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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Cao Huang Lifu Du Yu Peng Gang Xiang Yang Yu Jingyi Chen School of Electronics Information Engineering Habin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

A Pango FPGA-based solution for merging and processing multiple independent video streams and reconstructing a real-time display system is presented. The system handles each video stream individually, then produces a combined video output in HDMI format. To ensure high efficiency and low latency, a parallel processing scheme and a hardware-oriented architecture are employed. The Pango PG50K FPGA board acts as the central hardware for managing inputs, reconstructing video, and producing outputs. Rapid-access DDR3-SDRAM ensures efficient data caching and signal integrity. This system is ideal for applications requiring rapid response, such as traffic monitoring and industrial automation, and supports quick algorithm iteration and optimization, enhancing performance and adaptability.

关键词： Visualization Surveillance Display systems Streaming media Real-time systems Sensors Field programmable gate arrays Streams Image reconstruction Signal integrity

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Degree of Spiking Neural P Systems Without Delay

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Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants 5

Ethical Considerations in Explainable AI: Balancing Transpar...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Alijoyo, Franciskus Antonius Sandya Sneha Sri, S. Alapati, Purnachandra Rao Yuldashev, Dilyorjon Ponni Valavan, M. Center for Risk Management and Sustainability Indonesia School of Business and Information Technology Stmik Likmi Bandung Indonesia Krishna University Andhra Pradesh India Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada Kanuru India Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Trichy Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350385649

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1%• On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology. © 2024 IEEE.

关键词： Transparency

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Chattering Phenomena in Time-Optimal control for High-Order Chain-of-Integrator Systems With Full State Constraints

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IEEE Transactions on Automatic control 2025年

作者： Wang, Yunan Hu, Chuxiong Li, Zeyang Lin, Yujie Lin, Shize He, Suqin Department of Mechanical Engineering Tsinghua University Beijing China Mechatronic Control Engineering Zhejiang University Hangzhou China Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Mathematical Sciences Tsinghua University Beijing China

Time-optimal control for high-order chain-of-integrator systems with full state constraints remains an open and challenging problem within the discipline of optimal control. The behavior of optimal control in high-order problems lacks precise characterization. Even the existence of the chattering phenomenon, i.e., the control switches infinitely many times over a finite period, remains unknown. This paper establishes a theoretical framework for chattering in the problem, providing novel findings on the uniqueness of state constraints inducing chattering, the upper bound of switching times in an unconstrained arc during chattering, and the convergence of states and costates to the chattering limit point. For the first time, this paper proves the existence of chattering in the considered problem. The chattering optimal control for 4th-order problems with velocity constraints is precisely solved, providing an approach to plan time-optimal snap-limited trajectories. Other cases of order 4 are proved not to allow chattering. The conclusions rectify a prevalent misconception in the industry concerning the time-optimality of S-shaped trajectories with minimal switching times. © 1963-2012 IEEE.

关键词： Optimal control systems

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Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype

Designing Optimal Personalized Incentive for Traffic Routing...

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IEEE Conference on Decision and control

作者： Panagiotis D. Grontas Carlo Cenedese Marta Fochesato Giuseppe Belgioioso John Lygeros Florian Dörfler Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zürich Zürich Switzerland

We study the problem of routing plug-in electric and conventional fuel vehicles on a city scale using incentives. In our model, commuters selfishly aim to minimize a local cost that combines travel time and the financial expenses of using city facilities, i.e., parking and service stations. The traffic authority can influence the commuters' routing choice via personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of optimally designing these monetary incentives to induce traffic decongestion as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient scalable solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for bilevel games. Finally, we validate our approach via numerical simulations over the Anaheim's traffic network, showcasing its advantages in terms of traffic decongestion and scalability.

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Fixed-time adaptive parameter estimation for magnetostrictive actuated positioning system

Fixed-time adaptive parameter estimation for magnetostrictiv...

