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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->Artificial Intelligence]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Research review of graph reinforcement learning algorithms and their applications in the industrial field]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250408]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Successful application of reinforcement learning in decision support, combinatorial optimization, and intelligent control has driven its exploration in complex industrial scenarios. However, existing reinforcement learning methods face challenges in adapting to graph-structured data in non-Euclidean spaces. Graph neural networks have demonstrated exceptional performance in learning graph-structured data. By integrating graphs with reinforcement learning, graph-structured data was introduced into reinforcement learning tasks, enriching knowledge representation in reinforcement learning and offering a novel paradigm for addressing complex industrial process problems. The research progress of graph reinforcement learning algorithms in industrial domains was systematically reviewed, summarized graph reinforcement learning algorithms from the perspective of algorithm architecture and extracted three mainstream paradigms, explored their applications in production scheduling, industrial knowledge graph reasoning, industrial internet, power system and other fields, and analyzed current challenges alongside future development trends in this field.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[LI Dazi, LIU Zibo, BAO Yanyang, DONG Caibo, XU Xin]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>LI Dazi, LIU Zibo, BAO Yanyang, DONG Caibo, XU Xin</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250408]]></guid><cfi:id>11</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Review of diffusion models for inverse problems in image processing]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250409]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Diffusion models represent a novel type of generative artificial intelligence models. Compared to traditional networks such as generative adversative networks, variational autoencoders, and flow models, diffusion models are characterized by their robust training, high fidelity and diversity in generation, and strong mathematical interpretability, and so they are widely used in fields of computer vision, signal processing, multi-modal learning and so on. Diffusion models are capable of sufficiently learning and exploring the deep generative priors from the training images, providing a novel paradigm for solving inverse problems in image processing. In order to systematically sort out the development status of diffusion model, especially the latest progress in solving the inverse problem of image processing, the research of diffusion model for the inverse problem of image processing was reviewed. The basic principle and development status of diffusion model was expounded,the main technical route of using diffusion model to solve the inverse problem of image processing and some specific application results in this direction were emphatically introduced, and the future research directions were envisioned.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[WANG Zelong, WU Yuhang, LI Jian, YANG Xuan]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>WANG Zelong, WU Yuhang, LI Jian, YANG Xuan</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250409]]></guid><cfi:id>10</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Asymmetric Actor-Critic reinforcement learning for long-sequence autonomous manipulation]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250410]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Long-sequence autonomous manipulation capability becomes one of the bottlenecks hindering the practical application of intelligent robots. To address the diverse long-sequence operation skill requirements faced by robots in complex scenarios, an efficient and robust asymmetric Actor-Critic reinforcement learning method was proposed. This approach aims to solve the challenges of high learning difficulty and complex reward function design in long-sequence tasks. By integrating multiple Critic networks to collaboratively train a single Actor network, and introducing GAIL (generative adversarial imitation learning) to generate intrinsic rewards for the Critic network, the learning difficulty of long-sequence tasks was reduced. On this basis, a two-stage learning method was designed, utilizing imitation learning to provide high-quality pre-trained behavior policies for reinforcement learning, which not only improves learning efficiency but also enhances the generalization performance of the policy. Simulation results for long-sequence autonomous task execution in a chemical laboratory demonstrate that the proposed method significantly improves the learning efficiency of robot long-sequence skills and the robustness of behavior policies.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[REN Junkai, QU Yuke, LUO Jiawei, NI Ziqi, LU Huimin, YE Yicong]]></author>
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<atom:name>REN Junkai, QU Yuke, LUO Jiawei, NI Ziqi, LU Huimin, YE Yicong</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250410]]></guid><cfi:id>9</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Shapley value decomposition method in dynamic force deployment strategy planning]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250411]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Aiming at the dynamic force deployment problem, a multi-agent reinforcement learning strategy planning method based on SVD (Shapley value decomposition)was proposed. The reward distribution among cooperative multi-agents was explained by SVD, and the reward distribution was analysed by SVD reinforcement learning method to solve Markov convex game strategy. Secondly, based on the scenario of naval and air cross-domain cooperative confrontation, the allocation of space domain combat resources in heterogeneous multi-entity cooperative confrontation was analysed, a dynamic force deployment strategy planning model was built, and the state space, action space and reward function of the problem were designed. Finally, based on typical application scenarios, simulation experiments were organized to verify the dynamic force deployment problem with the military chess deduction system. Results show that compared with the multi-class baseline algorithm, the proposed method has excellent performance in strategic planning of dynamic force deployment, and it is theoretically interpretable. The proposed method learns the strategy of "layer upon layer interception, zone confrontation, core cover, and hierarchical breaking".]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[LUO Junren, ZHANG Wanpeng, SU Jiongming, LI Shengqiang, CHEN Jing]]></author>
