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American control Conference (ACC)

作者： Anantha Sai Hariharan Vinjarapu Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Hungary

Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.

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Stock Price Forecasting Based on Feature Fusion Deepar Model

Stock Price Forecasting Based on Feature Fusion Deepar Model

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Chinese control and Decision Conference, CCDC

作者： QingLin Xie Qi Lang Xiaodong Liu School of Control Engineering Dalian University of Technology Dalian China School of Computer Science and Information Technology Northeast Normal University Changchun China School of Control Science and Engineering Dalian University of Technology Dalian China

Stock forecasting has always attracted the attention of the people. However, existing studies rarely use long-term price patterns as input features. Therefore, this paper proposes a new deep learning model that integrates the features of long-term price models to predict stock closing prices. The clustering algorithm is used to extract the features of the long-term price model, and the prediction model is selected based on the deep autoregressive model . The method of output probability distribution of this model is suitable for time series data with large uncertainty such as financial data. The way of maximizing the likelihood function of the future sequence can better reflect the inherent randomness of the data. It can not only predict the value, but also predict the future fluctuation, and has high prediction accuracy. Compared with other deep learning models, the results show that the feature fusion the deep autoregressive model has lower prediction error and higher goodness of fit.

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A Method for Reducing Speech Distortion in Minimum Variance Distortionless Response Beamformer

A Method for Reducing Speech Distortion in Minimum Variance ...

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2023 Seminar on Signal Processing, SoSP 2023

作者： The, Quan Trong Huy, Nguyen Ba Anh, Pham Tuan Ha Noi University of Science and Technology Falculty of Information Technology Ha Noi Viet Nam University ITMO Falculty of Control Systems and Robotics Saint-Petersburg Russia University ITMO Falculty of Software Engineering and Computer Systems Saint-Petersburg Russia

ISBN: (纸本)9798350371086

Speech processing devices such as mobile telephone, hearing aid, cochlear implant is commonly equipped with a designed microphone array (MA) to capture the acoustic environment. The MA signals often contain a mixture of desired speech component and unwanted surrounding noise (a combination of reverberation, different interferences, complex recording environment, third-party speaker). Minimum Distortionless Variance Response (MVDR) beamformer is one the most useful methods for reducing background noise and improving speech enhancement has been recently studied. MVDR beamformer merges the predefined constraints of response from a priori relative transfer function and the criteria of minimum total output noise power. Because of the microphone mismatches, the different microphone sensitivities, or the error of the direction of arrival of the interest signal, the output final signal of MVDR beamformer often corrupted. In this paper, the authors proposed an additive gain function for reducing speech distortion and enhancing MVDR beamformer's performance. © 2023 IEEE.

关键词： Signal to noise ratio

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FlexPDD: Enabling Proportional Delay Differentiation Service on Programmable Switches

FlexPDD: Enabling Proportional Delay Differentiation Service...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinyue Jiang Dezhang Kong Zhengyan Zhou Di Wang Shuangxi Chen Chunming Wu College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Jiaxing Vocational and Technical College Jiangxing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Quality-of-Service (QoS) guarantees are crucial for meeting the diverse performance requirements of applications in packet networks. The Proportional Delay Differentiation (PDD) model offers relative service differentiation based on the delay requirements of different traffic classes. However, implementing PDD on current hardware switches faces challenges due to the lack of inherent queuing behavior description in switch ASICs. This paper introduces FlexPDD, a dynamic and adaptive packet prioritization mechanism designed to implement the PDD model on programmable switches. FlexPDD leverages the flexibility of programmable switch to adjust the mapping between packet classes and output queues dynamically, ensuring precise control over delay differentiation. Our implementation of FlexPDD on a Barefoot Tofino switch and an NS3 simulator demonstrates its feasibility and effectiveness. The results indicate that FlexPDD successfully maintains approximate delay differentiation among service classes proportional to their delay weights, highlighting its potential as a practical solution for achieving advanced service differentiation in modern network infrastructures.

关键词： Adaptation models Quality of service Switches control systems Hardware Scheduling Delays Global communication Faces

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Towards an Integrated Framework for Fugitive Methane Source Localization and Monitoring in a Wireless Sensor Network

Towards an Integrated Framework for Fugitive Methane Source ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amira Al Jawhari Yasmine Souissi Jawhar Ghommam Yassine Jamoussi Faisal Mnif Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Department of Engineering GUtech University Muscat Oman Laboratory of Control Energy Management (Cem-Lab) ENIS Tunisa Departement of Computer Science College of Science SQU

Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and detection of methane leakage using a diffusion model based on the gas diffusion theory. Given that centralized Least Square methods are not efficient and robust as they require the gathering and processing of large-scale measurements on a central node. We propose a detection technique which makes use of the distributed (Non-linear) least squares method to overcome this problem. Then, a network of connected methane sensors is used to detect gas leaks. In order to estimate the parameters of the diffusive model for the gas leakage on each sensor node, a distributed recursive estimator of the consensus plus an innovation type technique is used. The characteristics being estimated include the gas source’s distance, which will be effectively triangulated to determine the source’s precise location. The targeted location is subsequently estimated using a location dispersed algorithm-based LS.

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Decomposition Based MOEA with Unique Subregions and Stable Matching

Decomposition Based MOEA with Unique Subregions and Stable M...