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第43届中国控制会议

作者： Shengbin Wu Jing Na Yingbo Huang Faxiang Zhang Xinkai Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Department of Electronic and Information Systems Shibaura Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An adaptive parameter estimation method that ensures fixed-time convergence is proposed for micro-nano positioning system driven by magnetostrictive actuator(i.e., magnetostrictive actuated positioning system). Firstly, the hysteresis nonlinearity of the magnetostrictive actuated positioning system is characterized using the generalized Prandtl–Ishlinskii(GPI) ***, the low-pass filter is introduced to design the adaptive estimation law driven by the estimation error, by which the unmeasured intermediate hysteresis variable can be avoided, and the estimation error converges to zero within a fixed time. Specifically,a modified error variable is designed to deal with the influence of the regression vector, thereby improving convergence performance and simplifying parameter adjustment. Finally, the effectiveness of the presented method is verified by simulation and experimental outcomes.

关键词： Adaptive parameter estimation Fixed-time convergence Magnetostrictive actuator Micro/nano positioning system

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Sika Deer Facial Recognition Model Based on SE-ResNet

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Computers, Materials & Continua 2022年第9期72卷 6015-6027页

作者： He Gong Lin Chen Haohong Pan Shijun Li Yin Guo Lili Fu Tianli Hu Ye Mu Thobela Louis Tyasi College of Information Technology Jilin Agricultural UniversityChangchun130118China College of Electronic and Information Engineering Wuzhou UniversityWuzhou543003China Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center Changchun130118China Jilin Province Intelligent Environmental Engineering Research Center Changchun130118China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543003China Department of Agricultural Economics and Animal Production University of Limpopo0727PolokwaneSouth Africa

The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual *** this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high *** model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the *** Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the *** preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals *** setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the *** the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is *** results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.

关键词： Sika deer facial recognition model ResNet-50 se module shortcut connection ELU

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An Efficient GCD-Based Cancelable Biometric Algorithm for Single and Multiple Biometrics

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Computers, Materials & Continua 2021年第11期69卷 1571-1595页

作者： Naglaa F.Soliman Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Ghada M.El Banby Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)*** objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT *** original biometric is blurred with two co-prime ***,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred *** changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD ***,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric ***,we test the utility of the proposed algorithm in the single-and multi-biometric *** multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable *** metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are *** results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.

关键词： Cloud IoT cancelable biometrics GCD single-and multi-biometrics security applications

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Classification of Motor Imagery EEG Signals Based on Channel Attention Mechanism

Classification of Motor Imagery EEG Signals Based on Channel...

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Data Driven control and Learning Systems (DDCLS)

作者： Yue Yu Wenkai Ji Liming Zhao Zhongbo Sun Keping Liu Department of Control Engineering Changchun University of Technology Changchun China School of Electrical and Information Engineering Jilin Engineering Normal University Changchun China

Brain-computer interface (BCI) technology establishes communication between the brain and external devices by decoding EEG signals. BCI technology based on motor imagery (MI) has great application potential. There are many different methods to extract motor intention from electroencephalogram (EEG) based on motor imagery (MI).These methods rely on extracting the unique features of EEG in the process of imaginary movement, which directly affect the performance of neural decoding algorithm of BCI. Convolutional neural network (CNN) shows outstanding advantages in automatic extraction of image features. In this paper, an image representation method based on the EEG is proposed as the input of the network. Then, a CNN and a CNN based on Channel Attention Mechanism (CAM) are built as the classifier, convolution layers and activation functions of different sizes are validated. The performance of the method is evaluated. A CNN framework based on CAM, which contained three convolution layers (3-L) is better than the other state-of-the-art approaches. The accuracy on dataset IV from BCI competition II reaches 72.6%.

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Online Residual Learning from Offline Experts for Pedestrian Tracking

Online Residual Learning from Offline Experts for Pedestrian...

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IEEE Conference on Decision and control

作者： Anastasios Vlachos Anastasios Tsiamis Aren Karapetyan Efe C. Balta John Lygeros Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich Switzerland Control and Automation Group inspire AG 8005 Zürich Switzerland and with the Automatic Control Laboratory

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance.

关键词： Pedestrians Aggregates Prediction algorithms Trajectory Drones

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