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19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018

作者： Ghiţă, Ştefania Alexandra Barbu, Miruna-Ştafania Gavril, Alexandru Trăscău, Mihai Sorici, Alexandru Florea, Adina Magda Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania

ISBN: (纸本)9783319967271

The field of assistive robotics is gaining traction in both research as well as industry communities. However, capabilities of existing robotic platforms still require improvements in order to implement meaningful human-robot interactions. We report on the design and implementation of an external system that significantly augments the person detection, tracking and identification capabilities of the Pepper robot. We perform a qualitative analysis of the improvements achieved by each system module under different interaction conditions and evaluate the whole system on hand of a scenario for elderly care assistance. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Face recognition

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Module's overheating assessment with the use of supply current waveform analysis

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IFAC-PapersOnLine 2018年第6期51卷 306-311页

作者： Grzechca, Damian Rybka, Pawel Temich, Sebastian Silesian University of Technology Faculty of Automation Control Electronics and Computer Science Gliwice44-100 Poland

Design and manufacturing of electronic modules is a difficult process - on the one hand there arises a problem of developing the inner circuitry of best possible performance, on the other hand -building it at the lowest possible price. When it comes to cutting expenses on production, even the smallest changes in materials can critically influence the total manufacturing costs. The paper introduces a method of detecting certain module's overheating levels without a necessity of using temperature sensors - devices generating unnecessary fabrication expenses. The assessment of temperature can be performed with the use of supply current waveform analysis - particular changes in current function caused by overheating may be processed and correlated with a certain temperature. Finding those differences in supply waveforms and classifying them may be done with either analytic methods or heuristic algorithms. © 2018

关键词： Heuristic methods Data acquisition Genetic algorithms Heuristic algorithms Manufacture Neural networks Radar Temperature measurement Waveform analysis Analytic method Current function Electronic modules Manufacturing cost Supply currents Wave forms

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Structural Decomposition Approach to Design of No-Wait Cyclic Schedules for Repeatedly Operating Transport System Dedicated to Supply Loops

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IFAC-PapersOnLine 2020年第2期53卷 10542-10549页

作者： Robert Wójcik Arkadiusz Gola Jarosław Pempera Justyna Patalas-Maliszewska Department of Computer Engineering Faculty of Electronics Wrocław University of Science and Technology Wrocław Poland Institute of Technological Systems of Information Lublin University of Technology Lublin Poland Department of Control Systems and Mechatronics Faculty of Electronics Wrocław University of Science and Technology Wrocław Poland Institute of Mechanical Engineering University of Zielona Góra Zielona Góra Poland

The paper presents a method allowing to construct no-wait cyclical schedules for repetitive transport systems (e.g. the milk-run) servicing cyclic material supply loops in the production system using selected means of transport (e.g. AGVs). The transport means are following established routes and given arrival times. The routes are composed of sectors linking workstations. Transport trolleys may share specific sectors of the route in mutual exclusion mode and must wait in a given sector to enter the next sector of the route when another trolley occupies it. The job-shop repetitive transportation system is a system of cyclic processes with a fixed structure that are executing sequences of operations (routes) using shared resources (sectors). The work aims to find a no-wait cyclic schedule that guarantees the required delivery dates or establish that such a schedule does not exist. It considers cyclic process systems for which each resource can be used by at most two operations, and the deadlock state cannot occur as a result of waiting processes on shared resources. For specified initial operations of cyclic processes and their start times (the initial system state), the problem of determining no-wait cyclical schedules decomposes into subproblems. Each subproblem consists of the verification of necessary and sufficient conditions for the existence of solutions for each of 2-process subsystems composed of one shared resource and two processes using this resource. The method aims of prototyping various variants of process starting times for which the conditions guaranteeing no-wait property of the system hold simultaneously for each of the 2-process subsystems. It allows designing cyclic schedules for complex systems composed of 2-process subsystems that are structurally deadlock-free. The class of cyclical processes considered in this article is broader than the class of cascade-like (chain-like, sequential) process systems analysed so far in the literature. In this

