检索结果-内蒙古大学图书馆

Multiphysics-Coupled Stress analysis of Hydrogen Storage Filling Using Diverse Composite Layer Winding Patterns

学校读者我要写书评

暂无评论

Multiphysics-Coupled Stress Analysis of Hydrogen Storage Fil...

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yi Chen Zuoqin Qian Jianxiao Wang Jie Song College of Engineering Peking University Beijing China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University Of Technology Wuhan China National Engineering Laboratory for Big Data Analysis and Applications Peking University Ordos Research Institute of Energy Beijing China College of Engineering Peking University Ordos Research Institute of Energy Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Hydrogen storage technology, as one of the core technologies in the hydrogen energy industry chain, is directly related to the efficient utilization and safe transportation of hydrogen energy. Currently, existing literature overlooks the impact of the wrapping patterns of composite layers, which can cause hydrogen storage containers to exhibit different stress characteristics. In this paper, a Type III hydrogen storage tank model is established to analyze the flow field during the filling process, obtaining the internal pressure load distribution. Subsequently, three different wrapping patterns are modeled to comparatively analyze the stress conditions of the hydrogen storage tank under these patterns. Case studies based on ANSYS reveal that when the wrapping angle tends more towards 90° or 0°, the stress conditions on the composite layers tend to be more extreme, while the stress conditions on the inner liner improve, resulting in a higher maximum stress. This trend gradually diminishes from the inner layers to the outer layers. Moreover, the stress distribution in each layer is related to the wrapping direction of that layer, i.e., the fiber direction, and the position of the layer within the stack, and is almost unaffected by other layers.

关键词： Analytical models Hydrogen storage Hydrogen Windings Transportation Filling Finite element analysis Wrapping Stress Load modeling

PWG-IDS: An intrusion detection model for solving class imbalance in IIoT networks using generative adversarial networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Lei Jiang, Shuaimin Shen, Xiajiong Gupta, Brij B. Tian, Zhihong Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng47500 China Institute of Data and Knowledge Engineering Henan University Kaifeng47500 China School of Computer and Information Engineering Henan University Kaifeng47500 China National Institute of Technology Kurukshetra India Asia University Taichung Taiwan The Department of Cyberspace Insitute of Advanced Technology GuangZhou University GuangZhou510006 China

With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Intrusion detection

Urban flows prediction from spatial-temporal data using machine learning: A survey

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Xie, Peng Li, Tianrui Liu, Jia Du, Shengdong Yang, Xin Zhang, Junbo School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Institute of Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Urban Computing Business Unit JD Finance China

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems. Copyright © 2019, The Authors. All rights reserved.

关键词： data fusion

Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Hu, Shishuai Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China Ningbo Institute Northwestern Polytechnical University Ningbo315048 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a Channel-level Contrastive Single Domain Generalization (C2SDG) model for medical image segmentation. In C2SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. The segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C2SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C2SDG and also indicate that C2SDG outperforms the baseline and all competing methods with a large margin. The code will be available at https://***/ShishuaiHu/CCSDG. © 2023, CC BY-NC-ND.

关键词： Deep learning

A Survey of Route Recommendations: Methods, applications, and Opportunities

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Shiming Luo, Zhipeng Yang, Li Teng, Fei Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens' travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of classic methods and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) application-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Cooperative Ramp Merging in Mixed Traffic: Hybrid Model Predictive Control and Real Time Computations

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Sun, Zhanbo Gao, Ziyan Li, Zheyi Huang, Tianyu School of Transportation and Logistics Southwest Jiaotong University China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Intelligent autonomous vehicle group Sensetime Shanghai China Department of Civil and Environmental Engineering Carnegie Mellon University United States Civil Aviation Flight University of China China

This paper focuses on the problem of decision making in mixed traffic with conventional human-operated vehicles (HVs) and connected automated vehicles (CAVs). To better handle the stochasticity in HV behaviors, the paper applies a closed-loop optimal control approach to extend the previously developed deterministic cooperative decision-making framework for mixed traffic (Deterministic-CDMMT). The proposed framework is illustrated using a ramp merging example. This framework can be described as an optimization problem in which a merge sequencing problem and a trajectory planning problem are embedded and solved by a bi-level hybrid centralized-decentralized model predictive control (HMPC) approach. The upper-level merge sequencing problem is solved using a dynamic-programming-based approach. Considering real-time computational performance, three solution methods, including dynamic programming (DP), dynamic matrix predictive control (DMC), and simplified discrete control (SDC), are developed to solve the lower-level trajectory planning problem. Simulation results show that compared to open-loop control, the proposed framework can effectively address the uncertainty caused by the stochastic driving behaviors of HVs. In addition, it is found that hybrid centralized-decentralized control can reduce the computational time to the millisecond-level and provide a similar, system-efficient performance compared to centralized control, potentially meeting real-time computational requirements. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hu, Shishuai Liao, Zehui Liu, Zeyou Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained models to test data, have been employed to mitigate such discrepancies. However, existing TTA methods primarily focus on manipulating Batch Normalization (BN) layers or employing prompt and adversarial learning, which may not effectively rectify the inconsistencies arising from divergent data distributions. In this paper, we propose a novel Human-in-the-loop TTA (HiTTA) framework that stands out in two significant ways. First, it capitalizes on the largely overlooked potential of clinician-corrected predictions, integrating these corrections into the TTA process to steer the model towards predictions that coincide more closely with clinical annotation preferences. Second, our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities, through the careful calibration of BN parameters. Our HiTTA is distinguished by its dual-faceted capability to acclimatize to the distribution of test data whilst ensuring the model’s predictions align with clinical expectations, thereby enhancing its relevance in a medical context. Extensive experiments on a public dataset underscore the superiority of our HiTTA over existing TTA methods, emphasizing the advantages of integrating human feedback and our divergence loss in enhancing the model’s performance and adaptability across diverse medical centers. © 2024, CC BY-NC-ND.

关键词： Forecasting

Model-enhanced vector index 23

学校读者我要写书评

暂无评论

Model-enhanced vector index

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Hailin Zhang Yujing Wang Qi Chen Ruiheng Chang Ting Zhang Ziming Miao Yingyan Hou Yang Ding Xupeng Miao Haonan Wang Bochen Pang Yuefeng Zhan Hao Sun Weiwei Deng Qi Zhang Fan Yang Xing Xie Mao Yang Bin Cui School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and Microsoft Microsoft Microsoft and Aerospace Information Research Institute & Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Microsoft and Institute of Information Engineering Chinese Academy of Sciences Carnegie Mellon University National University of Singapore School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and National Engineering Laboratory for Big Data Analysis and Applications Peking University

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

关键词：