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Configuration and characters of novel Li2TiO3 tritium breeder cubic units with tuned channel structure via lithography-based 3D printing

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2023年第5期43卷 2151-2163页

作者： Cai, Liang Tan, Guangfan Hu, Xin Shen, Zhen Wan, Li Zhang, Yingchun Univ Sci & Technol Beijing Sch Mat Sci & Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst 95 Zhongguancun E Rd Beijing 100190 Peoples R China Ten Dimens Guangdong Technol Co Ltd 33 Informat Rd Foshan 528225 Peoples R China

Li2TiO3 is regarded as a vital tritium breeder candidate due to its favorable properties in Helium-Cooled Pebble Bed. However, the point contact stress breakage for traditional pebble structure can cause the blocking of tritium recovery system, and seriously reduce the safety and tritium self-sufficiency ability of fusion reactor. For addressing above problems, in this paper, complex-shaped Li2TiO3 cubic units were manufactured firstly via lithography-based 3D printing. According to the principle of tritium transport, the cubic unit structures with ordered channels were built and evaluated by finite element analysis. The photocuring parameters were opti-mized to improve printing accuracy by establishing an overgrowth model. After optimizing the sintering schedule, the Li2TiO3 cubic units with excellent relative density (92.3%TD) and packing factor (80.99%) were yielded, and it also had an outstanding compressive strength (84.4 MPa). The results of thermal cycling test shown that the Li2TiO3 cubic units possessed a prominent thermal cyclic durability.

关键词： 3D printing Digital light process Tritium breeder material Lithium titanate

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Smart management of Sand-Breaking systems for Taklimakan SandSea Highway: A Parallel Intelligence and Fuzzy control Approach

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7348-7358页

作者： Chang, Fangle Wang, Xiujuan Kang, Mengzhen Tao, Yu Yang, Jiahong Lei, Jiaqiang Wang, Fei-Yue Zhejiang Univ Ningbo Innovat Ctr Ningbo 315100 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 650100 Peoples R China

The planning, construction, and maintenance of sand-breaking systems around highway in Taklimakan SandSea mainly depend on expert's empirical knowledge, and face the problems of time-consuming management, unpredictable system performance, and few available data. This work proposes a management and control method of sand-breaking systems that follows artificial system, computational experiments, and parallel execution (ACP) theory. Expert knowledge was extracted to develop artificial sand-breaking system. conditional tabular generative adversarial network (CTGAN) was developed to achieve data augmentation. Computational experiments were implemented to evaluate artificial system performance by predicting whether the system can reach the set age limit. Using fuzzy control, the real sand-breaking system and the artificial one can learn from each other in parallel execution to provide decision support for sand-breaking system management. This approach provides a human-machine hybrid parallel intelligence system for complex sand-breaking systems management.

关键词： Road transportation Disasters Indexes Wind speed Vectors System performance Fuzzy control Conditional tabular generative adversarial network (CTGAN) fuzzy control parallel intelligence sand-breaking systems Taklimakan SandSea

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Towards City-Scale Traffic Prediction: Cluster Graph Wavelet Networks 25

Towards City-Scale Traffic Prediction: Cluster Graph Wavelet...

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IEEE 25th International Conference on intelligent Transportation systems (ITSC)

作者： Wang, Jingcheng Zhang, Yong Lv, Yisheng Hu, Yongli Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Inst Artificial Intelligence Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

With the development of intelligent transportation systems, the potential need for city-level traffic prediction is emerging. However, some large-scale traffic forecasting methods divide urban areas into grids, which cannot achieve nodelevel prediction. Meanwhile, mainstream graph convolution models have limitations on the scale of the input graph. For this problem, we propose cluster graph convolution networks with spectral graph wavelet kernels. The proposed method divides the large graph into subgraphs and then builds localized spatial dependency with spectral graph wavelet convolution. The model could achieve node-level traffic prediction on a large graph with an end-to-end framework. The experiments are conducted on the historical dataset of Beijing, covering more than 8,000 road segments. The experimental results demonstrate the effectiveness of the proposed method.

关键词： Convolution Roads Clustering methods Urban areas Predictive models Feature extraction Prediction algorithms

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Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON intelligent VEHICLES 2023年第3期8卷 2049-2061页

作者： Chen, Junzhou Zhao, Nan Zhang, Ronghui Chen, Long Huang, Kai Qiu, Zhijun Sun Yat sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Sun Yat sen Univ Guangdong Prov Key Lab Fire Sci & Intelligent Emer Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat sen Univ Sch Data & Comp Sci Guangzhou 510275 Peoples R China Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2R3 Canada

Due to the rising cost of human resources in road maintenance and the pursuit of efficiency, autonomous road inspection vehicles are developed for intelligent detection of road disease to prevent severe traffic disasters in the early stages. Nevertheless, as a prevalent road disease, road cracks are diverse and susceptible to shadows, weather changes, and noise in data acquisition. Moreover, they usually appear with thin shapes that are hard to detect correctly by existing methods. To handle this problem, more details of the road cracks need to be better analyzed. In this article, we propose a refined road crack detection method named locally enhanced cross-shaped windows transformer (LECSFormer), which adopts a delicate design of the encoder-decoder structure. The encoder employs window-based transformer blocks to model long-range dependencies. Each transformer block ensembles the locally enhanced module to enrich the local contextual information, and token shuffle operation is applied to build cross-windows connections. The decoder uses dense connections to fuse multi-scale information, and the feature fusion module fuses hierarchical features and reweights them by the channel attention mechanism. The proposed method outperforms other state-of-the-art methods with ODS of 0.963, 0.917, 0.952, and 0.953 on four challenging datasets, CrackTree260, CrackLS315, Stone331, and CRKWH100. It can accurately detect cracks in road surfaces and support intelligent preventive maintenance of roads.

