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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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Reliability Analysis of a k-out-of-n Single Server System Extending Service to External Customers Under N-Policy and Server Vacations 27th

Reliability Analysis of a k-out-of-n Single Server System ...

引用

27th international conference on Distributed computer and Communication Networks, DCCN 2024

作者： Joseph, Binumon Jose, K.P. Government Engineering College Idukki Painavu Kerala Idukki685603 India PG and Research Department of Mathematics St. Peter’s College Kerala Kolenchery682311 India

ISBN: (纸本)9783031808524

This study investigates the reliability of a k-out-of-n repairable system with a single server repairing failed system components. The server makes use of idle time by providing service to external customers. An N-policy is used to ensure the system’s reliability, even when it provides service to external components. The service for internal failed components begins only after N internal failed components have been accumulated. Furthermore, the external service is preempted when the server is busy with external failed components and the number of failed internal components reaches N. Once the internal component service is started, all failed internal components are repaired one by one. When both internal and external failed components get cleared from the system, the server goes on multiple vacations. The failure times of the system’s components follow an exponential distribution, while external customer’s arrival follows the Poisson process. The service times of internal and external customers follow a phase type distribution. Vacation time has an exponential distribution. Matrix Analytic Method is used to discuss system stability and steady-state distributions. The N-policy level is numerically optimised using an appropriate cost function. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sales

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Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance 1st

Multi-task Framework for Myocardial Pathological Segmentati...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Wu, Yilin Yang, Xiao Liu, Yongli Zheng, Jiahao Yang, Mingjing College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031870088

Accurate segmentation of myocardial pathology, including scar and edema, from magnetic resonance images (MRI) is of significant clinical importance for diagnosing, planning treatment, and assessing prognosis in cardiovascular diseases. Multi-sequence cardiac magnetic resonance images provide additional information for pathological segmentation, while ventricular blood pool regions offer positional anatomical priors for identifying scar and edema. Therefore, we propose a multi-task framework based on SimAM-UNet, where SimAM generates feature weights to better focus on modality-specific features for the segmentation of myocardial pathology using multi-sequence cardiac magnetic resonance images. The first task involves segmenting and extracting features of the myocardium and blood pool, which are relatively easy to obtain. The second task focuses on segmenting scars and edema by integrating positional feature constraints derived from the myocardium and blood pool. To address the multi-center data problem encountered in real-world data, we incorporate a data augmentation pipeline that simulates the generation of data samples from different centers, enhancing the domain generalization of our method. Our proposed method was evaluated on the MyoPS++ track of the CARE2024 Challenge, and the experimental results demonstrated the effectiveness of our multi-task framework for myocardial pathological segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Improved nn-UNet: Generalizable Multi-scale Attention-Driven Segmentation of Multi-sequence Myocardial Pathology 1st

Improved nn-UNet: Generalizable Multi-scale Attention-Driven...

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作者： Tang, Mengshi Li, Nuoxi Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou China School of Medical Imaging Fujian Medical University Fuzhou China

ISBN: (纸本)9783031870088

Multi-sequence cardiac magnetic resonance (MS-CMR) images are capable of providing myocardial pathology information for patients with myocardial infarction. Precise myocardial structure and pathology segmentation hold significant importance for subsequent diagnosis and treatment. Nevertheless, traditional manual myocardial structure and pathology segmentation is not only time-consuming and labor-intensive but also has a low accuracy rate, particularly when identifying pathology such as scars and edema that are small in volume and have low contrast with the surrounding tissues, it becomes even more challenging. To address this issue, this paper proposes an improved nn-UNet for fully automatic segmentation of myocardial pathologies. In this network, based on nn-Unet, we use multi-modal data as input to make up for the lack of information in a single mode. For multi-modal data, we utilize cross normalization to improve the generalization performance. Meanwhile, multi-scale attention modules are integrated to process features at different resolutions, thus improving the feature representation capability of neural networks. Through feature fusion and attention weighting, the model can better capture the global and local information of myocardial pathologies, and achieve more accurate segmentation of myocardial pathologies. To verify the effectiveness of the proposed method, we conducted an evaluation using five-fold cross-validation in the dataset of the MyoPS++ challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Attention-Guided Energy-Based Model for Out-of-Distribution Data Detection 27th

Attention-Guided Energy-Based Model for Out-of-Distribution ...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Cao, Zongjing Li, Yan Shin, Byeong-Seok Department of Computer Science and Engineering Inha University Incheon Korea Republic of

ISBN: (纸本)9783031783944

Detecting out-of-distribution (OOD) data is crucial for the safe and reliable deployment of deep learning models in open-world scenarios. While energy-based models (EBMs) have shown promising potential in OOD detection through the use of an energy function to capture the underlying probability distribution of data, previous approaches have primarily utilized logits or class probabilities from the fully connected layer to compute energy scores. However, logits are inherently class-specific and focus mainly on the relationship between the input and known classes, potentially ignoring the rich information embedded in raw feature representations that are essential for identifying OOD samples. This study introduces a novel approach that utilizes patterns within the feature space to calculate energy scores instead of relying on logits or class probabilities. We propose a spatial attention score to generate class-specific features for each category, which are then used to compute the energy score. Furthermore, we develop a new energy function that transforms these features into energy scores, significantly improving the OOD detection performance of EBMs. In experiments conducted on a Cifar-10 pre-trained ResNet-50, our feature-based energy score method reduced the average false positive rate at a true positive rate of 95% by 5.33% compared to the logits-based approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-based model Out-of-distribution detection Uncertainty quantification.

