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A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 307-315页

作者： Guangyu Xiong Xiuqin Shang Gang Xiong Timo R.Nyberg IEEE the Silk Road Business School Sanya University the Department of Industrial Engineering and Management School of ScienceAalto University the State Key Laboratory for Management and Controlof Complex Systems Institute of AutomationChinese Academy of Sciences(CAS) Qingdao Academy of Intelligent Industries the Cloud Computing Center CAS the Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationCAS the School of Science at Aalto University

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing *** on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

关键词： Lean approach non-manufacturing process optimized order handling process removing wastes value chain win-win scenario

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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Reachability of black-box nonlinear systems after koopman operator linearization

arXiv

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arXiv 2021年

作者： Bak, Stanley Bogomolov, Sergiy Duggirala, Parasara Sridhar Gerlach, Adam R. Potomkin, Kostiantyn Department of Computer Science Stony Brook University NY United States School of Computing Newcastle University Newcastle Upon Tyne United Kingdom Department of Computer Science University of North Carolina Chapel HillNC United States Aerospace Systems Directorate United State Air Force Research Laboratory Wright-Patterson Air Force BaseOH United States

Reachability analysis of nonlinear dynamical systems is a challenging and computationally expensive task. computing the reachable states for linear systems, in contrast, can often be done efficiently in high dimensions. In this paper, we explore verification methods that leverage a connection between these two classes of systems based on the concept of the Koopman operator. The Koopman operator links the behaviors of a nonlinear system to a linear system embedded in a higher dimensional space, with an additional set of so-called observable variables. Although, the new dynamical system has linear differential equations, the set of initial states is defined with nonlinear constraints. For this reason, existing approaches for linear systems reachability cannot be used directly. In this paper, we propose the first reachability algorithm that deals with this unexplored type of reachability problem. Our evaluation examines several optimizations, and shows the proposed workflow is a promising avenue for verifying behaviors of nonlinear systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear systems

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Comparison of genotypes and phenotypes for von Willebrand factor gene variants using Japanese genome database

Blood Vessels, Thrombosis & Hemostasis

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Blood Vessels, Thrombosis & Hemostasis 2025年

作者： Takafumi Akimoto Hiroshi Inaba Soichi Ogishima Kazuki Kumada Ayano Mitsuhashi Ryui Miyashita Tomoko Yamaguchi Masato Bingo Yushi Chikasawa Keiko Shinozawa Takeshi Hagiwara Kagehiro Amano Eiichi N. Kodama Ei Kinai Department of Laboratory Medicine Tokyo Medical University Tokyo Japan Department of Informatics for Genomic Medicine Group of Integrated Database Systems Tohoku Medical Megabank Organization Tohoku University Japan Advanced Research Center for Innovations in Next-Generation Medicine Tohoku University Japan Department of Biobank Group of Biobank Tohoku Medical Megabank Organization Tohoku University Japan Department of Infectious disease Graduate School of Medicine International Research Institute of Disaster Science Tohoku Medical Megabank Organization Tohoku University Japan

von Willebrand disease (VWD) is a common inherited bleeding disorder. The aim of this study was to determine the predicted disease states associated with various pathogenic VW factor (VWF) variants and their phenotypes using the largest Japanese whole-genome database. Of the 5,857 VWF gene variants registered in the Japanese Multi-Omics Reference Panel (jMorp), variants with the following criteria were extracted; 1) caused protein abnormalities due to genetic alterations, 2) have already been detected and included in a database, including known association with VWD, and 3) highly-likely pathogenic by in silico analysis. We measured VWF activity, antigen, propeptide, and collagen binding activity in stored plasma samples obtained from heterozygous carriers of the selected variants. A total of 29 VWF variants (26 single nucleotide and 3 small insertions/deletions) were detected and six of these were found in Leiden Open Mutation database. We obtained 43 plasma samples from individuals carrying these 29 variants as heterozygous. For the 43 variant-carriers, their mean age was 43.0 and blood group was type O in 17 (39.5%). Analysis of these plasma samples showed low VWF levels (<50%) in 6 (14.0%). Low VWF levels were found in 2/8 (25%) of the nonsense and 4/31 (12.9%) of the missense variants. Taking into consideration the limitation of using stored plasma samples, analysis of the jMorp indicated that most VWF gene variants with predicted pathogenic potential did not correlate with phenotypic expression. Our results supported incomplete penetrance and variable expressivity of the VWF gene variants.

关键词： Von Willebrand disease Prevalence Pathogenic variant Genotype Phenotype

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Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM over Few Mode Fiber based on Electrical Equalization 2

Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM ove...

引用

2nd Joint International Conference on Emerging computing Technology and Sports, JICETS 2019

作者： Ghazi, Alaan Fareed, Alaa Masunda, Tendai Aljunid, S.A. Idrus, Syed Zulkarnain Syed Amphawan, Angela Al-Dawoodi, Aras Rashidi, C.B.M. Fadhel, Ibrahim Eskandar Ibrahim School of Human Development and Techno-communication Universiti Malaysia Perlis Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq School of Computing Universiti Utara Malaysia Malaysia Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Department of Computer and Information Systems National Institute of Administrative Science Yemen

Space division multiplexing, in conjunction with wavelength division multiplexing, is a powerful mechanism that increases spatial channels significantly in a single optical fiber. Spiral Phased-Laguerre-Gaussian (SP-LG) modes indicate that they effectively reduce mode coupling, thereby increasing the channel response and performance positively. However, mode-coupling influence optical fiber system leads to inter-symbol interference (ISI) between the channels and reduces both capacity and distance. In this paper, we apply the SP-LG modes in a space wavelength division multiplexing over a few-mode fiber system. The results demonstrate significant improvements in reducing channels effects and ISI. The performance evaluations are based on the bit-error-rate (BER), eye diagram and spectrum analyzer. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Spectrum analyzers

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Predicting extreme events from data using deep machine learning: When and where

引用

Physical Review research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

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Water Induced Ferroelectric Switching: The Crucial Role of Collective Dynamics

arXiv

引用

arXiv 2023年

作者： Aslam, Muhammad Awais Stankovic, Igor Murastov, Gennadiy Carl, Amy Song, Zehao Watanabe, Kenji Taniguchi, Takashi Lugstein, Alois Teichert, Christian Gorbachev, Roman Rodriguez, Raul D. Matković, Aleksandar Department Physics Mechanical Engineering and Electrical Engineering Montanuniversität Leoben Franz Josef Strasse 18 Leoben8700 Austria Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Belgrade11080 Serbia Department of Physics and Astronomy National Graphene Institute University of Manchester Manchester United Kingdom Institute of Solid State Electronics TU Wien Gußhausstraße 25-25a Vienna1040 Austria Research Center for Electronic and Optical Materials National Institute for Materials Science 1- 1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Tomsk Polytechnic University Lenina ave. 30 Tomsk634034 Russia

The interaction mechanisms of water with nanoscale geometries remain poorly understood. This study focuses on behaviour of water clusters under varying external electric fields with a particular focus on molecular ferroelectric devices. We employ a two-fold approach, combining experiments with large-scale molecular dynamics simulations on graphene nanoribbon field effect transistors. We show that bilayer graphene nanoribbons provide stable anchoring of water clusters on the oxygenated edges, resulting in a ferroelectric effect. A molecular dynamics model is then used to investigate water cluster behaviour under varying external electric fields. Finally, we show that these nanoribbons exhibit significant and persistent remanent fields that can be employed in ferroelectric heterostructures and neuromorphic circuits. © 2023, CC BY.

关键词： Nanoribbons

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An Epitrochoidal Rotary Reactor for Solar-Driven Hydrogen Production Based on the Redox Cycling of Ceria: Thermodynamic Analysis and Optimization

SSRN

引用

SSRN 2022年

作者： Wang, Bo Li, Xian Zhu, Xuancan Wang, Yuesen Tian, Tian Dai, Yanjun Wang, Chi-Hwa Singapore138602 Singapore NUS Environmental Research Institute National University of Singapore Singapore138602 Singapore Department of Systems Science Institute of High Performance Computing A*STAR Singapore138632 Singapore School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Sloan Automotive Laboratory Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering National University of Singapore Singapore117585 Singapore

A novel epitrochoidal rotary reactor was proposed to perform efficient solardriven water-splitting based on thermochemical redox cycling of ceria. This study is focused on the geometry optimization of the epitrochoidal rotary reactor to achieve the optimal thermodynamic solar-to-fuel energy conversion efficiency. The mathematical descriptions of kinematic synthesis of the conjugate rotor and stator profiles are derived in detail. The geometric compression ratio is statistically estimated using a hit-or-miss Monte Carlo method. A thermodynamic model considering the solid and gas phase heat recovery is developed to evaluate the performance of the epitrochoidal rotary reactors in various geometries. Among all geometrical parameters instigated, the cam-to-rotor size ratio and the number of rotor sides are found to have the most significant impact on the solar-to-fuel energy conversion efficiency. The optimal design of the epitrochoidal rotary reactor with a curved triangle rotor offers a geometrical compression ratio of 13 and a solar-to-fuel efficiency of 17% under a concentrated solar radiative flux of 3000 suns. © 2022, The Authors. All rights reserved.

关键词： Thermoanalysis

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Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

arXiv

引用

arXiv 2022年

作者： Hennebelle, Alain Ismail, Leila Materwala, Huned Kaabi, Juma Al Ranjan, Priya Janardhanan, Rajiv School of Computing and Information Systems The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates College of Health Sciences Department of Internal Medicine United Arab Emirates University United Arab Emirates Mediclinic Al Ain Abu Dhabi United Arab Emirates Bhubaneswar Institute of Technology India Faculty of Medical & Health Sciences SRM Institute of Science & Technology India

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user’s data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III. © 2022, CC BY.

关键词： Logistic regression

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Quality Matters:Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological databases

引用

Genomics, Proteomics & Bioinformatics 2020年第2期18卷 91-103页

作者： Qingyu Chen Ramona Britto Ivan Erill Constance J.Jeffery Arthur Liberzon Michele Magrane Jun-ichi Onami Marc Robinson-Rechavi Jana Sponarova Justin Zobel Karin Verspoor School of Computing and Information Systems University of MelbourneMelbourneVIC 3010Australia European Molecular Biology Laboratory European Bioinformatics Institute(EMBL-EBI)Wellcome Trust Genome CampusCambridge CB101SDUK Department of Biological Sciences University of MarylandBaltimoreMD 21250USA Department of Biological Sciences University of Illinois at ChicagoChicagoIL 60607USA Broad Institute of MIT and Harvard CambridgeMA 02142USA Japan Science and Technology Agency National Bioscience Database CenterTokyo 102-8666Japan National Institute of Health Sciences Tokyo 158-8501Japan Swiss Institute of Bioinformatics CH-1015 LausanneSwitzerland Department of Ecology and Evolution University of LausanneCH-1015 LausanneSwitzerland Nebion AG 8048 ZurichSwitzerland

Introduction Biological databases represent an extraordinary collective volume of *** built up over decades and comprising many millions of contributions from the biomedical research community,biological databases provide worldwide access to a massive number of records(also known as entries)[1].Starting from individual laboratories,genomes are sequenced.

关键词： Impact collective massive

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