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A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy

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computers in Biology and Medicine 2024年 181卷 109052-109052页

作者： Mamalakis, Michail Macfarlane, Sarah C. Notley, Scott V. Gad, Annica K.B. Panoutsos, George School of Electrical and Electronic Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for in-silico Medicine University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease Department of Computer science Sheffield United Kingdom Department of Psychiatry Cambridge University Cambridge United Kingdom Department of Oncology and Metabolism The Medical School University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Madeira Chemistry Research Centre University of Madeira Funchal Portugal Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden

Metastasis driven by cancer cell migration is the leading cause of cancer-related deaths. It involves significant changes in the organization of the cytoskeleton, which includes the actin microfilaments and the vimentin intermediate filaments. Understanding how these filament change cells from normal to invasive offers insights that can be used to improve cancer diagnosis and therapy. We have developed a computational, transparent, large-scale and imaging-based pipeline, that can distinguish between normal human cells and their isogenically matched, oncogenically transformed, invasive and metastasizing counterparts, based on the spatial organization of actin and vimentin filaments in the cell cytoplasm. Due to the intricacy of these subcellular structures, manual annotation is not trivial to automate. We used established deep learning methods and our new multi-attention channel architecture. To ensure a high level of interpretability of the network, which is crucial for the application area, we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The methods offer detailed, objective and measurable understanding of how different components of the cytoskeleton contribute to metastasis, insights that can be used for future development of novel diagnostic tools, such as a nanometer level, vimentin filament-based biomarker for digital pathology, and for new treatments that significantly can increase patient survival. © 2024

关键词： Fluorescence microscopy

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Adaptive Fuzzy Dynamic Surface control of Flexible-Joint Robot systems With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 97-107页

作者： Song Ling Huanqing Wang Peter X.Liu the School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University the Department of Mathematics Bohai University IEEE the Department of Systems and Computer Engineering Carleton University

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

关键词： Adaptive fuzzy control dynamic surface control (DSC) flexible-joint (FJ) robots single-link tracking control

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Possibility of Using Technologies of Augmented and Virtual Reality for Acquisition Hard and Soft Skills of Electrical engineering Specialists 4

Possibility of Using Technologies of Augmented and Virtual R...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Chencheva, Olha Chenchevoi, Volodymyr Perekrest, Andriy Morozov, Yurii Zbyrannyk, Oksana Lytvynenko, Mykhaylo Kremenchuk Mykhailo Ostrohradskyi National University Department of Civil and Labour Safety Geodesy and Land Management Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Systems of Automatic Control and Electric Drive Department Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk Ukraine 'MOBIMODS' Sp. Z O.o. Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Economics and Management Kremenchuk Ukraine

ISBN: (纸本)9798350346831

The paper explores the possibilities of using augmented and virtual reality technologies to create distance online courses in order to ensure the acquisition of hard and soft skills of electrical engineering specialists. The use of 3D visualization, animation, sound accompaniment, subtitling in the development and formation of training courses of educational components allows you to fill it with the necessary informational content, immersing the student in a real production environment. The use of augmented reality allows you to adapt the educational process and provide the necessary competencies for the real technological process and equipment. © 2022 IEEE.

关键词： Visualization

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Capacity Optimization of Hydrogen Buffer Tanks in Renewable Power to Ammonia (P2A) System

Capacity Optimization of Hydrogen Buffer Tanks in Renewable ...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Li, Jiarong Lin, Jin Song, Yonghua Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China Department of Electrical Engineering Department of Electrical and Computer Engineering Beijing China

ISBN: (纸本)9781728155081

Power to hydrogen is a promising method to consume surplus renewable energy existing in the world. However, due to bottlenecks in hydrogen storage and transportation, converting hydrogen into ammonia which is a kind of hydrogen carrier with satisfied quality in storage and transportation is considered. Industrial ammonia synthesis reactors always work in constant conditions with constant flow rate of the feed gas, while hydrogen output of electrolyzers may vary sharply across renewable energy, thus hydrogen buffer tanks are required between electrolyzers and reactors to eliminate the fluctuation to some extent. The capacity of the buffer needs to be optimized due to relatively expensive cost of tanks, and the optimal capacity of the buffer actually depends on the operation flexibility of the reactor which is rarely discussed in existing literatures. This paper studies operation flexibility of the reactor first and establishes operation model of ammonia synthesis system under variable working conditions, on this basis the capacity optimization model is proposed and verified by a P2A system. At last the operation performance and the economy of P2A system are discussed, and case studies show that about 24% of total investment cost can be reduced with the consideration of operation flexibility of the reactor. © 2020 IEEE.

