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2022 International Conference on intelligent Systems and Computational Intelligence, ICISCI 2022

作者： Yang, Jianyu He, Xing Southwest University Chongqing Key Laboratory Of Nonlinear Circuits And Intelligent Information Processing College Of Electronic And Information Engineering Chongqing400715 China

ISBN: (数字)9781665456197

ISBN: (纸本)9781665456197

In this paper, a hybrid neurodynamics optimizatio (HNO) algorithm is proposed, which is a distributed convex optimization algorithm combined with stochastic neurodynamics. The state solution of the HNO algorithm can avoid falling into local optimal solution by adding neural white noise. The results of numerical experiments show that HNO algorithm has better global optimization ability. In addition, we build a distributed load share (DLS) model. Different from the current model, which only focuses on the quadratic economic cost function, we not only consider the valve point effect and the environmental cost function of the fuel generator, but also consider the constraints of the transmission line which connects the bus. A distributed non-convex nonsmooth optimization problem is constructed. To solve this problem, we first smoothed each local objective function, then dealt with nonconvex constraints utilizing penalty function, and transformed the original problem into a distributed nonconvex optimization problem. Finally, the HNO algorithm is used to solve the problem. The optimization values of each variable in the power grid show that the power grid can operate in an optimal state under the condition of power limitation. © 2022 IEEE.

关键词： Global optimization

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Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Motor Fault Diagnosis Under Complex Working Conditions Based on Signal Fusion and CA-CNN

Motor Fault Diagnosis Under Complex Working Conditions Based...

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2024 China Automation Congress, CAC 2024

作者： Cai, Mingxuan Liu, Qi Liu, Yuxiang Yang, Bo Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350368604

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multi-class motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Online Training for Zero-Shot Time-Lapse Microscopy Denoising and Super-Resolution 39

Efficient Online Training for Zero-Shot Time-Lapse Microscop...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Ruian Cheng, Ri Lyu, Xinkai Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)157735897X

In time-lapse microscopy, inherent noise significantly limits imaging sensitivity and increases measurement uncertainty. Due to the scarcity of clean data, zero-shot approaches have emerged as highly data-efficient solutions for microscopy denoising. However, existing methods typically process video frames independently, resulting in long training times and issues such as temporal noise and over-smoothing. In this paper, we introduce MDSR-Zero, a zero-shot online learning method designed for plug-and-play noise suppression and super-resolution of microscopy videos. Our approach leverages an efficient online training strategy that reuses denoising models from previous frames. By treating the video as a continuous stream, our model significantly reduces training time and ensures temporally consistent denoising. Additionally, we propose a novel loss function tailored for denoising in the context of super-resolution, which enhances the detail in the denoised results. Extensive experiments on both synthetic and real-world noise demonstrate that our method achieves state-of-the-art performance among zero-shot denoising approaches and is competitive with self-supervised methods. Notably, our method can reduce training time by up to 10x compared to the previous SOTA method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Foundation Model Driven Appearance Extraction for Robust Multiple Object Tracking 39

Foundation Model Driven Appearance Extraction for Robust Mul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fu, Teng Yu, Haiyang Niu, Ke Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

ISBN: (纸本)157735897X

Multiple Object Tracking (MOT) is a fundamental task in computer vision. Existing methods utilize motion information or appearance information to perform object tracking. However, these algorithms still struggle with special circumstances, such as occlusion and blurring in complex scenes. Inspired by the fact that people can pinpoint objects through verbal descriptions, we explore performing long-term robust tracking using semantic features of objects. Motivated by the success of the multimodal foundation model in text-image alignment, we reconsider the appearance feature extraction module in MOT and propose a Foundation model Driven multi-object tracker (FDTracker). Specifically, we propose a two-stage trained appearance feature extractor. In the first stage, using a single image of the object as input, the model could capture the attributes of objects with the assistance of natural language instructions. In the second stage, using a sequence of images of objects as input, the model learns how to use these attributes to distinguish between different objects and connect the same object at different times. Finally, for coordinating appearance and motion information, we propose a reasonable combined strategy, which better facilitates trajectory assignment and reconnection. Extensive experiments on benchmarks demonstrate the robustness of FDTracker. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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Fault-Tolerant Control for Underwater Vehicle with Actuator Saturation and Faults 5th

Fault-Tolerant Control for Underwater Vehicle with Actuato...

