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Volume 8 期 2，2026 2026年第8卷第2期

  • Review Intelligence

    Wang Fei-Yue

    Vol. 8, Issue 2, Pages: 131-148(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26067
    摘要：The rise of artificial intelligence is not merely a consequence of advances in computing technology, but the result of a long-term convergence of philosophical reflection, mathematical logic, and cognitive science. From the perspective of knowledge origins, this paper systematically examines the intellectual foundations and disciplinary evolution of artificial intelligence. At the philosophical level, it traces early forms of intelligent activities to the Eastern tradition of "Fu, Bi, Xing" and the Western trivium of "Grammar, Logic, Rhetoric", and further interprets intelligence through ontology, parallel intelligence, and the triadic framework of Being, Becoming, and Believing. At the mathematical level, the paper reviews the role of the three foundational crises of mathematics in promoting formalization, computability, and complexity theory, and reconstructs a logical trajectory from Hilbert's Program, Gödel's Incompleteness Theorem, the Turing Machine, Cybernetics, and McCulloch-Pitts' Model of Artificial Neurons to Category Theory and the Langlands Program. At the level of cognitive science, it discusses the Lighthill Report, the AI Winter, Longuet-Higgins's naming of cognitive science, Miller's cognitive revolution, as well as the hexagonal framework of cognitive science, showing how cognitive science helped artificial intelligence move beyond symbolic limitations toward deep learning, large models, and embodied intelligence. The paper argues that artificial intelligence is a product of the sustained interaction among humanistic thought, formal rationality, and cognitive mechanisms. Reconstructing its knowledge origins provides a fundamental framework for understanding the boundaries, limitations, evolution, and future directions of intelligence.  
    关键词：artificial intelligence;knowledge origin;parallel intelligence;philosophy of intelligence;cognitive science   
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  • Surveys and Prospectives

    Liu Chuanyang, Wu Yiquan, Liu Jingjing

    Vol. 8, Issue 2, Pages: 149-168(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26042
    摘要：Efficient and accurate detection of foreign object intrusion on transmission lines based on UAV aerial images and deep learning algorithms enables rapid identification of potential safety hazards, thereby improving the lean operation and maintenance capability of power grids. The research progress of deep learning-based foreign object intrusion detection methods for transmission lines using UAV aerial images was reviewed. First, deep learning-based foreign object detection methods for UAV aerial images were systematically reviewed from four dimensions: Faster R-CNN, SSD, the YOLO series algorithms, and other derivative algorithms. Second, an improved model for UAV aerial image-based foreign object intrusion detection in transmission lines was elaborated. It illustrated the application strategies and performance optimization effects of core technologies in detection tasks, including attention mechanisms, multi-scale feature fusion, and lightweight networks. Subsequently, the DETR-based foreign object detection models built on the vanilla Transformer architecture were investigated, as well as large Transformer models tailored for the power industry and their engineering applications. Finally, in view of the current challenges in UAV aerial image-based intelligent foreign object detection for transmission lines, future research prospects were proposed.  
    关键词：transmission line;electrical inspection;UAV aerial image;deep learning;foreign object detection   
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  • Surveys and Prospectives

    Zhang Chao, Yang Jungang, Lu Zhenhai, Li Yao, Wang Lianqing, Jiang Chao

    Vol. 8, Issue 2, Pages: 169-182(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26037
    摘要：To address the challenges faced in the analysis of information needs in time-sensitive task scenarios, an intelligent parsing method based on ontology was proposed. Firstly, an ontology model was designed to describe tasks and forces formally. Secondly, a semantic transformation and logical reasoning method based on the ontology model was established to automatically generate a structured list of information needs from unstructured task descriptions. The experimental results show that this method can generate a standardized and detailed list of information needs. Compared with traditional manual analysis, the efficiency of information assurance is significantly improved.  
    关键词：information need;intelligent parsing;ontology;emergency command   
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  • Surveys and Prospectives

