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Improved Model-Free Adaptive Control for MIMO Nonlinear Systems With Event-Triggered Transmission Scheme and Quantization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第9期53卷 5867-5880页

作者： Zhu, Panpan Jin, Shangtai Bu, Xuhui Hou, Zhongsheng Beijing Jiaotong Univ Sch Elect & Informat Engn Inst Adv Control Syst Beijing 100044 Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Henan Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.

关键词： Quantization (signal) MIMO communication Process control Control systems Adaptation models Task analysis Jacobian matrices Bounded convergence encoding-decoding event-triggered transmission scheme and quantization (ETQ) improved model-free adaptive control (iMFAC) multi-input multioutput (MIMO) partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ) pseudo partitioned Jacobean matrix (PPJM)

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Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第8期41卷 2048-2066页

作者： Zavala-Mondragon, Luis A. Rongen, Peter Bescos, Javier Olivan de With, Peter H. N. van der Sommen, Fons Eindhoven Univ Technol Elect Engn Dept VCA Lab NL-5612 AZ Eindhoven AZ Netherlands Philips Med Syst Nederland BV NL-5684 PC Best Netherlands

encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep convolutional framelets (TDCF), revealing important links between signal processing and CNNs. Specifically, the TDCF demonstrates that ReLU CNNs induce low-rankness, since these models often do not satisfy the necessary redundancy to achieve perfect reconstruction (PR). In contrast, this paper explores CNNs that do meet the PR conditions. We demonstrate that in these type of CNNs soft shrinkage and PR can be assumed. Furthermore, based on our explorations we propose the learned wavelet-frame shrinkage network, or LWFSN and its residual counterpart, the rLWFSN. The ED path of the (r)LWFSN complies with the PR conditions, while the shrinkage stage is based on the linear expansion of thresholds proposed Blu and Luisier. In addition, the LWFSN has only a fraction of the training parameters (<1%) of conventional CNNs, very small inference times, low memory footprint, while still achieving performance close to state-of-the-art alternatives, such as the tight frame (TF) U-Net and FBPConvNet, in low-dose CT denoising.

关键词： Noise reduction Discrete wavelet transforms Convolutional neural networks Convolution Computed tomography Thresholding (Imaging) encoding Convolutional neural networks wavelet frames noise reduction encoding-decoding

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Optimal Tracking Performance Analysis of MIMO Control Systems Under Multiple Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2734-2743页

作者： Cheng, Lingli Yan, Huaicheng Chi, Ming Zhan, Xisheng Zhou, Ge East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Hubei Normal Univ Coll Mechatron & Control Engn Huangshi 435002 Hubei Peoples R China Huazhong Univ Sci & Technol Coll Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shanghai Electromech Engn Inst Shanghai 201109 Peoples R China

This article is concerned with the optimal tracking performance on linear time-invariant (LTI) discrete-time multi-input-multi-output (MIMO) systems based on quantization, channel noise, and encoding-decoding, as well as bandwidth constraints. Meanwhile, power constraint, feedback and feedforward loops constraints of the systems are also taken into account. The tracking performance expression is described by inner-outer factorization, which is obtained by all stabilizing two-degree-of-freedom compensators. The results will illustrate that tracking performance limitation is decided by nonminimum phase zeros and unstable poles of the given plant. Moreover, time delay, encoding-decoding, channel noise, quantization, and bandwidth constraints also impact the performance. The accuracy of the method is verified by simulation examples.

关键词： Quantization (signal) MIMO communication Bandwidth Linear systems Delay effects decoding Communication channels Bandwidth constraints encoding-decoding noise quantization tracking performance

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Quantized Data Driven Model-Free Adaptive Predictive Control for a Class of Nonlinear Systems 12

Quantized Data Driven Model-Free Adaptive Predictive Control...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Liu, Genfeng Hou, Zhongsheng Henan Univ Technol Coll Elect Engn Zhengzhou 450001 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

ISBN: (纸本)9798350321050

In this paper, a quantized data driven model-free adaptive predictive control algorithm is presented for a class of nonlinear systems to deal with the limited network transmission capacity. Firstly, a uniform quantizer is designed to handle the network bandwidth limitation and an encoding-decoding mechanism is introduced to reduce the effects of quantization errors. Secondly, based on compact form dynamic linearization method, by only utilizing the input and output data of the control system, a quantized data driven control scheme is designed. Finally, a simulation is given to show the effectiveness and robustness of the presented control algorithm.

关键词： Quantizer encoding-decoding model-free adaptive predictive control MFAC data driven control

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Radio edutainment and participatory communication for social change: A case of lived reality among a rural Malawian audience

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JOURNAL OF AFRICAN MEDIA STUDIES 2022年第2期14卷 309-325页

作者： Kamlongera, Mtisunge Isabel Oslo Metropolitan Univ Oslo Norway

This article draws upon research exploring a project that combines edutainment and participatory communication strategies as an approach to social change in Malawi. Throughout the research, I take a critical stance that seeks to uplift voices of an audience attending a radio listening club (RLC) and therefore utilize participatory methods to co-create the body of knowledge with study participants. In this article, I query RLC audience's thoughts of the project's relevance and influence in their daily lives. Oriented by Hall's concepts of encoding and decoding, I analyse my data focusing on the dynamics between the objectives of the producers and the lived reality of the participants. Findings reveal that audience's motivation for continued attendance of the RLC is different to that intended in the encoding process;rather audiences negotiate the relevance of messages to their own needs, those of their children and utilize the space to extend their social capital.

