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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462006

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1st to the 4th century CE and is common to many languages from the Indian subcontinent. © 2022 IEEE.

关键词： Character recognition

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Modular Model Reduction of Interconnected Systems: A Top-Down Approach

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IFAC-PapersOnLine 2023年第2期56卷 4246-4251页

作者： Lars A.L. Janssen Bart Besselink Rob H.B. Fey Nathan van de Wouw Dynamics and Control group Department of Mechanical Engineering Eindhoven University of Technology Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen

Complex systems often involve interconnected subsystem models developed by multi-disciplinary teams. To simulate and control such systems, a reduced-order model of the interconnected system is required. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. However, reducing subsystem models affects not only the subsystems accuracy, but also the interconnected model accuracy, making it difficult to predict a priori the accuracy impact of subsystem reduction. To address this challenge, we introduce a top-down approach using mathematical tools from robust performance analysis, enabling the translation of accuracy requirements from the interconnected model to the subsystem level. This enables independent subsystem reduction while ensuring the desired accuracy of the interconnected model. We demonstrate the effectiveness of our approach through a structural dynamics case study.

关键词： Complex systems Model reduction control of interconnected systems Robust performance Top-down approach Error bounds

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

Out-of-distribution Object Detection through Bayesian Uncert...

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International Conference on Information Fusion

作者： Tianhao Zhang Shenglin Wang Nidhal Bouaynaya Radu Calinescu Lyudmila Mihaylova Automatic Control and Systems Engineering The University of Sheffield UK Department of Electrical and Computer Engineering Rowan University USA Department of Computer Science University of York UK

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

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U-net Model with Particle Swarm Optimization for Floating Raft Information Extraction

U-net Model with Particle Swarm Optimization for Floating Ra...

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International Conference on Information science and Technology (ICIST)

作者： Yuanyue Liu Jin Zhao Jianchao Fan Jun Wang School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Control Science and Engineering Dalian University of Technology Dalian China

Extracting the sea area of Marine floating raft culture is contributed to the rational use of aquaculture resources. The availability of remote sensing technology can dynamically detect aquaculture rafts. As a classical semantic segmentation model, U - net can also be beneficial to extract the synthetic aperture radar images of marine cultivation. However, the parameters of U - net are customarily set by experts based on experience and cannot be changed dynamically. In this paper, a method for flexibly designing U - net model parameters by particle swarm optimization is presented. Treat individuals with diverse parameters as a swarm and randomly initialize the encoded information, allowing each individual to apply appraisal indi-cators for fitness evaluation. Implementing particle updates and iteratively improving model parameters in interactive learning between individuals and populations. The experimental results demonstrate that the model can obtain competitive outcomes on multiple evaluation indicators after parameter optimization.

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Adaptive control Scheme for Clustering of Nodes Based on the Signs of Connections in Dynamical Signed Networks 19th

Adaptive Control Scheme for Clustering of Nodes Based on ...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Qi Wang, Yinhe Gao, Zilin Gao, Peitao Xiong, Jianbin Cen, Jian Gao, Ying School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou510665 China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory Guangzhou510665 China School of Computer Science and Engineering South China University of Technology Guagnzhou510641 China

ISBN: (纸本)9783031651229

The concept of clustering has garnered significant attention within the field of complex networks. Numerous clustering algorithms have been documented in the extant academic literature. However, most of them consider static signed networks, which are not suitable for dynamical networks. For dynamical signed networks, there is a class of algorithms called cluster dynamics. Nevertheless, they regard the connection to be time-invariant, which is a constraint. In this paper, for a kind of dynamical signed networks with time-varying connections, we discuss the adaptive control scheme for clustering of nodes, such that the network evolves asymptotically into a classifiable network that contains several friends’ groups with clear sign boundaries. In other words, through the careful design of the controller for the nodes and the establishment of coupling relationships between the nodes and their connections, we ensure that the connection of the dynamical signed network can approximate asymptotically the connection of a given classifiable network, as measured by the concept of uniformly ultimately bounded (UUB). Finally, the simulation is used to illustrate the validity of the method proposed in this paper. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Adaptive control Classifiable networks Multi-clustering

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Generic,efficient,and effective deobfuscation and semantic-aware attack detection for Power Shell scripts

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Frontiers of Information Technology & Electronic engineering 2022年第3期23卷 361-381页

作者： Chunlin XIONG Zhenyuan LI Yan CHEN Tiantian ZHU Jian WANG Hai YANG Wei RUAN College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Electrical Engineering and Computer Science Northwestern UniversityEvanstonIL 60208USA College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Magic Shield Co. Ltd.Hangzhou 310027China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

In recent years,Power Shell has increasingly been reported as appearing in a variety of cyber ***,because the PowerShell language is dynamic by design and can construct script fragments at different levels,state-of-the-art static analysis based Power Shell attack detection approaches are inherently vulnerable to *** this paper,we design the first generic,effective,and lightweight deobfuscation approach for PowerShell *** precisely identify the obfuscated script fragments,we define obfuscation based on the differences in the impacts on the abstract syntax trees of PowerShell scripts and propose a novel emulation-based recovery ***,we design the first semantic-aware PowerShell attack detection system that leverages the classic objective-oriented association mining algorithm and newly identifies 31 semantic *** experimental results on 2342 benign samples and 4141 malicious samples show that our deobfuscation method takes less than 0.5 s on average and increases the similarity between the obfuscated and original scripts from 0.5%to 93.2%.By deploying our deobfuscation method,the attack detection rates for Windows Defender and VirusTotal increase substantially from 0.33%and 2.65%to 78.9%and 94.0%,***,our detection system outperforms both existing tools with a 96.7%true positive rate and a 0%false positive rate on average.

关键词： PowerShell Abstract syntax tree Obfuscation and deobfuscation Malicious script detection

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A Convolutional Neural Network Approach for Optimal Steering control of Self-Driving Cars 3

A Convolutional Neural Network Approach for Optimal Steering...

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3rd International Conference on Image Processing and Intelligent control, IPIC 2023

作者： Hezam, Tameem Al mahbashi, Mohammed Almadwami, Hashem Almadwami, Ali School of Computer Science Nanjing University of Science and Technology 200 Xiaolingwei Street Jiangsu Nanjing210094 China School of Electronic and Control Engineering Chang'an University Shaanxi Xi'an710064 China School of Mechatronic Engineering Department of Mechanical Engineering Wuhan University of Technology 205 Luoshi Road Hongshan Dist. Hubei Wuhan430070 China School of Mechanical Engineering Nanjing University of Science and Technology 200 Xiaolingwei Street Jiangsu Nanjing210094 China

ISBN: (纸本)9781510668140

The end-to-end learning framework for autonomous cars has attracted considerable interest in recent years from both industry and academia. To control an autonomous car, this approach requires developing a model that links sensory perception, such as picture frames from a camcorder, to learn the optimal and safe driving actions. To develop a completely automated driving, it is possible to use professional driver experience more efficiently by using the end-to-end learning approach rather than spending a significant amount of money on labelling like bounding boxes for things. The conventional self-driving system, which typically consists of detection, positioning, mapping, and route planning manually fails to perform well with dynamic autonomous vehicles. We proposed a Convolutional Neural Network (CNN) framework for learning the autonomous driving to convert front-facing camera data from raw pixels and learn the steering commands. This end-to-end strategy has shown very promising results compared to the traditional self-driving models. The proposed CNN framework learns to intelligently drive in traffic on local roads with or without lane markings, on motorways, and with the least amount of training data from experts. Additionally, it also works smartly in places with obscure visual cues, including parking lots and gravel roadways. The system spontaneously picks up internal demonstrations of the essential processing phases, like identifying convenient road objects, by learning the human steering angle as the training input. © 2023 SPIE.

关键词： Autonomous vehicles

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Geometric Reachability for Attitude control Systems via Contraction Theory

arXiv

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arXiv 2025年

作者： Xu, Chencheng Jafarpour, Saber Zhao, Chengcheng Shi, Zhiguo Chen, Jiming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States College of Control Science and Engineering Zhejiang University Hangzhou China

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold SO(3) × 3 and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

arXiv

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arXiv 2024年

作者： He, Jiabao Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

— Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a nonparametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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Language-Queried Target Sound Extraction Without Parallel Training Data

Language-Queried Target Sound Extraction Without Parallel Tr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2025 IEEE.

关键词： contrastive language-audio pre-training retrieval-augmented strategy target sound extraction uni-modal training for multi-modal tasks

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