International Journal of Advanced Network, Monitoring and Controls

Remote Sensing Object Tracking refers to the process of detecting, recognizing, and tracking targets on the ground or at sea using remote sensing technology, particularly sensors mounted on satellite or aerial platforms to obtain high-resolution remote sensing image sequences. Current methods for remote sensing object tracking face challenges such as low tracking success rates and inefficiencies. This paper proposes a neural network for remote sensing object tracking based on SiamRPN++, which introduces an improved network structure incorporating the C3Minus module and a coordinate attention mechanism within the backbone extraction network. Furthermore, we design a feature extraction module, ResSwinT, that combines ResNet and Swin Transformer architectures to integrate local and global information obtained from feature maps as foundational features. This approach effectively addresses the aforementioned issues, and quantitative experiments demonstrate an increase in accuracy and success rates by 1.9% and 4.7%, respectively, indicating that our method effectively handles object tracking in remote sensing images.

To enhance the safety and efficiency of coal mine gas monitoring, this study develops an intelligent Internet of Things (IoT) monitoring system incorporating a Fuzzy-PID control algorithm. The system is structured into four layers—sensing, network transmission, control service, and mobile application—ensuring real-time data acquisition, stable transmission, intelligent processing, and remote monitoring. The Fuzzy-PID algorithm dynamically adjusts control parameters to improve response time and accuracy under nonlinear and uncertain conditions. Simulation experiments validate the system's performance, comparing traditional PID, Fuzzy, and Fuzzy-PID control strategies. Results indicate that the traditional PID algorithm achieves a response time of 2.0 s but exhibits oscillations of ±0.1 concentration units. The Fuzzy control algorithm stabilizes gas concentration within 4.0 s with deviations below ±0.05 units. The proposed Fuzzy-PID algorithm achieves an optimal balance, stabilizing gas concentration within 2.5 s with deviations reduced to less than ±0.03 units. These improvements enhance mine safety by reducing gas concentration fluctuations and providing real-time risk alerts. Practical deployment in a coal mining enterprise confirms the system’s capability in reducing manual intervention by 30% and improving early warning accuracy by 25%, demonstrating its potential for intelligent mine development.

To address the motor synchronization errors in the take-up system, an improved deviation-coupled synchronous control strategy based on fuzzy adaptive PID control is proposed. Traditional dual-motor synchronization methods struggle under conditions of high speed, highly dynamic operation, and significant disturbances, particularly in mitigating synchronization errors and system oscillations caused by parameter variations and sensor delays. This paper establishes a pendulum-angle feedback model, introduces a dynamic velocity compensation mechanism and adaptive synchronization gains, and develops a fuzzy adaptive PID controller. The controller dynamically adjusts PID parameters in real-time using pendulum-angle errors and their rate of change as inputs, achieving precise control of the motors' speed difference. Simulink simulation results indicate that, compared to traditional PID control, the proposed method reduces motor speed overshoot by 75% and decreases disturbance recovery time by over 50%. Experimental validations confirm that the method significantly reduces pendulum-angle fluctuations, enhances dynamic response speed, and improves system stability, thus fulfilling practical engineering requirements.

Compared with SC decoding, BP decoding with the parallel mechanism has higher throughput and lower latency, which is more suitable for the demand of 5G scene. To further improve its FER performance and reduce the memory overhead, a recurrent neural network-aided bit-flipping BP decoding of polar codes is proposed. Firstly, it uses bit flip to correct the wrong decoded bits during the decoding iteration. And then, the offset min-sum approximation is used to replace multiplication operation. Lastly the improved recurrent neural network architecture is adopted to realize parameter sharing. The simulation shows that the proposed scheme has a better error correction ability with fewer flipping times, and can effectively reduce the computational resource consumption and extra memory overhead of BP decoding.

This study investigates dictionary-based word segmentation algorithms, which are essential in Natural Language Processing (NLP). Chinese word segmentation poses significant challenges due to the lack of clear word delimiters in the language. This paper explores the advantages and limitations of dictionary-based segmentation algorithms, focusing on how data structures such as Trie and Double-Array Trie (DAT) can enhance segmentation efficiency. An analysis of Trie and DAT structures leads to an optimization achieving constant-time state transitions. This paper evaluates and compares various segmentation algorithms, including full segmentation, forward maximum matching, backward maximum matching, and bidirectional maximum matching. The inherent limitations of dictionary-based segmentation, particularly its dependence on dictionaries and poor disambiguation capability, are also discussed.

In high-concurrency scenarios, network and disk I/O-intensive operations often compete for shared resources, resulting in a decline in the server's load capacity. To address this challenge, this paper proposes a sophisticated high-concurrency server optimization solution. It utilizes various Reactor models in the Linux system, combined with the powerful Epoll mechanism and thread pool, to conduct research and optimization on the server's load capacity.Firstly, the event-driven and other modules required by the Web server are implemented and integrated. Secondly, the number of Reactors, the number of threads, and the business processing time under the Linux system are designed and controlled, and the design and implementation scheme of the high-concurrency server based on the Reactor model with the Epoll mechanism and thread pool are determined. Finally, the performance differences and the best usage scenarios of Web servers with different Reactor models in high-concurrency environments are analyzed through stress tests. The comparison results show that the QPS (Queries Per Second) indicator of the Web server based on the multi-Reactor multi-thread model is three times higher than that of the single-Reactor single-thread Web server, verifying its overall advantages in high-concurrency and long-term business processing. The research results demonstrate the applicable scenarios of different Reactor models, providing theoretical basis, implementation examples, and data support for choosing the appropriate Reactor model in actual server development, helping developers select the most suitable Reactor model according to specific server requirements to ensure higher efficiency in high-concurrency scenarios.

