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学者姓名：刘际明
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Abstract ：
High-strength steel columns are commonly connected by welding. However, the construction environment and quality of construction workers have a large impact on welding quality, especially on a construction site. This paper investigates the mechanical behavior of bolted built-up steel column, which is built using bolts instead of welds. Six bolted built-up steel columns were made using high-strength steel, referred as to H-SA700, and tested under axial loading to investigate the bearing capacity and buckling deformation. The main experimental parameter was the slenderness ratio. In addition, finite element models were established on the same scale as the tested specimens, and a supplementary parametric analysis on the effects of intervening plate thickness, intervening plate width, angle section side length, and bolt pitch on the bearing capacity of specimens was conducted. Moreover, the experimental and numerical results were compared with the calculated ultimate strength obtained from the corresponding codes of GB 50017-2017, ANSI/AISC 360-10, EN 1993-1-1, and AIJ-2002. The results showed that both slenderness ratio and angle section side length were the main factors on the bearing capacity. Japanese code gave the best predictions for the ultimate load of bolted built-up steel columns.
Keyword ：
Built-up steel column Built-up steel column Parametric analysis Parametric analysis Axial compression loading test Axial compression loading test Finite element analysis Finite element analysis
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| GB/T 7714 | Mou, Ben , Zhou, Yang , Qiao, Qiyun et al. Compressive behavior of bolted built-up steel columns fabricated using angle section columns [J]. | JOURNAL OF BUILDING ENGINEERING , 2021 , 44 . |
| MLA | Mou, Ben et al. "Compressive behavior of bolted built-up steel columns fabricated using angle section columns" . | JOURNAL OF BUILDING ENGINEERING 44 (2021) . |
| APA | Mou, Ben , Zhou, Yang , Qiao, Qiyun , Liu, Jiming . Compressive behavior of bolted built-up steel columns fabricated using angle section columns . | JOURNAL OF BUILDING ENGINEERING , 2021 , 44 . |
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Abstract ：
Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.
Keyword ：
Recurrent neural networks Recurrent neural networks Computational modeling Computational modeling Feature extraction Feature extraction medication knowledge medication knowledge critical patients critical patients attention mechanism attention mechanism simple graph convolution simple graph convolution Correlation Correlation medical records medical records Predictive models Predictive models Medication combination prediction Medication combination prediction Medical diagnostic imaging Medical diagnostic imaging temporal sequence temporal sequence Medical services Medical services
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| GB/T 7714 | Wang, Haiqiang , Wu, Yinying , Gao, Chao et al. Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution [J]. | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2021 , 25 (10) : 3995-4004 . |
| MLA | Wang, Haiqiang et al. "Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution" . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 25 . 10 (2021) : 3995-4004 . |
| APA | Wang, Haiqiang , Wu, Yinying , Gao, Chao , Deng, Yue , Zhang, Fan , Huang, Jiajin et al. Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2021 , 25 (10) , 3995-4004 . |
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| GB/T 7714 | Zhong, Ning , Liu, Jiming , Shi, Yong et al. An interview with Professor Raj Reddy on Web Intelligence (WI) and Computational Social Science (CSS) [J]. | Web Intelligence , 2018 , 16 (3) : 143-146 . |
| MLA | Zhong, Ning et al. "An interview with Professor Raj Reddy on Web Intelligence (WI) and Computational Social Science (CSS)" . | Web Intelligence 16 . 3 (2018) : 143-146 . |
| APA | Zhong, Ning , Liu, Jiming , Shi, Yong , Yao, Yiyu . An interview with Professor Raj Reddy on Web Intelligence (WI) and Computational Social Science (CSS) . | Web Intelligence , 2018 , 16 (3) , 143-146 . |
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Abstract ：
A Bayesian network (BN) is an important probabilistic model in the field of artificial intelligence and a powerful formalism used to describe uncertainty in the real world. As science and technology develop, considerable data on complex systems have been acquired by various means, which presents a significant challenge regarding how to accurately and robustly learn a network structure for a complex system. To address this challenge, many BN structure learning methods based on swarm intelligence have been developed. In this study, we perform a systematic comparison of three typical methods based on ant colony optimization, artificial bee colony algorithm, and bacterial foraging optimization. First, we analyze and summarize their main characteristics from the perspective of stochastic searching. Second, we conduct thorough experimental comparisons to examine the roles of different mechanisms in each method by means of multiaspect metrics, i.e., the K2 score, structural differences, and execution time. Next, we perform further experiments to validate the robustness of different algorithms on some benchmark data sets with noise. Finally, we present the prospects and references for researchers who are engaged in learning BN networks.
