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EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features 1st

EEG Signals Classification in Time-Frequency Images by Fus...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Hu, Zhongyi Luo, Zhenzhen Jin, Shan Li, Zuoyong College of Computer and Artificial Intelligence Intelligent Information Systems Institute Wenzhou University Wenzhou China Zhejiang Topcheer Information Technology Co. Ltd Zhejiang China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030930455

Automatic epilepsy diagnosis system based on EEG signals is critical in the classification of epilepsy. This disease classification through doctors’ visual observation of transient EEG signals is more art than science. This paper proposes a novel EEG signals classification method based on time-frequency images by fusing rotation-invariant local binary pattern and gray level co-occurrence matrix features. Specifically, a continuous wavelet transform is applied to perform wavelet decomposition of mutated EEG signals and obtain their time-frequency images. Then, the binary particle swarm optimization algorithm is used to eliminate redundant features in feature selection and optimize the hyperparameters of SVM. The proposed method was verified by its comparison with other available cutting-edge classification methods. The proposed EEG signals classification framework achieved a better classification effect, which will help expert clinicians and neuroscientists to make more accurate and rapid epilepsy diagnosis. © 2021, Springer Nature Switzerland AG.

关键词： Neurology

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Blockchain and Trusted Reputation Assessment-Based Incentive Mechanism for Healthcare Services

SSRN

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SSRN 2023年

作者： Liu, Yanhua Liu, Zhihuang Zhang, Qiu Su, Jinshu Li, Xiaoyan College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Blockchain-based healthcare IoT technology research promotes increased security for smart healthcare services such as real-time monitoring and remote disease diagnosis. In a healthcare IoT service system, it remains a challenging problem to explore effective incentive mechanisms throughout the healthcare service process and feedback to facilitate the efficiency of the blockchain consensus process. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and stores the assessment results credibly on the blockchain. Then, we propose an improved PBFT algorithm based on the reputation assessment to enhance blockchain consensus efficiency. Finally, BtRaI designs a token reward and punishment mechanism to motivate all parties to participate in the blockchain honestly, inhibit potential misbehavior, and encourage the healthcare system to provide better service quality. Theoretical analysis and experimental evaluation demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node failure tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 seconds in a 100-node consortium chain. The reputation assessment and token incentive mechanism of BtRaI have a realistic differentiation granularity and change curve, making it suitable for dynamic and complex healthcare service scenarios. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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SAM-PS: Zero-shot Parking-slot Detection based on Large Visual Model

SAM-PS: Zero-shot Parking-slot Detection based on Large Visu...

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IEEE Symposium on intelligent Vehicle

作者： Heng Zhai Jilin Mei Liang Chen Fangzhou Zhao Xijun Zhao Yu Hu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies(SHIC) China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory (CIC) School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Large visual models have recently demonstrated their promising performance on zero-shot transfer. However, so far, none of the existing methods explicitly possess the ability to perform zero-shot transfer on parking-slot detection, which results in current deep-learning based methods relying on training datasets, and methods based on traditional computer vision exhibiting poor robustness. In this paper, we propose a large visual model-based parking-slot detection method, which utilizes a large visual model (segment anything) to segment an around-view image and infer parking-slots by analyzing the relationship of marking-points in masks. In addition, we classify real-world parking-slots into two categories, line-based and area-based. The proposed method employs a two-stage approach which has a manually designed post-processing step without training. Multiple experiments have been carried out on public benchmarks, and our method demonstrates the capability for zero-shot transfer. The code will be released at https://***/Zhai0123/SAM-PS.

关键词： Training Learning systems Visualization Image segmentation Analytical models intelligent vehicles Computational modeling

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Application of Fuzzy Neural Network in Chromatographic Separation 6th

Application of Fuzzy Neural Network in Chromatographic Separ...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Xie, Chao-Fan Xu, Lu-Xiong Wang, Ruobin Zhang, Fuquan Department of Electrical Engineering I-Shou University Kaohsiung84001 Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China School of Information Science and Technology North China University of Technology Beijing100144 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811650352

