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CLEO: science and Innovations, S and I 2022

作者： Xing, Peng Chen, George F.R. Gao, Hongwei Chia, Xavier Agarwal, Anuradha M. Kimerling, Lionel C. Tan, Dawn T.H. Photonics Devices and System Group Singapore University of Technology and Design 8 Somapah Rd Singapore487372 Singapore Institute of Microelectronics A*STAR 2 Fusionopolis Way #08-02 Innovis Tower Singapore138634 Singapore Microphotonics Center Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Materials Research Laboratory Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States

We demonstrate high-speed transmission of 30Gbaud NRZ and PAM4 data using a silicon nitride microresonator frequency comb. We achieve bit-error-rates below the FEC limit and a low power penalty of 0.1dB compared to B2... 详细信息

ISBN: (纸本)9781557528209

关键词： Microresonators

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Learning User-Preferred Robot Navigation Based on Social Force Model from Human Feedback in Virtual Reality Environments

Learning User-Preferred Robot Navigation Based on Social For...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Shintaro Nakaoka Yosuke Kawasaki Masaki Takahashi Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Japan Department of System Design Engineering Keio University 3-14-1 Hiyoshi Kohokuku Yokohama Japan

Autonomous service robots are increasingly necessary to move without impeding the movement of pedestrians. Previous studies have determined optimal input for robots by minimizing a multi-objective function that includes the cost of reaching the destination and avoiding surrounding pedestrians. However, it is challenging to adjust the weights of each term in the cost function since they depend on the users and environment. In this study, we used the Social Force Model (SFM) as the base cost function and proposed a method to estimate SFM weights preferred by general user based on population density from human feedback. To achieve this, first we use Bayesian optimization and derive each user’s evaluation map of SFM in a virtual reality environment that provides a realistic and immersive experience for subjects to provide feedback on the robot’s movement. Second, we aggregated each user’s evaluation map to estimate a general user’s evaluation map. Finally, we have derived a functional relationship between the preferred SFM weights of general users and population density by Gaussian process regression. This relationship empowers the robot to navigate in a manner preferred by the general public, contingent on population density, even in the absence of human feedback obtained through virtual reality experimentation.

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Human-Computer Interaction and Knowledge Transfer: Study of E-Learning Achievement

Human-Computer Interaction and Knowledge Transfer: Study of ...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Doni Purnama Alamsyah Norfaridatul Ahkmaliah Othman Johan Muliadi Kerta Doni Morika Yudi Ramdhani Creativepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Design Interior Department School of Design Bina Nusantara University Jakarta Indonesia Information System Program Satu University Bandung Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance.

关键词： Human computer interaction Analytical models Electronic learning Data models Informatics Knowledge transfer

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Human-in-the-loop in smart manufacturing (H-SM): A review and perspective

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Journal of Manufacturing systems 2025年 82卷 178-199页

作者： Duck Bong Kim Mahdi Sadeqi Bajestani Ju Yeon Lee Seung-Jun Shin Goo-Young Kim Seyed Mohammad Mehdi Sajadieh Sangdo Noh Department of Manufacturing and Engineering Technology Tennessee Technological University Cookeville TN 38505 USA Department of Mechanical Engineering Tennessee Technological University Cookeville TN 38505 USA Department of Mechanical System Design Engineering Seoul National University of Science and Technology Seoul South Korea School of Interdisciplinary Industrial Studies Hanyang University 222 Wangsimni-ro Seongdong-gu Seoul 04763 Republic of Korea Department of Systems Management Engineering Sungkyunkwan University Suwon-si South Korea

Smart manufacturing, also known as Industry 4.0, is a manufacturing paradigm that aims to realize autonomous processes, minimizing human involvement. In the advent of manufacturing-unfriendly situations (e.g., pandemics), it has been learned that the paradigm does not work correctly and has limitations in handling those situations. There is a consensus that humans still play a crucial role in manufacturing, and the ultimate goal of manufacturing is to benefit them. To align with this, the European Commission introduced Industry 5.0, targeting human centricity, sustainability, and resilience. Operator 5.0 has also been presented to improve the physical and cognitive capabilities of shop operators. In contrast, the new concept of human-in-the-loop in smart manufacturing (H-SM), aiming for the involvement of diverse stakeholders, has been recently proposed. In this paper, we introduce the research methodology to elaborate on the current application fields of the H-SM concept. For this, we revisit the existing paradigms and their case studies. Also, we categorize them in terms of different components in H-SM and with respect to different levels of physical and cognitive capabilities and experiences. Then, we identify seven technology clusters and twenty-one key-enabling technologies for the H-SM implementation. It can be concluded the H-SM is well-aligned with human-intervened autonomous manufacturing.

