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3rd International Symposium on Instrumentation systems, Circuits and Transducers: Chip in the Pampa, INSCIT 2018

作者： De Oliveira, Michael Lopes Torres, Frank Sill Da Silva Fernandes, Keyliane Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Cyber-Physical Systems DFKI GmbH Bremen Germany Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9781538672983

A wide range of applications in the field of the Internet-of-Things (IoT) possess only strongly limited energy resources. This rises the need for Ultra-Low Power (ULP) designs that enable processing with lowest energy consumption. This, however, comes at considerable performance costs. Consequently, there is a need for flexible design flows that enable the designer to select the best tradeoff between energy consumption and performance for the destined application. Towards this end, we present in this paper a complete flow for the design of a ULP standard cell library based on the subthreshold methodology. The flow considers both the selection of the appropriate technology parameters, like supply voltage and base sizing, as well as the actual design and characterization of the standard cells. Simulation results for an ARM-based processor and ITC'99 and EPFL benchmark circuits realized in a commercial 130 nm technology indicate a possible reduction of power consumption of several factors, while performance degrades between two and three orders of magnitude. © 2018 IEEE.

关键词： Energy utilization

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Development and validation of a novel combinational index of liquid biopsy biomarker for longitudinal lung cancer patient management

The journal of liquid biopsy

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The journal of liquid biopsy 2024年 6卷 100167页

作者： Min-Yi Liao Yun-Jie Hao Ching-Shan Luo Ching-Mei Chen Po-Hao Feng Hsin-Yu Yang Da-Jeng Yao Kang-Yun Lee Fan-Gang Tseng Department of Power Mechanical Engineering National Tsing Hua University Hsinchu 30013 Taiwan. Department of Engineering and System Science National Tsing Hua University Hsinchu 30013 Taiwan. International Ph.D. Program in Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan. Division of Pulmonary Medicine Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City 23561 Taiwan. TMU Research Center of Thoracic Medicine Taipei Medical University Taipei 11031 Taiwan. Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan. Nano Science and Technology Program National Tsing Hua University Hsinchu 30013 Taiwan. Institute of Nano Engineering and Micro Systems (NEMS) National Tsing Hua University Hsinchu 30013 Taiwan. Graduate Institute of Clinical Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan. Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu 30013 Taiwan.

Objectives:Many cancer biomarkers such as the circulating tumor cells/microemboli (CTCs/CTM) have been reported significant associations with clinical outcomes. However, different biomarkers have different sensitivities and specificities for cancer types and cohort patients, and synergistic effects between certain biomarkers have also been observed, leading to the inaccurate, fluctuating, and even controversial results when multiple biomarkers are analyzed together. In this paper, a novel combinational index, P-score, was developed for monitoring and predicting the disease condition of lung cancer patients during follow-up visits. Materials and methods:There were totally 13 return patients with 54 blood samples involved in this study to examine the number of CTC and CTM. Information from one group of 7 patients including 27 blood samples with published clinical data was employed to develop while those from another group of 4 patients containing 14 blood samples with unpublished clinical data were used to validate the P score in prediction. Enumerations were based on immunofluorescent staining images. Distributions of CTC/CTM and their frequencies in stratified patients were carefully examined and analyzed the ROC curve and AUC value to develop the P score and P score-based prediction model. Results and conclusion:We found that the predictive power of P-score was not only comparable to the traditional cancer marker, in comparison with individual CTC/CTM, more false positives could be corrected by using P-score, thereby to improve the accuracy of analysis. From our preliminary validation tests, the prognosis and disease progression monitored longitudinally by P-score were further confirmed by clinical outcome data from physicians and its sensitivity was even better than those from individual biomarkers. We believe that this novel combinational indicator could be a promising tool to interpret clinical outcomes more accurately from multiple factors, particularly useful

关键词： Circulating tumor cells/microemboli Combinational index Disease monitoring and prognosis Drug screening Lung cancer

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Addressing knowledge management transfer in project management at digital manufacturing

Addressing knowledge management transfer in project manageme...

