International Journal of Network Dynamics and Intelligence

Security Coordination Control for the Belt Conveyor Systems with False Data Injection Attacks

Lei Ma — 2025-03-25

Article

Security Coordination Control for the Belt Conveyor Systems with False Data Injection Attacks

Lei Ma ^1,2, Hao Zhang ^1,2, Guoqing Wang ^1,2, Chunyu Yang ^1,2,*, and Linna Zhou ^1,2

¹ School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

² Xuzhou Coal Mining Group Co., Ltd, Xuzhou 221116, China

^* Correspondence: E-mail: chunyuyang@cumt.edu.cn

Received: 30 July 2024

Accepted: 11 October 2024

Published: 25 March 2025

Abstract: This paper addresses the issue of security coordination control for belt conveyor systems (BCSs) vulnerable to false data injection (FDI) attacks in an industrial Internet of Things scenario. A networked BCSs drive model is established for the first time consisting of flexibly connected dual permanent magnet synchronous motors. Subsequently, a novel security control strategy based on quadratic regulators is proposed to preserve the drive motor synchronization in response to FDI attacks that compromise the signal interaction from sensors to controllers. Afterward, a composite sub-optimal security coordination controller is presented based on the singular perturbation theory and the corresponding solution approach, recognizing the two-time-scale dynamics inherent to motor systems. The proposed criteria both eliminate ill-conditioned numerical issues and assure system stability. Furthermore, simulations exhibit the advantages and physical experiments confirm the efficiency of the developed algorithms.

Concatenated Vector Representation with the Asymmetric GloVe Model

Junfeng Shi — 2025-03-25

Article

Concatenated Vector Representation with the Asymmetric GloVe Model

Junfeng Shi ^*, and Jihong Li

School of Modern Educational Technology, Shanxi University, Taiyuan 030006, China

^* Correspondence: sjf@sxu.edu.cn

Received: 7 November 2023

Accepted: 31 December 2023

Published: 25 March 2025

Abstract: The GloVe model is a widely used model for word vector representation learning. The word vector trained by the model can encode some semantic and syntactic information, and the conventional GloVe model trains the word vector representation by collecting the context word within a symmetric window for a given target word. Obviously, such collection does not obtain the left/right side information between the context word and the target word, which is linguistically critical information for learning a word representation of syntactic information. Therefore, the word vector trained by the GloVe model performs poorly in syntax-based tasks such as the part-of-speech tagging task (abbreviated as the POS task) and the chunking task. In order to solve this problem, a concatenated vector representation is proposed with the asymmetric GloVe model, which distinguishes left contexts from right contexts of the target word and exhibits more syntactic similarity than the original GloVe vector representation in looking for the target word’s neighbor words. By using the syntactic test set, the concatenated vector representation performs well for the word analogy task, and the syntax-based tasks such as the POS task and the chunking task. At the same time, the dimension of the concatenated vector representation is the half dimension of the original GloVe vector representation, reducing the running time greatly.

Torus-Event-Based Sliding Mode Control for Networked Interval Type-2 Fuzzy Systems Under Deception Attacks

Xingwang Liu — 2025-03-25

Article

Torus-Event-Based Sliding Mode Control for Networked Interval Type-2 Fuzzy Systems Under Deception Attacks

Xingwang Liu ¹, Zhi Ling ^2,*, and Yang Zhang ²

¹ College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China

² School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

^* Correspondence: Lingzhi@njust.edu.cn

Received: 21 November 2023

Accepted: 29 June 2024

Published: 25 March 2025

Abstract: In this paper, the security control problem is investigated for the discrete networked interval type-2 (IT2) fuzzy system under limited communication resources. A torus-event-triggering protocol is developed via two thresholds to regulate the transmission of data, ensuring avoiding the transmission of abnormal data. The deception attacks considered are assumed to have the ability of injecting false information into the data transmitted between sensor and controller. By constructing the new membership functions, a security sliding mode controller is proposed and the theoretical analysis proves that the stochastic stability of the closed-loop system and the reachability of the prescribed sliding surface can be guaranteed. Finally, an illustrative numerical example is proposed to demonstrate the effectiveness of the proposed control strategy.

Privacy-Preserving Distributed Entropy Filtering for Microgrids With Innovation Decomposition

Yan Liang — 2025-03-25

Article

Privacy-Preserving Distributed Entropy Filtering for Microgrids With Innovation Decomposition

Yan Liang ¹, Yangkai Chen ², Dengfeng Pan ^3,*, and Haifang Song ²

¹ Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

² Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

³ School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

^* Correspondence: dengfengpan@swin.edu.au

Received: 31 December 2024

Accepted: 11 March 2025

Published: 25 March 2025

Abstract: Data leakage and cyberattacks are usually inevitable due mainly to the vulnerability of deployed communication networks. The paper proposes a distributed privacy-preserving filter based on the maximum correlation criteria for microgrids. An improved distributed structure is first constructed via adding decomposed innovation from neighbors in update steps. Then, an improved version of the maximum correntropy criterion is defined to evaluate the local filtering performance as well as the consensus performance by adding an innovation-related term. In light of fixed-point iterations and the adopted filter structure, the desired filter gains are obtained recursively by optimizing the proposed index. Furthermore, the profound analysis is performed to disclose that the filtering covariance of external eavesdroppers is larger than target-side filters and hence the privacy of the microgrids can be protected. Finally, an example is exploited to verify its effectiveness.

Linear State Estimation for Multi-rate NCSs With Multi-channel Observation Delays and Unknown Markov Packet Losses

Zhenglu Sun — 2025-03-25

Highlights

Linear State Estimation for Multi-rate NCSs With Multi-channel Observation Delays and Unknown Markov Packet Losses

Zhenglu Sun and Chunyan Han ^*

School of Electrical Engineering, University of Jinan, Jinan, 250022, China

^* Correspondence: cyhan823@hotmail.com

Received: 19 October 2023

Accepted: 27 November 2024

Published: 25 March 2025

Abstract: This paper is concerned with the linear minimum mean square error estimation (LMMSE) for the multi-rate sampling systems with multi-channel observation delays and unknown Markovian packet losses. The original system is firstly transformed into a single-rate jumping parameter system with multi-channel and delay-free observations by employing the lifting technique and introducing a set of reorganized observations and Markov chains. Then, the single-rate system is converted into a general linear system without delays by defining a new group of extended states. Based on the innovation analysis method, a liner minimum mean square error estimator is developed, and the estimator gain is obtained in terms of generalized Riccati difference equations based on a set of coupled Lyapunov equations. Therefore, the original state estimation problem is solved via the jumping parameter property. Finally, the convergence of the Riccati equation is analyzed and a stationary filter is obtained. The novelty of this paper lies in the introduction of the reorganized observations and multi-state Markov chains.

Real-Time Semantic Segmentation of Road Scenes via Hybrid Dilated Grouping Network

Yan Zhang — 2025-03-25

Article

Real-Time Semantic Segmentation of Road Scenes via Hybrid Dilated Grouping Network

Yan Zhang ¹, Xuguang Zhang ^1,*, Deting Miao ¹, and Hui Yu ²

¹ School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China

² cSCAN, University of Glasgow, G12QB, United Kingdom

^* Correspondence: zhangxuguang78@163.com

Received: 10 January 2024

Accepted: 23 April 2024

Published: 25 March 2025

Abstract: Real-time semantic segmentation is a critical step for various real-world application scenarios such as autonomous driving systems. How to achieve a high accuracy while keeping a high inference speed has become a challenging issue for real-time semantic segmentation. To tackle this challenge, we propose a Hybrid Dilated Grouping Network (HDGNet) for real-time semantic segmentation of outdoor scenes in this study, which not only improves the accuracy of image segmentation, but also considers the inference speed. To reduce model parameters to speed up inference, we propose to use factorization convolution to replace ordinary two-dimensional convolution. However, simply reducing the amount of model parameters may sacrifice segmentation accuracy. We thus further introduce dilated convolution to extract multi-scale spatial information. The HDG module is constructed by combining factorization convolution and dilated convolution, which not only reduces the model parameters and improves the model inference speed, but also extracts local and more contextual information. And furthermore, to enhance the feature expression ability of the network, we introduce a channel attention mechanism to capture the information interaction between channels. After obtaining the shallow features and deep high-level semantic information, we design the skip layer connections to fuse the feature branches from different stages to improve the segmentation accuracy. The experiments conducted on the widely used datasets show that the proposed model achieves superior real-time performance over existing methods but using significantly fewer model parameters.

Basic theories and methods of target's height and distance measurement based on monocular vision

Jiafa Mao — 2025-03-25

Article

Basic theories and methods of target’s height and distance measurement based on monocular vision

Jiafa Mao ^1,*, and Lu Zhang ²

¹ College of Computer Science and Technology, ZheJiang University of Technology, ZheJiang, Hang Zhou 310023, China

² China Telecom Hangzhou, Hangzhou 311121, China

^* Correspondence: maojiafa@zjut.edu.cn

Received: 23 December 2023

Accepted: 28 June 2024

Published: 25 March 2025

Abstract: The existing object tracking, localization, measurement, and other technologies mostly concentrate on dual cameras or using single camera plus the non-visual sensor technology. These technologies are achieved by increasing the amount of data at the expense of lowing the processing speed to achieve precise localization of machine vision. If machine vision localization can be achieved without increasing the amount of data processing, then only the monocular ranging method can be used. Therefore, monocular ranging is obviously more challenging in actual research. Motivated by this, this paper proposes a novel object learned method based on monocular vision. According to the geometric model of camera imaging and the basic principle of converting analog signals to digital signals, we derive the relationship model between the object distance, object height, camera height, image resolution, image target size, and camera parameters. We theoretically prove the infinite solvability of “self-invariance” and the solvability of “self-change”, which provides a theoretical basis for the object tracking, localization and measurement based on monocular vision. The experimental results show the correctness of our theory.