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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Wen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything"to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA's LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS's unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes. © 2023 IEEE.

关键词： Multi-omics Data Pathological Images Segment Anything Survival Analysis

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The Effect of Noisy and Blurry Data on Deep Learning: Application in Brain Image Classification

The Effect of Noisy and Blurry Data on Deep Learning: Applic...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Fadillah, Muhammad Fajar Azka Rumala, Dewinda Julianensi Purnomo, Mauridhi Hery Purnama, I. Ketut Eddy Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember University Center of Excellence on Uce AIHeS Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Convolutional Neural Networks (CNN) is one of the best Deep Learning algorithms commonly used for computer vision tasks, including medical image analysis. CNN can learn the representational features from images starting from the lower to complex features. However, noisy data can affect the generalization of the networks, which we can often find in medical images, such as Magnetic Resonance Imaging (MRI). In this paper, we intend to find the correlation between noisy data and the performance of CNN models. We build automatic CNN-based classifiers for normal brain MR images based on axial view by setting up three different data scenarios to train the classifiers: 1) original data, 2) blurred data, and 3) noisy data. We also evaluate the relationship between the prediction accuracy and kernel size of the convolutional layers. Based on our investigation, deeper layers and smaller kernels in the CNN models give better generalization. © 2022 IEEE.

关键词： Magnetic resonance imaging

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Yijia Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. © 2023 IEEE.

关键词： deep learning multimodal pathogenicity protein structure variant

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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Wide Area Control of Distributed Resources through 5G Communication to Provide Frequency Support 8

Wide Area Control of Distributed Resources through 5G Commun...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Hadjidemetriou, L. Akrytov, A. Kyriakou, K. Charalambous, C. Asprou, M. Ciornei, I. Ellinas, G. Panayiotou, C. Kios Research and Innovation Center of Excellence University of Cyprus Cyprus University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Towards the green transition of modern power systems, a massive deployment of distributed energy resources (DERs) based on renewable sources is required. These DERs are grid integrated through power electronics converters which reduce the commitment of synchronous generators and as a result, the overall inertia of the system. Maintaining the frequency stability in such low-inertia power systems is a crucial aspect for the system operators;therefore, local control schemes are integrated with conventional generators and DERs to ensure fast reaction in case of frequency disturbances so as to preserve system stability. Existing practices for maintaining the frequency stability rely on the response of a local governor controller integrated with the synchronous generators. Some new approaches introduce frequency support services by DERs, by considering the droop control and virtual inertia concept within the inverter local controller. In another approach, wide area control schemes can also be utilized to further enhance the system stability. Such a wide area control scheme is introduced in this work to coordinate the operation of DERs through 5G communication for enabling fast frequency support services. Due to the fast dynamics of a power system, a reliable and high-speed communication is particularly important for the deployment of the proposed method. Therefore, an experimental hardware in the loop setup has been developed to investigate how the communication performance can affect the stability of the power system. Moreover, a benchmarking is implemented to evaluate the frequency stability when local controllers are providing support and when the proposed wide area controller is applied under a different communication infrastructure. Experimental results demonstrate that when a reliable 5G network is used, then a significant stability improvement can be achieved by the proposed wide area controller. © 2022 IEEE.

关键词： Controllers

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset 4th

Cross-Spectral Image Registration: a Comparative Study and...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Rivadeneira, Rafael E. Velesaca, Henry O. Sappa, Angel ESPOL Polytechnic University Escuela Superior Politécnica Del Litoral ESPOL Guayaquil Ecuador Software Engineering Department University of Granada Granada18014 Spain Computer Vision Center Barcelona Bellaterra08193 Spain

ISBN: (纸本)9789819769919

The field of cross-spectral imaging has significantly advanced, driven by its diverse applications, including environmental monitoring and medical imaging enhancements. The integration of images from different parts of the electromagnetic spectrum, particularly the fusion of thermal and visible images, is a crucial task for different applications. It provides a comprehensive picture of a scene, combining the clarity of visible light imaging with the contrast of thermal imaging. This research investigates the efficiency of various techniques and architectures for local feature matching between visible and thermal images, essential for accurate image registration. Through evaluating a wide array of methods against a novel acquired cross-spectral dataset encompassing varied real-world scenarios, the study provides detailed insights into their effectiveness and limitations under different conditions. It also presents quantitative benchmarks on computational speed, offering a clearer perspective on each method’s performance and applicability in practical, especially resource-constrained, settings. The results indicate that these architectures exhibit remarkable capabilities in accurately and efficiently registering images from the thermal and visible domains. Their inherent flexibility in handling complex problems, along with their computational speed, suggests that these approaches hold significant promise for addressing cross-spectral imaging challenges. The dataset is available at:https://***/vision-cidis/CIDIS-dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image registration

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Dynamic Adaptation Gain Design and Tuning for Threat Discrimination 63

Dynamic Adaptation Gain Design and Tuning for Threat Discrim...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Kangkang Chen, Kaiwen Polycarpou, Marios M. Parisini, Thomas University of Cyprus Kios Research and Innovation Center of Excellence Dept. of Electrical and Computer Engineering Nicosia1678 Cyprus Imperial College London Dept. of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily obscured by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented. © 2024 IEEE.

关键词： Cyber attacks

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing 15

PowerGen: Resources Utilization and Power Consumption Data G...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ismail, Leila Materwala, Huned Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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A Method for Extracting Small Water Areas Based on an Improved FPN Network 19

A Method for Extracting Small Water Areas Based on an Improv...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yu, Yufeng Wang, Meixiang Zhao, Qun Wang, Yong Chen, Qingqing Department of Computer and Software Hohai University Jiangsu Nanjing211108 China School of Computer Engineering Nanjing Institute of Technology Jiangsu Nanjing211167 China Bank of Nanjing Co. Nanjing Jiangsu211800 China Aerosun Corporation Information Center Jiangsu Nanjing China

ISBN: (纸本)9798331507817

Small water areas such as river branches, lakes, and ponds are vital components of aquatic ecosystems closely linked to human activities. Extracting small water areas from remote sensing images holds significant importance in water resource planning, investigation, sustainable water resource management, and environmental protection. However, the current small water area extraction methods based on remote sensing often encounter the problems of small target, few features, difficult localization and easy loss of edge features. Therefore, this paper proposes a small water area extraction model based on IFPN_BEM network. The model leverages a Transformer Encoder module with deformable attention mechanisms to globally model deep features of the FPN network and optimizes small water area edges using the BEM module, which further improves the extraction accuracy of small water areas. Experimental results on the GID dataset demonstrate that this model provides more complete and accurate extraction of small water areas, achieving precision, F1 score, and IoU accuracy metrics of 81.3%, 84.4%, and 73.4% respectively. These metrics show improvements of 9.5%, 8.2%, and 8.5% over the original FPN network, outperforming UNet, DeepLabV3+, and the improved FPN + PointRend networks across all metrics. © 2025 IEEE.

关键词： Lakes

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