Cancers doi: 10.20517/ces.2025.70

Authors: Yufan Wang,Ning Zhao

This paper focuses on the problem of boundary control for a distributed parameter system (DPS) under denial-of-service (DoS) attacks. Initially, a DPS model is employed. Considering the incomplete measurement of the DPS's state, a novel boundary observer is then proposed, which only relies on the right boundary state instead of full-domain information to achieve accurate state estimation, significantly reducing the measurement cost. Subsequently, an anti-DoS observer-based boundary controller is designed, which is applied only to the spatial boundary to lower actuator deployment costs while improving robustness to intermittent DoS attacks. In addition, a Lyapunov-Krasovskii functional is introduced, and the design methods for the controller and observer are derived by solving linear matrix inequalities. Finally, the feasibility of the control strategy is verified through an example.

Cancers doi: 10.20517/ces.2025.72

Authors: Fei Han,Longkang Ma,Yanhua Song,Jinnan Zhang,Shikun Shao

This paper addresses the distributed H_∞-consensus state estimation issue for a class of discrete time-varying systems operating within binary sensor networks. An integral measurement output model is developed for each node to formulate the time intervals associated with sampling. Every binary sensor is equipped with an energy harvester to improve power efficiency. Information transmission between sensor nodes and their neighboring nodes is carefully orchestrated through a dynamic event-triggering protocol. Valuable information for estimation purposes is obtained by analyzing the discrepancies between the real and predicted inputs of binary sensors. Information from neighboring nodes is only transmitted when the node’s energy level is positive and the event-triggering condition is met. Two random variables are introduced to represent the energy level and the information from neighboring nodes to be received or not, respectively. Based on the available information, a distributed estimator is constructed for every binary sensor, and the expected performance constraints are given for the dynamic characteristics of estimation errors within a finite horizon. Sufficient conditions are constructed to obtain the desired distributed estimation performance constraint, and associated estimator gains are achieved by resolving the recursive linear matrix inequalities at each node, indicating the excellent scalability of the proposed approach. Ultimately, the effectiveness of the distributed estimation algorithm proposed in this paper is validated through an extensive simulation analysis.

Cancers doi: 10.20517/ces.2025.82

Authors: Hai-Feng Zhang,Yu-Miao Zhang,Xiao Ding,Chuang Ma,Yongxiang Xia,Chi K. Tse

With the ongoing evolution of modern power grids, power flow calculation, which is the cornerstone of power system analysis and operation, has become increasingly complex. While promising, existing data-driven methods struggle with key challenges: poor generalization in data-scarce scenarios, efficiency bottlenecks when integrating physical laws, and a failure to capture higher-order interactions within the grid. To address these challenges, this paper proposes a Spatial Multi-scale Reservoir Computing framework that seamlessly incorporates functional matrix and physical information to solve power flow calculation. The framework utilizes parallel readout layer parameters to construct the functional matrix and integrates physical information to create a multi-scale information processing mechanism and readout constraints. By improving the reservoir computing model, the framework also combines the reservoir paradigm with the inherent physical characteristics of power grids while maintaining computational efficiency. Experimental results demonstrate that the presented framework achieves exceptional performance across various IEEE bus systems, showcasing superior generalization in data-scarce scenarios, as well as improvement in computational speed, prediction accuracy, and robustness, while ensuring the feasibility of the output results.

Cancers doi: 10.20517/ces.2025.77

Authors: Jien Chen,Xiaokang Wang,Yang Song

With increasing train speeds, intensified vibrations in the pantograph-catenary (PC) system make separation between the pantograph and the contact wire - and the resulting arcing - more likely. These arcs degrade the current collection quality of high-speed trains, destabilize the traction drive system, and generate harmonics and electromagnetic interference that affect onboard communication equipment. This study investigates the impact of PC arcing at different train speeds on the traction system’s input voltage and rectifier-side direct-current (DC) voltage, and determines the corresponding acceptable arc length thresholds. An equivalent impedance model of the autotransformer traction network incorporating messenger wires and droppers is developed, together with a Habedank black-box arc model that accounts for the dynamic variations in dissipated power and voltage gradient with changing arc length. In parallel, an alternating current traction drive system model incorporating a pulse rectifier and traction motor is constructed, and its validity is verified by comparing simulation results with experimental data. Results indicate that within the allowable voltage disturbance range (input voltage 1,085-1,922 V; DC-side fluctuation ≤ 5%), the input voltage arc length threshold decreases from 0.887 cm to 0.826 cm as train speed increases from 200 km/h to 300 km/h, while the DC-side threshold drops from 1.28 cm to 0.75 cm. These findings reveal that higher speeds increase the sensitivity of the traction converter to voltage disturbances - identical arc lengths cause stronger voltage fluctuations. Moreover, for a given train speed, the input voltage arc length threshold remains higher than that of the rectifier-side DC voltage.