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Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories

Procedia CIRP 2024年 128卷 424-429页

作者： Julian Weller Nico Migenda Sebastian von Enzberg Martin Kohlhase Wolfram Schenck Roman Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 33102 Paderborn Germany Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 33330 Gütersloh Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 33102 Paderborn Germany

The emerging trend of Prescriptive Analytics tries to prescribe actionable decisions. The goal is to help production experts in automating their decisions in the factory. Decision making processes in factories mostly rely on experts inside the company. Current trends such as demographic shifts and shortages of skilled personnel are challenging organizations to find ways to become less dependent on the ad hoc availability of expert knowledge through digitalization and data analytics. Existing data analytics approaches for smart factories usually focus on description, diagnosis, and prediction. An approach to differentiate between different application areas of Prescriptive Analytics in a smart factory is presented. Focus areas for a useful application and integration of Prescriptive Analytics are derived. Based on these applications, design principles are constructed. They help decision makers to select future lighthouse use cases for development and help smart factory advocates to strategically decide which prescriptive analytics approaches to highlight. The resulting design principles are based on a literature review and validated in a workshop with experts from research. Approaches are differentiated and key characteristics for future Use Case design decisions are derived.

关键词： Prescriptive Analytics Smart Factory Design methodology tools technologies

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Automatic Stiffness Measurement of Small Objects Using an Integrated System of Simultaneous Multi-scale Observation and Two-Directional Force Measurement

Automatic Stiffness Measurement of Small Objects Using an In...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Masaru Kojima Ryo Yoshikawa Yasushi Mae Tatsuo Arai Science Graduate School of Engineering Science Osaka University Toyonaka Osaka Japan Department of Mechanical Engineering Faculty of Engineering Science Kansai University Suita Osaka Japan Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Recently, the field of life sciences has witnessed significant advancements, leading to the exploration of various cell characteristics to evaluate cellular behavior. Among these characteristics, cell stiffness has garnered considerable attention. It is widely acknowledged that cell stiffness undergoes changes based on the cell’s physiological state, rendering stiffness measurement a critical parameter in cell evaluation. However, the extensive research into cell stiffness has been hindered by the challenges associated with manipulating micron-scale cells and measuring nano-scale forces. Previous studies have proposed a cell stiffness measurement system utilizing a two-fingered microhand equipped with a micro force sensor. Nonetheless, this approach required manual manipulation of the target object, resulting in potential errors in stiffness measurement. Additionally, manual manipulation restricted the workspace, limiting its applicability. To address these limitations, this paper introduces an automated stiffness measurement technique that integrates force data and image data. Furthermore, a multi-scale simultaneous observation system is presented, combining optical systems from two microscopes with different magnification levels.

关键词： Force measurement Optical microscopy Microscopy Measurement uncertainty Measurement techniques Rendering (computer graphics) Optical imaging Physiology Force sensors Optical sensors

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ScMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

ScMonica: Single-cell Mosaic Omics Nonlinear Integration and...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoli Zhang, Rui Aslam, Saba Li, Huijun Chen, Yuxi Zhang, Zequn Huang, Ruey-Song Wu, Hongyan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Centre for Cognitive and Brain Sciences University of Macau Taipa China Faculty of Science and Technology University of Macau Taipa China Shenzhen college of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Faculty of Health Sciences University of Macau Taipa China

ISBN: (纸本)9798350386226

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica's potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains. © 2024 IEEE.

关键词： Gene expression

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PARE: A Plane-Assisted Autonomous Robot Exploration Framework in Unknown and Uneven Terrain

PARE: A Plane-Assisted Autonomous Robot Exploration Framewor...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Pu Xu Zhaoqiang Bai Haoming Liu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Identifying traversable areas is a critical task for unmanned vehicles exploring safely through unstructured environments. In practice, the ambiguity in perceiving terrain traversability usually brings great challenges for autonomous exploration in unknown and uneven terrain, which often leads to conservative strategies or potential risk of vehicle damage, resulting in many unexplored areas in the environment. To that end, this paper proposes a plane-assisted autonomous robot exploration framework (PARE) to achieve maximum volume and safe autonomous exploration. The process is carried out by a three-step dual-layer framework: constructing a local tree using Plane-Assisted RRT* (PA-RRT*), calculating exploration gain based on terrain information, and maintaining a global search graph. Firstly, the planar feature metrics (flatness, sparsity, elevation variation, slope and slope variation) are introduced to determine the terrain traversability. Secondly, to completely explore the rugged environment, we propose a dual-layer exploration framework comprising local and global strategies. A local planner based on PA-RRT* is proposed to find the best path by evaluating the planar information and the volumetric gain within the local exploration tree. Meanwhile, a global planner constructed by graph is proposed to record unexplored nodes with high exploration gain from the local tree to ensure a high level of exploration volume. Extensive simulation and real-world experiments demonstrate that our method significantly outperforms existing frameworks, with an average improvement of more than 12% in exploration volume.

关键词： Measurement intelligent robots Autonomous vehicles Autonomous robots

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Models of Exchanged datasets and Interactions of Buyers in the data Market: Toward Multi-Agent Simulators for System Design 26

Models of Exchanged Datasets and Interactions of Buyers in t...

