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Towards Interoperability of Industrial Automation Systems Using SystemJ and TwinCAT 18

Towards Interoperability of Industrial Automation Systems Us...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Kang, Owen Salcic, Zoran University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350312201

This paper proposes extending the system-level programming language SystemJ to interact with the industry control software TwinCAT to introduce dynamic behaviors into TwinCAT-based systems. The approach extends abstract SystemJ signal classes using the Automation Device Specification (ADS) protocol to access TwinCAT status and control variables, allowing new high-level concepts like dynamic reconfigurability of industrial automation systems to control systems originally designed using traditional languages and tools. We created prototypes of realistic manufacturing systems testbeds and conducted experiments to validate the proposed approach by accessing different types of TwinCAT variables. © 2023 IEEE.

关键词： Interoperability

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Use of Audience Response Systems to Enhance Student Engagement in Online Synchronous Environments: An Exploratory Study 27

Use of Audience Response Systems to Enhance Student Engageme...

引用

27th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Nesbit, Trevor Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (纸本)9781713880325

Many higher education institutions adapted to the Covid-19 pandemic by switching their teaching into online mode making use of online synchronous sessions using technologies such as Zoom. It was common for lecturers to find it disconcerting that many students were not turning on their cameras and microphones and how this made it difficult to ascertain whether their students were engaged in the sessions at all. This paper examines the experiences of teaching staff in eight courses relating to the use of audience response systems (ARS) to improve the experiences of students and teaching staff when conducting synchronous online teaching sessions or hybrid sessions when they had some face-face students and some online students joining sessions synchronously. Literature is examined that shows the benefits relating to the use of ARS in synchronous online teaching sessions to include anonymity of student responses;enhancement of feedback between teaching staff and students;and teaching staff getting a better sense of student engagement during a session. An analysis of the eight cases presented confirms these benefits in the literature from the perspective of the teaching staff. The findings apply irrespective of the ARS being used and will be of relevance and interest to any teaching staff seeking to improve the experiences of students and teaching staff involved in synchronous online teaching sessions. © 2023 Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Students

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PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE DATA-FREE CLASS-INCREMENTAL LEARNING 11

PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE DAT...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Ma, Chunwei Ji, Zhanghexuan Huang, Ziyun Shen, Yan Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present iVoro, a novel framework derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner - the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Furthermore, we bridge DNN and VD using Power Diagram Reduction, and show that the VD structure can be progressively refined along the phases using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multilayer VD that efficiently captures multi-grained features from DNN. Importantly, iVoro is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ∼0.9). Putting everything together, iVoro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, iVoro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

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State-of-the-Art Wireless Charging Systems for E-Bikes: Technologies and Applications 14

State-of-the-Art Wireless Charging Systems for E-Bikes: Tech...

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14th IEEE International Conference on Power Electronics and Drive Systems, PEDS 2023

作者： Shrestha, Niranjan Samanta, Akash Fietosa, Filipe Costa Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350399967

Several advancements were made in wireless charging for e-transportation however, the knowledge acquired in the context of wireless charging of electric cars are not sufficient to apply to electric bicycle (e-bikes). Therefore, this paper reviewed state-of-the-art wireless charging technologies with a special emphasis on e-bike charging. A comparative analysis of different kinds of wireless charging techniques is also presented to provide a clear understanding and guidelines to choose the best suitable technology for implementation in e-bikes. In addition, current issues, challenges, and future research scopes are identified where necessary modifications need to be done before implementing the technology for e-bikes. © 2023 IEEE.

关键词： Electromagnetic induction

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Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

Dense Error Map Estimation for MRI-Ultrasound Registration i...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Salari, Soorena Rasoulian, Amirhossein Rivaz, Hassan Xiao, Yiming Concordia University Department of Computer Science and Software Engineering MontrealQC Canada Concordia University Department of Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350346459

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time. © 2023 IEEE.

