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International Conference on Computer Supported Cooperative Work in design

作者： Quanlong Guan Jinneng He Zhao-Rong Lai Yuyu Zhou Quming Jiang Ziliang Chen College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory Zhuhai China Chutian Dragon Co. Ltd Dongguan China School of Intelligent Systems Science and Engineering Jinan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. This paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. The approach uniquely combines ℓ 1 and ℓ 2 -regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. This algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Computational modeling Benchmark testing Market research Robustness Regulation Computational efficiency Resource management

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Research on the Path Planning of the Construction Robot Based on BIM

Research on the Path Planning of the Construction Robot Base...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Zhu, Fenghao Liu, Zhenzhong Tianjin University of Technology Tianjin Key Laboratory For Advanced Mechatronic System Design Intelligent Contr. Natl. Demonstration Center For Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering China

ISBN: (纸本)9781665441285

Aiming at the problem of the optimal global path planning of the construction robot entering the target point of the construction site and the efficient construction of the navigation map, a BIM(Building information modeling)-based construction robot job planning method is proposed. The traditional method of building a map environment for construction robots is obtained by its own sensors, which has a large amount of calculation. Firstly, the real construction environment is established through the BIM model, which effectively improves the efficiency of generating navigation map. Secondly, adding goal-directed functions to the RRT algorithm, combined with the target bias expansion algorithm based on the idea of artificial potential fields to further accelerate the search efficiency. Finally, the pruning algorithm is used to reduce the redundant points, which shortens the running time of the algorithm and the simulation time of the robot. The improved RRT algorithm before and after was compared and simulated in the construction environment of BIM in V-rep. The experimental results demonstrate a reduction of more than 50% in the final path simulation time for the improved RRT algorithm. © 2021 IEEE.

关键词： Motion planning

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC

arXiv

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arXiv 2024年

作者： Yang, Sikun Koeppl, Heinz School of Computing and Information Technology Great Bay University Dongguan523000 China Great Bay Institute for Advanced Study Great Bay University Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay University China Technische Universität Darmstadt Darmstadt Germany

The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior is adopted to infer the appropriate number of latent communities, and each vertex is endowed with a gamma distributed positive memberships vector. Despite having many attractive properties, inference in the EPM is typically performed using Markov chain Monte Carlo (MCMC) methods that prevent it from being applied to massive network data. In this paper, we generalize the EPM to account for dynamic enviroment by representing each vertex with a positive memberships vector constructed using Dirichlet prior specification, and capturing the time-evolving behaviour of vertices via a Dirichlet Markov chain construction. A simple-to-implement Gibbs sampler is proposed to perform posterior computation using Negative-Binomial augmentation technique. For large network data, we propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in the proposed model. The experimental results show that the novel methods achieve competitive performance in terms of link prediction, while being much faster. © 2024, CC BY.

关键词： Monte Carlo methods

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Adversarial Learning Network for Recovering Rppg Signals from Low-Resolution Images for Remote Heart Rate Measurement

SSRN

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SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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A Novel Tunable Stiffness Method Using Gecko Setae-Inspired Adhesive Interlayer Films for Soft Actuator

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Journal of Bionic Engineering 2025年 1-16页

作者： Wang, Zhihuan Xu, Linsen Ye, Liangzhi Wang, Mingming Zhang, Zhihua Wu, Tao College of Mechanical and Electrical Engineering Hohai University Changzhou213000 China Changzhou Key Laboratory of Intelligent Manufacturing Technology and Equipment Changzhou213000 China Suzhou Research Institute Hohai University Suzhou215000 China Changzhou Institute of Advanced Manufacturing Technology Changzhou213000 China Wuhan Second Ship Design and Research Institute Wuhan430000 China

Soft actuators are inherently flexible and compliant, traits that enhance their adaptability to diverse environments and tasks. However, their low structural stiffness can lead to unpredictable and uncontrollable complex deformations when substantial force is required, compromising their load-bearing capacity. This work proposes a novel method that uses gecko setae-inspired adhesives as interlayer films to construct a layer jamming structure to adjust the stiffness of soft actuators. The mechanical behavior of a single tilted microcylinder was analyzed using the energy method to determine the adhesion force of the adhesives. The gecko-inspired adhesive was designed under the guidance of the adhesion force model. Testing under various loads and directions revealed that the tilted characteristic of microcylinders can enhance the adhesion force in its grasping direction. The adhesive demonstrated excellent adhesion performance compared to other typical adhesives. A tunable stiffness actuator using gecko setae-inspired adhesives (TSAGA), was developed with these adhesives serving as interlayer films. The stiffness model of TSAGA was derived by analyzing its axial compression force. The results of stiffness test indicate that the adhesives serve as interlayer films can adjust the stiffness in response to applied load. TSAGA was compared with other typical soft actuators in order to evaluate the stiffness performance, and the results indicate that TSAGA exhibits the highest stiffness and the widest tunable stiffness range. This demonstrates the superior performance of the setae-inspired adhesives as interlayer films in terms of stiffness adjustment. © Jilin University 2025.