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An Efficient Device Placement Method for Distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. The core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. Through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. The results indicate that the method finds non-trivial device placements outperforming baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixed-integer linear programming

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Automating Persona Generation: Leveraging ChatGPT-4 in Educational Digital Services

Automating Persona Generation: Leveraging ChatGPT-4 in Educa...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Thanatat Wongabut Unhawa Ninrutsirikun Chonlameth Arpnikanondt School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research provides a preliminary study on automating user persona generation via ChatGPT-4 with a case study on educational digital services. Data were collected from 40 undergraduate students at one of Thailand’s science and technology higher education institutes through questionnaires and semi-structured interviews, with participants grouped via K-means clustering based on similar responses. The study relied on ChatGPT-4 to generate personas based on empathy maps, jobs-to-be-done analysis, and Hofstede’s cultural dimensions. The validity of the generated personas was assessed using SHapley Additive exPlanations to identify key influencing features, revealing that integrating cultural contexts enhances persona specificity and relevance. The findings demonstrate that personas generated through this approach accurately reflect unique traits of target clusters, while significantly reducing the time and resources required. This study highlights a practical method for using ChatGPT-4 to generate personas, focusing on Thai science and tech undergraduates. It shows how tailored data collection improves ChatGPT’s understanding of specific user groups. Future research could expand sample sizes and explore broader contexts to enable generalization of the approach to become a framework that works across multiple target users and application contexts. Additionally, exploring methods like hierarchical K-means may improve clustering accuracy. Incorporating expert evaluations using the Persona Perception Scale could further ensure the real-world applicability.

关键词： Accuracy Large language models Education Psychology Focusing Data collection Cultural differences Reliability Prompt engineering Interviews

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Plant Leaf Disease Identification Based on Machine and Deep Learning Methods

Plant Leaf Disease Identification Based on Machine and Deep ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P. Kalaiarasi R. Ramalakshmi V. Kannan A. Bhuvanesh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are laborintensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models.

关键词： Deep learning Visualization Plant diseases Machine learning algorithms Microorganisms Plants (biology) Production Manuals Medical diagnosis Diseases

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Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification 27th

Confidence-Guided Feature Alignment for Cloth-Changing Pers...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment Network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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An Effective Ensemble Algorithm for Short-Term Load Forecasting 25th

An Effective Ensemble Algorithm for Short-Term Load Forecas...

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25th international conference on Information Security Applications, WISA 2024

作者： Zhang, En-Wei Wu, Luo-Fan Tsai, Chun-Wei Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9789819616237

Short-term load forecasting (STLF) is a critical issue for managing electricity distribution systems because its accuracy might strongly impact the performance and security of the power system. An effective load forecasting system, of course, can also be used to detect and prevent abnormal behaviors (e.g., electricity theft detection) to further provide a stable and safe power system. That is why extensive studies have been presented by using statistical, machine learning, and deep learning methods. However, each learning model has its strengths to capture specific patterns in the load profile. To integrate the strengths of different deep learning algorithms, an ensemble model, which contains three deep learning mechanisms, including convolution, self-attention, and recurrent, is proposed in this paper. Because of the high uncertainty and volatility, a single-step horizon will be focused on while predicting the load at the residential level to ensure timely and precise responses to security threats. Multiple-step forecasting will also be conducted at the aggregated level to further enhance the robustness of the power grid security. Furthermore, considering the distinct behavior in the load profile, the proposed method is designed accordingly to adapt to each level. The experimental results show the effectiveness of the proposed method compared with deep learning models on both levels across two well-known datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Design of athermal SiON waveguide structure for telecom photonics integrated circuits 23

Design of athermal SiON waveguide structure for telecom phot...

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23rd Slovak-Czech-Polish Optical conference on Wave and Quantum Aspects of Contemporary Optics

作者： Chovan, J. Uherek, F. Seyringer, D. Dohnal, F. Seyringer, H. Latal, J. Siska, P. International Laser Centre Slovak Centre of Scientific and Technical Information Ilkovičova 3 Bratislava841 04 Slovakia OptoNet Slovakia s. r. o. Detvianska Huta 215 Hriňová962 05 Slovakia Institute of Electronics and Photonics Slovak University of Technology Ilkovičova 3 Bratislava812 19 Slovakia Hochschulstraße 1 Dornbirn6850 Austria V-Research GmbH Industrial Research & Development CAMPUS V Stadtstraße 33 Dornbirn6850 Austria Dept. of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

The rapid advancement of optical communication technologies has necessitated the development of highly efficient and stable photonic integrated circuits that enable coherent signal processing in the optical domain. Among them, Arrayed Waveguide Gratings (AWG) has emerged as a crucial component in Dense Wavelength Division Multiplexing (DWDM) systems. However, the performance of conventional AWGs is strongly affected by temperature fluctuations, resulting in central wavelength shifts and increased insertion losses. The aim of this study is to achieve a significant reduction of this dependency by using silicon oxynitride and polymethyl methacrylate on a silicon substrate with a thermal silicon dioxide buffer layer, thereby improving the overall performance and reliability of AWG-based DWDM systems. To achieve this, we have designed an athermal waveguide structure that can operate efficiently over a wide temperature range of -40°C to 80°C without the need for active temperature control mechanisms. Such a waveguide creates the basis for the AWGs in photonics integrated circuits. © 2025 SPIE.

关键词： Dense wavelength division multiplexing

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