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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Lin, Junguang Pan, Zhaoguang Zhao, Shenyi Wu, Fan Jiang, Yuehong Shi, runye International Joint Lab on Low-grade Energy Utilization of Zhejiang Province Zhejiang Energy Group R&D Institute Co. Ltd.2 Hangzhou China Department of Electrical Engineering and Applied Electronics Technology Tsinghua University Beijing China Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China

ISBN: (纸本)9781665434256

Combined heat and electric power energy networks are common as multi-energy systems and its optimal operation is a hot topic. The goals of this manuscript is to achieve the optimal heat and power flow for the coupling network with tree-topology heat network and dC power network. The study aims to minimize the total deviations between user setting points and building indoor temperatures, decrease total energy consumption and cost. An optimization model is proposed using a steady-state method. The effectiveness of this method is proved by simulation results. Optimal operational schedule is obtained which can considers the complexity of the combined heat and electric networks. © 2021 IEEE

关键词： Electric load flow

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Meal intention recognition system based on gaze direction estimation using deep learning for the user of meal assistant robot

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Journal of Institute of Control, robotics and Systems 2021年第5期27卷 334-341页

作者： Park, Hyunji Jang, Inhoon Ko, Kwangeun Applied Robot R&D Department Korea Institute of Industrial Technology Korea Republic of Department of Computer Science & Engineering Hanyang University Korea Republic of Industry Academic Cooperation Foundation Hankyong National University Korea Republic of

Care robots that provide dietary supplements to the elderly and the disabled should be able to intelligently interact with the users’ intention to eat in real-time and provide the food they want. To this end, based on deep learning, we have developed a user interface that facilitates the selection of food to be eaten according to six user gaze directions. A single-stage object-detection deep learning model has been implemented as a lightweight, single-stage object-detection deep learning model that can be installed on commercial tablets, and the accuracy of this model was 0.9857. © ICrOS 2021.

关键词： Human robot interaction

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day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros d. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MdP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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Kinesthetic sensing of hole position by 3-finger gripper

Kinesthetic sensing of hole position by 3-finger gripper

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International Conference on Ubiquitous robots and Ambient Intelligence (UrAI)

作者： Myoung-Su Choi dong-Hyuk Lee Jae-Han Park Ji-Hun Bae Robotics R&D Group Korea Institute of Industrial Technology Ansan Repulbic of Korea(South) Applied Robot R&D Departmment Korea Institute of Industrial Technology Ansan Repulbic of Korea(South)

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

In this study, we present a novel method for `peg-in-hole' assembly using a multi-joint three-fingered gripper. In previous studies, we have conducted peg-in-hole assembly based on spiral search method, not knowing the exact hole position. However, search time varies greatly depending on the error between the peg and the hole, it was difficult to estimate tact time of the assembly task. In this paper, in order to reduce the search time deviation of the hole, we propose a method that estimating the direction of the hole first before conducting the search motion. direction of the hole is estimated by `kinesthetic sensing' using the fingers grasping the peg. The feasibility of the proposed method has been verified through peg-in-hole assembly experiments using the three-fingered robot gripper.

关键词： robot sensing systems Grippers Navigation Grasping Performance evaluation Estimation

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Plasmonic nanoparticle-based surface-enhanced raman spectroscopy-guided photothermal therapy: emerging cancer theranostics

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Nanomedicine 2023年第6期18卷 555-576页

作者： Anindita das Hsieh-Chih Tsai Tapas Sen rakesh S Moirangthem Department of Physics Nanophotonics Lab Indian Institute of Technology (Indian School of Mines) Dhanbad Jharkhand 826004 India Graduate Institute of Applied Science & Technology National Taiwan University of Science & Technology Taipei 106 Taiwan Advanced Membrane Materials Center National Taiwan University of Science & Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Chungli Tao-Yuan 320 Taiwan Nano-biomaterials Research Group School of Natural Sciences Faculty of Science & Technology University of Central Lancashire Preston PR1 2HE UK Department of Physics Manipur University Canchipur Imphal Manipur 795003 India

Optical imaging modalities have emerged as a keystone in oncological research, capable of providing molecular and cellular information on cancer with the advantage of being minimally invasive toward healthy tissues. Photothermal therapy (PTT) has shown great potential, with the exceptional advantages of high specificity and noninvasiveness. Combining surface-enhanced raman spectroscopy (SErS)-based optical imaging with PTT has shown tremendous potential in cancer theranostics (therapeutics + diagnosis). This comprehensive review article provides up-to-date information by exploring recent works focused mainly on the development of plasmonic nanoparticles for medical applications using SErS-guided PTT, including the fundamental principles behind SErS and the plasmon heating effect for PTT.

关键词： cancer theranostics photothermal therapy plasmonic nanoparticles surface-enhanced raman spectroscopy surface functionalization toxicity

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Watt-level all polarization-maintaining femtosecond fiber laser source at 1100 nm for multicolor two-photon fluorescence excitation of fluorescent proteins

arXiv

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arXiv 2024年

作者： Wen, Junpeng Pilger, Christian Wang, Wenlong Erapaneedi, raghu Xiu, Hao Fan, Yiheng Hu, Xu Huser, Thomas Kiefer, Friedemann Wei, Xiaoming Yang, Zhongmin School of Physics and Optoelectronics South China University of Technology Guangzhou510640 China State Key Laboratory of Luminescent Materials and Devices Institute of Optical Communication Materials South China University of Technology Guangzhou510640 China Biomolecular Photonics Research Group Faculty of Physics Bielefeld University BielefeldD-33615 Germany European Institute for Molecular Imaging University of Münster Röntgenstraße 16 MünsterD-48149 Germany Max Planck Institute for Molecular Biomedicine Röntgenstraße 20 MünsterD-48149 Germany Guangdong Engineering Technology Research Development Center of Special Optical Fiber Materials and Devices South China University of Technology Guangzhou510640 China Guangdong Provincial Key Laboratory of Fiber Laser Materials and Applied Techniques South China University of Technology Guangzhou510640 China Research Institute of Future Technology South China Normal University Guangdong Guangzhou510006 China

We demonstrate a compact watt-level all polarization-maintaining (PM) femtosecond fiber laser source at 1100 nm. The fiber laser source is seeded by an all PM fiber mode-locked laser employing a nonlinear amplifying loop mirror. The seed laser can generate stable pulses at a fundamental repetition rate of 40.71 MHz with a signal-to-noise rate of >100 dB and an integrated relative intensity noise of only ~0.061%. After two-stage external amplification and pulse compression, an output power of ~1.47 W (corresponding to a pulse energy of ~36.1 nJ) and a pulse duration of ~251 fs are obtained. The 1100 nm femtosecond fiber laser is then employed as the excitation light source for multicolor multi-photon fluorescence microscopy of Chinese hamster ovary (CHO) cells stably expressing red fluorescent proteins. Copyright © 2024, The Authors. All rights reserved.

关键词： Proteins

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Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

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Frontiers in Energy research 2024年 12卷

作者： Shanmugasundar, G. Manjunatha, r. Cep, robert Logesh, K. Kaushik, Vikas raju, S. Srinadh Elangovan, Muniyandy Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai India Karnataka Bangalore India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of Mechanical Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Punjab Mohali India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (dFd) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of dFd anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the dFd for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, r2, and AAPrE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings. Copyright © 2024 Shanmugasundar, Manjunatha, Cep, Logesh, Kaushik, raju and Elangovan.

关键词： Gas industry

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Finite State Machine based Vehicle System for Autonomous driving in Urban Environments

Finite State Machine based Vehicle System for Autonomous Dri...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sang-Hyeon Bae Sung-Hyeon Joo Jung-Won Pyo Jae-Seong Yoon Kwanghee Lee Tae-Yong Kuc Sungkyunkwan University Suwon Korea Robot R&D Group Korea Institute of Industrial Technology Ansan Korea

ISBN: (数字)9788993215205

ISBN: (纸本)9781728185620

In autonomous driving systems, many approach have been developed based on high-precision sensors, high computing power, development of various networks in academia and industry. The key point of an autonomous car is that it needs to know what is happening in the driving situation and an action corresponding to this situation must be created and operated. In this paper, we propose the system structure and method of a autonomous driving car consisting of three main parts:perception, planning, control. Each main part is designed to recognize the real-time driving environment, make action plan based on finite state machine, and activate kinematic model based control. The proposed vehicle system copes with six major situations of urban environment driving, and we verified system through the simulation.