    Cheng Kaiwen, Du Yuanyuan, Zhao Yuhao

    Vol. 8, Issue 2, Pages: 183-195(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26030
    摘要：Brain language decoding provides a critical pathway for restoring communication in patients with aphasia and serves as an important driving force for the development of brain-computer interface (BCI) technology. In recent years, substantial progress has been made in multi-level brain language decoding based on both invasive and non-invasive brain signals. However, different linguistic levels vary markedly in brain signal representation, task complexity, and model selection, which has led to fragmented technical pathways and research emphases, while a systematic level-based synthesis is still lacking. Based on this, the major advances in brain language decoding research from 2008 to 2025 were reviewed across different linguistic levels, including phonemes, words, phrases, sentences, and discourse. The influence of brain signal modality, task setting, and model architecture on decoding accuracy was analyzed, and further key issues such as multi-level decoding, open-vocabulary decoding, and the integration of pre-trained language models were discussed. On this basis, several future research pathways were proposed for the development of language brain-computer interfaces and intelligent language rehabilitation, with the aim of providing theoretical support and technical reference for related interdisciplinary studies.  
    关键词：brain signal;brain language decoding;artificial intelligence;language brain-computer interface;multi-level decoding   
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  • Papers and Reports

    Hu Zhenxiao, Yao Xiaoting, Xi Zhiyuan, Wang Xiao

    Vol. 8, Issue 2, Pages: 196-206(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26020
    摘要：In autonomous driving perception, accurate understanding of interaction signals is the key to safe, human-like decisions. Real-time detection of these signals is found difficult to achieve with current high-precision models. Effective modeling of dynamic temporal signals was generally lacking. However, effective modeling of dynamic temporal signals, such as turn signal flashes and pedestrian gestures. As a result, missed detections and false alarms occur frequently in complex scenarios, creating safety risks. To address this, a lightweight-temporal-knowledge distillation collaborative optimization model for autonomous driving edge devices was proposed, named lightweight temporal knowledge-distilled YOLO (YOLO-TKD). The model was designed to resolve the conflict between the need for temporal modeling in interactive signal detection and the constraints of computational resources. Firstly, the YOLOv8n baseline was adopted, with MobileNetV2 utilized as the backbone network to compress the model size. Subsequently, a temporal convolutional network (TCN) module was introduced to capture dynamic dependencies between consecutive frames. Finally, a knowledge distillation (KD) strategy was incorporated to maintain recognition performance. Experimental results on the dataset show that the proposed method achieves precision, recall, and mean average precision of 87.5%, 86.4%, and 84.3%, respectively, which represent improvements of 3.2%, 2.6%, and 1.1% over the baseline YOLOv8n. At the same time, the model parameter count is reduced by 26.3%. The proposed YOLO-TKD not only significantly reduces resource consumption but also effectively improves the accuracy and temporal robustness of interaction signal detection, providing a viable technical solution for autonomous driving edge devices.  
    关键词：interaction signal detection;lightweight model;temporal convolutional network;knowledge distillation;YOLOv8   
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  • Papers and Reports

    Wang Tao, Zhang Gening, Xie Yuanhan

    Vol. 8, Issue 2, Pages: 207-218(2026) DOI: 10.11959/j.issn.2096-6652.ZNKX26048
    摘要：Traditional occupational models were limited by a static perspective centered on the biological human. To overcome this, a "tangram"-inspired dynamic triadic collaboration framework was proposed that preconceived occupations as networks of digital humans, robots, and biological humans. By employing task-atomic decomposition and dynamic weighting quantification, the framework systematically unpacked how technological penetration reconstructed occupational task bundles. This approach breaks through the static "human task bundle" paradigm of occupational science and advances the discipline toward a human-machine collaboration modeling paradigm, which provides a scientific foundation for occupational forecasting, policy formulation, and educational restructuring in the artificial general intelligence era.  
    关键词：professional reconfiguration;human-machine collaboration;tangram occupational model;sociology of human-machine interaction   
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Digital chefs and intelligent cooking systems based on multimodal large language model

A digital chef and an intelligent cooking method were proposed to achieve high-quality, precise cooking results. In the offline phase, visual, auditory and thermal sensors record professional chefs' continuous cooking operations. The collected frame-by-frame images and multi-round Q&A texts form a culinary expert knowledge base. A low-rank adaptation method was applied to fine-tune a pretrained multimodal large language model, enabling it to understand cooking intentions. In the online phase, real-time sensory data were converted into image-text inputs for the fine-tuned model, which then generated cooking instructions to guide users through the cooking steps. A hardware-software cooking system was implemented and tested with a pan-frying steak task. Experimental results show that the fine-tuned system effectively controls the steak's doneness and quality, and significantly improves the accuracy and rationality of cooking instructions compared to the model before fine-tuning.

> Papers and Reports

LI Xinyuan, LI Bai, SUN Yueshuo, ZHANG Tantan, TIAN Yonglin, YIN Zhuyan, WANG Fei-Yue

Digital chefs and intelligent cooking systems based on multimodal large language model
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