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学者姓名：陈国龙
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Abstract ：
对体育赛事转播的保护，司法实践倾向于采用将体育赛事节目认定为作品的方式，但该做法对“独创性”判断标准依赖较强，且与“著作权-邻接权”的二元区分逻辑也稍有相悖，难以用来规范赛事传播中的盗播行为。相较而言，广播组织权作为规制盗播的天然制度工具，能够较好地实现对传播行为的保护。但是，随着网络转播的普及，网播组织已成为赛事转播的重要主体，碍于其不属于广播组织者，导致广播组织权无法全面保护体育赛事转播。有鉴于此，可参照国际层面共识和立法经验，扩大广播组织权的主体保护范围，使网播组织能够适用《著作权法》第47条，以广播组织权为基础性保护手段，同时结合其他保护方式，全面保障体育赛事转播权益。
Keyword ：
体育赛事节目 体育赛事节目 广播组织权 广播组织权 网播组织 网播组织 视听作品 视听作品
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| GB/T 7714 | 李智 , 陈国龙 . 体育赛事转播的著作权法保护研究——以广播组织权为分析中心 [J]. | 太原理工大学学报(社会科学版) , 2024 , 42 (04) : 57-65 . |
| MLA | 李智 等. "体育赛事转播的著作权法保护研究——以广播组织权为分析中心" . | 太原理工大学学报(社会科学版) 42 . 04 (2024) : 57-65 . |
| APA | 李智 , 陈国龙 . 体育赛事转播的著作权法保护研究——以广播组织权为分析中心 . | 太原理工大学学报(社会科学版) , 2024 , 42 (04) , 57-65 . |
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Abstract ：
对体育赛事转播的保护,司法实践倾向于采用将体育赛事节目认定为作品的方式,但该做法对"独创性"判断标准依赖较强,且与"著作权-邻接权"的二元区分逻辑也稍有相悖,难以用来规范赛事传播中的盗播行为.相较而言,广播组织权作为规制盗播的天然制度工具,能够较好地实现对传播行为的保护.但是,随着网络转播的普及,网播组织已成为赛事转播的重要主体,碍于其不属于广播组织者,导致广播组织权无法全面保护体育赛事转播.有鉴于此,可参照国际层面共识和立法经验,扩大广播组织权的主体保护范围,使网播组织能够适用《著作权法》第47条,以广播组织权为基础性保护手段,同时结合其他保护方式,全面保障体育赛事转播权益.
Keyword ：
体育赛事节目 体育赛事节目 广播组织权 广播组织权 网播组织 网播组织 视听作品 视听作品
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| GB/T 7714 | 李智 , 陈国龙 . 体育赛事转播的著作权法保护研究 [J]. | 太原理工大学学报（社会科学版） , 2024 , 42 (4) : 57-65 . |
| MLA | 李智 等. "体育赛事转播的著作权法保护研究" . | 太原理工大学学报（社会科学版） 42 . 4 (2024) : 57-65 . |
| APA | 李智 , 陈国龙 . 体育赛事转播的著作权法保护研究 . | 太原理工大学学报（社会科学版） , 2024 , 42 (4) , 57-65 . |
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Social Internet of Things is the fusion carrier of social network and Internet of Things. In the social Internet of Things, millions of different intelligent objects connect and communicate with each other, and social data emerge rapidly, which puts forward higher requirements for the rapid dissemination of valuable information. As an important means of information retrieval, the research and application of multi-label classification technology in social Internet of Things environment is relatively few. Characterized by multi-labels cognitive learning, fast response, concept drifting and huge solution space, the multi-label learning becomes more complicated under data stream environment. To overcome the problems above, this paper proposed a multi-label algorithm based on kernel extreme learning machine. In the training phase, both Cholesky matrix decomposition inverse method and matrix block method were deployed on the Spark platform to solve the inverse problem of large matrix, thus improving the modeling efficiency. In addition, incremental learning of the model was realized from the two aspects of example increment and class increment. The online sequential extreme learning machine is improved to adjust the network weights and realize incremental updating of input dimension. Meanwhile, the output layer nodes were divided and assembled in the way of "concept group," which made the single classifier model structure scalable. And the label incremental learning is realized in the output dimension. Experimental results on large-scale datasets and practical datasets under stream environment demonstrate that the proposed method provides an efficient solution to multi-label classification.
