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2022 World Automation Congress, WAC 2022

作者： Tehrani, Kambiz Simde, Diane Fozing, Jourdain Jamshidi, Mo Departement of Energy and Control University of Normandy-ESIGELEIRSEEM Rouen France Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States

ISBN: (数字)9798888314449

ISBN: (纸本)9798888314449

This paper discusses a 3D design of multi-source energy system. This modeling considers several parameters that influence the energy produced such as sunlight, temperature, humidity, speed and wind direction, hub height, time and season. This technique is validated with several simulation results using a Multiphysics approach for a small hybrid farm that can be applied to microgrids. © 2022 TSI Enterprises.

关键词： 3D modeling

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Analysis and Mitigation of Soft-errors in GPU-accelerated Hyperspectral Image Classifiers

Analysis and Mitigation of Soft-errors in GPU-accelerated Hy...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Sergiu-Mohamed Abed Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Gianluca De Lucia Marco Lapegna Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino CASD Scuola Superiore Universitaria Rome Italy Department of Mathematics and Applications University of Naples Federico II

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

This work assesses the reliability of a hyperspectral image classifier for edge devices under transient faults by using a fine-grain strategy based on the Hardware Injection Through Program Transformation (HITPT) technique. The results identified the most vulnerable software parts and the corruption effects due to hardware faults (from 5.1% to 100.0% of accuracy drop). Then, the results supported the adoption of a selective-hardening software mechanism (based on the Duplication with Comparison strategy) to effectively mitigate the most critical effects under limited costs.

关键词： Costs Graphics processing units Reliability engineering Hardware Software Software reliability Circuit faults Transient analysis Principal component analysis Hyperspectral imaging

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Fusion Learning of Multimodal Neuroimaging with Weighted Graph AutoEncoder

Fusion Learning of Multimodal Neuroimaging with Weighted Gra...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Gen Zhu, Yifan Zhang, Fuquan Liu, Wenjin Yao, Yuxiang Li, Xuesong Beijing Institute of Technology School of Computer Science and Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China Minjiang University College of Computer and Control Engineering Fuzhou China Yangzhou University Northern Jiangsu People's Hospital Department of Nephrology Yangzhou China

ISBN: (纸本)9781665468190

Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction. © 2022 IEEE.

关键词： Neuroimaging

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Low-Distortion Clustering with Ordinal and Limited Cardinal Information

arXiv

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arXiv 2024年

作者： Burkhardt, Jakob Caragiannis, Ioannis Fehrs, Karl Russo, Matteo Schwiegelshohn, Chris Shyam, Sudarshan Department of Computer Science Aarhus University Åbogade 34 Aarhus N8200 Denmark Department of Computer Control Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we would like to evaluate clustering algorithms in terms of social cost objectives that are defined using d. This is done using the notion of distortion, which measures how far from optimality a clustering can be, taking into account all underlying metrics that are consistent with the ordinal information available. Unfortunately, the most important clustering objectives (e.g., those used in the well-known k-median and k-center problems) do not admit algorithms with finite distortion. To sidestep this disappointing fact, we follow two alternative approaches: We first explore whether resource augmentation can be beneficial. We consider algorithms that use more than k clusters but compare their social cost to that of the optimal k-clusterings. We show that using exponentially (in terms of k) many clusters, we can get low (constant or logarithmic) distortion for the k-center and k-median objectives. Interestingly, such an exponential blowup is shown to be necessary. More importantly, we explore whether limited cardinal information can be used to obtain better results. Somewhat surprisingly, for k-median and k-center, we show that a number of queries that is polynomial in k and only logarithmic in n (i.e., only sublinear in the number of agents for the most relevant scenarios in practice) is enough to get constant distortion. © 2024, CC BY.

关键词： Clustering algorithms

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Embedded Multi-Port Converters for Synthesis and Enhancement of Hybrid-Clamped Multilevel Converters

Embedded Multi-Port Converters for Synthesis and Enhancement...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Yuzhuo Li Pasan Gunawardena Hao Tian Yunwei Li Department of Electrical and Computer Engineering University of Alberta Edmonton Canada School of Control Science and Engineering Shandong University Jinan China

Leveraging a number of inner capacitors/inductors, hybrid-clamped multilevel converters (MLCs) normally face great challenges among good performance (proper charge/discharge of these devices), high efficiency (maintaining low losses) and high power density (compact profile). On the other hand, these multiple-device energy-processing requirements have been addressed well in some promising multi-port converters (MPCs), and, therefore, inspire us to implement well-developed compact MPCs to facilitate the voltage/current level generation process in hybrid-clamped MLCs. Though recently, some researchers started to integrate active cells into hybrid-clamped MLCs and improve capacitor voltage control and generate extra output levels, the systematic synthesis method is still unclear and rarely discussed in the literature. To address this gap, we propose a systematic synthesis method for those hybrid clamped MLCs that can benefit from embedding well-developed MPCs. The approach can be applied for both voltage-source and current-source hybrid-clamped MLCs, covering emerging MLCs. In particular, we also derived and verified a new mixed hybrid MLC family through an emerging current-fed dual-input isolated multi-port converter. This topology features both current-source and voltage-source benefits and is ideal for future renewable generation integration.

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Decoding AI Judgment: How LLMs Assess News Credibility and Bias

arXiv

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arXiv 2025年

作者： Loru, Edoardo Nudo, Jacopo Marco, Niccolò Di Cinelli, Matteo Quattrociocchi, Walter Department of Computer Control and Management Engineering Sapienza University of Rome Viale Ariosto 25 Rome00185 Italy Department of Computer Science Sapienza University of Rome Viale Regina Elena 295 Rome00161 Italy

Large Language Models (LLMs) are increasingly used to assess news credibility, yet little is known about how they make these judgments. While prior research has examined political bias in LLM outputs or their potential for automated fact-checking, their internal evaluation processes remain largely unexamined. Understanding how LLMs assess credibility provides insights into AI behavior and how credibility is structured and applied in large-scale language models. This study benchmarks the reliability and political classifications of state-of-the-art LLMs—Gemini 1.5 Flash (Google), GPT-4o mini (OpenAI), and LLaMA 3.1 (Meta)—against structured, expert-driven rating systems such as NewsGuard and Media Bias Fact Check. Beyond assessing classification performance, we analyze the linguistic markers that shape LLM decisions, identifying which words and concepts drive their evaluations. We uncover patterns in how LLMs associate credibility with specific linguistic features by examining keyword frequency, contextual determinants, and rank distributions. Beyond static classification, we introduce a framework in which LLMs refine their credibility assessments by retrieving external information, querying other models, and adapting their responses. This allows us to investigate whether their assessments reflect structured reasoning or rely primarily on prior learned associations. © 2025, CC BY.

关键词： Structured Query Language

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Minor Surface Cracks Detection using SCD11 Convolutional Neural Network

Minor Surface Cracks Detection using SCD11 Convolutional Neu...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Adil Hussain Kashif Naseer Qureshi Faizan Zaman Ayesha Aslam Tariq School of Electronics and Control Engineering Chang’an University Xi'an China Department of Electronics and Computer Engineering University of Limerick Limerick Ireland School of Information Engineering Chang’an University Xi'an China

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The manual detection of road cracks is a time-consuming process. On the other hand, solutions that are based on deep learning are both speedy and accurate. Recently, several different Convolutional Neural Networks (CNN) based on deep learning have been proposed. However, the performance of the CNN models has varied. The major challenge is the computational resources required to train a pre-trained CNN model; however, a lightweight CNN is more suitable for better training efficiency. In this study work, the SCD11 CNN model is implemented and compared with the pre-trained CNN models, including Inception V2, VGG19, and Xception CNN. The models are trained and tested using the public dataset i.e., the Surface Cracks Dataset. The dataset is divided into training, validation and test sets. The SCD11 CNN along with the pre-trained CNN models are trained and validated and then tested using the splitting of the public dataset. Furthermore, the model’s performance evaluation is performed by using a private dataset. The results show that the SCD11 CNN performs better than the pre-trained CNN models for both the public and private datasets.

关键词： Training Deep learning Performance evaluation Accuracy Computational modeling Roads Manuals

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Data-Efficient control Barrier Function Refinement

Data-Efficient Control Barrier Function Refinement

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American control Conference (ACC)

作者： Bolun Dai Heming Huang Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY

control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a two-link arm.

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Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective 41

Rethinking Guidance Information to Utilize Unlabeled Samples...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples.A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their ***, EntMin emphasizes prediction discriminability while neglecting prediction *** alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled *** analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label *** by this finding, we propose a Label-Encoding Risk Minimization (LERM).It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label *** a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a ***, we analyze the relationships between LERM and ERM as well as ***, we verify the superiority of the LERM under several label insufficient *** codes are available at https://***/zhangyl660/LERM. Copyright 2024 by the author(s)

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Optimization of Cube Storage Warehouse Scheduling Using Genetic Algorithms

Optimization of Cube Storage Warehouse Scheduling Using Gene...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Won Yong Ha Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA

In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time.

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