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Few-shot gearbox fault diagnosed based on meta-learning and time convolution network

Few-shot gearbox fault diagnosed based on meta-learning and ...

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Chinese Automation Congress (CAC)

作者： Yuan Dai Yanghui Ou Junwei Hu Zuowei Ping Xinghua Xu Yong Zhang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan China

In order to resolve the problem that the traditional fault diagnosis methods can not accurately diagnose under complex working conditions and few fault data, a fault diagnosis method based on meta-learning and time convolution network (TCN) is proposed in this paper. Firstly, Ensemble Empirical Mode Decomposition-Independent Component Analysis (EEMDICA) is used to denoise the original signals under different working conditions, and the acquired signals are randomly sampled to form meta tasks according to the meta-learning protocol. Through the TCN model training, the priori knowledge is obtained by optimizing the initialization parameters. The knowledge is used to improve accuracy in unforeseen condition tasks. Finally, simulation experiments are carried out on the gearbox dataset. The results indicate that the method has advantages in solving the fault classification problem of few shot gearbox under complex working conditions.

关键词： Employee welfare Fault diagnosis Training Protocols Automation Convolution Task analysis

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Experimental analysis of chemical removal effect in RISA grinding of optical glass

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Procedia CIRP 2024年 123卷 304-309页

作者： Kodai Shimodo Hinata Takamaru Yusuke Chiba Hidebumi Kato Mikinori Nagano Masahiko Fukuta Katsutoshi Tanaka Kentaro Watanabe Kazuhisa Hamazono Yasuhiro Kakinuma Department of System Design Engineering Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Production Headquarters Nikon Corp. 1-10-1 Asamizo-dai Minami-ku Sagamihara 252-0328 Japan Nano processing System Division Shibaura Machine Co. Ltd. 2068-3 Ohoka Numazu 410-0022 Japan

In the large-aperture lens manufacturing, not only high surface quality but also high productivity is required. From this perspective, Reaction-Induced-Slurry-Assisted grinding (RISA grinding), in which cerium oxide slurry is supplied instead of using general grinding fluid, has been developed to achieve high surface quality at high removal rate. However, the surface profile error becomes large due to chemical removal effect of the cerium oxide slurry. Therefore, the purpose of this study is to experimentally investigate the influence of the chemical removal effect on the surface profile error using AE signals and servo current signals in RISA grinding. First, relation between dwell time and removal depth is analyzed by conducting a basic grinding test without the workpiece rotation. The result shows that the actual removal depth increases with increase of dwell time despite applying the constant depth of cut. Then, zero-cut RISA grinding test is conducted. It is found that the removal process proceeds even though zero-cut grinding, and its removal rate varies according to the wheel feed rate. Furthermore, the amount of cerium oxide abrasives adhered to the grinding wheel can be estimated from the AE signals and servo current signals.

关键词： Ultra-precision grinding Chemical mechanical grinding Optical lens

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Multi-objectives design optimization based on multi-objectives Gaussian processes for system-in-Package

Multi-objectives design optimization based on multi-objectiv...