Keyword ：
Class-incremental learning Class-incremental learning Data stream Data stream Example-incremental learning Example-incremental learning Extreme learning machines Extreme learning machines Internet of Things Internet of Things Kernel Kernel Kernel extreme learning machine Kernel extreme learning machine Matrix decomposition Matrix decomposition Multi-label learning Multi-label learning Periodic structures Periodic structures Prediction algorithms Prediction algorithms SIoT SIoT Training Training
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| GB/T 7714 | Luo, Fangfang , Liu, Genggeng , Guo, Wenzhong et al. ML-KELM: A Kernel Extreme Learning Machine Scheme for Multi-Label Classification of Real Time Data Stream in SIoT [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2022 , 9 (3) : 1044-1055 . |
| MLA | Luo, Fangfang et al. "ML-KELM: A Kernel Extreme Learning Machine Scheme for Multi-Label Classification of Real Time Data Stream in SIoT" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 9 . 3 (2022) : 1044-1055 . |
| APA | Luo, Fangfang , Liu, Genggeng , Guo, Wenzhong , Chen, Guolong , Xiong, Naixue . ML-KELM: A Kernel Extreme Learning Machine Scheme for Multi-Label Classification of Real Time Data Stream in SIoT . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2022 , 9 (3) , 1044-1055 . |
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Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today's microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.
Keyword ：
biology patterns biology patterns data-driven data-driven digital image correlation digital image correlation medicine medicine microscopes microscopes
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| GB/T 7714 | Ni, Shiwei , Chen, Fei , Chen, Guolong et al. Mathematical model and genomics construction of developmental biology patterns using digital image technology [J]. | FRONTIERS IN GENETICS , 2022 , 13 . |
| MLA | Ni, Shiwei et al. "Mathematical model and genomics construction of developmental biology patterns using digital image technology" . | FRONTIERS IN GENETICS 13 (2022) . |
| APA | Ni, Shiwei , Chen, Fei , Chen, Guolong , Yang, Yufeng . Mathematical model and genomics construction of developmental biology patterns using digital image technology . | FRONTIERS IN GENETICS , 2022 , 13 . |
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Abstract ：
As the scale of integrated circuits grows, the number of nets greatly increases, which makes the runtime of layer assignment algorithm increase and becomes an important limiting factor of efficient routing algorithm. Besides, in the manufacture, vias always take high cost. Accordingly, this paper presents two strategies to reduce runtime and the number of vias: (1) an efficient region-division based parallel strategy, which realizes load balancing of parallel routing to improve the efficiency of routing algorithm; (2) an equivalent routing solution aware via optimization strategy, which determines the priority of each net in using routing resource to reduce the number of vias of layer assignment. Furthermore, combining the above two strategies, this paper proposes a via-aware parallel layer assignment algorithm for very large scale integration (VLSI) physical design. The experimental results show that the proposed algorithm is able to optimize the number of vias significantly and reduce runtime simultaneously. © 2022 Chinese Institute of Electronics. All rights reserved.