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2021 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2021

作者： Wang, Zhixue Chen, Hanning Liang, Xiaodan He, Maowei Tiangong University School of Control Science and Engineering Tianjin China Tiangong University School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441308

The Multiobjective Evolutionary Optimization algorithms (MOEAs) have attracted lots of attention and have been used for resolving engineering problems, such as production scheduling, logistics planning, and intelligent storage. This paper focuses on the modification of the Stable Matching-based Selection (STM) in Evolutionary Multiobjective Optimization (MOEA/D-STM). Generally speaking, the mechanism would strike an equilibrium between the preferences among scalar problems and solutions. Nevertheless, the initial stable matching model may have a high weakness of mismatching with a certain solution. To address the drawback, this paper proposes the Decomposition Based MOEA with Unique Subregions and Stable Matching (MOEA/D-RM). MOEA/D-RM decomposes the multiobjective optimization problem into a number of various subregions for remedying the loss of diversity and introduces the incomplete preference lists into the STM model for repairing the loss of convergence. The effectiveness and competitiveness of MOEA/D-RM are corroborated and compared with several evolutionary multiobjective optimization algorithms. © 2021 IEEE.

关键词： Multiobjective optimization

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MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation

MemSAM: Taming Segment Anything Model for Echocardiography V...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaolong Deng Huisi Wu Runhao Zeng Jing Qin College of Computer Science and Software Engineering Shenzhen University College of Mechatronics and Control Engineering Shenzhen University Centre for Smart Health School of Nursing The Hong Kong Polytechnic University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a novel echocardiographical video segmentation model by adapting SAM to medical videos to address some long-standing challenges in ultrasound video segmentation, including (1) massive speckle noise and artifacts, (2) extremely ambiguous boundaries, and (3) large variations of targeting objects across frames. The core technique of our model is a temporal-aware and noise-resilient prompting scheme. Specifically, we employ a space-time memory that contains both spatial and temporal information to prompt the segmentation of current frame, and thus we call the proposed model as MemSAM. In prompting, the memory carrying temporal cues sequentially prompt the video segmentation frame by frame. Meanwhile, as the memory prompt propagates high-level features, it avoids the issue of misidentification caused by mask propagation and improves representation consistency. To address the challenge of speckle noise, we further propose a memory reinforcement mechanism, which leverages predicted masks to improve the quality of the memory before storing it. We extensively evaluate our method on two public datasets and demonstrate state-of-the-art performance compared to existing models. Particularly, our model achieves comparable performance with fully supervised approaches with limited annotations. Codes are available at https://***/dengxl0520/MemSAM.

关键词： Image segmentation Adaptation models computer vision Ultrasonic imaging Echocardiography Annotations Noise

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Deep Reinforcement Learning-based Computing Offloading and Resource Allocation Algorithm in Internet of Vehicles

Deep Reinforcement Learning-based Computing Offloading and R...

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International Conference on Information, Communication and Networks (ICICN)

作者： Yiping Yuan Xin Chen Libo Jiao Yijie Wang School of Control Science and Engineering Beijing Information Science & Technology University Beijing China School of Computer Science Beijing Information Science & Technology University Beijing China

With the rapid emergence of the Internet of Vehicles (IoV) and 6G networks, the exponential growth in data traffic has presented a formidable challenge to the limited computational capabilities of in-vehicle devices. To overcome this challenge, mobile edge computing (MEC) offers a viable solution by offloading computationally intensive tasks to the edge servers. In this study, we design a novel joint optimization approach that leverages deep reinforcement learning (DRL) to inform decisions on computational offloading, energy consumption, and resource allocation. Our proposed scheme considers the involvement of both the base station (BS) and road side units (RSUs) in providing computing resources to the user while simultaneously minimizing energy consumption and reducing latency of computational tasks. Our simulations unequivocally establish the efficacy of the Deep Reinforcement Learning-based computing offloading and resource allocation (DCORA) algorithm. The proposed DCORA algorithm outperforms alternative baseline schemes by approximately 15% in direct comparison.

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A Learning-Based Stochastic Game for Energy Efficient Optimization of UAV Trajectory and Task Offloading in Space/Aerial Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Jialiuyuan Shi, You Dai, Chen Yi, Changyan Yang, Yuxiao Zhai, Xiangping Zhu, Kun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing211106 China

In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) and low earth orbital (LEO) satellite assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs). The key challenge involves how to efficiently execute tasks offloaded from IoT devices, given limited on-board computation capabilities of UAVs and delay constraints of tasks. To address it, UAVs are first allowed to adjust their trajectories to accommodate as many tasks as possible from IoT devices. Subsequently, UAVs compute a portion of these tasks while delegating the rest to LEO satellites for further execution. Accordingly, we formulate a joint optimization problem including UAV trajectory planning, task offloading, and bandwidth allocations, aiming to maximize the long-term energy efficiency of UAVs and LEO satellites (i.e., the total data size in computation offloading divided by energy consumption of UAVs and LEO satellites). Considering the interactions among intelligent UAVs, this problem is reformulated as a set of interconnected multi-agent stochastic games, and the existence of corresponding Nash Equilibrium (NE) is theoretically proved. To obtain the NE, a multi-agent reinforcement learning-based algorithm, namely UTPTR, is developed concerning system dynamics and uncertainties. Simulations evaluate the performance of the UTPTR and demonstrate its superiority compared to existing approaches. © 1967-2012 IEEE.

关键词： Stochastic systems

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Semantic Correspondence with Peripheral Position Coding

Semantic Correspondence with Peripheral Position Coding

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jinjian Chen Zuoyong Li Taotao Lai Huosheng Xie College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM.

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