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International Carpathian Control Conference (ICCC)

作者： Vinícius Berno Da Silva Paulo César Barros Rodrigues Murillo Ferreira Dos Santos Paolo Mercorelli Milena Faria Pinto Wolmar Araújo Neto Department of Electroelectronics CEFET-MG Leopoldina Brasil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Germany Graduate Program in Electrical Engineering CEFET-RJ Rio de Janeiro Brasil Federal University of Espírito Santo UFES Vitória Brazil

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Gain scheduling has been successfully applied in many different areas due to the positive results achieved when applied to nonlinear control. Its utilization enables the controller to respond rapidly to changes in operating conditions. This study demonstrates the application of gain scheduling to a SMAR didactic level system to implement adaptive control. The results exhibited a strong response with minor errors using a Proportional and Integral (PI) controller, which facilitated the development of two generic equations for determining the values of K p (proportional gain) and K i (integral gain) based on the desired set point.

关键词： Adaptive scheduling PI control Liquids System dynamics Integral equations Noise Gain measurement

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New statistics for simultaneously machine incipient fault detection and monotonic degradation assessment 4

New statistics for simultaneously machine incipient fault de...

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4th International Conference on Mechanical, Electric and Industrial engineering, MEIE 2021

作者： Hou, Bingchang Chen, Yikai Deng, Yanqing Wang, Yuting Wang, Dong State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai200240 China Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

Machine condition monitoring (MCM) has become an important tool to avoid sudden machine breakdown and gaining more economic profits. Tasks including early fault detection and monotonic degradation assessment are important in MCM. For the incipient fault detection, statistics such as kurtosis, Gini index are widely utilized, but they cannot give an accurately incipient fault detection time, and many fluctuations may exhibit. For the monotonic degradation assessment, root-mean-square are commonly used, however, it is sensitive to energy, and cannot show distinct degradation tendency in an early fault state. Those drawbacks have limited the development of practical MCM algorithms. To address those issues, this paper proposed four parameterized statistics for simultaneously early fault detection and monotonic degradation assessment. The four parameterized statistics can be health indicators and simplify the MCM algorithms, which can be beneficial to the practical MCM applications. © Published under licence by IOP Publishing Ltd.

关键词： Fault detection

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Identifying monitoring indicators for emerging technological opportunities 26

Identifying monitoring indicators for emerging technological...

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26th International Association for management of Technology Conference, IAMOT 2017

作者： Oh, Eunkyeong Lee, Yongseung Shin, Juneseuk Sungkyunkwan University Department of Graduate School of Management of Technology Korea Republic of Sungkyunkwan University Department of Systems Management Engineering Korea Republic of

ISBN: (纸本)9783200049864

Monitoring of emerging technologies needs indicators which are more sensitive to changes in context and time. To this purpose, organizations have depended on qualitative expert judgment, but it has increasingly suffered from reduced sensitivity and accuracy. A better way of monitoring is needed. As either alternative or supplement to expert judgment, some researchers suggested various quantitative indicators including number of patents, number of new products and others. However, it is not clear what indicators are good for monitoring of emerging technologies, and how we can identify such indicators because each indicator has advantages as well as disadvantages. Therefore, in this paper, we suggest a method of identifying appropriate quantitative monitoring indicators for emerging technological opportunities. In the existing literature, appropriateness of monitoring indicators can be evaluated from three perspectives: 1) sensitivity to technological changes, 2) sensitivity to changes in external context, and 3) sensitivity to the value of a technological opportunity. In other words, good monitoring indicators send signals when important changes occur. Through literature review, we collect monitoring indicators and their time-series data of two emerging technologies over last nine years. Selected technologies consist of building-integrated photovoltaic module and photovoltaic module. Reviewing existing literature, we construct a pool of monitoring indicators. Using autoregression and correlation analysis, we can choose monitoring indicators that can explain the variance of value of a technological opportunity. Then, sensitivity analysis helps us identify indicators that are sensitive to changes in technology and context For emerging technologies, we find some indicators that are sensitive to more than 70% of changes in technology and external context, but are sensitive to less than 10% of noises. However, some indicators are sensitive to more than 90% of changes in e

关键词： Monitoring

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Mrf-Former: A Multi-Scale Resolution Fusion Approach for Addressing Scale Discrepancies in Remote Sensing Image Change Detection

SSRN

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SSRN 2024年

作者： Yang, Hualin Ouyang, Jiarui Cheng, Junhao Zhang, Wen Li, Hui Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Institute of Artificial Intelligence Guangzhou University Guangzhou China

Dealing with resolution variations in dual-temporal remote sensing images is challenging due to differences in sensor capabilities, altitudes, footprints, resampling, and data fusion. Conventional methods for different-resolution change detection struggle to effectively address these challenges, primarily due to insufficient consideration of global and local feature interactions. In this paper, we propose a novel change detection model that leverages the power of multi-scale resolution fusion(MRF). By incorporating the MRF-Block, our approach extracts hierarchical multi-scale features from the input data, enabling effective capture of contextual information at various granularities. This facilitates improved handling of resolution disparities and promotes interactions between global and local features. Experimental evaluations conducted on diverse datasets, demonstrate the effectiveness of our proposed model in achieving accurate change detection in dual-temporal remote sensing imagery, even in scenarios characterized by varying resolutions. Code is available on-line at https://***/yanghl1914/MRF-Former. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Acceptance and Use of Sensor Technology in Diagnosis of Gender Dysphoria

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Sensors and Materials 2023年第8期35卷 2771-2782页

作者： Shih, Chun-Ming Lai, Jiin-Chyuan Mark Chan, Yung-Kuan Hsieh, Ming Yuan Meen, Teen-Hang Graduate School of Human Sexuality Shu-Te University No. 59 Hengshan Rd Yanchao District Kaohsiung City824 Taiwan Department of Applied Foreign Languages TransWorld University 1221 Zhenan Road Yunlin County Douliu City640 Taiwan Department of Management Information Systems National Chung Hsing University 145 Xingda Road West District Taichung City40227 Taiwan Department of International Business National Taichung University of Education 140 Minsheng Road South District Taichung City40306 Taiwan Graduate School of Human Sexuality Shu-Te University No. 59 Hengshan Rd Yanchao District Taichung City40306 Taiwan Department of Electronic Engineering National Formosa University 64 Wunhua Road Yunlin County Huwei Township632 Taiwan

The degree of acceptance and the impact of sensor technology on diagnosing gender dysphoria (GD) were investigated, cross-employing the technology acceptance model (TAM), follower theory (FT), and social identity theory (SIT). Through interviews with 20 invited experts and a questionnaire survey of 243 participants, alternatives (attitude toward using technology, behavioral intention to use, and actual system use), factors (innovative sensor technology, user’s perception of the technology, and social identity), and subfactors (innovative sensor technology, behaviors within user’s perception of the technology, and social identity) were defined. The analysis results showed that innovative sensor technology is important in the users’ perception of the technology for the diagnosis of GD based on gender characteristics. The participant expected the actual use of the technology for the precise and reliable identification of GD. This result implies that sensor technology helps solve social problems caused by GD, and its applications can be evaluated in diversified behaviors in accordance with GD. Sensors that measure psychological disturbance were considered important for psychological and physical assessments. The results of this research reveal that more opportunities are required for the use of sensor technology as an efficient method for interdisciplinary research. © MYU K.K.

关键词： Behavioral research

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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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RadonPy:automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

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npj Computational Materials 2022年第1期8卷 2155-2169页

作者： Yoshihiro Hayashi Junichiro Shiomi Junko Morikawa Ryo Yoshida The Institute of Statistical Mathematics(ISM) Research Organization of Information and Systems10-3 Midori-choTachikawaTokyo190-8562Japan Department of Mechanical Engineering The University of Tokyo7-3-1 HongoBunkyoTokyo113-8656Japan Institute of Engineering Innovation The University of Tokyo2-11 YayoiBunkyo-kuTokyo113-0032Japan Department of Materials Science and Engineering School of Materials and Chemical TechnologyTokyo Institute of Technology2-12-1 OokayamaMeguro-kuTokyo152-8552Japan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)1-2-1 SengenTsukubaIbaraki305-0047Japan Department of Statistical Science School of Multidisciplinary ScienceThe Graduate University of Advanced Studies(SOKENDAI)10-3 Midori-choTachikawaTokyo190-8562Japan

The spread of data-driven materials research has increased the need for systematically designed materials property ***,the development of polymer databases has lagged far behind other material *** present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric ***,15 different properties were calculated for more than 1000 amorphous *** MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning *** the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying *** to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.