关键词： edutainment participatory communication dialogue encoding-decoding Freire culture circles

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A two-branch encoder-decoder network for image tampering localization

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APPLIED SOFT COMPUTING 2024年 164卷

作者： Luo, Yuling Liang, Ce Qin, Sheng Liu, Junxiu Fu, Qiang Yang, Su Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China Swansea Univ Dept Comp Sci Swansea Wales

Tampered images with false information can mislead viewers and pose security issues. Tampering traces in images are difficult to detect. To locate tampering traces effectively, a dual-domain deep-learning-based image tampering localization method based on RGB and frequency stream branches is proposed in this work. The former branch learns and extracts tampered features on the image and content features of the tampered region. The latter branch extracts tampered features from the frequency domain to complement the RGB stream branch. In addition, an attention mechanism is used to integrate the features from both branches at the fusion stage. In the experiments, the F1 score of the proposed method outperformed those of the baselines on the NIST16 dataset (with a 15.3% improvement), and the AUC score outperformed those of the baselines on the NIST16 and COVERAGE datasets (improvements of 3.9% and 4.7%, respectively). This study provides a beneficial alternative to image tampering localization techniques.

关键词： Image tampering localization Image forensics encoding-decoding Attention mechanism

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WET-UNet: Wavelet integrated efficient transformer networks for nasopharyngeal carcinoma tumor segmentation

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SCIENCE PROGRESS 2024年第2期107卷 368504241232537页

作者： Zeng, Yan Li, Jun Zhao, Zhe Liang, Wei Zeng, Penghui Shen, Shaodong Zhang, Kun Shen, Chong Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou Peoples R China Hainan Normal Univ Sch Informat Sci & Technol Haikou Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South China 58 Renmin Rd Haikou 570228 Hainan Peoples R China

Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verific

关键词： Nasopharyngeal carcinoma wavelet transform self-attention encoding-decoding medical segmentation

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DCCaps-UNet: A U-Shaped Hyperspectral Semantic Segmentation Model Based on the Depthwise Separable and Conditional Convolution Capsule Network

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REMOTE SENSING 2023年第12期15卷 3177-3177页

作者： Wei, Siqi Liu, Yafei Li, Mengshan Huang, Haijun Zheng, Xin Guan, Lixin Gannan Normal Univ Coll Phys & Elect Informat Ganzhou 341000 Peoples R China

Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentation model (DCCaps-UNet) based on the depthwise separable and conditional convolution capsule network was proposed in this study. The whole network is an encoding-decoding structure. In the encoding part, image features are firstly fully extracted and fused. In the decoding part, images are then reconstructed by upsampling. In the encoding part, a dilated convolutional capsule block is proposed to fully acquire spatial information and deep features and reduce the calculation cost of dynamic routes using a conditional sliding window. A depthwise separable block is constructed to replace the common convolution layer in the traditional capsule network and efficiently reduce network parameters. After principal component analysis (PCA) dimension reduction and patch preprocessing, the proposed model was experimentally tested with Indian Pines and Pavia University public hyperspectral image datasets. The obtained segmentation results of various ground objects were analyzed and compared with those obtained with other semantic segmentation models. The proposed model performed better than other semantic segmentation methods and achieved higher segmentation accuracy with the same samples. Dice coefficients reached 0.9989 and 0.9999. The OA value can reach 99.92% and 100%, respectively, thus, verifying the effectiveness of the proposed model.

关键词： hyperspectral image semantic segmentation capsule network depthwise separable convolution dynamic routing encoding-decoding

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A Novel Tree-Based Genetic Algorithm for the Multicast Protocol in Two-Tiered WSNs

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Chinese Journal of Electronics 2020年第5期29卷 852-858页

作者： ZHANG Qiongbing XIE Chengwang JIANG Lei School of Computer Science and Engineering Hunan University of Science and Technology School of Computer and Information Engineering Nanning Normal University

Genetic algorithms(GAs) serve as a class of powerful tools to search for an effective multicast routing scheme among multiple cluster header nodes,which strongly affects the lifetime of two-tiered Wireless sensor networks(WSNs). This paper proposes a novel Genetic algorithm(GA) with a new crossover mechanism called Leaf crossover for the multicast routing among upper tier nodes in two-tiered WSNs, which outperforms the existing popular tree-based GAs by not requiring the global network link information, encoding/decoding or repair operations. Our simulation study indicates that the proposed algorithm could prolong the lifetime of multicast service, increase the packet delivery ratio as well as converge fast by comparison with existing GAs.

关键词： packet delivery ratio upper tier nodes tree-based genetic algorithm decoding Genetic algorithm (GA) Leaf crossover encoding-decoding multicast protocol multicast protocols two-tiered WSN Multicast genetic algorithms two-tiered wireless sensor networks network coding Wireless sensor networks (WSNs) leaf crossover effective multicast routing scheme wireless sensor networks telecommunication network reliability global network link information crossover mechanism multiple cluster header nodes tree-based GA trees (mathematics)

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Brain tumour segmentation based on an improved U-Net

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BMC MEDICAL IMAGING 2022年第1期22卷 199-199页

作者： Zheng, Ping Zhu, Xunfei Guo, Wenbo Anhui Univ Sci & Technol Huainan 232001 Anhui Peoples R China Yunnan Univ Kunming 650500 Yunnan Peoples R China

Background Automatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours. Methods To solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding-decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics. Results The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions Our algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.

关键词： Brain tumour Dice loss encoding-decoding HDC Segmentation U-Net

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