Keyword ：
Bayesian network structure learning Bayesian network structure learning Artificial bee colony algorithm Artificial bee colony algorithm Bacterial foraging optimization Bacterial foraging optimization Ant colony optimization Ant colony optimization Swarm intelligence Swarm intelligence
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| GB/T 7714 | Ji, Junzhong , Yang, Cuicui , Liu, Jiming et al. A comparative study on swarm intelligence for structure learning of Bayesian networks [J]. | SOFT COMPUTING , 2017 , 21 (22) : 6713-6738 . |
| MLA | Ji, Junzhong et al. "A comparative study on swarm intelligence for structure learning of Bayesian networks" . | SOFT COMPUTING 21 . 22 (2017) : 6713-6738 . |
| APA | Ji, Junzhong , Yang, Cuicui , Liu, Jiming , Liu, Jinduo , Yin, Baocai . A comparative study on swarm intelligence for structure learning of Bayesian networks . | SOFT COMPUTING , 2017 , 21 (22) , 6713-6738 . |
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Abstract ：
Bacterial foraging optimization (BFO) has attracted much attention and been widely applied in a variety of scientific and engineering applications since its inception. However, the fixed step size and a lack of information communication between bacterial individuals during the optimization process have significant impacts on the performance of BFO To address these issues on real-parameter single objective optimization problems, this paper proposes a new bacterial foraging optimizer using new designed chemotaxis and conjugation strategies (BFO-CC). Via the new chemotaxis mechanism, each bacterium randomly selects a standard-basis-vector direction for swimming or tumbling; this approach may obviate calculating a random unit vector and could effectively get rid of interfering with each other between different dimensions. At the same time, the step size of each bacterium is adaptively adjusted based on the evolutionary generations and the information of the globally best individual, which readily makes the algorithm keep a better balance between a local search and global search. Moreover, the new designed conjugation operator is employed to exchange information between bacterial individuals; this feature can significantly improve convergence. The performance of the BFO-CC algorithm was comprehensively evaluated by comparing it with several other competitive algorithms (based on swarm intelligence) on both benchmark functions and real-world problems. Our experimental results demonstrated excellent performance of BFO-CC in terms of solution quality and computational efficiency. (C) 2016 Elsevier Inc. All rights reserved.
Keyword ：
Standard basis vector Standard basis vector Nonuniform step size Nonuniform step size Bacterial foraging optimization Bacterial foraging optimization Conjugation Conjugation
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| GB/T 7714 | Yang, Cuicui , Ji, Junzhong , Liu, Jiming et al. Bacterial foraging optimization using novel chemotaxis and conjugation strategies [J]. | INFORMATION SCIENCES , 2016 , 363 : 72-95 . |
| MLA | Yang, Cuicui et al. "Bacterial foraging optimization using novel chemotaxis and conjugation strategies" . | INFORMATION SCIENCES 363 (2016) : 72-95 . |
| APA | Yang, Cuicui , Ji, Junzhong , Liu, Jiming , Yin, Baocai . Bacterial foraging optimization using novel chemotaxis and conjugation strategies . | INFORMATION SCIENCES , 2016 , 363 , 72-95 . |
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Abstract ：
Community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of the method lies in the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the community detection problem. First, the method uses a connection-based encoding scheme to model an agent and a random-walk behavior to construct a solution. Next, it applies three evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents and solution evolution. We tested the performance of our method using synthetic and real-world networks. The results show its capability in effectively detecting community structures.
Keyword ：
multiagent evolutionary method multiagent evolutionary method complex network complex network random walk random walk community structure detection community structure detection
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| GB/T 7714 | Ji, Junzhong , Jiao, Lang , Yang, Cuicui et al. A Multiagent Evolutionary Method for Detecting Communities in Complex Networks [J]. | COMPUTATIONAL INTELLIGENCE , 2016 , 32 (4) : 587-614 . |
| MLA | Ji, Junzhong et al. "A Multiagent Evolutionary Method for Detecting Communities in Complex Networks" . | COMPUTATIONAL INTELLIGENCE 32 . 4 (2016) : 587-614 . |
| APA | Ji, Junzhong , Jiao, Lang , Yang, Cuicui , Liu, Jiming . A Multiagent Evolutionary Method for Detecting Communities in Complex Networks . | COMPUTATIONAL INTELLIGENCE , 2016 , 32 (4) , 587-614 . |
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| GB/T 7714 | Chen, Jianhui , Ma, Jianhua , Zhong, Ning et al. WaaS: Wisdom as a Service [J]. | IEEE INTELLIGENT SYSTEMS , 2014 , 29 (6) : 40-47 . |
| MLA | Chen, Jianhui et al. "WaaS: Wisdom as a Service" . | IEEE INTELLIGENT SYSTEMS 29 . 6 (2014) : 40-47 . |
| APA | Chen, Jianhui , Ma, Jianhua , Zhong, Ning , Yao, Yiyu , Liu, Jiming , Huang, Runhe et al. WaaS: Wisdom as a Service . | IEEE INTELLIGENT SYSTEMS , 2014 , 29 (6) , 40-47 . |
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| GB/T 7714 | Zhong, Ning , Liu, Jiming , Ma, Jianhua . Guest editorial-Wisdom Web of Things (W2T) [J]. | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS , 2013 , 16 (4) : 355-356 . |
| MLA | Zhong, Ning et al. "Guest editorial-Wisdom Web of Things (W2T)" . | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 16 . 4 (2013) : 355-356 . |
| APA | Zhong, Ning , Liu, Jiming , Ma, Jianhua . Guest editorial-Wisdom Web of Things (W2T) . | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS , 2013 , 16 (4) , 355-356 . |
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Abstract ：
The rapid development of the Internet and the Internet of Things accelerates the emergence of the hyper world. It has become a pressing research issue to realize the organic amalgamation and harmonious symbiosis among humans, computers, and things in the hyper world, which consists of the social world, the physical world, and the information world (cyber world). In this paper, the notion of Wisdom Web of Things (W2T) is proposed in order to address this issue. As inspired by the material cycle in the physical world, the W2T focuses on the data cycle, namely "from things to data, information, knowledge, wisdom, services, humans, and then back to things." A W2T data cycle system is designed to implement such a cycle, which is, technologically speaking, a practical way to realize the harmonious symbiosis of humans, computers, and things in the emerging hyper world.