In view of the challenges posed by traditional methods in accurately identifying the damage types of film images, this paper proposes an improved approach by leveraging transfer learning based on ResNet50, incorporating the channel attention mechanism from the Convolutional Block Attention Module (CBAM). Additionally, the AlphaDropout model is integrated with the SeLU activation function to enhance stability and performance, and the resultant model is named CBAM-ResNet50. The film dataset employed in this study comprises four types of damage: cracks, dewetting, particles, and scratches. Extensive training and testing of the CBAM-ResNet50 model on this dataset demonstrate significant improvements in classification accuracy. Compared to the original ResNet50 model, the proposed method achieves a remarkable 25.57% improvement in accuracy, reaching 90.58%. This work highlights the potential of combining attention mechanisms and advanced activation strategies to address complex image classification tasks. Furthermore, the approach paves the way for practical applications in quality inspection and automated defect detection in industrial processes.

Face mask wearing detection is an important application scenario in current technology. This study proposes a method based on the YOLOv5 object detection algorithm to address this issue. Traditional methods face challenges such as the diversity of mask-wearing postures and variations in lighting conditions, which affect their performance. To tackle these challenges, this research presents a new approach that combines the YOLOv5 object detection algorithm with an improved ResNet network architecture. By integrating the detection capabilities of YOLOv5 with the enhanced ResNet network, the method can more accurately detect masks and their wearing status, effectively capturing mask features in images, thereby significantly improving recognition accuracy and stability. The use of a custom mask dataset enables the model to better adapt to diverse lighting and posture conditions. Using deep learning frameworks like PyTorch for inference tools has significantly improved inference speed on GPUs. Experimental results show that after 200 training epochs, the proposed method achieved an accuracy exceeding 85% in face mask wearing detection tasks, with detection accuracy surpassing 98% on certain test datasets. Furthermore, the mean average precision (mAP) reached 97.5%, demonstrating the model's robustness under complex backgrounds and diverse populations. Finally, this paper discusses potential future development directions in the field of face mask wearing detection, including further enhancing the model's adaptability to varying environmental conditions and its application in real-time detection systems.

Aoday, the Internet of Things (IoT) is changing fields by allowing interconnected devices to collect, share, and process data. As for traditional IoT networks that depend on centralized cloud computing, they come with high latency, redundant bandwidth consumption and energy inefficiency. This paper examines edge computing and identifies it as an enabling solution to these challenges. This facilitates real-time analytics of larger groups of data from smaller inputs and is the key characteristic of the edge computing model, by processing data closer to the source; edge computing minimizes latency, optimizes bandwidth usage, and enhances scalability. It examines architectural designs, optimization techniques, and practical applications of edge computing. The empirical evidence also shows that edge computing achieves up to 80% latency reduction, compared to the cloud, a bandwidth saving due to the fact that edge computing could process data at the source (thereby reducing data transfer to the cloud), and that edge computing could reduce overhead energy consumption by approximately 90% compared to cloud computing. The solutions proposed include hierarchical architectures, dynamic resource allocation, and integration with the blockchain, tackling challenges such as scalability, security, and energy efficiency. This work concludes that edge computing is a major breakthrough in iot networks and an enabling technology for real-time, efficient and sustainable applications.

Fiber optic gyro belongs to highly reliable and long-life components, which cannot be realized by traditional reliability assessment methods due to the difficulty of obtaining failure data; the Wiener process model can better model the degradation process of the device, thus realizing the reliability assessment based on the performance degradation. However, the performance degradation of fiber optic gyro exhibits nonlinear characteristics, and there is significant variability in degradation patterns among individual units within the same batch. Traditional Wiener process modeling fails to account for these two critical features. In this paper, a reliability assessment method based on the nonlinear random effect Wiener process is proposed. The nonlinear relationships are first transformed into linear forms through time-scale transformation, while the drift coefficients of the Wiener process are randomized to construct a more comprehensive stochastic degradation model. Subsequently, the Gibbs sampling method is introduced to achieve precise parameter estimation and model resolution. The proposed methodology is then applied to zero-bias performance degradation data from fiber optic gyros for reliability evaluation, generating corresponding reliability curves. The experiments show that the Akaike Information Criterion (AIC) value of the model in this paper is significantly reduced by 28.7% compared with the traditional method, indicating that the model achieves a better balance between complexity and goodness-of-fit. Therefore, the developed methodology provides a more accurate representation of the nonlinear degradation characteristics in fiber optic gyro, thereby significantly enhancing the credibility of the assessment outcomes.