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Issue:2026 - Vol. 11 - Iss.1
Published:2026.03
Articles:14 articles
International Journal of Advanced Network, Monitoring and Controls
IJANMC
The journal aims to providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills, especially in the fields of advanced network, future network, monitoring, sensors and controls. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed, Only original… More
2026 Vol.11 Iss.1 (14 articles)
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202601Cover
IJANMC 2026, 11(1), 0-0;
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Copyright2026-01
IJANMC 2026, 11(1), 0-0;
Content
202601-Content
IJANMC 2026, 11(1), 0-0;
Research paper
Lightweight Legal Named Entity Recognition via Incremental Fine-Tuning
Abstract: In the digital transformation of the judiciary, legal entity recognition is a foundational prerequisite for building intelligent judicial systems. To address the limited domain adaptability of generic pre-trained models and the computational burden of training large legal models, this paper proposes a lightweight yet effective legal entity recognition optimizer built upon the BERT-BiLSTM-CRF architecture. Empirical results demonstrate substantial gains in accuracy, efficiency, and deployability. With a legal-specia…
Keywords: Entity Extraction; Machine Learning; Neural Network; Model Training; Long Document Modeling
IJANMC 2026, 11(1), 1-7;
https://doi.org/10.58244/ijanmc.263670
Research paper
Research on Instrument Digital Recognition Technology Based on Yolov8
Abstract: To improve the efficiency and reliability of industrial digital instrument reading, this paper proposes an automatic recognition method based on YOLOv8. Aiming at the low efficiency and poor robustness of traditional manual and rule-based methods, YOLOv8 is used to accurately detect the digital display area of instruments, benefiting from its fast inference speed and strong adaptability to complex environments. Subsequently, image preprocessing operations, including grayscale conversion, denoising, binarization, an…
Keywords: Industrial Digital Instrument Recognition; Yolov8; Resnet34; Opencv
IJANMC 2026, 11(1), 8-18;
https://doi.org/10.58244/ijanmc.263671
Research paper
An Agentic Email Spam Detection System Based on DeepSeek-R1, Dify, and Fine-Tuned BERT
Abstract: Spam email has long threatened communication security and work efficiency. To address this, we design and implement an email spam-detection agent that integrates the DeepSeek large language model, the Dify agent-development platform, and a fine-tuned BERT model. The system uses BERT as the core classifier, leveraging its strengths in semantic understanding and deep feature extraction; it adopts a binary scheme (label 0 = ham, 1 = spam), and fine-tuning enables effective recognition of email text features. Meanwhile…
Keywords: Deepseek; Dify; BERT Model; Email Detection; Agent
IJANMC 2026, 11(1), 19-30;
https://doi.org/10.58244/ijanmc.263672
Research paper
Front-End Optimization of ORB-SLAM3 Using Adaptive Thresholding and PROSAC
Abstract: The performance of visual SLAM is strongly influenced by the quality of front-end feature detection and correspondence matching. To improve ORB-SLAM3 in weak-texture environments, under feature clustering, and in the presence of mismatches, this paper optimizes the front-end pipeline in three stages. First, an adaptive threshold is introduced into FAST detection to improve keypoint extraction in weak-texture regions. Second, an improved quadtree-based distribution strategy is adopted to reduce feature over-concentr…
Keywords: Visual SLAM; Feature Extraction and Matching; Quad-Tree Coding; PROSAC Algorithm
IJANMC 2026, 11(1), 31-42;
https://doi.org/10.58244/ijanmc.263673
Research paper
AI-Enhanced Quantum Key Distribution with Adaptive Error Correction and Entanglement Optimization
Abstract: This paper proposes an AI-enhanced QKD protocol, which uses machine learning-based adaptive control to dynamically optimize error correction and entanglement quality in view of the dynamics of network conditions. The proposed framework integrates the AI prediction model with Low-Density Parity-Check codes and entanglement swapping, aiming at the intelligent regulation of photon loss, QBER, and the key generation rate. An AI model predicts real-time channel noise and thus dynamically adjusts LDPC parameters and enta…
Keywords: Quantum Key Distribution (QKD); Artificial Intelligence; Entanglement Swapping; Adaptive Error Correction; LDPC Codes; Machine Learning; Quantum Networks
IJANMC 2026, 11(1), 43-50;
https://doi.org/10.58244/ijanmc.263674
Research paper
Research of a UWB-Based Localization Algorithms