关键词： repetitive delivery systems milk-run routing scheduling vehicle routing problem job-shop type transportation systems resource blocking deadlock problem no-wait cyclic schedule two cyclic processes with a shared resource decomposition of the system structure

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Matching evaluation based on image content discriminative features for different image types

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The Journal of Photographic science 2024年第1期72卷

作者： Eman S. Sabry Salah Elagooz Fathi E. Abd El-Samie Nirmeen A. El-Bahnasawy Ghada M. El-Banby Rabie A. Ramadan a Department of Communications and Computers Engineering Higher Institute of Engineering El-Shorouk Academy El-Shorouk City Egypt b Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egyptc Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia d Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt e Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt f Computer Engineering Department College of Engineering Cairo University Giza Egypt

ABSTRACTSearch by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is very challenging problem, which is commonly based on edge feature detectors to extract features from sketches. However, they are inconvenient, and it is crucial to select different methods. Thus, methods assessment is conducted for similarity matching between sketched and real images for different drawing contents and clarity levels. The assessment approach includes comparing feature keypoints gathered using the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) methods. In addition, the comparison includes matching of objects inside High-Deﬁnition (HD) images and 3D images based on their generated features. The textual-Hcontent image search is another search type that is frequently used in brand logo cases. Hence, it is presented for text-logo image search based on visual features. Besides, the motives and downsides of the considered methods are declared, including the space and time complexities within test cases.

关键词： SIFT ORB sketch-real image retrieval objects matching spatial distance 3D image matching

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COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

arXiv

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arXiv 2020年

作者： Hemdan, Ezz El-Din Shouman, Marwa A. Karar, Mohamed Esmail Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Computer Engineering College of Computing and Information Technology Shaqra University Saudi Arabia

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework;namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. The worst classification performance is obtained for the InceptionV3 model with f1-scores of 0.67 for normal cases and 0.00 for COVID-19 cases. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work. Copyright © 2020, The Authors. All ri

关键词： Coronavirus

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Small-gain theorem for stability, cooperative control and distributed observation of infinite networks

arXiv

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arXiv 2020年

作者： Noroozi, Navid Mironchenko, Andrii Kawan, Christoph Zamani, Majid Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Magdeburg Germany Faculty of Computer Science and Mathematics University of Passau Passau94032 Germany Institute of Informatics Ludwig Maximilian University of Munich Germany Computer Science Department University of Colorado BoulderCO80309 United States Computer Science Department Ludwig Maximilian University of Munich Germany

Motivated by a paradigm shift towards a hyper-connected world, we develop a computationally tractable small-gain theorem for a network of infinitely many systems, termed as infinite networks. The proposed small-gain theorem addresses exponential input-to-state stability with respect to closed sets, which enables us to analyze diverse stability problems in a unified manner. The small-gain condition, expressed in terms of the spectral radius of a gain operator collecting all the information about the internal Lyapunov gains, can be numerically computed for a large class of systems in an efficient way. To demonstrate broad applicability of our small-gain theorem, we apply it to the stability analysis of infinite time-varying networks, to consensus in infinite-agent systems, as well as to the design of distributed observers for infinite networks. Copyright © 2020, The Authors. All rights reserved.

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Breast Cancer Heterogeneity Investigation: Multiple k-Means Clustering Approach

Breast Cancer Heterogeneity Investigation: Multiple k-Means ...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Joanna Tobiasz Christos Hatzis Joanna Polanska Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Medicine Yale School of Medicine New Haven USA

