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2023 china Automation Congress, CAC 2023

作者： Tang, Yongda Ye, Hongtao Luo, Wenguang School of Automation Guangxi University of since and Technology Liuzhou China

ISBN: (纸本)9798350303759

Traditional methods for determining the freezing point of diesel fuel are complex, expensive, and susceptible to human and environmental factors. Soft measurement methods offer a technical solution for estimating and controlling the prediction of such variables. In this paper, we apply adaptive Lasso penalties to sparse kernel principal component analysis (SKPCA), resulting in adaptive sparse kernel principal component analysis (ASKPCA). ASKPCA achieves non-linear dimensionality reduction while applying different penalties to features with varying importance. This reduces the dimension dependence and improving the model's explanatory power while preserving essential features. The data processed by ASKPCA is used as input for the least squares support vector machine (LSSVM), leading to the establishment of a soft measurement model based on ASKPCA-LSSVM. Simulation results show that the ASKPCA-LSSVM model has better prediction accuracy than the four other methods, underscoring its efficacy as a modeling approach. © 2023 IEEE.

关键词： Support vector machines

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Attention Makes Breast Cancer Segmentation in Ultrasound Image Better 13

Attention Makes Breast Cancer Segmentation in Ultrasound Ima...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Cao, Shuang Toe, Teoh Teik School of Information and Communication Engineering University of Electronic and since Technology of China Cheng Du China NanYang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350319156

According to the International Agency for Research on Cancer (IARC) statistics, starting from the year 2020, breast cancer has officially surpassed lung cancer and becomes the world's leading cancer. Early detection of tumors plays a crucial role in clinical treatment, and ultrasound scanning is a cheap and efficient imaging technique that is widely used. Today, with the development of deep learning, an increasing number of network architectures have demonstrated promising performance in medical image processing, among which UNet stands out as an excellent example. In this paper, we compare the effectiveness of UNet,and UNet with attention mechanism,such as TransUnet and SwinUnet in detecting and segmenting breast cancer lesion area based on ultrasound images. From the experiment, UNet with attention show an excellent performance, especially for the TransUnet, which has the best indicators among all the models. © 2023 IEEE.

关键词： Network architecture

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A soft measurement method for the freezing point of diesel based on the ASKPCA-LSSVM model

A soft measurement method for the freezing point of diesel b...

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Chinese Automation Congress (CAC)

作者： Yongda Tang Hongtao Ye Wenguang Luo School of Automation Guangxi University of Since and Technology Liuzhou China

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Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation

Mining High Utility Itemset with Multiple Minimum Utility Th...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Naji Alhusaini Jing Li Philippe Fournier-Viger Ammar Hawbani Guilin Chen School of Computer and Information Engineering. Chuzhou University Chuzhou China School of Computer Since and Technology University of Since and Technology of China Hefei China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

High Utility Itemset Mining (HUIM) is the task of extracting actionable patterns considering the utility of items such as profits and quantities. An important issue with traditional HUIM methods is that they evaluate all items using a single threshold, which is inconsistent with reality due to differences in the nature and importance of items. Recently, algorithms were proposed to address this problem by assigning a minimum item utility threshold to each item. However, since the minimum item utility (MIU) is expressed as a percentage of the external utility, these methods still face two problems, called “itemset missing” and “itemset explosion”. To solve these problems, this paper introduces a novel notion of Utility Deviation (UD), which is calculated based on the standard deviation. The U D and actual utility are jointly used to calculate the MIU of items. By doing so, the problems of “itemset missing” and “itemset explosion” are alleviated. To implement and evaluate the U D notion, a novel algorithm is proposed, called HUI-MMU-UD. Experimental results demonstrate the effectiveness of the proposed notion for solving the problems of “itemset missing” and “itemset explosion”. Results also show that the proposed algorithm outperforms the previous HUI-MMU algorithm in many cases, in terms of runtime and memory usage.

关键词： Runtime Itemsets Conferences Explosions Data mining Task analysis Standards

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Design and Analysis of a Compact Tensioning Device for a Cable Drive Manipulator

Design and Analysis of a Compact Tensioning Device for a Cab...

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Yang Lu Xianmin Zhang Yanlin Chen Yanbin Wu Yanjiang Huang School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology since 2010 School of Mechanical and Automotive Engineering at South China University of Technology since 2013 Guangdong Key Laboratory of Precision Equipment and Manufacturing Technology School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China South China University of Technology Guangzhou China

The cable-driven robot has the advantage of having a terminal motion part capable of generating high velocity and acceleration during movement, providing benefits such as high payload and a larger workspace. Currently, there is a lack of compact devices that can achieve adaptive tension control and force feedback, and can be integrated into common cable-driven robotic arms. This makes it difficult to precisely adjust the tension of the cables in cable-driven robots, greatly limiting their operational precision and payload capacity. To solve these problems, this paper will propose a horizontal moving and stretching tensioning device based on the screw-nut pair. Additionally, an experimental platform is constructed in this study to verify the effects of tension variation and its impact on stiffness. It is demonstrated that the tensioning device can achieve linear changes in tension and affect the elbow joint torsional stiffness of the cable-driven robotic arm.