Abstract: To address the problem of excessive outlier errors caused by noise in indoor non-line-of-sight (NLOS) environments—particularly in positioning and robot localization—two Chan–Taylor cooperative algorithms are proposed and implemented to suppress NLOS-induced errors. The first approach integrates Kalman filtering for error mitigation, while the second reconstructs distance measurements based on statistical characteristics. By combining multiple positioning algorithms, the proposed methods effectively reduce the impa…
Keywords: Indoor Positioning; Non-Line-of-Sight (NLOS); Kalman Filter; Chan-Taylor Algorithm
IJANMC 2026, 11(1), 51-60;
https://doi.org/10.58244/ijanmc.263675
Research paper
Research on Obstacle Avoidance Decision-Making for Unmanned Vehicles based on Reinforcement Learning
Abstract: In response to the limitations of traditional obstacle avoidance algorithms for unmanned vehicles in dealing with unknown obstacles and complex dynamic environments, this paper proposes Q Mixing Network and Video Delivery Network reinforcement learning algorithms, specifically for the research of obstacle avoidance decision-making for unmanned vehicles. By constructing a mapping relationship between local function values and global function values, it is possible to guide obstacle avoidance decisions for unmanned v…
Keywords: Reinforcement Learning; Unmanned Vehicle; Obstacle Avoidance Decision-Making; Autonomous Driving
IJANMC 2026, 11(1), 61-75;
https://doi.org/10.58244/ijanmc.263676
Research paper
Extracting Features from Radar Spectrograms Using Deep Learning for Target Detection
Abstract: Rooted in Convolutional Neural Networks (CNNs), translation invariance inherently imposes a fundamental constraint on their ability to analyze radar spectrograms, resulting in inadequate feature extraction for distant and small targets. To overcome these limit ations, this paper proposes IPRadar_Net. This novel model, built on the Transformer architecture, marks a departure from conventional convolutional and hybrid paradigms. The model exploits the Transformer's lack of translation invariance and it's positional e…
Keywords: Radar Object Detection; Feature Extraction; Vision Transformer; Adaptive Positional Encoding; Radar Spectrogram
IJANMC 2026, 11(1), 76-87;
https://doi.org/10.58244/ijanmc.263677
Research paper
System Electromagnetic Compatibility Analysis Method Based on Cascaded Multi-Port Networks
Abstract: To address the challenges in efficiently modeling and simulating Electromagnetic Compatibility (EMC) for complex electrical and electronic systems, a system-level EMC analysis method based on cascaded multi-port network theory is proposed. According to the topological structure of the system, this method decomposes the complex system into multiple cascaded multi-port network modules, where electromagnetic energy transmission and coupling occur via ports. In this method, the Electromagnetic Interference (EMI) transf…
Keywords: System-Level EMC; Multi-Port Network; Cascaded Network; Impedance Matrix; Electromagnetic Port; Electromagnetic Coupling
IJANMC 2026, 11(1), 88-98;
https://doi.org/10.58244/ijanmc.263678
Research paper
Research on Style Transfer of Unpaired Images Based on Improved CycleGAN
Abstract: To address the issues of uneven texture distribution, background mis-migration, and training imbalance in traditional CycleGAN for style transfer of non-paired horse and zebra images, this study proposes an improved model integrating dynamic attention mechanisms, semantic segmentation constraints, and adaptive training strategies. By embedding lightweight space-channel hybrid attention modules in generator residual blocks, the model enhances feature extraction in target regions. A lightweight semantic segmentation…
Keywords: Cyclegan; Image Style Transfer; Attention Mechanism; Semantic Segmentation; Adaptive Training Strategy
IJANMC 2026, 11(1), 99-109;
https://doi.org/10.58244/ijanmc.263679
Research paper
Fusion of Multi-Instrument Time-Energy Data with Self-Attention for Enhanced GRB Detection
Abstract: Gamma-ray burst (GRB) detection is crucial for triggering rapid follow-up observations and enabling subsequent multi-wavelength studies, yet traditional methods are limited by their difficulty in modeling long-range temporal dependencies in noisy, non-stationary data. In this work, we propose GRBNet, a self-attention–based neural network for GRB detection from same-source multi-detector time-tagged event (TTE) sequences. GRBNet leverages multi-head self-attention to adaptively aggregate discriminative evidence over…
Keywords: GRB; Self-Attention; Style; Multi-Detector Fusion; Deep Learning
IJANMC 2026, 11(1), 110-116;
https://doi.org/10.58244/ijanmc.263680