关键词： property calculation dynamics

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A CHARACTER-LEVEL SPAN-BASED MODEL FOR MANDARIN PROSODIC STRUCTURE PREDICTION

arXiv

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arXiv 2022年

作者： Chen, Xueyuan Song, Changhe Zhou, Yixuan Wu, Zhiyong Chen, Changbin Wu, Zhongqin Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong TAL Education Group Beijing China

The accuracy of prosodic structure prediction is crucial to the naturalness of synthesized speech in Mandarin text-to-speech system, but now is limited by widely-used sequence-to-sequence framework and error accumulation from previous word segmentation results. In this paper, we propose a span-based Mandarin prosodic structure prediction model to obtain an optimal prosodic structure tree, which can be converted to corresponding prosodic label sequence. Instead of the prerequisite for word segmentation, rich linguistic features are provided by Chinese character-level BERT and sent to encoder with self-attention architecture. On top of this, span representation and label scoring are used to describe all possible prosodic structure trees, of which each tree has its corresponding score. To find the optimal tree with the highest score for a given sentence, a bottom-up CKY-style algorithm is further used. The proposed method can predict prosodic labels of different levels at the same time and accomplish the process directly from Chinese characters in an end-to-end manner. Experiment results on two real-world datasets demonstrate the excellent performance of our span-based method over all sequence-to-sequence baseline approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Structural optimization

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Multi-sensor cyber-physical sorting system (CPSS) based on Industry 4.0 principles: A multi-functional approach 4

Multi-sensor cyber-physical sorting system (CPSS) based on I...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Konstantinidis, Fotios K. Sifnaios, Savvas Tsimiklis, Georgios Mouroutsos, Spyridon G. Amditis, Angelos Gasteratos, Antonios Institute of Communication and Computer Systems National Technical University of Athens 9 Iroon. Polytechniou Str. AthensGR-157 73 Greece Department of Production and Management Engineering Democritus University of Thrace 12 Vas. Sophias XanthiGR-671 32 Greece Department of Electrical and Computer Engineering Democritus University of Thrace XanthiGR-671 00 Greece

Products global demand constantly grows, along with drifts of the volume of waste produced, thus having a negative impact on the environment. Besides the advancement of artificial intelligence and cutting-edge sensing and actuation systems, allow for traditional recycling procedures to be digitised and become more efficient. Among these, automated sorting machines allow such efficacy, by using multiple recognition technologies to categorise items, instead of relying on humans to separate them. In this sense, this paper presents a new approach being included in the Cyber-Physical Sorting System (CPSS) capable to be adapted in different industries such as urban wastes, construction & demolition wastes as well as mineral sorting. The materials are laid out over a conveyor belt, where multi-purpose visual sensors (hyper-spectral, industrial and short-wave infrared) acquire images in order to classify and sort them in different bins using robotic arms. The architecture of the proposed CPSS allows the integration of additional sensors, increasing the interoperability with upcoming sensing devices. Furthermore, an intelligent hyper-spectral component adapted to urban wastes was developed that classifies glass, metal, paper, and plastic using multi-spectral data in visual and near-infrared ranges between 470nm - 975nm. Scatter-correction methods and derivatives were applied to extract noise-free features, while an exploratory analysis based on simple and robust Principal component analysis (PCA) was performed to identify the most significant principal components for the training phase. During the evaluation, the neural network performed better when normalisation and Savtizky and Golay (SG) filtering was applied to the dataset against other classifiers. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Computer vision

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