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17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Intelligent Diagnosis Platform for Alzheimer’s Disease Based on Multi-modal Knowledge Graph 17th

Intelligent Diagnosis Platform for Alzheimer’s Disease Base...

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17th International Conference on Brain Informatics, BI 2024

作者： Li, Jiaqiang Yang, Peng Cao, Jiuwen Guan, Zhenghua Qu, Junlong Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound. School of Biomedical Engineering Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang310010 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is an incurable chronic neurodegenerative disease. If it can be diagnosed early and intervened in time clinically, it can effectively delay the development. However, early diagnosis of AD is difficult and there is still a lack of intelligent diagnosis platforms for AD. In order to solve this problem, we propose an AD intelligent diagnosis platform based on a multimodal knowledge graph. We apply the AD intelligent diagnosis model on the intelligent diagnosis platform, which realizes the early diagnosis of AD based on patient data and gives AD diagnosis advice. We verify the diagnostic model based on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We build a medical knowledge graph based on the current open source medical knowledge database. The experimental results show that the proposed method can be applied to the early diagnosis of AD. Furthermore, it can give appropriate diagnosis advice, which is expected to reduce the burden on doctors and realize the intelligent diagnosis and treatment of AD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Detailed Routing Short Violations Prediction Method Using Graph Convolutional Network 15

Detailed Routing Short Violations Prediction Method Using Gr...

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15th IEEE International Conference on Solid-State and Integrated Circuit technology, ICSICT 2020

作者： Chen, Xuan Di, Zhi-Xiong Wu, Wei Feng, Quan-Yuan Shi, Jiang- Yi School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Schoo1 of Microelectronics XiDian University Xian710071 China

ISBN: (纸本)9781728162355

With the continuous shrink of IC manufacturing process, how to accurately predict the routing violations before detailed routing is becoming more and more important to improve the placement quality. In this paper, we propose a detailed routing short violations prediction model based on the Graph Convolutional Network (GCN). Based on the key features extracted from the placement and detailed routing stage separately, we train a GCN model to build a map relationship between these two stages. Through this model, we can predict the detailed routing short violations at placement stage successfully. Experiments show that the average prediction accuracy of our model is 94% which is higher than existing method based on machine learning. © 2020 IEEE.

关键词： Forecasting

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Detailed Routing Short Violations Prediction Method Using Graph Convolutional Network

Detailed Routing Short Violations Prediction Method Using Gr...

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International Conference on Solid-State and Integrated Circuit technology

作者： Xuan Chen Zhi-Xiong Di Wei Wu Quan-Yuan Feng Jiang- Yi Shi The School of Information Science and Technology Southwest Jiaotong University Chengdu China Schoo1 of Microelectronics XiDian University Xian China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

关键词： Predictive models Routing Feature extraction Pins Machine learning Computational modeling Benchmark testing

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Conflict Analysis and Detection Based on Model Checking for Spatial Access Control Policy

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Tsinghua science and technology 2017年第5期22卷 478-488页

作者： Aijuan Zhang Cheng Ji Yu Bao Xin Li the School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China the Schoo1 of Environment Science and Spatial Inform atics China University of M ining and TechnologyXuzhou 221116China

In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.

关键词： spatial object multi-granularity conflict detection model-checking

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Optimization of FinFET-based circuits using a dual gate pitch technique 15

Optimization of FinFET-based circuits using a dual gate pitc...

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IEEE International Conference on computer-Aided Design

作者： Sravan K. Marella Amit Ranjan Trivedi Saibal Mukhopadhyay Sachin S. Sapatnekar Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN Schoo1 of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (纸本)9781467383899

Source/drain stressors in FinFET-based circuits lose their effectiveness at smaller contacted gate pitches. To improve circuit performance, a dual gate pitch technique is proposed in this work, where standard cells with double the gate pitch are selectively used on the gates of the circuit critical paths, at minimal area and power costs. A stress-aware library characterization is performed for FinFET-based standard cells by obtaining stress distributions using finite element simulations on a subset of structures. The stresses are then employed to create look-up tables for mobility multipliers and threshold voltage shifts, for subsequent performance characterization of FinFET-based standard cells. Finally, a circuit delay optimizer is applied using the dual gate pitch approach and is compared with an alternative gate sizing approach. Using a combination of gate sizing and the dual gate pitch approach, it is shown that the average power delay product improves by 12.9% and 15.9% in 14nm and 10nm technologies, respectively.

关键词： Logic gates Stress Standards FinFETs Layout Metals

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Localization based stereo speech separation using deep networks

Localization based stereo speech separation using deep netwo...