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26th International Conference on Knowledge-Based and intelligent Information and engineering systems, KES 2022

作者： Hayashi, Teruaki Matsushima, Hiroyasu Sakaji, Hiroki Fukami, Yoshiaki Shimizu, Takumi Department of Systems Innovation School of Engineering The University of Tokyo Tokyo Japan Center for Data Science Education and Research Shiga University Shiga Japan Department of International Digital and Design Management Tokyo University of Science Tokyo Japan Faculty of Policy Management Keio University Kanagawa Japan

Value creation by reusing data as exchangeable resources has been widely studied as a new source of innovation, resulting in the establishment of a data market and its ecosystem. However, owing to the nascent nature of the market, observable information on exchanged datasets and interactions among stakeholders, such as buyers and providers, in the market are limited. Therefore, a lack of observable information that contributes to designing the market system and formulating regulations to promote the sound growth of data markets exists. This study modeled exchanged datasets and data buyers as agents, the smallest units of the data market components, and prepared seven scenarios for four market sizes. We simulated the effects of datasets and agent models with different market sizes on the agents' data purchases and emergence of popular datasets and discussed the factors that affect the purchase frequency distribution of the datasets. We present the experimental results and new implications for developing a data market simulator. The development of this simulator is expected to significantly advance research on the market understanding and system design in data markets. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and intelligent Information & engineering systems (KES 2022)

关键词： systems analysis

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Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision

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Tsinghua science and Technology 2025年第2期30卷 633-649页

作者： Yi Lei Dawei Shu Miao Yu Donglin Shi Jianqiang Li Yanjie Chen Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Automation Tsinghua UniversityBeijing 100084China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research InstituteXiamen 361000China Institute of Systems Engineering Dalian University of TechnologyDalian 116024China Physical Education Department Hebei Sports UniversityShijiazhuang 050049China Department of Children’s Health Care Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China

Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

关键词： developmental coordination disorder action quality assessment pose estimation dynamic time warping decision support system

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Comments on "A Lightweight Privacy Preserving Authentication Protocol for VANETs"

Comments on "A Lightweight Privacy Preserving Authentication...

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International Conference on Computer and Communications (ICCC)

作者： Syed Muhammad Awais Wu Yucheng Khalid Mahmood Rupak Kharel School of Microelectronics and Communication Engineering Chongqing University Chongqing China School of Psychology and Computer Science and Graduate School of Intelligent Data Science University of Central Lancashire Preston United Kingdom School of Psychology and Computer Science and Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Taiwan School of Psychology and Computer Science and Faculty of Electrical and Electronics Engineering University of Central Lancashire Preston United Kingdom School of Psychology and Computer Science and Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

Vehicle use and the concept of a "smart city" are developing quickly. As a result of this progression, the Vehicular Ad-Hoc Network (VANET) is a popular network for inter-vehicular communication. The data gathering about the state of the road, the whereabouts of vehicles, their speeds, and the description of traffic congestion are obtained using the VANET. The public nature of information flow on the VANET creates serious security risks. data security is typically one of the most important duties of VANET. As a result, it becomes one of the researchers’ top priorities. Li et al. [1] introduced a Lightweight Privacy Preserving Authentication Protocol for VANETs. They stated that several attacks could be resisted using their secure authentication devised protocol. However, after analyzing we discovered that their authentication protocol is susceptible to roadside impersonation attack and it does not provide vehicle anonymity. In the end, we have provided a few suggestions to cope with Li et al.’s devised protocol current shortcomings.

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End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality systems

arXiv

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arXiv 2025年

作者： Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Alsheikh, Mohammad Abu Kuhn, Carlos C.N. Alem, Yibeltal F. Radwan, Ibrahim School of Electrical Data Engineering University of Technology SydneyNSW2007 Australia School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia Faculty of Science & Technology University of Canberra CanberraACT2617 Australia

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 37% for the reconstructed human poses compared to the baseline. © 2025, CC BY.

关键词： Mean square error

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Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第3期8卷 891-894页

作者： Dev, Kapal Chih-Lin, I. Boutaba, Raouf Khowaja, Sunder Ali Lien, Shao-Yu Wang, Yue Munster Technological University Department of Computer Science CorkT12 P928 Ireland China Mobile Research Institute Beijing100053 China University of Waterloo School of Computer Science WaterlooONN2L 3G1 Canada University of Sindh Faculty of Engineering and Technology Jamshoro76080 Pakistan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan 6G Research Centre Samsung Electronics SurreyKT16 0RS United Kingdom

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Feasibility Study of Hybrid Energy systems at Remote Locations in Australia

Feasibility Study of Hybrid Energy Systems at Remote Locatio...

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Australasian Universities Power engineering Conference, AUPEC

作者： Sami Ullah Narottam Das M A Parvez Mahmud School of Engineering and Technology Central Queensland University Melbourne VIC Australia Centre for Intelligent Systems Central Queensland University Brisbane QLD Australia Faculty of Science University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Reliance on conventional fossil fuels to fulfil the energy demands in this era of industrial development is becoming a financial burden and a huge source of global warming. Almost 70% of Australia’s electricity generation from conventional energy resources, such as coal (57%) and gas (13%). Using traditional fuels at vast level causes considerable greenhouse gas emission. Because the integration of renewable energy production systems has potential to address the electrical demands of distant places, this research provides a feasibility study concentrating on integrating hybrid energy generation into Australia’s energy system. This research investigates the design and implementation of hybrid energy systems (HES) evolved to the specific needs of distant commercial markets in three remote regions across Australia: Norseman in Western Australia, Marla in Southern Australia, and Traralgon in Victoria. Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software has adopted to model the HES comprising photovoltaic systems, wind turbines, diesel generators, and energy storage systems as primary and secondary backups for the selected locations. These results show surplus electricity production in all locations, with Marla having the best financial outcomes. Norseman and Marla have similar fuel consumption patterns and emissions; however, Traralgon has higher fuel consumption and emissions due to increased energy demand. The study outlines future research and recommendations, emphasizing cooperation and sustainability in improving renewable energy technologies.

关键词： Renewable energy sources Energy consumption Production systems Power engineering Software packages Electricity Hybrid power systems Wind turbines Australia Sustainable development

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