关键词： Roads Transformers Feature extraction Diseases Inspection Shape intelligent vehicles Refined crack detection window-based trans-former semantic segmentation autonomous road inspection vehicles

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A Deep Learning Method for Breast Cancer Classification in the Pathology Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 5025-5032页

作者： Liu, Min Hu, Lanlan Tang, Ying Wang, Chu He, Yu Zeng, Chunyan Lin, Kun He, Zhizi Huo, Wujie Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Qingdao Peoples R China

Objective: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

关键词： Feature extraction Pathology Deep learning Breast cancer Training Transfer learning Smoothing methods Deep learning breast cancer transfer learning loss function AlexNet

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A Paradigm Shift for Modeling and Operation of Oil and Gas: From Industry 4.0 in CPS to Industry 5.0 in CPSS

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9186-9193页

作者： Wang, Xingxia Wang, Yutong Yang, Jing Wang, Xiao Meng, Zonglin Liu, Zhaohai Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 266114 Peoples R China Qingdao Acad Intelligent Ind Qingdao 230031 Peoples R China Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90024 USA Tangshan Jidong Petr Machinery Co Ltd Tangshan 063299 Hebei Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Under the impetus of Industry 4.0, oil and gas is undergoing an unprecedented digital transformation, and many innovative ideas are proposed. However, the achieved higher efficiency comes at the expense of reduced social consideration, which necessitates more society-related research and calls for a technological paradigm shift. To meet this challenge, this article introduces parallel oil and gas within the framework of parallel intelligence-based Industry 5.0, providing a pivotal transition from cyber-physical systems (CPS) to cyber-physical-social systems (CPSS). A comprehensive review of oil and gas industrial chain that covers upstream, midstream, and downstream is first outlined. Grounded in Industry 5.0, the main principles of parallel oil and gas are then provided, where three kinds of workers (biological workers, digital workers, and robotic workers) and three operation modes (autonomous modes, parallel modes, and expert/emergency modes) collaborate to develop more human-oriented and resilient systems. To realize the desired vision, some enabling technologies, including blockchain, smart contracts, and industrial foundation models, are thereafter listed. Furthermore, computational experiments on fault diagnosis of sucker rod pumps are conducted to illustrate the feasibility and effectiveness of our proposed mechanism. Finally, the future trend toward imaginative intelligence is envisaged.

关键词： Oils Fifth Industrial Revolution Production Fourth Industrial Revolution Biology Service robots Transportation Cyber-physical systems (CPS) cyber-physical-social systems (CPSS) Industry 4.0 Industry 5.0 parallel intelligence parallel oil and gas

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Platform development and gliding optimization of a robotic flying fish with morphing pectoral fins

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BIOINSPIRATION & BIOMIMETICS 2023年第3期18卷 036010-036010页

作者： Chen, Di Wu, Zhengxing Dong, Huijie Meng, Yan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Syst Minist Educ Taiyuan 030024 Peoples R China

The aquatic-aerial robot with the free interface crossing can enhance adaptability in complex aquatic environments. However, its design is extremely challenging for the striking discrepancies in propulsion principles. The flying fish in nature exhibits remarkable multi-modal cross-domain locomotion capability, such as high-maneuvers swimming, agile water-air crossing, and long-distance gliding, providing extensive inspiration. In this paper, we present a unique aquatic-aerial robotic flying fish with powerful propulsion and a pair of morphing wing-like pectoral fins to realize cross-domain motion. Furthermore, to explore the gliding mechanism of flying fish, a dynamic model with a morphing structure of pectoral fins is established, and a double deep Q-network-based control strategy is proposed to optimize the gliding distance. Finally, experiments were conducted to analyze the locomotion of the robotic flying fish. The results suggest that the robotic flying fish can successfully perform the 'fish leaping and wing spreading' cross-domain locomotion with an exiting speed of 1.55 m s(-1) (5.9 body lengths per second, BL/s) and a crossing time of 0.233 s indicating its great potential in cross-domain. Simulation results have validated the effectiveness of the proposed control strategy and indicated that the dynamical adjustment of morphing pectoral fins contributes to improving the gliding distance. The maximum gliding distance has increased by 7.2%. This study will offer some significant insights into the system design and performance optimization of aquatic-aerial robots.

关键词： bioinspired robotic fish morphing pectoral fin aquatic-aerial crossing motion gliding motion optimization

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Pavement Defect Detection With Deep Learning: A Comprehensive Survey

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON intelligent VEHICLES 2024年第3期9卷 4292-4311页

作者： Fan, Lili Wang, Dandan Wang, Junhao Li, Yunjie Cao, Yifeng Liu, Yi Chen, Xiaoming Wang, Yutong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Lab Electromagnet Space Cognit & Intelligent Contr Beijing 100081 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Nanjing 213300 Jiangsu Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.

关键词： Deep learning pavement defect detection computer vision image processing 3D image

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning 19

Geometry Problem Solving Based on Counter-Factual Evolutiona...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Song, Bing Xiong, Gang Shen, Zhen Zhu, Fenghua Lv, Yisheng Ye, Peijun Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology China Chinese Academy of Sciences The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266000 China

ISBN: (纸本)9798350320695

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models. © 2023 IEEE.

关键词： Geometry

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