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Electronic Components and technology Conference (ECTC)

作者： Weijing Dai Zhenkun Wang Ke Xue School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

Since electronic devices based on system-in-Package technologies become sophisticated, the number of components and the complexity of internal structure of individual devices increase dramatically. Therefore, how to optimally design such packages turns into one of the most crucial issues. However, this increasing complexity greatly enlarges not only the amount of design variables, but also the design objectives, making it extremely burdensome to examine every design neither by prototyping nor by numerical simulation. For the past, design of experiment, response surface analysis and other common statistical methods are widely used for such purpose, but their effectiveness drops rapidly with the intricacy increasing. To overcome this limitation, we implement multi-objective Gaussian process, Bayesian optimization and multi-objective multifactorial evolution algorithm to form a framework in this study to tackle this design problem with high-dimensional inputs and multiple outputs. Some variations of this framework were tested, and the modelling accuracy and optimization efficiency were used as the indicative merits to evaluate the performance of this framework. This interdisciplinary study shows benefits in introducing advance intelligent algorithms into packaging design process and present a viable approach to achieve both automation and optimization for complicated packaging design.

关键词： Correlation Statistical analysis Gaussian processes Packaging Complexity theory Bayes methods Response surface methodology

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Performance Analysis of a New Low Power BIST Technique in VLSI Circuit by Reducing the Input Vectors

Performance Analysis of a New Low Power BIST Technique in VL...

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International Conference on Electrical Information and Communication technology (EICT)

作者： S. M Kifayat Kabir Raihan Motalib Fakrul Islam Javed Chamak Ganguly Saeed Hossen Rakib Satyendra N. Biswas Circuit and System Design ULKASEMI Pvt. Limited Dhaka Bangladesh IC Physical Design ULKASEMI Pvt. Limited Dhaka Bangladesh ASIC Physical Design PrimeSilicon Technology (BD) Ltd Dhaka Bangladesh Electrical and Electronic Engineering Primeasia University Dhaka Bangladesh Physical Design Neural Semiconductor Ltd Dhaka Bangladesh Dept. of BiCEP Bloomsburg University PA USA Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

VLSI circuit testing is complex and costs over 30% of the total IC design. The built-in self-testing technique is popular for its extensive fault coverage and efficiency. BIST refers to a hardware device testing construction used to perform fault detection and improve the device's performance to ensure a fault- free environment to avoid causing damage within the device's lifetime. As ICs are getting more complex day by day, different BIST modules are needed to test different circuits, which increase complication in design. As a result, power consumption and clock cycle increase. This research introduces a new Built-in Self-Test (BIST) technique for VLSI circuits. To deal with the shortcomings of conventional techniques, a BIST technique is proposed, which can test the circuit's functionality and detect faults successfully. The proposed model has been implemented using 22nm PTM model by employing LTSpice simulation software. The proposed model decreases power consumption upto 47.54% in without fault, 45.34% in open circuit fault and 45.63% in short circuit fault conditions compared to conventional technique and reduces the input vectors which in turn minimizes design intricacy and ensures an improved and novel approach for self-testing compared to many other existing BIST techniques.

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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Chinese Automation Congress (CAC)

作者： Yining Wu Runfeng Zhang Fang Liu Zhenzhong Liu Jianguo Wu Haonan Dong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Yichang Research Institute of Testing Technology Yichang China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m 2 , 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height.

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Trajectory Planning for Formation Variation of UGVs in Cluttered Environment

Trajectory Planning for Formation Variation of UGVs in Clutt...

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International Conference on Intelligent Transportation

作者： Jie Cheng Ruishuang Chen Zhihui Liang Shuan Ding Zaiyue Yang Shenzhen Key Laboratory of Control The-ory and Intelligent Systems School of System Design and Intelligent Manufacturing Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

The problem of trajectory planning for formation variation of unmanned ground vehicles (UGVs) is challenging due to the coupled non-collision constraints that apply to multiple vehicles. To address this issue, an innovative frame-work based on the alternating direction method of multipliers (ADMM) is proposed in this paper. The framework decouples complex constraints into two categories: individual constraints and mutual constraints. The former category involves dynamic, formation, and static obstacle constraints, which can be solved in parallel by each vehicle. Convex feasible set (CFS) algorithm is employed in this paper to simplify the static obstacle constraints. The latter category deals with collision avoidance among vehicles during the variation process and can be solved by nonlinear solvers. The convergence of the ADMM for the optimization problem is proved in this paper. Compared with solving the entire problem directly, the proposed framework reduces computation time and improves the quality of solution.

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