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Issue:2026 - Vol. 11 - Iss.3
Published:2026.08
Articles:12 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.3 (12 articles)
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Cover202603
IJANMC 2026, 11(3), 0-0;
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Contents202603
IJANMC 2026, 11(3), 1-1;
https://doi.org/10.58244/ijanmc.263669
Research paper
DART-DETR: Adaptive Receptive Fields and Dynamic Feature Fusion for Efficient Real-Time Aerial Object Detection
Abstract: Object detection in Unmanned Aerial Vehicle (UAV) scenarios faces significant challenges, including low resolution, a high prevalence of small objects, extreme scale variations, and dense occlusion. To address these issues, this paper proposes DART-DETR, a novel object detection architecture based on adaptive receptive fields and dynamic feature fusion. Specifically, this work introduces the DAFPN, which achieves content-adaptive fusion across multi-scale features via dual-path dynamic weight prediction, a cross-in…
Keywords: Object Detection; Unmanned Aerial Vehicle; Small Object Detection; Transformer-based Detector
IJANMC 2026, 11(3), 1-13;
https://doi.org/10.58244/ijanmc.263989
Research paper
The Review of Image Style Transfer
Abstract: Image style transfer, as a core cross-disciplinary technology in computer vision and non-photorealistic rendering, aims to preserve the semantic structure of the content image while transferring the artistic textures and brushstroke patterns of the style image. Early traditional methods struggled with complex scenes due to the semantic gap. With the advancement of deep learning, neural style transfer (NST) achieved a leap from "pixel-level statistics" to "feature-level perception." Crucially, while early optimizati…
Keywords: Image Style Transfer; Neural Style Transfer; Deep Learning; Diffusion Models; Generative Adversarial Networks
IJANMC 2026, 11(3), 14-26;
https://doi.org/10.58244/ijanmc.263991
Research paper
Dynamic Clustering Hierarchy for Vehicular Communication (DCHVC)
Abstract: The number of vehicles with network access keeps growing, and such growth brings more serious channel congestion and node overload problems to VANET systems, these problems will become much worse if the network only uses one cluster head node, to solve the above existing defects, this paper puts forward a dynamic clustering scheme named DCHVC, this scheme takes the motion similarity of vehicles as a reference standard, and uses a multi-index dual cluster head selection rule to raise the efficiency of vehicle data t…
Keywords: Vanets; Double Cluster Head; Dynamic Clustering; Multi-Criteria Decision-Making; Congestion Control
IJANMC 2026, 11(3), 27-42;
https://doi.org/10.58244/ijanmc.263992
Research paper
Mapping Method Based on Improved Cartographer Algorithm
Abstract: This research introduces an enhanced version of the Cartographer algorithm for laser-based simultaneous localization and mapping (SLAM) to address common challenges in traditional LiDAR methods, such as incomplete point cloud features, low-quality data, and pose drift induced by measurement noise. The proposed approach incorporates an Adaptive Unscented Kalman Filter (AUKF) during sensor fusion, which effectively predicts and updates sensor measurements with adaptive noise optimization to mitigate interference duri…
Keywords: Cartographer; Sensor Fusion; Adaptive Unscented Kalman Filter; Improved Voxel Filtering (Key Words)
IJANMC 2026, 11(3), 43-57;
https://doi.org/10.58244/ijanmc.263993
Research paper
High-Efficiency Distillation Network for Lightweight Single-Image Super-Resolution
Abstract: Lightweight single-image super-resolution is important for edge-side imaging systems where reconstruction accuracy, model size, memory movement, and inference latency must be considered together. Although many compact SR networks reduce floating-point operations through channel distillation or depth wise-style operators, fewer FLOPs do not necessarily lead to faster execution on real hardware because intermediate feature access may still be expensive. In addition, when the distillation branch is overly compressed…
Keywords: Single-Image Super-Resolution; Lightweight Network; Partial Convolution; Feature Distillation; Edge Computing