Chromatographic separation process is a continuous and discrete mixed system. It is a fixed bed consisting of chromatographic columns or similar chromatographic columns. The components are separated by valve switching. Therefore, many small and medium-sized enterprises need to control system parameters, so they are vulnerable to external interference. In the actual production process, it is often in the safety aspect, the system operation will deviate from the best operating conditions, so it is difficult to give full play to the separation ability of SMB chromatographic separation process. In order to make the separation system of small and medium-sized enterprises play its potential, the results based on simulation model are particularly important. This paper firstly digitalization the simulation process, then uses FNN to training the simulation data to the influence of observation parameters on the results, searching for the trend of complex parameter change from neural network learning, foundation for verifying the controllability of the system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition

SSRN

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SSRN 2022年

作者： Jiang, Zheyong Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang330090 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology China

Accurate wind speed forecasting is beneficial to reduce the risk of power system caused by wind power uncertainty, which is of great significance for wind power grid connection. However, the randomness and intermittence of wind speed bring great challenges to the accurate wind speed forecasting. In this study, a CGRU multi-step wind speed forecasting model based on secondary decomposition (SD) and multi-label specific XGBoost feature selection method is proposed, which achieves good forecasting performance. By using the secondary decomposition and sample entropy analysis, the original wind speed series is decomposed into multiple sub-series, and the frequency division characteristics of wind speed fluctuations are further extracted. Previous studies rarely focused on feature selection for multi-step wind speed forecasting, the feature selection method suitable for multi-step wind speed forecasting is proposed in this study. By using this method, the optimal input features of each sub-series are selected, which simplifies the data structure and improves the modeling efficiency. An efficient CGRU forecasting model is developed in this study, which can capture the depth characteristics and obtain the time-dependent relationship of wind speed. Experiments conducted in four seasons show that the model can adapt to long-term dependence and extract effective information from original data. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Discrete-time inverse linear quadratic optimal control over fnite time-horizon under noisy output measurements

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Control Theory and technology 2021年第4期19卷 563-572页

作者： Han Zhang Yibei Li Xiaoming Hu Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Mathematics KTH Royal Institute of Technology10044 StockholmSweden

In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is *** goal is to recover the corresponding quadratic objective function using noisy ***,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally ***,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are *** formulate the problem as a risk minimization problem and approximate the problem using empirical *** is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative *** then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective *** efectiveness of our results are demonstrated by numerical examples.

关键词： Inverse optimal control Linear quadratic regulator Statistical consistency EM-algorithm

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Lossless and Universal 3D Object Encryption with Differentiated Visual Effects upon Decryption: A Novel Paradigm

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhao, Ruoyu Zhang, Yushu Mou, Jun Puech, William Weng, Jian School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China School of Information Science and Engineering Dalian Polytechnic University Dalian116000 China Centre National de la Recherche Scientifique Université de Montpellier Montpellier34095 France College of Information Science and Technology Jinan University Guangzhou510632 China

A recently proposed 3D object encryption scheme enables hierarchical decryption, allowing a single encryption to display varied visual effects upon decryption. This has potential applications in complex scenarios where 3D objects require access at different security levels. However, it has two main issues: lossless decryption is not possible due to precision loss from the IEEE 754 standard, and it is customized for a specific AES algorithm, limiting support for others. Motivated by this, a novel paradigm for 3D object encryption with differentiated visual effects upon decryption is proposed. In this paradigm, the precision loss is preserved within the 3D encrypted object, allowing the 3D object after fully decrypting to be identical to the original, and thus it is lossless. Meanwhile, the data to be encrypted is reduced to three blocks, which are generalized bitstreams. Bitstream encryption algorithms that do not produce ciphertext expansion can be applied, and it operates independently of the other components in the paradigm, making it universal. Finally, a prototype of this paradigm is constructed, using the same encryption algorithm as in the previous scheme for comparative experiments. Two 3D object datasets are used to conduct the experiments and results demonstrate that it achieves lossless decryption and has a time advantage, while also undergoing security analysis and verification against smoothing attacks to confirm the security. © 2025 IEEE. All rights reserved,

关键词： Differential privacy

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RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

arXiv

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arXiv 2023年

作者： Zhu, Yabin Li, Chenglong Wang, Xiao Tang, Jin Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes School of Electronic and Information Engineering Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets. In particular, the improvements of ProFormer on the LasHeR dataset are up to +6.2%/+ 4.1% in PR/SR compared with existing best RGBT tracker. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

arXiv

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arXiv 2022年

作者： Yang, Guang Cao, Juan Sheng, Qiang Qi, Peng Li, Xirong Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors;2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://***/guang-yanng/DRAG. © 2022, CC BY.

关键词： Object detection

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