Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames

Existing physics-based modeling approaches do not have a good compromise between performance and computational efficiency in predicting the seismic response of reinforced concrete (RC) frames, where high-fidelity models (e.g., fiber-based modeling method) have reasonable predictive performance but are computationally demanding, while more simplified models (e.g., shear building model) are the opposite. This paper proposes a novel artificial intelligence (AI)-enhanced computational method for seismic response prediction of RC frames which can remedy these problems. The proposed AI-enhanced method incorporates an AI technique with a shear building model, where the AI technique can directly utilize the real-world experimental data of RC columns to determine the lateral stiffness of each column in the target RC frame while the structural stiffness matrix is efficiently formulated via the shear building model. Therefore, this scheme can enhance prediction accuracy due to the use of real-world data while maintaining high computational efficiency due to the incorporation of the shear building model. Two data-driven seismic response solvers are developed to implement the proposed approach based on a database including 272 RC column specimens. Numerical results demonstrate that compared to the experimental data, the proposed method outperforms the fiber-based modeling approach in both prediction capability and computational efficiency and is a promising tool for accurate and efficient seismic response prediction of structural systems.     

Mutual information-enhanced digital twin promotes vision-guided robotic grasping

Vision-guided learning for autonomous robotic manipulations is a wide-ranging and high-impact topic in the context of smart manufacturing. Most learning strategies are object-centered or prior information-dependent, which likely lead to the problems of generalization across objects or scenes. To alleviate this, this work presented an embodiment decision-making method by the marriage between the digital twin epistemology and information-theoretic approach. The initial insight was that the mutual information generated in the interactions between the available vision models and real-world perceptions could decrease the uncertainty of sensing-action processes. Further, the real-time interactive information gains and visual templates constitute the digital twin through bidirectional data flowing and real-time optimization. As a demonstration of concept, on the conveyor-based and vision-guided robotic grasping platform, the robotic grasping experiments of freely placed and moving parts were performed. Experimental results indicated that the autonomous and real-time optimization of the conveyor-based and vision-guided robotic grasping system happens and the adaptability to the real-world changes had been clearly increased. This research suggested that the representation and dynamic capture of the complex interactions between both sides of cyber-physical system could generate new possibilities to the evolution of decision-making paradigm in more complex industrial processes.     

Graph-based reinforcement learning for discrete cross-section optimization of planar steel frames

A combined method of graph embedding (GE) and reinforcement learning (RL) is developed for discrete cross-section optimization of planar steel frames, in which the section size of each member is selected from a prescribed list of standard sections. The RL agent aims to minimize the total structural volume under various practical constraints. GE is a method for extracting features from data with irregular connectivity. While most of the existing GE methods aim at extracting node features, an improved GE formulation is developed for extracting features of edges associated with members in this study. Owing to the proposed GE operations, the agent is capable of grasping the structural property of columns and beams considering their connectivity in a frame with an arbitrary size as feature vectors of the same size. Using the feature vectors, the agent is trained to estimate the accurate return associated with each action and to take proper actions on which members to reduce or increase their size using an RL algorithm. The applicability of the proposed method is versatile because various frames different in the numbers of nodes and members can be used for both training and application phases. In the numerical examples, the trained agents outperform a particle swarm optimization method as a benchmark in terms of both computational cost and design quality for cross-sectional design changes; the agents successfully assign reasonable cross-sections considering the geometry, connectivity, and support and load conditions of the frames.     

CTU depth decision algorithms for HEVC: A survey

High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).     

Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

With the increasing maturity of video editing technology, forgers are more inclined to transcode videos to High Efficiency Video Coding (HEVC) videos, as HEVC not only enables people to enjoy high-definition videos but also allows broadcasters to stream it more efficiently across networks. Therefore, to verify the originality and authenticity, it is of great significance to propose an algorithm for detecting transcoded HEVC videos. In this paper, a theoretical model of video transcoding is first constructed, and a novel transcoding detection algorithm based on In-loop Filtering and Prediction Units (PU) Partition (IFPP) is proposed. By analyzing the statistical characteristics of strong and normal filtering modes in deblocking filtering and calculating offset values in Sample Adaptive Offset (SAO) filtering, the transcoding traces in inter-coded frames can be captured. In addition, PU partition statistics are also extracted to make full use of traces in intra-coded frames. By fusing these subfeatures, the proposed IFPP feature with 17 dimensions can be obtained, which is further fed to the Support Vector Machine (SVM) classifier. Finally, experiments are conducted on datasets with various coding parameters. Results show that the proposed algorithm outperforms existing algorithms and has better robustness.     

加权因子的PSO-SVR区域空气PM2.5浓度预报方法

结合支持向量回归机(SVR)和粒子群优化算法(PSO),本文提出了一种加权因子的区域大气PM2.5浓度预测方法(W-PSO-SVR)。该方法对预测模型的输入变量进行[0,1]间的不平等加权赋值,权重值由PSO寻优求得,通过不断寻优迭代,赋予输入变量不平等的权重,从而建立预测模型。对区域大气PM2.5浓度预报结果表明,与单独的支持向量回归机模型和0或1的加权因子的支持向量回归模型相比,W-PSO-SVR预报精度提高明显,且该方法不用考虑对历史数据时滞因子的影响,较好地实现了模型输入参数的有效选择,从而可降低输入参数的维数。     

A fine grained digital textile printing system based on image registration

Printed fabrics differ from non-planar fabrics (e.g., embroidery) in their rich colours, various patterns and low costs. However, non-planar fabrics are considered more attractive, expensive in appearance and luxurious owing to their solid texture patterns, floats and luster contrasts. To produce fabrics that are both colorful and lavish and also have color gradients, this paper proposes a novel fine-grained digital printing system that combines digital textile printing and non-planar fabrics. Note that because distortion is one of the characteristics of the fabric, directly printing an ink-design pattern onto non-planar fabric gives poor performance. This work develops a solution to accurately align the ink-design pattern with the fabric, consisting of both hardware and software parts that are used to take pictures of the fabric, employ image registration-based computer vision technology to deform the ink-design pattern according to the deformability of the fabric, and print the deformed ink-design pattern onto the fabric. The system is flexible and highly automated, and it makes mass production possible. In the prototype, fine quality fabrics are produced efficiently (a fabric of 0.12 m² in less than 4 s).     

平行学习——机器学习的一个新型理论框架

本文提出了一种新的机器学习理论框架.该框架结合了现有多种机器学习理论框架的优点,并针对如何使用软件定义的人工系统从大数据提取有效数据,如何结合预测学习和集成学习,以及如何利用默顿定律进行指示学习等目前机器学习领域面临的重要问题进行了特别设计.     

Rademacher复杂度在统计学习理论中的研究

假设空间复杂性是统计学习理论中用于分析学习模型泛化能力的关键因素.与数据无关的复杂度不同,Rademacher复杂度是与数据分布相关的,因而通常能得到比传统复杂度更紧致的泛化界表达.近年来,Rademacher复杂度在统计学习理论泛化能力分析的应用发展中起到了重要的作用.鉴于其重要性,本文梳理了各种形式的Rademacher复杂度及其与传统复杂度之间的关联性,并探讨了基于Rademacher复杂度进行学习模型泛化能力分析的基本技巧.考虑样本数据的独立同分布和非独立同分布两种产生环境,总结并分析了Rademacher复杂度在泛化能力分析方面的研究现状.展望了当前Rademacher复杂度在非监督框架与非序列环境等方面研究的不足,及其进一步应用与发展.     

Augmented Reality-assisted Intelligent Window for Cyber-Physical Machine Tools

Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.