ISBN: (纸本)9781728146188

Breast cancer, a frequent cause of mortality among women, is a heterogeneous disease with multiple subtypes, which identification has a great impact on the prognosis and the choice of therapy. Various attempts are being made worldwide to classify breast tumors and to discover differences between subtypes. The purpose of this study is to divide a diverse group of patients with a multiple k-means clustering approach in order to investigate the complex structure of the dataset. Primary tumor samples collected from 859 females within The Cancer Genome Atlas Breast Invasive Carcinoma project served as the material for an RNA-Seq experiment. Expression levels measured with the RNA-Seq procedure were downloaded from the Genomic Data Commons Data Portal and used for this study. The proposed multilevel clustering algorithm is based on the idea of treating each resulting cluster as an input for a new k-means clustering as long as a given cluster is not too small or integrated. Each clustering is preceded with feature filtration and selection, which are based on the Gaussian Mixture Model decomposition of gene expression level amplitude or variance, respectively. The gap statistic is used for the selection of the optimal number of clusters as well as for deciding whether a group should be divided. Results were compared to the subtype labels either obtained from the PAM50 classifier or based on the HER2 and hormone-receptor markers. The performed multilevel clustering produced a large number of small clusters. During the first division five clusters were obtained, three of them containing almost only hormone-receptor positive (HR+) tumors. One cluster consisted of similar numbers of HER2-enriched, triple-negative breast cancer (TNBC), and HR+ cases. Obtained results confirm a great heterogeneity of breast cancer. However, they also suggest that commonly used breast tumor classification corresponding to the receptor markers may not entirely reflect the complexity of subtypes of

关键词： Breast cancer Clustering algorithms Tumors Filtration Gene expression Bioinformatics

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3D Smith chart constant quality factor semicircles contours for positive and negative resistance circuits

arXiv

引用

arXiv 2020年

作者： Asavei, Victor Muller, Andrei A. Sanabria-Codesal, Esther Moldoveanu, Alin Ionescu, Adrian M. Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania Lausanne1015 Switzerland Departamento de Matemática Aplicada Universitat Politècnica de València Valencia Spain

The article proves first that the constant quality factor (Q) contours for passive circuits, while represented on a 2D Smith chart, form circle arcs on a coaxal circle family. Furthermore, these circle arcs represent semi-circles families in the north hemisphere while represented on a 3D Smith chart. On the contrary we show that the constant Q contours for active circuits with negative resistance form complementary circle arcs on the same family of coaxal circles in the exterior of the 2D Smith chart. Also, we find out that these constant Q contours represent complementary semi-circles in the south hemisphere while represented on the 3D Smith chart for negative resistance circuits. The constant Q - computer aided design (CAD) implementation of the Q semi-circles on the 3D Smith chart is then successfully used to evaluate the quality factor variations of newly fabricated Vanadium dioxide inductors first, directly from their reflection coefficient, as the temperature is increased from room temperature to 50 degrees Celsius (°C). Thus, a direct multi-parameter frequency dependent analysis is proposed including Q, inductance and reflection coefficient for inductors. Then, quality factor direct analysis is used for two tunnel diode small signal equivalent circuits analysis, allowing for the first time the Q and input impedance direct analysis on Smith chart representation of a circuit, including negative resistance. Copyright © 2020, The Authors. All rights reserved.

关键词： Timing circuits

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FES based wrist tremor suppression using multi-periodic repetitive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10135-10140页

作者： Zan Zhang Bing Chu Yanhong Liu D.H. Owens School of Electrical Engineering Zhengzhou University Zhengzhou China Department of Electronics and Computer Science university of southampton Southampton SO17 1BJ UK Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street Sheffield S1 3JD UK

Tremor is a very common motor disorder, mainly manifested as involuntary, periodic and rhythmic movement in any part of the body, especially in hands and upper-limbs, which seriously affects the life quality of patients. Functional electrical stimulation (FES) has been shown a promising technique to suppress tremor. Most existing FES based design methods assume tremor is a single frequency signal which however is a highly idealized simplification of the real case which contains multiple-frequency or even a frequency band, therefore limiting their practical performance. To address this problem, this paper proposes a controller design method based on multi-periodic repetitive control that is capable of suppressing tremor signal with multiple frequencies. Simulation and experimental results verify the effectiveness of the proposed method.

关键词： Functional electrical stimulation (FES) tremor suppression multi-periodic repetitive control simulation experimental verification

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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