关键词： Grading

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Online Distributed Optimization with Efficient Communication via Temporal Similarity 42

Online Distributed Optimization with Efficient Communication...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Juncheng Liang, Ben Dong, Min Boudreau, Gary Afana, Ali University of Toronto Department of Electrical and Computer Engineering Canada Ontario Tech University Department of Electrical Computer and Software Engineering Canada Ericsson Canada Canada

ISBN: (纸本)9798350334142

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which completely removes the commonly assumed Slater's condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. As an example application, we apply ODOTS to enable communication-efficient federated learning. Our experimental results based on real-world image classification demonstrate that ODOTS obtains higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions. © 2023 IEEE.

关键词： Computational efficiency

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Photovoltaic Maximum Power Point Tracking Based on Bayesian Optimization Neural Network 49

Photovoltaic Maximum Power Point Tracking Based on Bayesian ...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Hu, Pengcheng Ukil, Abhisek Nair, Nirmal-Kumar C University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350331820

This paper uses the neural network to realize a photovoltaic (PV) system's maximum power point tracking. After Bayesian optimization of hyperparameters, the model can accurately determine the maximum power point voltage according to solar irradiance and temperature. The optimized PV system not only adapts to different operating conditions but also achieves the optimal power output under uniform irradiance, static irradiance shading, and dynamic shading, ensuring the maximum efficiency of the PV system. Moreover, the optimized model is superior to the unoptimized model and the traditional perturbation and observation method in terms of accuracy. The improved model enhances the practicality and reliability of the PV system, helping to improve PV efficiency and reduce energy loss. © 2023 IEEE.

关键词： Maximum power point trackers

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A Method to Evaluate User-Centered Self-Adaptive Systems for Smart Environments 10

A Method to Evaluate User-Centered Self-Adaptive Systems for...

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10th International Conference on eDemocracy and eGovernment, ICEDEG 2024

作者： Avila, Marcelo Quito, Christian Valdez, Wilson Cardenas-Delgado, Paul Cedillo, Priscila Universidad de Cuenca Computer Science Department Cuenca Ecuador University of Szeged Software Engineering Department Szeged Hungary

ISBN: (纸本)9798350365535

With the advent of Industry 4.0, Ambient Intelligence, and other related technologies, self-Adaptation has been seen in modern systems. In this sense, Human-computer Interaction (HCI) requires an approach focused on the user and the environment, which facilitates and enhances the use of the systems. In this context, the quality of Self-Adaptive Systems (SAS) is essential for their adoption, dissemination, and service. Thus, this study introduces a detailed evaluation method to analyze the quality of HCI in SAS, providing a solution applicable to various contexts aligned with the standard ISO/IEC 25040. This study also presents a quality model aligned with the ISO/IEC 25010 standard. The quality evaluation method allows the objective definition for the measurement application through specific metrics that determine the degree of functional adequacy and interaction capability. Finally, this study demonstrates how to perform a detailed evaluation of the quality of HCI in SAS, using as an example the evaluation of the Spotify system as a case study, applying metrics to evaluate functional adequacy and interaction capacity aligned with ISO/IEC 25040 and 25010 standards. © 2024 IEEE.

关键词： ISO Standards

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Development of a Directional Element for Solar Inverter-Rich Distribution Networks

Development of a Directional Element for Solar Inverter-Rich...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： The Hoang, Tran Nair, Nirmal-Kumar C. Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9798350396782

The high penetration of electronic-coupled distributed energy sources, especially photovoltaic systems into distribution networks has created new challenges for traditional non-directional protection systems. Fault currents can now come from multiple sources, making protection location and direction detection difficult. Although conventional negative-sequence directional elements have been proposed as alternative solutions for detecting unbalanced fault direction, the unusual waveforms of current and voltage contributed by photovoltaic systems can adversely affect their performance. This paper proposes a new directional approach that utilizes prefault voltage and during-fault negative-sequence current component to determine the direction of unbalanced faults in photovoltaic system-dominated distribution networks while considering the dynamic voltage support of photovoltaic systems as imposed by new grid code requirement. The proposed method is validated through numerical analysis and EMT simulations in the software environment PowerFactory/DIgSILENT. © 2023 IEEE.

关键词： Directional element distribution network EMT simulation photovoltaic system unbalanced faults

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