关键词： Glues

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Construction and Application of the Teaching Cloud Platform for Engineering Drawing Course

Construction and Application of the Teaching Cloud Platform ...

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International Conference on Educational and Information Technology (ICEIT)

作者： Yahong Xue Haozhi Mu Lijun Xue Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

A cloud platform for engineering drawing course teaching was proposed based on cloud station. The cloud station can solve the common problems existed in engineering drawing course, such as the difficulty of teaching, the heavy work of tutorship, the poor teaching effect, the weak spatial imagination of students, as well as the current difficulty to develop the virtual model resource base and the interaction is not strong. The platform uses Solidworks software to build 3D model library, and combines with CADbro's powerful cloud service and 3D file viewing function to realize the interaction and sharing with cloud model by using web browser. At the same time, we choose bilibili, a popular cultural site for teenagers, to release the explanation videos of exercises, and make use of the convenient way to build the cloud station to integrate, release and manage the resources. The platform realizes self-study and self-test at anytime and anywhere on both PC and mobile devices, and fast jumps the corresponding explanation video and cloud virtual model according to the demand. The virtual model in the cloud can be rotated, cut, distance measured, exploded, parts hidden, moved, 2D viewed and so on by touch. The platform is innovative, extensible, interesting and practical, which can greatly enhance the students' enthusiasm for independent learning, and it can be used reference to the development of engineering drawing teaching resources.

关键词： Cloud computing Solid modeling Three-dimensional displays Engineering drawings Education Buildings Software

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design and Practice of Blended Teaching Model of Engineering Professional Courses based on Outcome-based Education

Design and Practice of Blended Teaching Model of Engineering...

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Educational Technology (ICET), IEEE International Conference on

作者： Jinyou Xu Lei Zhang Chengjie Rui Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China

In the context of engineering education, to improve the practical engineering skills of engineering students, the teaching ideas and implementation methods of the curriculum are designed based on Outcome-based Education. A blended teaching approach was used to design the implementation process to achieve the curriculum objectives. In this paper, we explain the process of blended teaching from the two aspects of basic knowledge and competency training. Active learning of basic knowledge is implemented based on a three-step blended teaching model before, after, and during classes. It adopts the project-driven learning approach of the flipped classroom model and cultivates students' ability to solve complex engineering problems through collaborative group and intra-group mutual assessment. Based on the original objective assessment of the curriculum and under the current teaching model, we design a diversified evaluation approach to comprehensively assess the effectiveness of student competence achievement. Practice has shown that project-based inquiry-based teaching methods based on the flipped classroom model can promote learning outcomes for students and increase their interest in learning. It avoids the blind and aimless theoretical learning process of the student and improves the practical ability of the student. The methods and means of instruction of teachers have been further extended, and the ability of students to learn independently has been improved.

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Teaching Innovation and Practice of Mind Mapping Applied to Engineering Drawing Course

Teaching Innovation and Practice of Mind Mapping Applied to ...

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International Conference on Educational and Information Technology (ICEIT)

作者： Yahong Xue Haozhi Mu Lijun Xue Xiaofei Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Engineering drawing is a compulsory technical foundation course for mechanical majors. Its main task is to cultivate students' ability to draw and read engineering drawings. The content of this course is complicated and abstract and not easy to understand, which makes it difficult for students to learn. Mind mapping is a visual thinking tool to systematize and organize abstract and complicated knowledge. Taking the application of mind mapping to various teaching processes of engineering drawing course, such as the construction of framework, the sorting out of fragmented knowledge, the solution of drawing geometry and sectional view, and the group project for important and difficult content as examples, we illustrate the importance of mind map in cultivating students' knowledge generalization ability, independent learning ability, enhancing students' logical thinking, creative thinking, divergent thinking, and stimulating students' interest in learning.

关键词： Knowledge engineering Geometry Visualization Technological innovation Engineering drawings Education Task analysis

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