检索结果-内蒙古大学图书馆

Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Shengbo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness. © 2021, CC BY-NC-ND.

关键词： Gradient methods

Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Shengbo Lyu, Bo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefited from the dynamic regressor extension and mixing (DREM), an extended element-wise parameter identification law is utilized to dismiss the uncertainty. On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study. On the other hand, the element-wise estimation process in DREM can minimize conservatism of the safe adaptive process compared to other existing adaptive CBF algorithms. The stability as well as the forward invariance of the presented safe control-estimation scheme is proved. Furthermore, the robustness of the scheme under bounded disturbances is analyzed, where a robust CBF with modest conditions is used to ensure safety. The framework is illustrated by simulations on adaptive cruise control, where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances and the potential crash risk is avoided by the proposed safe control scheme. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

学校读者我要写书评

暂无评论

South African Journal of Chemical Engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

Ultrathin-shell epitaxial Ag@Au core-shell nanowires for high-performance and chemically-stable electronic, optical, and mechanical devices

学校读者我要写书评

暂无评论

Nano Research 2021年第11期14卷 4294-4303页

作者： Yangzhi Zhu Sanggon Kim Xuezhi Ma Peter Byrley Ning Yu Qiushi Liu Xiaoming Sun Da Xu Sangshan Peng Martin C.Hartel Shiming Zhang Vadim Jucaud Mehmet R.Dokmeci Ali Khademhosseini Ruoxue Yan Department of Chemical and Environmental Engineering Bourns College of EngineeringUniversity of California-RiversideRiversideCA92521USA Terasaki Institute for Biomedical Innovation Los AngelesCA90064USA Department of Electrical and Computer Engineering University of California-RiversideRiversideCA92521USA State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan UniversityChangsha410082China Department of Bioengineering University of California-Los AngelesLos AngelesCA90095USA Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Material Science and Engineering Program University of California-RiversideRiversideCA92521USA

Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.

关键词： epitaxial growth core-shell nanowire plasmonic waveguides atomic force microscopy(AFM)probe transparent electrode wearable electronics

AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Lyu, Bo Wang, Shengbo Wen, Shiping Shi, Kaibo Yang, Yin Zeng, Lingfang Huang, Tingwen Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional computing architectures (CPU, GPU, or TPU). But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption, to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static, and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model (LSTM, combined with the dynamic-fill scheme) generates remarkable mapping performance on a small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms. Copyright © 2021, The Authors. All rights reserved.

关键词： Memory architecture

3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning

学校读者我要写书评

暂无评论

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

International Conference on Information science and Control Engineering (ICISCE)

作者： Obed Tettey Nartey Guowu Yang Sarpong Kwadwo Asare Jinzhao Wu Dorothy Araba Yakoba Agyapong Lady Nadia Frempong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.

关键词： Training Deep learning Image segmentation Information science Three-dimensional displays Computed tomography Liver

Room-temperature multiple ligands-tailored SnO_(2) quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high V_(OC)

学校读者我要写书评

暂无评论

Light(science & Applications) 2021年第12期10卷 2467-2481页

作者： Zhiwei Ren Kuan Liu Hanlin Hu Xuyun Guo Yajun Gao Patrick W.K.Fong Qiong Liang Hua Tang Jiaming Huang Hengkai Zhang Minchao Qin Li Cui Hrisheekesh Thachoth Chandran Dong Shen Ming-Fai Lo Annie Ng Charles Surya Minhua Shao Chun-Sing Lee Xinhui Lu Frédéric Laquai Ye Zhu Gang Li Department of Electronic and Information Engineering Research Institute for Smart Energy(RISE)Guangdong-Hong Kong-Macao(GHM)Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and DevicesThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518057China Hoffmann Institute of Advanced Materials Shenzhen Polytechnic7098 Liuxian BoulevardShenzhen 518055China Department of Applied Physics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina King Abdullah University of Science and Technology(KAUST) KAUST Solar Center(KSC)Physical Sciences and Engineering Division(PSE)Material Science and Engineering Program(MSE)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Physics The Chinese University of Hong KongShatin 999077 Hong KongChina Center of Super-Diamond and Advanced Films(COSDAF) Department of ChemistryCity University of Hong KongHong KongChina

The benchmark tin oxide(SnO_(2))electron transporting layers(ETLs)have enabled remarkable progress in planar perovskite solar cell(PSCs).However,the energy loss is still a challenge due to the lack of“hidden interface”*** report a novel ligand-tailored ultrafine SnO_(2) quantum dots(QDs)via a facile rapid room temperature ***,the ligand-tailored SnO_(2) QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite *** novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing,delivering reduced interface defects,suppressed non-radiative recombination and elongated charge carrier *** conversion efficiency(PCE)of 23.02%(0.04 cm^(2))and 21.6%(0.98 cm^(2),V_(OC) loss:0.336 V)have been achieved for the blade-coated PSCs(1.54 eV E_(g))with our new ETLs,representing a record for SnO_(2) based blade-coated ***,a substantially enhanced PCE(V_(OC))from 20.4%(1.15 V)to 22.8%(1.24 V,90 mV higher V_(OC),0.04 cm^(2) device)in the blade-coated 1.61 eV PSCs system,via replacing the benchmark commercial colloidal SnO_(2) with our new ETLs.

关键词： perovskite interface quantum

TargetNet: Functional microRNA target prediction with deep neural networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

Low-Velocity-Favored Transition Radiation

学校读者我要写书评

暂无评论

Physical Review Letters 2023年第11期131卷 113002-113002页

作者： Jialin Chen Ruoxi Chen Fuyang Tay Zheng Gong Hao Hu Yi Yang Xinyan Zhang Chan Wang Ido Kaminer Hongsheng Chen Baile Zhang Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center the Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa 32000 Israel Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Physics University of Hong Kong Hong Kong 999077 China Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge—a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the decrease of particle velocity v; as a result, low-energy particles are not favored in practice. Here, we find that there exist situations where transition radiation from particles with extremely low velocities (e.g., v/c<10−3) exhibits comparable intensity as that from high-energy particles (e.g., v/c=0.999), where c is the light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to 8 orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the interference of the excited Ferrell-Berreman modes in an ultrathin epsilon-near-zero slab. Our findings may provide a promising route toward the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles.

关键词： Atom or molecule scattering from surfaces Atomic & molecular collisions Cherenkov radiation Crystal optics Electron beams & optics Light-matter interaction Optical sources & detectors Optical transient phenomena Plasmonics