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2018 Institute of Industrial and systems Engineers Annual Conference and Expo, IISE 2018

作者： Muncinelli, Gianfranco De Lima, Edson Pinheiro Deschamps, Fernando Gouvea Da Costa, Sergio E. Beller, Cassiano S. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Superior Institute of Administration and Economy of Mercosul Curitiba Brazil

Manufacturing has been changing drastically the way it approaches facility design and implementation, that is, there is a growing adoption of digital techniques. This paper intends to determine the theoretical concepts and guidelines for assisting the study of alternatives for problem-solving in a specific case of an automaker digital manufacturing project. The main objective of the research is to determine the theoretical components that will assist in the forwarding of solutions to the problems observed in the current model of project management of digital manufacturing projects. The conceptual guidelines proposed based on systematic literature and guided the construction of research protocol that collects data from technical visits (semi-participative observation), technical documents and procedures, information systems log and archives, and semi-structured interviews. Data analysis use the mechanism of triangulation and workshops for producing a conceptual analysis about how each factor can assist in addressing the solving of the issues observed, building the theoretical constructs. The manufacturing engineering team could identify strengths and weakness on the ongoing practices and better plan improvements for reaching opportunities and mitigating threats/risks. The findings are classified in the following aspects: cultural, technology, knowledge content, organization, processes, and project management;and showed a complete analysis on how ready is the organization for developing digital manufacturing projects. © 2018 Institute of Industrial Engineers (IIE). All rights reserved.

关键词： Knowledge management

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NMPC Strategy for a Quadrotor UAV in a 3D Unknown Environment

NMPC Strategy for a Quadrotor UAV in a 3D Unknown Environmen...

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International Conference on Advanced Robotics (ICAR)

作者： Iuro B. P. Nascimento Antonio Ferramosca Luciano C. A. Pimenta Guilherme V. Raffo Graduate Program in Electrical Engineering Federal University of Minas Gerais (UFMG) CEP Belo Horizonte Minas Gerais Brasil CONICET - UTN Facultad Regional Reconquista Santa Fe Argentina UFMG INCT for Cooperative Autonomous Systems Applied to Security and Environment

This work presents a Nonlinear Model Predictive Control strategy for a quadrotor UAV with obstacle avoidance capability in a 3D unknown environment with static obstacles. The system aims to reach the target in minimum time while avoiding obstacles and also to take into account the energy of states and inputs. Sensor information is processed to detect the obstacles and obtain the inequality constraints of an obstacle-free zone. Numerical results are presented to attest the performance of the system.

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Multi-objective Support Vector Machines Ensemble Generation for Water Quality Monitoring

Multi-objective Support Vector Machines Ensemble Generation ...

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Congress on Evolutionary Computation

作者： Victor Henrique Alves Ribeiro Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

Real-world classification problems generally deal with imbalanced data, where one class represents the majority of the data set. The present work deals with event detection on a drinking-water quality time series, where the presence of a quality event is the minority class. In order to solve such problems, supervised learning algorithms are recommended. Researchers have also used multi-objective optimization (MOO) in order to generate diverse models to build ensembles of classifiers. Although MOO has been used for ensemble member generation, there is a lack on it's application for member selection, which is usually done by selecting a specific subset from the resulting models, or by using meta-algorithms, such as boosting. The proposed work comprises the application of MOO design in the whole process of ensemble generation. To do so, one multi-objective problem (MOP) is defined for the creation of a set of non-dominated solutions with Pareto-optimal support vector machines (SVM). After that, a second MOP is defined for the selection of such SVMs as members of an ensemble. Such methodology is compared to other member selection methods, such as: the single best classifier, an ensemble composed of the full set of non-dominated solutions, and the selection of a specific subset from the Pareto front. Results show that the proposed method is suitable for the creation of ensembles, achieving the highest classification scores.

关键词： Support vector machines Optimization Mood Training Event detection Kernel Supervised learning

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Multi-objective PID Controller Tuning for an Industrial Gasifier

Multi-objective PID Controller Tuning for an Industrial Gasi...

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Congress on Evolutionary Computation

作者： Victor Henrique Alves Ribeiro Gilberto Reynoso- Meza Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Control of industrial plants is an important engineering field of study, where the optimization of such processes can lead to better product quality and higher profit. The present work deals with the optimization of an industrial gasifier, proposed as a benchmark challenge by ALSTOM Power Technologies in 2002. Researchers from around the globe proposed different methods for optimizing such system, using linear and nonlinear system identification techniques, different controller schemes, such as proportional-integral-derivative and model predictive controllers, and search methods for controller tuning. The authors care for the study of a methodology that can generalize the controller optimization process of real industrial systems, where linear system identification can lead to simpler systems, which are used to design more robust controllers. The proposed work deals with linear system identification and multiobjective optimization for the gasifier's controller design. To do so, the authors divide the problem in two steps, the linear system identification, where step functions are fed to the gasifier in order to detect the output response of the system, and the design of a proportional-integral-derivative controller, where spherical pruned multi-objective differential evolution algorithm is used in order to optimize the controller gains. The proposed method, unfortunately, does not provide an optimal controller, which can be explained by the complexity of the benchmark system. With such results, the authors believe a better multi-objective problem definition is necessary. Also, it is concluded that such benchmark presents a real challenge for future controller design methndologies.