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<atom:name>LUO Junren, ZHANG Wanpeng, SU Jiongming, LI Shengqiang, CHEN Jing</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250411]]></guid><cfi:id>8</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-robot dynamic visualization research platform based on three-wheeled omnidirectional mobile robot]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250412]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To better investigate the complex collective behaviors in distributed collaborative robots, a multi-robot dynamic visualization research platform based on three-wheeled omnidirectional mobile robot was designed. The purpose of the platform is to provide an intuitive and flexible experimental environment for promoting the testing and development of multi-robot algorithms. The platform is composed of self-developed, low-cost, small, omnidirectional wheeled mobile robots and visual multi-touch screens that support gesture recognition and shape detection of objects, enabling the configuration of various dynamic rendering scenarios. With this platform, researchers are able to focus on the design and optimization of algorithms in multi-robot systems without being limited to specific scenarios or task settings. The robots motion performance was tested, and multi-robot algorithms were successfully tested in multiple task scenarios, initially validating the platforms effectiveness and flexibility.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[ZHU Pengming, YANG Jiaqi, LIU Peng, QIU Xuekai, DAI Wei, ZENG Zhiwen, LU Huimin, ZHOU Zongtan]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHU Pengming, YANG Jiaqi, LIU Peng, QIU Xuekai, DAI Wei, ZENG Zhiwen, LU Huimin, ZHOU Zongtan</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250412]]></guid><cfi:id>7</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Convolutional neural network mixed-precision quantization method considering layer sensitivity]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250413]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To address the problem of how to faithfully map neural networks to resource-constrained embedded devices, a mixed-precision quantization method for convolutional neural networks based on layer sensitivity analysis was proposed. The sensitivity of convolutional layer parameters was measured by calculating the average trace of the Hessian matrix, providing a basis for bit-width allocation. A layer-wise ascending-descending approach was employed for bit-width allocation, ultimately achieving mixed-precision quantization of the network model. Experimental results demonstrate that compared to the fixed-precision quantization methods DoReFa and LSQ+, the proposed mixed-precision quantization method improves recognition accuracy by 10.2% and 1.7%, respectively, at an average bit-width of 3 bit. When compared to other mixed-precision quantization methods, the proposed approach achieves over 1% higher recognition accuracy. Additionally, noise-injected training effectively enhances the robustness of the mixed-precision quantization method, improving recognition accuracy by 16% under a noise standard deviation of 0.5.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[LIU Haijun, ZHANG Chenxi, WANG Xiyu, CHEN Changlin, CHEN Jun, LI Zhiwei]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>LIU Haijun, ZHANG Chenxi, WANG Xiyu, CHEN Changlin, CHEN Jun, LI Zhiwei</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250413]]></guid><cfi:id>6</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Optimization method for deep image clustering based on alternating normalization and category-wise uniform prior]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250414]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Deep image clustering was employed to analyze the cluster structure of unlabeled image data through deep learning techniques. However, due to the absence of class labels that provide definitive information, uncertain clustering predictions may be yielded by unsupervised deep image clustering, introducing noise information that was found detrimental to performance enhancement and application development. Therefore, a clustering prediction optimization method based on alternating normalization and category-wise uniform prior was proposed to correct low confidence predictions and improve deep image clustering performance. At the same time, the method had a low degree of coupling with the model structure and training process, enabling cross-model optimization for deep image clustering frameworks. Experimental results on multiple datasets reveal that the effective clustering prediction optimization is achieved for various deep image clustering models through the approach.