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5th China Conference on intelligent Networked Things, CINT 2022

作者： Li, Hongfei Xu, Jing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9789811989148

Underwater vehicle system, as a kind of special machine capable of underwater resource exploration, submarine pipeline maintenance, marine environment reconnaissance and submarine rescue, is a common tool in modern marine exploitation. Underwater vehicles play an important role in the survey and exploration of marine resource. The fault tolerant control for underwater vehicle system with external disturbance and actuator faults is studied in this paper. The fault observer can estimate the fault in the existence of disturbance environment. A sufficient condition for the existence of fault observer is proposed which guarantees the stability of the closed-loop system under actuator faults. Moreover, a fault-tolerant controller is developed to compensate for the failure effects on the system by updating the design feedback matrices online. For the fault-tolerant controller, Lyapunov function is used to obtain the stability for underwater vehicle system about actuator saturation. Finally, an example is included to show the effectiveness of the presented theory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lyapunov functions

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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Computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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Diffusion Recursive Minimum Error Entropy Algorithm 22

Diffusion Recursive Minimum Error Entropy Algorithm

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6th International Conference on Digital Signal processing, ICDSP 2022

作者： Cai, Peng Lin, Dongyuan Yan, Wenxing Wang, Shiyuan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781450395809

Distributed estimation algorithms under the mean square error (MSE) criterion have received a lot of attention due to their simplicity and good estimation performance for distributed estimates in Gaussian noise environments. However, when the noise does not obey the Gaussian distribution, the performance of these algorithms can degrade seriously. In this paper, a robust distributed estimation algorithm, called the diffusion recursive minimum error entropy (DRMEE), is proposed by combining the diffusion strategy and the minimum error entropy (MEE) criterion. Since MEE criterion has been proved to be insensitive to many types of non-Gaussian noise models, the proposed algorithm is expected to improve the robustness of distributed estimation algorithms under the MSE criterion, significantly. The superior performance of DRMEE is confirmed by simulation results in the scenario of system identification with two multi-peak distribution noise environments. © 2022 ACM.

关键词： Diffusion

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Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model 38

Denoising Diffusion Path: Attribution Noise Reduction with A...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Lei, Yiming Li, Zilong Zhang, Junping Shan, Hongming Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Institute of Science and Technology for Brain-Inspired Intelligence MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University China

The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain predictions by accumulating gradients along a path from a baseline to the target image. However, noise accumulated during this process can significantly distort the explanation. While existing methods primarily concentrate on finding alternative paths to circumvent noise, they overlook a critical issue: intermediate-step images frequently diverge from the distribution of training data, further intensifying the impact of noise. This work presents a novel Denoising Diffusion Path (DDPath) to tackle this challenge by harnessing the power of diffusion models for denoising. By exploiting the inherent ability of diffusion models to progressively remove noise from an image, DDPath constructs a piece-wise linear path. Each segment of this path ensures that samples drawn from a Gaussian distribution are centered around the target image. This approach facilitates a gradual reduction of noise along the path. We further demonstrate that DDPath adheres to essential axiomatic properties for attribution methods and can be seamlessly integrated with existing methods such as IG. Extensive experimental results demonstrate that DDPath can significantly reduce noise in the attributions-resulting in clearer explanations-and achieves better quantitative results than traditional path-based methods. © 2024 Neural information processing systems foundation. All rights reserved.

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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence

A Novel Effectiveness Assessment Framework for Neoadjuvant C...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tian, Xiao Sui, Dong Liu, Weifeng Guo, Maozu Luo, Gongning Wang, Kuanquan Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350337488

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2% ∼ 6% improvement respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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