检索结果-内蒙古大学图书馆

The Method of Neural Network Control over the Process of Manufacturing Foil Solar Panels 2

学校读者我要写书评

暂无评论

The Method of Neural Network Control over the Process of Man...

2nd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2020

作者： Sachenko, Anatoliy Ivakhiv, Orest Vyshnia, Volodymyr Grzeszczyk, Konrad Osolinskyi, Oleksandr Novosad, Stanislav Kochan, Volodymyr Nakonechnyi, Markiyan Kochan, Orest Kopania, Lukasz Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics Radom Poland Lviv Polytechnic National University Department of Intelligent Mechatronics and Robotics Lviv Ukraine Dnipropetrovsk State University of Internal Affairs Department of Economic and Information Security Dnipro Ukraine International Vision Machinery VISORT Sp. J Radom Poland Ternopil National Economic University Department for Information Computer Systems and Control Ternopil Ukraine Ternopil National Economic University Research Institute for Intelligent Computer Systems Ternopil Ukraine Lviv Polytechnic National University Department of Computerized Automation Systems Lviv Ukraine Lviv Polytechnic National University Department of Measuring Information Technologies Lviv Ukraine

ISBN: (纸本)9781728190822

Authors have developed a method of control over one of the main technological processes of manufacturing foil solar panels, i.e., burning tracks in the layers of semiconductor material and insulation. The proposed method is based on neural networks trained directly on the control object. This eliminates the laborious process of identifying the parameters of the control object mathematical model and the impact of its error on the training outcomes. To clarify the conditions for optimizing a certain law for generating the control action by neural networks various laws of control have been simulated. It's established that laws of control can be changed according to the current situation of the control object. Simulation outcomes proofed that the most efficient is the system with the PI- controller. © 2020 IEEE.

关键词： Process control

ROBOTIC AND ARTIFICIAL-INTELLIGENCE SYSTEMS FOR THE NAVAL OPERATIONAL ENVIRONMENT

学校读者我要写书评

暂无评论

NAVAL ENGINEERS JOURNAL 1987年第4期99卷 74-86页

作者： HOGGE, SM The author earned her bachelor's of science degree in industrial engineering and operations research from Virginia Polytechnic Institute and State University. While at Virginia Tech she worked in the Robotics Laboratory supporting robotics research sponsored by the National Bureau of Standards. She completed her master's of science in computer science at The Johns Hopkins University. Until this past March Miss Hogge worked at the Naval Surface Weapons Center in Silver Spring Maryland. Her duties included the application of robotics and artificial intelligence to explosive ordnance disassembly mobile robotics remote controlled fire fighting vehicles human factors expert systems computer enhanced decision-making aids and hardware security. She published a two-volume study discussing shipboard operational applications of robotics and AI. In addition her publications include nine recent journal articles on robotics and computer security. Miss Hogge is currently employed with Automaker Inc. in Houston Texas. Her current role is manager of all government related robotic operations and vision systems. Miss Hogge's projects at Automaker include: an explosive ordnance disassembly gantry robot system a vision inspection system to examine bearings 3-part camouflage spray painting an investigation of the use of robotic welding in Navy shipyards and rework facilities and epoxy spray painting of missile radomes.

Due to demographical factors, there will be a 25% decline in the national labor pool of eligible 17 to 21 year old men by 1992. As the Navy faces fierce competition with other services and private industry for the dwindling personnel resources of the nation, efforts must be made to consider automating certain tasks, especially those that are hazardous, boring, and labor intensive onboard ships at sea. The Surface Ship Continuing Concept Formulation (CONFORM) Program (SEA-5014) sponsored the Naval Surface Weapons Center's robotics Laboratory to identify potential applications of robotic and artificial intelligence systems to operation and mission activities for shipboard use. The results of the investigation concerning applications of robotics to automate the aforementioned tasks are presented. Changes in current manning levels for selected tasks and ship classes, i.e., CVs, CVNs, LPDs, LPNs, and auxiliaries are examined. Future ship designs incorporating robotics, such as the advance based repair (ABR) ship are discussed. Twenty-four applications were cited in the study which include a remote controlled vehicle for flight deck operations, limited tasks in biological, chemical, and radiation environments, computer aided command decision aids, ordnance handling, undersea search, recovery, and salvage, and ventilation duct cleaning. Four of the applications and their subsequent hardware fruition will be discussed.

关键词： robotics

Self-slimmed vision Transformer

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zong, Zhuofan Li, Kunchang Song, Guanglu Wang, Yali Qiao, Yu Leng, Biao Liu, Yu School of Computer Science and Engineering Beihang University China SenseTime Research China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Shanghai AI Laboratory China

vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by 1.7× with negligible accuracy drop, and even speed up ViTs by 3.6× while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://***/Sense-X/SiT. © 2021, CC BY.