关键词： Optimization Process control Benchmark testing Tuning PD control Genetic algorithms

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Potential Impact of Social Media to Support Government Services in Jakarta Smart City 3

Potential Impact of Social Media to Support Government Servi...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Didik Madyatmadja, Evaristus Abdurachman, Edi Gaol, Ford Lumban Pudjianto, Bonifasius W. Hapsara, Manik Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Indonesian Creative Economy Agency Jakarta Indonesia University of New South Wales School of Engineering and Information Technology Australia

ISBN: (纸本)9781538658215

The purpose of this initiative is to ensure that citizens use social media to access information about the state of the city. Social media application offers benefits in the form of a channel of communication between the community and the government or the group that has public interest. Through this application, the public could channel complaints about public services. Perception becomes the important key for this condition because user action is determined by their perception toward the government e-services. The result of standard measurement for customer satisfaction on government organization is used to help find the weakness of the service and correct it. The interface is made to be more user-friendly to ensure the effective service. So, it is necessary to develop the system continuously. Perceived quality is one of the important determinants of the success of social media application, therefore needs identification of the user's perception and expectations and involving becomes the key to success besides the good quality that will increase the trust and satisfaction of users. Evaluation of social media applications is needed so that it can provide recommendations to the provincial government of DKI Jakarta related to what things can be done to improve community service. © 2018 IEEE.

关键词： Smart city

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Modeling of a 2-DOF Piezoelectric Micromanipulator at High Frequency Rates through Nonlinear Black-box System Identification

Modeling of a 2-DOF Piezoelectric Micromanipulator at High F...

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2018 Annual American Control Conference, ACC 2018

作者： Ayala, Helon Vicente Hultmann Rakotondrahe, Micky Coelho, Leandro Dos Santos Pontifical Catholic University of Rio de Janeiro Department of Mechanical Engineering Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Brazil FEMTO-ST Institute Université Bourgogne Franche-Comté CNRS Besançon France Department of Electrical Engineering Federal University of Paraná Brazil

ISBN: (纸本)9781538654286

In the present paper we proceed the data-driven modeling of a two degrees of freedom (2-DOF) piezoelectric micromanipulator through models with the Nonlinear AutoRegressive with eXogenous inputs (NARX) structure and real acquired data. We show the results when the system is excited at high frequencies, aiming towards rapid and precise micropositioning. The order of NARX the models are increased until they satisfy the statistical tests based on higher-order correlations and the multiple correlation coefficients, which are close to unity for both measured outputs. The results herein presented encourage the use of data-driven methods for modeling piezoelectric micromanipulators. © 2018 AACC.

关键词： Data models Mathematical model Micromanipulators Analytical models Actuators"

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Model of developing key performance indicator to increase the quality of education during the Covid-19 pandemic

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Journal of Physics: Conference Series 2021年第1期1832卷

作者： E Selviyanti W Sardjono M Mukhlis M Tohir M Maswar A Fariz Health Department Politeknik Negeri Jember Jember Jawa Timur Indonesia 68124 Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Faculty of Teacher Training and Education Islamic State Institute of Jember Jawa Timur INDONESIA Universitas Ibrahimy Situbondo Indonesia Computer Network Engineering SMK 3 Perguruan 'Cikini' Jakarta Indonesia 12640

Indonesia is one of the countries whose people are most affected by the 2019 corona virus (COVID-19). When the corona virus spreads faster, the Indonesian government imposed large-scale social restrictions in big cities to villages in Indonesia. With the changing situation in carrying out work activities during this pandemic, it will certainly affect the quality of organization in companies and the quality of education in schools and colleges. In order to identify all processes in teaching and learning activities that are running well or not, schools need to monitor and measure employee performance. Key performance indicators (KPIs) are generally used as benchmarks to determine how well a company's performance can be represented by employee performance. In this study, the key performance indicator preparation process was carried out through the stages of designing a questionnaire, distributing questionnaires to employees in educational institutions, conducting factor analysis and then developing a KPI model. The output is in the form of a KPI model that can be used as a reference in the formation of KPIs in educational institutions, namely Y = 5,360 + 1,380 X1-1, 068 X3.

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