Keyword ：
VLSI circuits VLSI circuits
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| GB/T 7714 | Liu, Geng-Geng , Li, Ze-Peng , Guo, Wen-Zhong et al. Via-Aware Parallel Layer Assignment Algorithm for VLSI Physical Design [J]. | Acta Electronica Sinica , 2022 , 50 (11) : 2575-2583 . |
| MLA | Liu, Geng-Geng et al. "Via-Aware Parallel Layer Assignment Algorithm for VLSI Physical Design" . | Acta Electronica Sinica 50 . 11 (2022) : 2575-2583 . |
| APA | Liu, Geng-Geng , Li, Ze-Peng , Guo, Wen-Zhong , Chen, Guo-Long , Xu, Ning . Via-Aware Parallel Layer Assignment Algorithm for VLSI Physical Design . | Acta Electronica Sinica , 2022 , 50 (11) , 2575-2583 . |
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As timing delay becomes a critical issue in chip performance, there is a burning desire for IC design under smart manufacturing to optimize the delay. As the best connection model for multi-terminal nets, the wirelength and the maximum source-to-sink pathlength of the Steiner minimum tree are the decisive factors of timing delay for routing. In addition, considering that X-routing can get the utmost out of routing resources, this article proposes a Timing-Driven X-routing Steiner Minimum Tree (TD-XSMT) algorithm based on two-stage competitive particle swarm optimization. This work utilizes the multi-objective particle swarm optimization algorithm and redesigns its framework, thus improving its performance. First, a two-stage learning strategy is presented, which balances the exploration and exploitation capabilities of the particle by learning edge structures and pseudo-Steiner point choices. Especially in the second stage, a hybrid crossover strategy is designed to guarantee convergence quality. Second, the competition mechanism is adopted to select particle learning objects and enhance diversity. Finally, according to the characteristics of the discrete TD-XSMT problem, the mutation and crossover operators of the genetic algorithm are used to effectively discretize the proposed algorithm. Experimental results reveal that TSCPSO-TD-XSMT can obtain a smooth trade-off between wirelength and maximum source-to-sink pathlength, and achieve distinguished timing delay optimization. © 2022 Association for Computing Machinery.
Keyword ：
Economic and social effects Economic and social effects Genetic algorithms Genetic algorithms Integrated circuit design Integrated circuit design Integrated circuits Integrated circuits Multiobjective optimization Multiobjective optimization Particle swarm optimization (PSO) Particle swarm optimization (PSO) Timing circuits Timing circuits Trees (mathematics) Trees (mathematics)
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| GB/T 7714 | Liu, Genggeng , Zhou, Ruping , Xu, Saijuan et al. Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing [J]. | ACM Transactions on Management Information Systems , 2022 , 13 (4) . |
| MLA | Liu, Genggeng et al. "Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing" . | ACM Transactions on Management Information Systems 13 . 4 (2022) . |
| APA | Liu, Genggeng , Zhou, Ruping , Xu, Saijuan , Zhu, Yuhan , Guo, Wenzhong , Chen, Yeh-Cheng et al. Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing . | ACM Transactions on Management Information Systems , 2022 , 13 (4) . |
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Abstract ：
随着集成电路规模的日益增长,需要处理的线网数量显著增多,层分配算法运行时间增大成为限制高效设计布线方案的重要因素;此外在生产工艺中,通孔的制造成本较高.针对以上两个问题,本文提出了两种新颖的策略分别用于优化算法运行时间和通孔数量:(1)一种高效的基于区域划分的并行策略,实现各区域在并行布线阶段负载均衡,以提高并行布线的效率;(2)基于线网等效布线方案感知的通孔优化策略,决定各线网对布线资源使用的优先级,进而减少层分配方案的通孔数量.最终将上述两种策略相结合,提出了一种面向超大规模集成电路物理设计的通孔感知的并行层分配算法.实验结果表明该算法对通孔数量和运行时间均有良好的优化效果.
