In response to the challenges of insufficient accuracy in face detection and missed small targets under low-light conditions, this paper proposes a detection scheme that combines image preprocessing and detection model optimization. Firstly, Zero-DCE low-light enhancement is introduced to adaptively restore image details and contrast, providing high-quality inputs for subsequent detection. Secondly, YOLOv11n is enhanced through the following improvements: a P2 small-target detection layer is added while the P5 layer is removed, addressing the original model's deficiency in detecting small targets and streamlining the computational process to balance model complexity and efficiency; the P2 up... More >

As the core component of the copper top-blown smelting, the service life of the injection lance critically affects production stability. To monitor the operating condition of the injection lance, a data-driven model is proposed to predict the Remaining Useful Life (RUL) or service life, namely, the DKT-LSSVM model. Firstly, to reduce noise interference, the Daubechies wavelet with four vanishing moments (DB4) denoising is used to process the raw data. Then, the Kernel Principal Component Analysis (KPCA) method is utilized to extract the principal components from the denoised data, which retains at least 90% information content (18 principal components are obtained). These principal component... More >

Federated learning is a collaborative machine learning paradigm that trains models across multiple computing nodes while aiming to preserve the privacy of local data held by participants. However, because of the open network environment, federated learning faces severe privacy and security challenges. Studies have shown that attackers can reconstruct original training data by intercepting gradients transmitted across the network, thereby posing a serious threat to user privacy. One representative attack is the Deep Leakage from Gradients (DLG), which iteratively recovers training data by optimizing dummy inputs to match the observed gradients. To address this challenge, this paper proposes a... More >

Artificial intelligence (AI) is reshaping engineering education by offering adaptive, interactive, and data-driven learning environments. This paper examines the primary roles of AI in enhancing control engineering education (CEE), with emphasis on sensing-oriented applications such as sensor-based system modelling, AI-assisted signal processing, and data-driven state estimation-domains that underpin modern sensing, communication, and control systems. The paper discusses how AI tools, particularly MATLAB and Simulink integrated with machine learning and reinforcement learning capabilities, can serve as effective pedagogical instruments for teaching complex topics including nonlinear contro... More >

A fixed-time adaptive optimal parameter estimation (FxT-AOPE) scheme is proposed to address the difficulties in estimating dead zone parameters and slow convergence speed of tracking errors in permanent magnet synchronous motor systems. First, the continuous piecewise linear neural network is used to model the nonlinear dead zone dynamics. Second, an auxiliary filter is constructed to extract estimation errors, and this filter is used to drive an adaptive law with time-varying gain, minimizing the cost function of estimation errors and achieving adaptive optimal parameter estimation (AOPE). Then, the AOPE method is introduced into the fixed-time non-singular terminal sliding mode control (Fx... More >

Strain sensors have become fundamental to contemporary sensing technology, driven by the growing demand for flexible, sensitive, and durable transducers across medical care, robotics, structural monitoring, and human-machine interfaces. Rapid advances in strain sensing performance---in terms of gauge factor, detection limit, working range, and operational stability---have enabled next-generation wearable and implantable sensing systems. This mini review examines sensing mechanism classifications, key performance parameters, sensor architectures, and application domains, with a unified focus on how sensing capability can be maximised for real-world deployment. Particular emphasis is placed on... More >

The Internet of Vehicles (IoV) is a core component of smart transportation systems, making it feasible to exchange information among vehicles, infrastructure, and central systems in real time. However, the effective use of resources and the efficient distribution of tasks in these dynamic environments is a challenging task. This paper presents a blockchain-based collaborative task allocation framework method that can solve these problems by using a greedy algorithm for general task allocation and adopting a dynamic collaboration scheduling algorithm for emergent tasks. Employing the blockchain-based reward mechanism, the transparency, fairness, and security in dynamic mobile crowdsensing (MC... More >

This study explains the main idea and structure of an integrated What-How intelligent control and universal control (I-U-control) system. The proposed framework consists of two control layers. The bottom layer uses the U-control framework to manage 'How' to control within a universal framework. The top layer uses intelligent control (I-control) to coordinate and guide 'What' to achieve both global and local control goals. This study also reviews the configurations, functions, and integration of these two control layers in analysis, design, and applications. More >