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NAVAL COASTAL systemS laboratory - UNIQUE INSITU TEST AND EVALUATION SITE

NAVAL ENGINEERS JOURNAL 1978年第2期90卷 119-129页

作者： JOLLIFF, JV CALLAHAN, CM USN Capt. James V. Jolliff USNgraduated from the U. S. Naval Academy in 1954. Following graduation he served in the USS S. N. Moore (DD—747) and USS Cimarron (AO—22). He received his MS degrees in Naval Architecture from Webb Institute of Naval Architecture and in Financial Management from The George Washington University. He culminated his education at The Catholic University of America where he was awarded his Doctorate in Ocean Engineering in 1972. Capt. Jolliff has served in Naval Shipyards as Ship Superintendent Assistant Repair Officer and Assistant Planning & Estimating Superintendent and as such was primarily concerned with the repair and conversion of U. S. Navy skips. In addition he has served as Maintenance Officer Staff of Commander Mine Force U. S. Pacific Fleet as Co—Chairman of the Naval Engineering Division Engineering Department U. S. Naval Academy and as CV Design Manager in the Advanced Concepts Division and as Head Ship Survivability Office Naval Ship Engineering Center. An active member of ASNE since 1966 he has served as a member of the National Council and is currently the Chairman of the Journal Committee. He has had several papers presented at ASNE Day and published in the Journal and in 1976 was one of the recipients of the ASNE President's Award. At the present time he is assigned as the Commanding Officer Naval Coastal Systems Laboratory (NCSL) Panama City Fla. Mr. Casville M. Callahanis a native of Southwest Virginia where he attended Elementary and Secondary School prior to his three year's service in the U. S. Navy during World War II. He graduated from Lincoln Memorial University Harrogate Tenn. in 1950 receiving his BS degree in Mathematics. In 1952 he received his MS degree in Mathematics from Auburn University Auburn Ala. and taught mathematics at Lincoln Memorial University and at Florida State University Tallahassee Fla. prior to joining the staff of the Mine Defense Laboratory in 1955. He has progressed through a variety of assignments as the Labo

Test and Evaluation have become paramount in today's Department of Defense acquisition process. Therefore, the U. S. Navy requires both private and public facilities to accomplish the final goals of the “Fly before Buy” concept. Such a facility exists at the Naval Coastal systems laboratory (NCSL); an integral part of the Chief of Naval Material's, Director of Navy Laboratories organization. This paper briefly addresses the laboratory, its mission, and its history. This is followed by an in—depth facilities overview in order to create an understanding of the slow but steady evolution of NCSL's unique fixed facilities. These facilities, when coupled to the local natural environment, provide a unique in situ test and evaluation capability which is unequalled in the United states for assessing seagoing coastal systems. Of prime consideration is the Range Date Acquisition Center (RADAC) and Its ancillary subsystems for tracking, environmental monitoring, communications, and post run analysis. The paper is concluded with a discussion of both past and present use of the aforementioned facilities with an emphasis on user acceptance and future potential growth.

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： Electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s = 5.02 TeV

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Journal of High Energy Physics 2025年第1期2025卷 1-49页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Putte, S. Vannerom, D. Clerbaux, B. Dansana, S. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Aga Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The inclusive jet cross section is measured as a function of jet transverse momentum pT and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment a... 详细信息

The inclusive jet cross section is measured as a function of jet transverse momentum p_T and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb⁻¹. The jets are reconstructed with the anti-k_T algorithm using a distance parameter of R = 0.4, within the rapidity interval |y| < 2, and across the kinematic range 0.06 < p_T< 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling α_S. © The Author(s) 2025.

关键词： Hadron-Hadron Scattering Jet Physics QCD

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Disjoint-Path and Golden-Pin Based Irregular PCB Routing with Complex Constraints

Disjoint-Path and Golden-Pin Based Irregular PCB Routing wit...

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Design Automation Conference

作者： Qinghai Liu Qinfei Tang Jiarui Chen Chuandong Chen Ziran Zhu Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China College of Computer and Date Science Fuzhou University Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Department of Microelectronics Southeast University Nanjing China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

PCB routing becomes time-consuming as the complexity of PCB design increases. Unlike traditional schemes that treat the two essential PCB routing processes separately, namely, escape and bus routing, we consider the continuity between them and present a golden-pin-based routing scheme to find the desired solution with angle and topology constraints. Further, conventional rip-up and reroute methods are often ineffective and inefficient for congestion alleviation and routability optimization. We construct a component graph by modeling components as vertices and applying the minimum weight vertex covering method to improve the routability. A self-adaptable ordering method is presented for escape routing to arrange the pin order on the component boundary, guaranteeing successful bus routing. In addition, escape routing is performed based on a disjoint path method. We construct a dynamic Hanan grid in bus routing and utilize a novel congestion adjustment technique to improve solution quality. Compared with FreeRouting and Allegro, the experiment results show that our algorithm achieves high routability and a significant 90% runtime reduction.