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[ZHU Yiming, MA Zheng]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHU Yiming, MA Zheng</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250414]]></guid><cfi:id>5</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Lightweight photovoltaic module defect detection with YOLOv8-DM]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250415]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Given the challenges posed by photovoltaic component damage detection and the high demands placed on human and computational resources by existing detection technologies, an improved lightweight model named YOLOv8-DM was proposed on the basis of the YOLOv8n.The integration ofelectroluminescence imaging with object detection methods was implemented to achieve photovoltaic defect detection. Innovative components were introduced, including a dynamic scale feature pyramid network and an inverted residual multiscale attention mechanism, along with a Ghost module enhanced by dynamic convolution. These modifications were specifically designed to address the deficiencies observed in the YOLOv8n model regarding feature representation and multiscale object recognition, which enhanced fine-grained detection capabilities and reduced computational complexity. When evaluated on the augmented PVEL-AD dataset, the model demonstrated an improvement of 3% in recall rate and 3.3% in mAP50 compared to the baseline model, with a 34% reduction in parameter count and a 20% decrease in computational demand. The optimized architecture was validated to effectively meet the practical requirements for high-accuracy photovoltaic defect detection with lower computational costs.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[YANG Wei, ZHANG Changsheng, LIU Hui]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>YANG Wei, ZHANG Changsheng, LIU Hui</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250415]]></guid><cfi:id>4</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Combinatorial enumeration and time-interval contrastive learning for sequential recommendation]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250416]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To address the problem of inadequate self-supervised signal quality in contrastive learning models for sequential recommendation tasks, a combinatorial enumeration and time-interval contrastive learning for sequential recommendation model was proposed. The model generated enhanced sequences which preserved temporal information through time-interval perturbation-based data augmentation. A combinatorial enumeration strategy was introduced to integrate user behavior and time-interval information, constructing multi-view augmented sequence pairs. The model employed a multi-head attention mechanism to encode user behavior sequences and optimized self-supervised signals through multi-task joint training, which improved model performance. The proposed model is well-suited for scenarios with high data sparsity and uneven interaction behaviors, effectively addressing challenges in self-supervised signal modeling. Experimental results on three real-world datasets demonstrate that the model outperforms the current state-of-the-art contrastive learning models in terms of HR (hit ratio) and NDCG (normalized discounted cumulative gain).]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[ZHANG Wenxuan, SUN Fuzhen, WANG Aofei, ZHANG Zhiwei, WANG Shaoqing]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHANG Wenxuan, SUN Fuzhen, WANG Aofei, ZHANG Zhiwei, WANG Shaoqing</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250416]]></guid><cfi:id>3</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Large language model-based legal defense document generation for power grid enterprises with data augmentation and rule guidance]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250417]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To enhance the ability of large language models to generate legal documents for the power grid sector under few-shot conditions, a few-shot legal document generation method based on LLM(large language models) was proposed, integrating data augmentation and rule guidance techniques. The proposed method addressed key challenges in power grid legal document generation, such as data scarcity, high domain specificity, and the complexity of legal practice. Experimental results show that the method achieves excellent performance in generating power grid legal defense documents, significantly improving the quality and professionalism of the generated texts.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[HUANG Chengyan, ZHA Xiaoyun, DING Qunyan, HU Wei]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>HUANG Chengyan, ZHA Xiaoyun, DING Qunyan, HU Wei</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250417]]></guid><cfi:id>2</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Fault sidebands cluster penalized regression extraction method for health monitoring of gear transmission systems]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250418]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Gear faults manifest in the frequency spectrum as multi-order modulation sideband clusters phenomenon center on the meshing frequency and its higher-order harmonics, spaced by the gear rotation frequency. In order to automatically focus the fault side frequency components, a method of fault side frequency cluster extraction with penalty regression was proposed. Adaptive sparse group lasso regression self-data-driven strategy was used to determine the penalty coefficient size and update the spectrum weight online to find the fault sideband clusters. Based on the sideband weight coefficients obtained from sparse group lasso regression, a new index called the sparse group lasso sidebands indicator was proposed for the health monitoring of gear transmission systems, enabling the early fault warning and location of gear transmission systems. Results analysis show that the proposed method can provide more accurate gear early fault detection and fault location.]]></description>
<pubDate>2025/7/23 0:00:00</pubDate>
<category><![CDATA[Artificial Intelligence]]></category>
<author><![CDATA[KONG Detong, LI Naipeng, LI Xinyu, LIU Chao, ZHANG Leping, HUANG Yuhao]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>KONG Detong, LI Naipeng, LI Xinyu, LIU Chao, ZHANG Leping, HUANG Yuhao</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/20250418]]></guid><cfi:id>1</cfi:id><cfi:read>true</cfi:read></item>
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