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Attention Makes Breast Cancer Segmentation in Ultrasound Image Better

Attention Makes Breast Cancer Segmentation in Ultrasound Ima...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Shuang Cao Teoh Teik Toe School of Information and Communication Engineering University of Electronic and Since Technology of China Cheng Du China NTU Business AI Lab NanYang Technological University Singapore

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Facial Landmark based BMI Analysis for Pervasive Health Informatics

Facial Landmark based BMI Analysis for Pervasive Health Info...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yujin Wang Zhi Jin Jia Huang Hongzhou Lu Wenjin Wang School of Intelligent Systems Since and Engineering Jinan University China School of Intelligent Systems Engineering Sun Yat-Sen University China Intensive Care Unit The Third People’s Hospital of Shenzhen China Department of Biomedical Engineering Southern University of Science and Technology China

Body Mass Index (BMI), calculated based on the ratio between a person’s height and weight, is a widely used metric for body weight or fatness. In this paper, we investigate the potential of face image-based BMI estimation using an RGB camera. We proposed a simple yet highly reproducible image processing framework that converts an input face image into a BMI value or obesity class (underweight, normal and overweight). In this framework, we explored the options of using 2D or 3D facial landmark models, view angle correction in 2D and 3D, different choices for facial feature extraction (landmark distances or coordinates), and different prediction models (regression or classification) based on shallow machine learning techniques. Our framework was thoroughly validated on two public datasets. The insights of this measurement are discussed, as well as the challenges and limitations, to increase the understanding for future improvement of camera-based BMI estimation. The source code of this study is available at https://***/hxfj/*** relevance— This contributes to simpler and more effective daily health management.

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Getting Undergraduates Ready for china’s Belt and Road Initiative (BRI) Through an Overseas Experiential Learning Project

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china and the World 2021年第3期4卷

作者： Andrew Min Han Chin Thomas Menkhoff Hans-Dieter Evers Hoong Hui Daniel Gn Kevin Koh Chester Wey Lee Patrick H. M. Loh Linda Low Sebastian Tan Teng Seng Teo Natalie Yap Lee Kong Chian School of Business Singapore Management University (SMU) 81 Victoria Street Singapore 188065 Singapore Andrew Chin Min Han graduated from Tsinghua University in Beijing China with a Tsinghua–MIT Sloan International Master of Business Administration degree under the Singapore Government’s Asian Business Fellowship (ABF) scholarship program. He is the Program Director of Singapore Management University (SMU) Executive Certificate in China Business and of the SMU Internationalisation Series. He teaches as an Adjunct Faculty of Strategy and Organisation at the SMU Lee Kong Chian School of Business since January 2017. He teaches effectively in English and Chinese. With over 23 years of experience in education and publishing industry in Asia Andrew Chin’s multi-sector career has enabled him to experience the full spectrum of the education industry in his roles as the Singapore EDB Officer handling the World-Class University portfolio and the Specialist Information and Publishing Industry portfolio Chief Representative and Country Manager (China) at Thomson Learning (Fortune 500 company) and Co-founder and CEO for an early childhood education business. Andrew was a Business Development Director (Universities) at Sodexo Group (Fortune 500 company) for the Asia-Pacific region. After working 10 years in Beijing China Andrew returned to Singapore in 2012 to work closely with the top leadership at Singapore Management University on the SMU China & East Asia Initiatives. Thomas Menkhoff is the Professor of Organisational Behaviour & Human Resources (Education) at the Lee Kong Chian School of Business Singapore Management University (SMU). Two of his recent publications include: (i) Menkhoff Thomas Ning Kan Siew Evers Hans-Dieter and Chay Yue Wah eds. Living in Smart Cities: Innovation and Sustainability (New Jersey: World Scientific Publishing 2018) and (ii) Chay Yue Wah Menkhoff Thomas and Low Linda eds. China’s Belt and Road Initiative — Understanding the Dynamics of a Global Transfor

In this paper, we explain how an experiential learning course and study tour to Gansu Province (People’s Republic of china) enabled undergraduates at the Singapore Management university (SMU) to acquire 21st-century competencies and higher-order thinking skills by analyzing and evaluating specific aspects of china’s Belt and Road Initiative (BRI) and china–Singapore (Chongqing) Connectivity Initiative — New International Land–Sea Trade Corridor (CCI-ILSTC) with emphasis on developing viable Go-To-Market (GTM) strategies aimed at selling Gansu produce in four Southeast Asian markets. We share how the course was designed to support the attainment of key learning goals and discuss how we turned pedagogical aspirations into concrete learning outcomes. We introduce key aspects of the so-called “SMU-XO” project that the students conducted in partnership with an industry partner, Pacific International Lines (PIL), and discuss how the project work helped learners to gain global competency by (i) examining critical issues related to BRI such as multi-modal infrastructure connectivity, (ii) appreciating the local perspectives of project stakeholders in Lanzhou and Shanghai and (iii) successfully interacting with people from different cultures, namely china, Indonesia, Vietnam, Malaysia and Thailand.

关键词： Experiential learning Singapore china Belt and Road Initiative (BRI)

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Semi-randomized hashing for large scale data retrieval

Semi-randomized hashing for large scale data retrieval

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Haichuan Yang Xiao Bai Jun Zhou Peng Ren Jian Cheng Lu Bai School of Computer Since and Engineering Beihang University China School of Information and Communication Technology Griffith University Australia College of Information and Control Engineering China University of Petroleum China Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of York UK

In information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic research problem in hashing. Most hashing methods used purely randomized or optimized learning strategy to achieve this goal. Our method, on the other hand, combines both randomized and optimized strategies. It starts from generating multiple random vectors, and then approximates them by a single projection vector. In the quantization step, it uses the orthogonal transformation to minimize an upper bound of the deviation between real-valued vectors and binary codes. The proposed method overcomes the problem that randomized hash functions are isolated from the data distribution. What's more, our method supports an arbitrary number of hash functions, which is beneficial in building better hashing methods. The experiments show that our approach outperforms the alternative state-of-the-art methods for retrieval on the large scale dataset.

关键词： Vectors Euclidean distance Binary codes Training Equations Principal component analysis

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