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Frontoparietal connectivity neurofeedback training for promotion of working memory: An fNIRS study in healthy male participants

arXiv

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arXiv 2020年

作者： Xia, Meiyun Xu, Pengfei Yang, Yuanbin Jiang, Wenyu Wang, Zehua Gu, Xiaolei Yang, Mingxi Li, Deyu Li, Shuyu Dong, Guijun Wang, Ling Wang, Daifa School of Biological Science and Medical Engineering Beihang University Beijing100083 China College of Computer Science Sichuan Normal University Chengdu610101 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100083 China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100083 China Department of Rehabilitation Medicine Wangjing Hospital China Academy of Chinese Medical Sciences Beijing100102 China Department of Neurological Rehabilitation Guangxi Jiangbin Hospital Nanning530021 China School of Sports and Health Shandong Sport University Jinan250102 China

Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently. In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory, involving healthy adults. Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation, as neurofeedback, yielding a strong targeting effect. In the experiment, 10 participants (test group) received three cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the control group. Frontoparietal FC was significantly increased in the test group (p = 0.03), and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: p = 0.0005, reaction time of 3-back test: p = 0.0009). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective, and widespread training approach for cognitive function enhancement. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Performance of VRF systems based on large scale monitoring

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IOP Conference Series: Materials Science and Engineering 2019年第5期609卷

作者： Hua Liu Mingyang Qian Da Yan Umberto Berardi State Key Laboratory of Air-conditioning Equipment and System Energy Conservation Zhuhai China School of Architecture Tsinghua University Beijing China Department of Architectural Science Ryerson University 350 Victoria Street Toronto Ontario M5B 2K3 Canada

Variable Refrigerant Flow (VRF) systems are refrigerant systems, which are generally comprised of an outdoor unit serving multiple indoor units connected by a refrigerant piping network. It is important to evaluate the performance of VRF systems, which can help the design and operate of VRF systems. Performance test done by manufactory can reveal the performance of VRF systems in designed conditions. However, it is hard to reveal effective performance in real buildings. The field test is complicated compared with the test in the laboratory and can only conduct on typical samples rather than large scale samples. However, typical samples are not enough for reflecting the performance of large scale VRF systems samples. A simple method of evaluating the performance of large scale VRF systems samples is necessary. This paper proposed and calculating model for electricity consumption and cooling demand of VRF systems based on measured operating data in the laboratory. The paper used the calculating model combined with 344 samples operating data from real residential buildings to calculate the performance of a large scale VRF. This paper analyzed the VRF systems' performance with different influencing factors such as climate zones, cooling duration and outdoor temperature for the recommendation for VRF systems' designing and operation.

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Soft-sensing of NOx content in power Station based on BP Neural Network, RBF Neural Network and PCA-RBF Neural Network

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IOP Conference Series: Materials Science and Engineering 2018年第6期392卷

作者： Peitao Hu Liu Changliang School of Control and Computer Engineering North China Electric Power University Baoding Hebei 071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing 102206 China

The timely and accurate measurement of the NOx content of the power plant denitrification system is very important for the precise regulation of the amount of ammonia and the control of the NOx emission. In this paper, we use 660MW coal-fired units as the research object. Combined with consideration of the poor accuracy and hysteresis of NOx measurement, the prediction models are established respectively by using RBF neural network, BP neural network and PCA-RBF neural network. The PCA-RBF model is based on principal component analysis (PCA) and RBF neural network. In order to improve the accuracy of the NOx soft-sensing model, this paper simplifies the sample model by optimizing the data samples, reducing the redundant information, and using dynamic neural network to achieve more accurate and faster training model. The results show that RBF neural network is superior to BP neural network in prediction accuracy and speed, while the improved and optimized PCA-RBF model has better prediction accuracy than the RBF neural network model. At the same time, this paper provides a theoretical basis for the real-time and accurate measurement of NOx content in power station, and provides some reference for improving the measurement and control of NOx in the actual production process.

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Carbon Nanotubes: A Molecular Dynamics Study on Self‐Assembly of Single‐Walled Carbon Nanotubes: From Molecular Morphology and Binding Energy (Adv. Mater. Interfaces 19/2019)

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Advanced Materials Interfaces 2019年第19期6卷

作者： Jianwei Zhang Jianlei Cui Yang Cheng Wenjun Wang Xiaoqiao He Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou 310027 P. R. China Shaanxi Key Laboratory of Intelligent Robots Xi'an Jiaotong University Xi'an 710049 P. R. China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai 200240 P. R. China State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences Xi'an 710119 P. R. China Department of Architecture and Civil Engineering City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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A joint learning and communications framework for federated learning over wireless networks

arXiv

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arXiv 2019年

作者： Chen, Mingzhe Yang, Zhaohui Saad, Walid Yin, Changchuan Vincent Poor, H. Cui, Shuguang Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Centre for Telecommunications Research Department of Informatics King's College London WC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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