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International Conference on Digital Signal Processing (DSP)

作者： Yang Yu Wenwu Wang Jian Luo Pengming Feng Schoo1 of Marine Science and Technology Northwestern Polytechnical University Xi'an China Centre for Vision Speech and Signal Processing University of Surrey UK

ISBN: (纸本)9781479980598

Time-frequency (T-F) masking is an effective method for stereo speech source separation. However, reliable estimation of the T-F mask from sound mixtures is a challenging task, especially when room reverberations are present in the mixtures. In this paper, we proposed a new stereo speech separation system where deep networks are used to generate soft T-F mask for separation. More specifically, the deep network, which is composed of two sparse autoencoders and a softmax classifier, is used to estimate the orientations of the target and interferers at each T-F unit, based on low-level features, such as mixing vector (MV), interaural level and phase difference (IPD/ILD). The deep network is trained by a greedy layer-wise method using a dataset that was generated by convolving room impulse responses (RIRs) with clean speech signals positioned in different angles with respect to the sensors. With the trained deep networks, the probability that each T-F unit belongs to the target or interferer can be estimated based on the localization cues for generating the soft mask. Experiments based on real binaural RIRs and TIMIT dataset are provided to show the performance of the proposed system for reverberant speech mixtures, as compared with a model based T-F masking technique proposed recently.

关键词： Speech Training Feature extraction Speech processing Source separation Neural networks Reverberation

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A new smart distribution grid fault self-healing system based on traveling-wave

A new smart distribution grid fault self-healing system base...

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2013 IEEE Industry Applications Society Annual Meeting, IAS 2013

作者： Qianqian, Liu Zeng, Xiangjun Xue, Ma Xiang, Li Changsha University of Science and Technology Chilin Street Changsha 410076 China Key Laboratory of Smart Grids Operation and Control Schoo1 of E1ectrical and Information Engineering China

ISBN: (纸本)9781467352024

Considering painful lessons caused by large scale blackouts at home and abroad, building a smart distribution grid fault self-healing system is of great urgency. Fast and accurate fault location is the key technology of self-healing, therefore, a new smart distribution grid self-healing system based on traveling-wave is proposed to greatly reduce users' outage time and range, which consists of three parts: fault detection, fault location, fault isolation and power service restoration. The fault detection is based on secondary side traveling-waves of voltage transformer. A fault is identified when the first measured traveling-wave is greater than the setting threshold. The fault location is based on multi-terminal traveling-wave arriving time. Fault distance is calculated precisely via the least square fitting result. Afterwards faults of distribution grid, which are distinguished, can be isolated intelligently. Extensive simulative and experimental results demonstrate that the accuracy and self-healing speed of the proposed system is significantly improved and "Seamless self-healing" is expected to achieve. © 2013 IEEE.

关键词： Electric fault location

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Cascading network failure based on local load distribution and non-linear relationship between initial load and capacity

Cascading network failure based on local load distribution a...

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Ma, Yi-Hua Zhang, Dong-Li School of Management Hebei University of Technology Tianjin 300401 China School of Business and Management Hebei University of Economics and Business Shijiazhuang 050061 China Schoo1 of Management and Economics Hebei University of Science and Technology Shijiazhuang 050018 China

ISBN: (纸本)9781467314855

Inspired by previous existing works, based on the local preferential redistribution rule of the load and the non-linear relation between load and capacity, we put forward a cascading model which is more practical and more suitable for real networks. We analyze the model theoretically and simulate it on BA scale-free network. In comparison with the strongest robustness against cascading failures of the linear load-capacity model in the case of α = 0.5 , we find that the robustness of the network can reach stronger in the case of δ ≠ 1, which is a tunable parameter controlling the strength of the capacity of node in our model. The results show that the model is effective. So it may be helpful to control cascading network failure and research on cascading failure deeply. © 2012 IEEE.

关键词： Complex networks

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A Simple Two-Level Bayesian Network and its Application in the HPLC-MS Data

A Simple Two-Level Bayesian Network and its Application in t...

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第2届大连国际色谱学术报告会、第37届国际高效液相色谱及相关技术会议、第18届全国色谱学术报告会

作者： Ping Ma Xiaohui Lin Peivuan Yin Guowang Xu Schoo1 of Computer Science and Technology Dalian University of Technology Dalian 116024 China CAS Key Laboratory of Separation Science for Analytical Chemistry Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China

Bayesian network (BN) [1] is a very simple and efficient classifier, and has a wide application in many *** its structure learning is quite difficult, especially for high dimensional data, such as liquid chromatography-mass spectrometry (LC-MS) *** the number of the possible structures grows exponentially with the number of *** process LC-MS data effectively, we applied an efficient BN structure learning method which constructs a two-level BN (BN-TwoL).BN-TwoL sets the class label C as the root of the *** variables which are conditional independent of each other lie in different sub-trees of the root.

关键词： Classification Bayesian Networks Liquid Chromatography

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