关键词： Autonomous vehicles Automobiles Sensors Laser radar Wheels Tires Planning

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Correlation between the bath composition and nanoporosity of dC-electrodeposited Ni-Fe alloy

Correlation between the bath composition and nanoporosity of...

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作者： Maizza, Giovanni Pero, renato Kaciulis, Saulius Bolli, Eleonora Eom, Hyeonjin Lee, Minsu Yim, Tai Hong Department of Applied Science and Technology Politecnico di Torino Torino Italy Department of Industrial Engineering University of Rome "Tor Vergata" Rome Italy Institute for the Study of Nanostructured Materials ISMN — CNR Rome Italy Thermochemical Energy R&D Group Korea Institute of Industrial Technology Cheonan-si Korea Republic of Department of Materials Science and Engineering Hanyang University Seoul Korea Republic of Surface Technology R&D Group Korea Institute of Industrial Technology Incheon Korea Republic of

The outstanding mechanical strength of as-deposited dC-electrodeposited nanocrystalline (nc) Ni-Fe alloys has been the subject of numerous researches in view of their scientific and practical interest. However, recent studies have reported a dramatic drop in ductility upon annealing above 350°C, associated with a concomitant abnormal rapid grain growth. The inherent cause has been ascribed to the presence of a detrimental product or by product in the bath, which affects either the microstructure or causes defects in the concentration and/or distribution of the as-deposited films. The present work has been inspired by the observed abnormal behaviour of annealed electrodeposited nc Ni-Fe alloy, which has here been addressed by considering the relationship between the composition of the bath (iron-chloride, nickel-sulphate solution, saccharin and ascorbic acid) and deposition defects (e.g. grain boundary pores) in the case of an nc Ni-Fe (Fe 48 wt%) alloy. The current investigations have included X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) in both as-deposited and post-annealed conditions (300°C–400°C). XPS depth profiling with Ar ion sputtering showed a significant amount of C and O impurities entrapped in the foils during deposition. As such impurities are often overlooked in common analytical techniques, new scenarios may need to be rationalised to explain the observed drop in tensile ductility of the as-deposited Ni-Fe alloys. © 2020 John Wiley & Sons, Ltd.

关键词： X ray photoelectron spectroscopy

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Color-avoiding percolation in edge-colored Erdős–rényi graphs

arXiv

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arXiv 2022年

作者： ráth, Balázs Varga, Kitti Fekete, Panna Tímea Molontay, roland Afréd Rényi Institute of Mathematics Hungary Department of Stochastics Institute of Mathematics Budapest University of Technology and Economics Hungary ELKH-BME Stochastics Research Group Chile Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary ELKH-ELTE Egerváry Research Group Hungary Department of Applied Analysis and Computational Mathematics Eötvös Loránd University Hungary

We study a variant of the color-avoiding percolation model introduced by Krause et al., namely we investigate the color-avoiding bond percolation setup on (not necessarily properly) edge-colored Erd̋ os–rényi random graphs. We say that two vertices are color-avoiding connected in an edge-colored graph if after the removal of the edges of any color, they are in the same component in the remaining graph. The color-avoiding connected components of an edge-colored graph are maximal sets of vertices such that any two of them are color-avoiding connected. We consider the fraction of vertices contained in color-avoiding connected components of a given size as well as the fraction of vertices contained in the giant color-avoiding connected component. Under some mild assumptions on the color-densities, we prove that these quantities converge and the limits can be expressed in terms of probabilities associated to edge-colored branching process trees. We provide explicit formulas for the limit of the normalized size of the giant color-avoiding connected component, and in the two-colored case we also provide explicit formulas for the limit of the fraction of vertices contained in color-avoiding connected components of a given size. Copyright © 2022, The Authors. All rights reserved.

关键词： Color

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