Keyword ：
区域划分 区域划分 层分配 层分配 并行算法 并行算法 负载均衡 负载均衡 超大规模集成电路 超大规模集成电路 通孔 通孔
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| GB/T 7714 | 刘耿耿 , 李泽鹏 , 郭文忠 et al. 面向超大规模集成电路物理设计的通孔感知的并行层分配算法 [J]. | 电子学报 , 2022 , 50 (11) : 2575-2583 . |
| MLA | 刘耿耿 et al. "面向超大规模集成电路物理设计的通孔感知的并行层分配算法" . | 电子学报 50 . 11 (2022) : 2575-2583 . |
| APA | 刘耿耿 , 李泽鹏 , 郭文忠 , 陈国龙 , 徐宁 . 面向超大规模集成电路物理设计的通孔感知的并行层分配算法 . | 电子学报 , 2022 , 50 (11) , 2575-2583 . |
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本发明涉及一种连续微流控生物芯片下存储最小化的高级综合设计方法，基于路径调度算法, 其特征在于，包括以下步骤：步骤S1 : 根据给定时序图，遍历计算所有操作的优先权，确定操作的调度顺序；步骤S2 : 优先调度具有较小优先权的操作，计算特定组件的准备时间，选择准备时间最早的组件，绑定并执行所调度的操作；步骤S3 : 调度该操作优先权最小且就绪的子操作，进行绑定；步骤S4 : 调度执行给定时序图中所有的操作，得到一组绑定和调度解，以及组件之间的流体运输任务，完成高级综合设计。本发明能获得具有较少存储次数的连续微流控生物芯片的高级综合设计方案。
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| GB/T 7714 | 刘耿耿 , 林泓星 , 黄兴 et al. 连续微流控生物芯片下存储最小化的高级综合设计方法 : CN202110337815.9[P]. | 2021-03-30 . |
| MLA | 刘耿耿 et al. "连续微流控生物芯片下存储最小化的高级综合设计方法" : CN202110337815.9. | 2021-03-30 . |
| APA | 刘耿耿 , 林泓星 , 黄兴 , 徐赛娟 , 郭文忠 , 陈国龙 . 连续微流控生物芯片下存储最小化的高级综合设计方法 : CN202110337815.9. | 2021-03-30 . |
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本发明涉及一种基于多策略优化的超大规模集成电路多层总体布线方法，包括步骤S1：在预连接布线阶段，采用虚拟容量的动态调整策略对通道容量进行适当调整缩减；步骤S2：在全局考量下的布线重组阶段找到最拥挤的布线区域，采用布线子区域的自适应扩展策略对其进行自适应扩展，根据布线后的不同拥堵度，对应地调整扩大的范围和扩张速度；步骤S3：在布线时采用虚拟容量的动态调整策略对通道虚拟容量进行动态调整，对不同通道方向上的通道容量进行相互补充，及时补充剩余通道容量较小的布线通道；步骤S4：采用基于A*算法的启发式搜索策略通过A*算法进行启发式搜索和布线。本发明能够提高布线容量的利用率，平衡布线器的布线效率和全局搜索的压力。
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| GB/T 7714 | 刘耿耿 , 裴镇宇 , 郭文忠 et al. 一种基于多策略优化的超大规模集成电路多层总体布线方法 : CN202110739932.8[P]. | 2021-06-30 . |
| MLA | 刘耿耿 et al. "一种基于多策略优化的超大规模集成电路多层总体布线方法" : CN202110739932.8. | 2021-06-30 . |
| APA | 刘耿耿 , 裴镇宇 , 郭文忠 , 郑筱媛 , 陈国龙 . 一种基于多策略优化的超大规模集成电路多层总体布线方法 : CN202110739932.8. | 2021-06-30 . |
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本发明涉及一种连续微流控生物芯片下基于序列对的流层物理设计方法，包括以下步骤 : 步骤S1 : 基于序列对表示方法，在组件布局阶段通过离散粒子群优化算法得到组件布局解; 步骤S2 : 在布线阶段将组件对之间的曼哈顿距离作为布线顺序的考虑依据，并通过基于协商布线算法进行布线；步骤S3 : 将根据布线的反馈信息进行针对流通道交叉点区域的布局调整; 步骤S4 : 从而衔接组件布局与流通道布线阶段, 并判断布局调整后的流层物理设计结果是否得到进一步优化，若是则循环步骤S3‑S4，若否则完成流层物理设计。本发明以优化流通道交叉点数量、芯片面积和流通道长度为目标，最终得到高质量流层物理设计方案。
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| GB/T 7714 | 刘耿耿 , 黄鸿斌 , 黄兴 et al. 连续微流控生物芯片下基于序列对的流层物理设计方法 : CN202111132198.5[P]. | 2021-09-27 . |
| MLA | 刘耿耿 et al. "连续微流控生物芯片下基于序列对的流层物理设计方法" : CN202111132198.5. | 2021-09-27 . |
| APA | 刘耿耿 , 黄鸿斌 , 黄兴 , 徐赛娟 , 郭文忠 , 陈国龙 . 连续微流控生物芯片下基于序列对的流层物理设计方法 : CN202111132198.5. | 2021-09-27 . |
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