关键词： Ammonia

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Adaptation Via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive 3d Object Detection

SSRN

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SSRN 2023年

作者： Li, Ziyu Yao, Yuncong Quan, Zhibin Qi, Lei Feng, Zhenhua Yang, Wankou School of Automation Southeast University Nanjing210096 China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-labels. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely Adaptation Via Proxy (AVP), that explicitly leverages cross-domain relationships to generate adequate high-quality samples without any target statistics, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on the source domain with curriculum example mining to enhance its generalization capability. Then, we fine-tune the pre-trained detector on the instance-aware proxy that is a data collection with stronger supervision. The instance-aware proxy utilizes the profitable instances from the source domain to provide more robust supervision for optimizing the detector to overcome domain gaps. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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Mini-batch stochastic subgradient for functional constrained optimization

arXiv

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arXiv 2024年

作者： Singh, Nitesh Kumar Necoara, Ion Kungurtsev, Vyacheslav Automatic Control and Systems Engineering Department University Politehnica Bucharest Spl. Independentei 313 Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania Computer Science Department Czech Technical University Karlovo Namesti 13 Prague12135 Czech Republic

In this paper we consider finite sum composite convex optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is amenable to proximal evaluations. We assume a generalized bounded gradient condition on the objective which allows us to simultaneously tackle both smooth and nonsmooth problems. We also consider the cases of both with and without a strong convexity property. Further, we assume that each constraint set is given as the level set of a convex but not necessarily differentiable function. We reformulate the constrained finite sum problem into a stochastic optimization problem for which the stochastic subgradient projection method from [17] specializes to a collection of mini-batch variants, with different mini-batch sizes for the objective function and functional constraints, respectively. More specifically, at each iteration, our algorithm takes a mini-batch stochastic proximal subgradient step aimed at minimizing the objective function and then a subsequent mini-batch subgradient projection step minimizing the feasibility violation. By specializing different mini-batching strategies, we derive exact expressions for the stepsizes as a function of the mini-batch size and in some cases we also derive insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime. We also prove sublinear convergence rates for the mini-batch subgradient projection algorithm which depend explicitly on the mini-batch sizes and on the properties of the objective function. Numerical results also show a better performance of our mini-batch scheme over its single-batch counterpart. © 2024, CC BY.

关键词： Constrained optimization

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Maximum Power Point Tracking Using Cross-Correlation Algorithm for Pv System

SSRN

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SSRN 2022年

作者： Abulanwar, Sayed Shahin, Ahmed Gad, Hesham H. Rizk, Mohammad E.M. Hu, Weihao Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Engineering Horus University Egypt

This paper suggests an online, rapid, dynamic, and pragmatic cross-correlation (CC) technique to eﬀectively realize maximum power point tracking (MPPT) of the photovoltaic (PV) array. The PV voltage and current are extracted, averaged, and rate of change of the power and terminal voltage dynamics are integrally cross-correlated to regulate the duty ratio of the converter. According to this approach, the maximum power point (MPP) is defned in terms of the polarity of the CC coefcient (rcc). If the CC coefcient is positive, the operating point is at left of the MPP, while a negative CC coefcient indicates that the operating point is located at right of the MPP, otherwise the MPP is attained zero CC coefcient with lower steady-state ﬂuctuations. In this regard, a tracking optimization can be realized with confned drift either under gradual or step solar irradiance changes. The proposed CC technique quickly converges to the MPP without identifcation of PV array parameters. Besides, the proposed CC tracking algorithm tracks MPP with wide-range small step-size iterations since the search area is restricted to the vicinity of the MPP rather than the entire PV curve. Moreover, the proposed technique is featured by robustness against system parameters and load variations with simple hardware implementation as compared to several existing techniques. The eﬀectiveness of the proposed technique is thoroughly explored via time-domain, experimental, and hardware-in-loop investigations and meanwhile compared to the two most popular techniques: P&O INC methods, for both gradual and step irradiance changes and also shadow eﬀects. © 2022, The Authors. All rights reserved.

关键词： Time domain analysis

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RGB Arabic Alphabets Sign Language Dataset

arXiv

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arXiv 2023年

作者： Al-Barham, Muhammad Alsharkawi, Adham Al-Yaman, Musa Al-Fetyani, Mohammad Elnagar, Ashraf Sa’aleek, Ahmad Abu Al-Odat, Mohammad MLALP Research Group University of Sharjah United Arab Emirates Mechatronics Engineering Department The University of Jordan Jordan AppsWave for Information Technology Jordan Department of Computer Science University of Sharjah United Arab Emirates Al-Wefaq Control Systems Doha Qatar Student Guidance Department The University of Jordan Jordan

This paper introduces the RGB Arabic Alphabet Sign Language (AASL) dataset. AASL comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset. AASL is made available to the public on Kaggle.1 © 2023, CC BY.

关键词： Classification (of information)

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