关键词： Distillation

Relative pose of three calibrated and partially calibrated cameras from four points using virtual correspondences

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tzamos, Charalambos Kocur, Viktor Barath, Daniel Haladová, Zuzana Berger Sattler, Torsten Kukelova, Zuzana Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Slovakia ETH Zürich Computer Vision and Geometry Group Switzerland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic

We study challenging problems of estimating the relative pose of three cameras and propose novel efficient solutions to the configurations (1) of four points in three calibrated cameras (the 4p3v problem), and (2) of four points in three cameras with unknown shared focal length (the 4p3vf problem). Our solutions are based on the simple idea of generating one or two additional virtual point correspondences in two views by using the information from the locations of the input correspondences. We generate such correspondences using a very simple and efficient strategy, where the new points are the mean points of three corresponding input points. The new solvers are efficient and easy to implement, since they are based on existing efficient minimal solvers, i.e., the well-known 5-point and 6-point relative pose solvers and the P3P solver. Extensive experiments on real data show that our solvers achieve state-of-the-art results. We also present a simple network that can improve the precision of the mean-point correspondences, showing the potential to learn better virtual point correspondences. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

Suborbital flight test of a prototype terrain-relative navigation system

学校读者我要写书评

暂无评论

Suborbital flight test of a prototype terrain-relative navig...

2007 AIAA InfoTech at Aerospace Conference

作者： Seybold, Calina Chen, George Bellutta, Paolo Johnson, Andrew Matthies, Larry Thurman, Sam Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States System Engineering Section M/S 233-201 4800 Oak Grove Drive United States AIAA United States EDL Systems and Advanced Technologies Group System Engineering Section M/S 301-490 Mobility and Robotic Systems Section M/S 198-235 United States Computer Vision Group Mobility and Robotics Systems Section M/S 198-235 United States Autonomous Systems Division M/S 198-105

ISBN: (纸本)1563478935

In April 2006, a terrain-relative navigation experiment successfully flew on a sounding rocket mission at White Sands Missile Range. The mission was designated by NASA/Wallops Flight Facility as 41.068 NT/Seybold ("41.068") and was flown as part of the New Millennium Program ST9 Terrain-Relative Guidance System (TRGS) Project's competitive Phase A effort. The TRN experiment had two objectives: 1) To demonstrate the viability of a navigation filter for precision landing that uses on-board IMU and descent image data to achieve position knowledge errors of less than 100m, validated with GPS data recorded during flight;and 2) To establish that commercial off-the-shelf cameras can be packaged sufficiently well to survive the sounding rocket launch environment. Both objectives were met. The 13 min flight provided a data set that was processed post-flight through the prototype algorithms to bring the ST9 TRGS Project to a technology readiness level of 4 in TRN. This paper describes the flight system development and operations of the TRN experiment on 41.068.

关键词： Flight control systems

Intervention AUVs: The Next Challenge

学校读者我要写书评

暂无评论

IFAC Proceedings Volumes 2014年第3期47卷 12146-12159页

作者： Pere Ridao Marc Carreras David Ribas Pedro J. Sanz Gabriel Oliver Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona CIRS Building C/ Pic de Peguera CO 17071 Girona Catalonia - Spain Universitat Jaume I (UJI) Av. Vicent Sos Banyat S/N 12006 Castelln Spain Universitat de les Illes Balears (UIB) Cra. Valldemossa Km 7.5 07122 Palma Spain

While commercially available AUVs are routinely used in survey missions, a new set of applications exists which clearly demand intervention capabilities. The maintenance of: permanent observatories underwater; submerged oil wells; cabled sensor networks; pipes; and the deployment and recovery of benthic stations are but a few of them. These tasks are addressed nowadays using manned submersibles or work-class ROVs, equipped with teleoperated arms under human supervision. Although researchers have recently opened the door to future I-AUVs, a long path is still necessary to pave the way to underwater intervention applications performed in an autonomous way. This paper reviews the evolution timeline in autonomous underwater intervention systems. Milestone projects in the state of the art are reviewed, highlighting their principal contributions to the field. To the best of the authors knowledge only three vehicles have demonstrated some autonomous intervention capabilities so far: ALIVE, SAUVIM and GIRONA 500 I-AUV. Next, GIRONA 500 I-AUV is presented and its software architecture discussed. Recent results in different scenarios are reported: 1) Valve turning and connector plugging/unplugging while docked to a sub-sea panel, 2) Free floating valve turning using learning by demonstration, and 3) Free floating multipurpose multisensory based object recovery. The paper ends discussing the lessons learned so far and presenting the authors view of the future.

关键词： Autonomous Vehicles Robotic Manipulators Marine Systems

PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Gu, Jinjin Cai, Haoming Chen, Haoyu Ye, Xiaoxing Ren, Jimmy S. Dong, Chao School of Data Science Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SenseTime Research SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

GM-DF: Generalized Multi-Scenario Deepfake Detection

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

TTPP: Temporal transformer with progressive prediction for efficient action anticipation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting