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State Derivative Normalization for Continuous-Time Deep Neural Networks

IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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OpenVNA: A Framework for Analyzing the Behavior of Multimodal Language Understanding System under Noisy Scenarios

arXiv

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arXiv 2024年

作者： Yuan, Ziqi Zhang, Baozheng Xu, Hua Liang, Zhiyun Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China China School of Information Science and Engineering Hebei University of Science and Technology China College of Information and Electrical Engineering China Agricultural University China Technology Development Co. Ltd. China

We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at https://***/thuiar/OpenVNA, with a demonstration video available at https://***/0Z9cW7RGct4. © 2024, CC BY-NC-SA.

关键词： Graphical user interfaces

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Reliability Evaluation and Improvement of Islanded Microgrid Considering Operation Failures of Power Electronic Equipment

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Journal of Modern Power systems and Clean Energy 2020年第1期8卷 111-123页

作者： Wen Zhong Lingfeng Wang Zhaoxi Liu Shiying Hou State Key Laboratory of Power Transmission Equipment&System Security and New Technology College of Electrical EngineeringChongqing UniversityChongqingChina Laboratory of Trustworthy Cyber-Physical Systems and Infrastructures Department of Electrical Engineering and Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

With the high integration of power electronic technologies in microgrids,the reliability assessment considering power electronic devices has become a hot ***,so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the *** paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources(RESs).In addition,to improve the reliability of islanded microgrid,the conventional battery energy storage system(BESS)is replaced by the hybrid energy storage system(HESS).Based on the proposed model,the operation failure models for the power electronic modules in microgrid are built and tested,and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

关键词： Reliability assessment reliability improvement operation failure rate power electronics islanded microgrid

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Guest Editorial Special Issue on Near-Field Communications (NFCs) in Internet of Everything

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IEEE Internet of Things Journal 2025年第12期12卷 18459-18460页

作者： Pin-Han Ho Miaowen Wen Zhiguo Ding Marco Di Renzo Wei Duan Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Communication Engineering Khalifa University Abu Dhabi UAE Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) Paris-Saclay University Gif-sur-Yvette France School of Information Science and Technology Nantong University Nantong China

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Effective Spline-GMPPT Approach Performance for Photovoltaic systems under Uniform Irradiance and Partial Shading Conditions

Effective Spline-GMPPT Approach Performance for Photovoltaic...

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Energy Transition and Security (ICETS), International Conference on

作者： Saidi Ahmed Abdelouahed Touhami Draoui Abdelghani Meziane Mohamed Amine Harrouz Abdelkader Department of Electrical Engineering Laboratory of Sustainable Development and Computer Science Ahmed Draia University Algeria Department of Electrical Engineering Laboratory of Smart Grid and Renewable Energy University of Tahri Mohamed Bechar Algeria Laboratory of Environmental and Energy Systems (LSEE) University Center Ali Kafi Tindouf Algeria NAAMA University Center Naama Algeria

In the literature, there are several criteria techniques classified to achieve the point of maximum power tracking (MPPT) in photovoltaic (PV) power systems, such as accuracy, speed, and simplicity. These criteria are often trade-offs; in this case, the higher accuracy generally achieved at the expense of two other criteria: speed and *** contribution proposes a new technique based on the Spline-Global-MPPT approach to provide its reliability in multiple criteria as an accurate, fast, and simple technique to find the MPP of PV power generation systems under two cases, such as uniform irradiance and partial shading conditions (PSCs), in the total distorted characteristics of the PV string. A cubic spline interpolation approach has proposed in this method, which defines from a few simple points an approximate *** localize the GMPP under standard (uniform) irradiance conditions, there are a huge number of interpolation-based approaches proposed in the literature. Unfortunately, this interpolation approach became incapable of detecting the point of the global maximum power (GMPP) under PSCs. The MPP of the system predicted by this new Spline-GMPPT method using a limited sample set of current and voltage, and it maintains this position as long as the external conditions stay unchanged. In the last part, the simulation results prove the reliability and robustness of the proposed approach.

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Towards a Longitudinal Comparison Between Different Strategies for Android Malware Detection

Towards a Longitudinal Comparison Between Different Strategi...

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International Conference on Theoretical and Applicative Aspects of computer science (ICTAACS)

作者： Abdelhak Mesbah Ibtihel Baddari Mohamed Amine Riahla Department of Computer Science Faculty of Sciences LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria Department of Electrical Systems Engineering Faculty of Technology LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions.

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Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System 1st

Adaptive RBF-Neural-Network Control with Force Observer for...

引用

1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment

arXiv

引用

arXiv 2023年

作者： Rezaei, Ahmad Taheri, Mahdi Mahani, Ali Magierowski, Sebastian Department of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany Tallinn University of Technology Tallinn Estonia Reliable & Smart Systems Laboratory Shahid Bahonar University Kerman Iran Department of Electrical Engineering and Computer Science York University Toronto Canada

The first important step in extracting DNA characters is using the output data of MinION devices in the form of electrical current signals. Various cutting-edge base callers use this data to detect the DNA characters based on the input. In this paper, we discuss several shortcomings of prior base callers in the case of time-critical applications, privacy-aware design, and the problem of catastrophic forgetting. Next, we propose the LRDB model, a lightweight open-source model for private developments with a better read-identity (0.35% increase) for the target bacterial samples in the paper. We have limited the extent of training data and benefited from the transfer learning algorithm to make the active usage of the LRDB viable in critical applications. Henceforth, less training time for adapting to new DNA samples (in our case, Bacterial samples) is needed. Furthermore, LRDB can be modified concerning the user constraints as the results show a negligible accuracy loss in case of using fewer parameters. We have also assessed the noise-tolerance property, which offers about a 1.439% decline in accuracy for a 15dB noise injection, and the performance metrics show that the model executes in a medium speed range compared with current cutting-edge models. © 2023, CC BY-NC-ND.

关键词： DNA

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Sparse common feature representation for undersampled face recognition

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IEEE Internet of Things Journal 2021年第7期8卷 5607-5618页

作者： Yang, Shicheng Wen, Ying He, Lianghua Zhou, MengChu Department of Computer Science and Technology Tongji University Shanghai200092 China Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University Shanghai200241 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21589 Saudi Arabia

This work investigates the problem of undersampled face recognition (i.e., insufficient training data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain samples captured by IoT devices may include background and facial disguise that makes face recognition more challenging than that with sufficient and reliable images. Many models work well in face recognition on a big data set, but when training data are insufficient, they achieve unsatisfactory performance. This work proposes a novel method named sparse common feature-based representation (SCFR) that provides a unique and stable result and completely avoids very time-consuming training required by a deep learning model. Specially, it constructs a common feature dictionary using both training and test images. Thereinto, a common feature is based on a discriminative common vector and learned by a Gaussian mixture model for both training and test images in a semisupervised learninig manner, which would reduce the difference among samples in each class. In the optimization, the latent indicator of test data is initialized by the estimated label. This can avoid learning invalid information and lead to good prototype images. A new variation dictionary characterizes variables that can be shared by different classes. Finally, this work adopts minimum reconstruction residuals to recognize test images, thus bringing about a substantial improvement in SCFR's performance. Extensive results on benchmark face databases demonstrate that the proposed method is better than the state-of-the-art methods handling undersampled face recognition. © 2014 IEEE.

关键词： Face recognition

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