating syn-thetic crankshaft speed data using a vessel engine simulator. We further introduce real-world anomaly scenarios, via fault injection, simulating a single cylinder producing higher-than-normal RPM. We implement a transformer-based encoder model specifically designed for multivariate time series data to detect these anomalies. The model leverages self-attention mechanisms and positional encodings to capture temporal dependencies and interactions between variables within the crankshaft speed data. Our proposal achieves a high accuracy of over 96% in anomaly detection. © 2024 IEEE.

关键词： Crankshafts

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Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems 41

Private and Federated Stochastic Convex Optimization: Effici...

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41st International Conference on Machine Learning, ICML 2024

作者： Reshef, Roie Levy, Kfir Yehuda Department of Electrical and Computer Engineering Technion Haifa Israel

This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze this setting within the Stochastic Convex Optimization (SCO) framework, and devise methods that ensure Differential Privacy (DP) while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions. Our approach, based on a recent stochastic optimization technique, offers linear computational complexity, comparable to non-private FL methods, and reduced gradient obfuscation. This work enhances the practicality of DP in FL, balancing privacy, efficiency, and robustness in a variety of server trust environments. Copyright 2024 by the author(s)

关键词： Federated learning

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Leveraging Convex Relaxation to Identify the Feasibility of Conducting AC False Data Injection Attack in Power Systems

Leveraging Convex Relaxation to Identify the Feasibility of ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Iranpour, Mohammadreza Rasoul Narimani, Mohammad Department of Electrical and Computer Engineering Los Angels United States

ISBN: (纸本)9798331541125

FDI (False Data Injection) attacks are critical to address as they can compromise the integrity and reliability of data in cyber-physical systems, leading to potentially severe consequences in sectors such as power systems. The feasibility of FDI attacks has been extensively studied from various perspectives, including access to measurements and sensors, knowledge of the system, and design considerations using residual-based detection methods. Most research has focused on DC-based FDI attacks;however, designing AC FDI attacks involves solving a nonlinear optimization problem, presenting additional challenges in assessing their feasibility. Specifically, it is often unclear whether the infeasibility of some designed AC FDI attacks is due to the nonconvexity and nonlinearity inherent to AC power flows or if it stems from inherent infeasibility in specific cases, with local solvers returning infeasibility. This paper addresses this issue by leveraging the principle that if a convexified AC FDI attack design problem is infeasible, the attack design itself is infeasible, irrespective of nonlinear solution challenges. We propose an AC FDI attack design based on convexified power flow equations and assess the feasibility of the proposed attack by examining the extent of the attackable region. This approach utilizes a Quadratic Convex (QC) relaxation technique to convexify AC power flows. To evaluate the proposed method, we implement it on the IEEE 118-bus test system and assess the feasibility of an AC FDI attack across various attack zones. © 2025 IEEE.

关键词： Electric load flow

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The internet of battle things: a survey on communication challenges and recent solutions

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Discover Internet of Things 2025年第1期5卷 1-28页

作者： Kufakunesu, Rachel Myburgh, Herman De Freitas, Allan Department of Electrical Electronic and Computer Engineering University of Pretoria Cnr Lynnwood and Roper Street Pretoria0028 South Africa

The use of Internet of Things (IoT) technology in military settings has introduced the notion of "Internet of Battle Things" (IoBT), transforming modern warfare by interconnecting various equipment and systems essential for battlefield operations. This connectivity facilitates real-time communication, data sharing, and collaboration among military assets, enhancing situational awareness, decision-making processes, and overall operational effectiveness. The domain for IoBT encompasses a broad range of military assets, from drones and ground vehicles to soldier-worn wearables, sensors, and munitions. These assets are capable of collecting and transmitting critical information from the battlefield, including location data, status updates, environmental conditions, and the movements of adversaries. IoBT networks depend on robust communication networks, secure data transmission protocols, advanced data analytics for processing vast datasets, and seamless integration with command-and-control infrastructures. However, IoBT devices and systems function in dynamic and challenging battlefield conditions which present unique communication challenges. This study aims to review research efforts that provide current state-of-the-art solutions, their limitations, and emerging technologies. We classify these challenges into interoperability, power and energy management, security, and network resilience, while also discussing future research directions to improve communication in IoBT networks. © The Author(s) 2025.

关键词： Interoperability

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Studying the Relationship Between Crossover Features and Performance on MNK-Landscapes Using Regression Models 16th

Studying the Relationship Between Crossover Features and Per...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nakashima, Teruhisa Aguirre, Hernán Tanaka, Kiyoshi Department of Electrical and Computer Engineering Shinshu University Japan

ISBN: (纸本)9789897587214

Crossover is a key component of evolutionary algorithms and has been the focus of numerous studies. Its effectiveness depends on the operator’s properties to mix information, the specific characteristics of the problem, and the diversity of the population, influenced by the dynamics of the algorithm. This study focuses on binary representations and introduces a method to examine the relationship between crossover features and the performance of a multi-objective evolutionary algorithm on problem subclasses with random and neighbor patterns of variable interactions. The aim is to identify the crossover features relevant to performance in each problem subclass through regression models. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Multiple linear regression

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