IJANMC 2026, 11(3), 58-66;
https://doi.org/10.58244/ijanmc.263994
Research paper
Circular Target Detection Method Based on Color Space Conversion and Morphological Filtering
Abstract: To address the low efficiency, high alignment error, and insufficient positioning reliability of single-sensor manual alignment in bulk cement truck loading, this paper proposes a circular target detection and localization method based on color space conversion, morphological filtering, and multi-sensor concurrent fusion. The proposed method first performs Gaussian preprocessing on industrial camera images to suppress high-frequency noise caused by dust, vibration, and illumination fluctuation. The filtered image i…
Keywords: Circular Target Detection; Hsv Threshold Segmentation; Morphological Filtering; Contour Extraction; Multi-Sensor Fusion
IJANMC 2026, 11(3), 67-73;
https://doi.org/10.58244/ijanmc.263995
Research paper
A Method for Constructing Causal Knowledge Graphs in Oil and Gas Fields that Integrates Data-Driven Approaches and Large Language Models
Abstract: To address the issues of lacking unified representation for multi-source heterogeneous data in oil and gas field development, poor interpretability of traditional data-driven methods, and the difficulty in structuring expert experience, a causal knowledge graph construction method integrating data-driven and large language models (LLMs) is proposed. This method first learns the causal skeleton from structured data using causal discovery algorithms (PC) and then utilizes LLMs to extract domain knowledge from enginee…
Keywords: Oil and Gas Field Development; Causal Knowledge Graph; Large Language Models; LLM Evaluation Agent
IJANMC 2026, 11(3), 74-86;
https://doi.org/10.58244/ijanmc.263996
Research paper
A CNN-BiLSTM Model with Multi-Head Attention for Temperature and Humidity Prediction in Grain Storage Facilities
Abstract: Accurate prediction of temperature and humidity within grain storage facilities is essential for ensuring long-term food safety and reducing post-harvest losses. Existing approaches based on standard Long Short-Term Memory (LSTM) networks or Sequence-to-Sequence LSTM (Seq2Seq-LSTM) architectures often fail to capture the complex multi-scale temporal dependencies inherent in grain depot microclimate data. In this paper, we propose a hybrid deep learning model that integrates Convolutional Neural Networks (CNN), Bidi…
Keywords: Grain Storage; Temperature Prediction; Humidity Prediction; Deep Learning; CNN-Bilstm; Attention Mechanism; Time Series Forecasting
IJANMC 2026, 11(3), 87-108;
https://doi.org/10.58244/ijanmc.263997
Research paper
Dual-Branch ConvNeXt with Parameter-Free 3D Attention for Person Re-Identification
Abstract: Retrieving specific individuals across disjoint camera networks, widely known as pedestrian re-identification (Re-ID), serves as a fundamental pillar for modern visual surveillance and smart security systems. However, practical deployments are frequently hampered by shifting viewpoints, background clutter, and diverse body poses. Consequently, obtaining representations that are both highly robust and discriminative remains a formidable obstacle. Motivated by these challenges, we design an innovative Re-ID architect…
Keywords: Person Re-Identification; Parameter-Free 3D Attention; ConvNeXt; Fine-Grained Feature Learning; Dual-Branch Architecture
IJANMC 2026, 11(3), 109-118;
https://doi.org/10.58244/ijanmc.263998
Research paper
A Method for Micro-Crack Detection Based on Improved YOLOv8
Abstract: Aiming at the problems of large-scale span, extreme aspect ratio and extremely low pixel proportion of micro-cracks on the surface of civil engineering structures, this paper proposes an improved object detection algorithm based on YOLOv8. Based on YOLOv8, the algorithm first integrates the Convolutional Block Attention Module (CBAM) into the key feature extraction stage of the backbone network. Through feature weighting in both channel and spatial dimensions, it effectively suppresses noise interference in the con…
Keywords: Deep Learning; Crack Detection; YOLOv8; CBAM Attention Mechanism; SAHI Slicing Inference; Small Object Detection
IJANMC 2026, 11(3), 119-129;
https://doi.org/10.58244/ijanmc.263999