Keyword ：
Transparent service Transparent service Internet of Things Internet of Things Data cycle Data cycle Wisdom Web of Things Wisdom Web of Things Active service Active service
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| GB/T 7714 | Zhong, Ning , Ma, Jian Hua , Huang, Run He et al. Research challenges and perspectives on Wisdom Web of Things (W2T) [J]. | JOURNAL OF SUPERCOMPUTING , 2013 , 64 (3) : 862-882 . |
| MLA | Zhong, Ning et al. "Research challenges and perspectives on Wisdom Web of Things (W2T)" . | JOURNAL OF SUPERCOMPUTING 64 . 3 (2013) : 862-882 . |
| APA | Zhong, Ning , Ma, Jian Hua , Huang, Run He , Liu, Ji Ming , Yao, Yiyu , Zhang, Yao Xue et al. Research challenges and perspectives on Wisdom Web of Things (W2T) . | JOURNAL OF SUPERCOMPUTING , 2013 , 64 (3) , 862-882 . |
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Abstract ：
The dynamic and robust characteristics of intrinsic functional connectivity of coherent spontaneous activity are critical for the brain functional stability and flexibility. Studies have demonstrated modulation of intrinsic connectivity within local spatial patterns during or after task performance, such as the default mode network (DMN) and task-specific networks. Moreover, recent studies have compared the global spatial pattern in different tasks or over time. However, it is still unclear how the large-scale intrinsic connectivity varies during and after a task. To better understand this issue, we conducted a functional MRI experiment over three sequential periods: an active semantic-matching task period and two rest periods, before and after the task respectively (namely, on-task state and pre-/post-task resting states), to detect task-driven effect on the dynamic large-scale intrinsic organization in both on-task state and post-task resting state. Three hierarchical levels were investigated, including (a) the whole brain small-world topology, (b) the whole pairwise functional connectivity patterns both within the DMN and between the DMN and other regions (i.e., the global/full DMN topography), and (c) the DMN nodal graph properties. The major findings are: (1) The large-scale small-world configuration of brain functional organization is robust, regardless of the behavioral state changing, while it varies adaptively with significantly higher local efficiency and lower global efficiency during the on-task state (P<0.05, Monte-Carlo corrected); (2) The DMN may be essentially engaged during both task and post-task processes with adaptively varied spatial patterns and nodal graph properties. The present study provides further insights into the robustness and plasticity of the brain intrinsic organization over states, which may be the basis of memory and learning in the brain. (C) 2012 Elsevier Inc. All rights reserved.
Keyword ：
Default mode network Default mode network Task-driven states Task-driven states Topology Topology Small world Small world Modulation Modulation
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| GB/T 7714 | Wang, Zhijiang , Liu, Jiming , Zhong, Ning et al. Changes in the brain intrinsic organization in both on-task state and post-task resting state [J]. | NEUROIMAGE , 2012 , 62 (1) : 394-407 . |
| MLA | Wang, Zhijiang et al. "Changes in the brain intrinsic organization in both on-task state and post-task resting state" . | NEUROIMAGE 62 . 1 (2012) : 394-407 . |
| APA | Wang, Zhijiang , Liu, Jiming , Zhong, Ning , Qin, Yulin , Zhou, Haiyan , Li, Kuncheng . Changes in the brain intrinsic organization in both on-task state and post-task resting state . | NEUROIMAGE , 2012 , 62 (1) , 394-407 . |
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