Computers and Artificial Intelligence is a double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of computer science and artificial intelligence. The journal provides an essential platform for researchers, practitioners, and educators to share innovative ideas, findings, and advancements that are shaping the future of these rapidly evolving fields.
LLM-Based Zero-Code Control for Robotic Arms: Representative Methods, Challenges, and Future Trends
With the rapid evolution of Large Language Models (LLMs), significant advancements have emerged in natural language processing, cross-modal reasoning, and complex task planning, heralding a paradigm shift in robotic manipulation. Traditional robotic arm control demands specialized programming and extensive engineering expertise, creating a high barrier to entry. This paper systematically reviews recent advancements in LLM-based zero-code implementation technologies for robotic arms, which enable direct control through natural language and substantially reduce development costs and operational complexity. We establish a clear technical classification framework, analyzing the performance characteristics and applicability of various methods such as direct mapping approaches (SayCan, Code as Policies, ProgPrompt) and feedback-driven architectures (Inner Monologue). We also explore multimodal fusion techniques (RT-2, PaLM-E, VIMA, VoxPoser) and their application across diverse scenarios including education, home services, and manufacturing. Furthermore, we identify key challenges—including natural language parsing accuracy, robustness of multimodal fusion, long-horizon planning, and safety—and propose future research directions aimed at fostering the integration of embodied intelligence with natural language interaction. This study clarifies the research trajectory in this field, offering theoretical support for the broader popularization and deployment of robotic technology.
Large Language Models (LLMs), robotic arms, zero-code control, natural language interaction, embodied AI
Copyright (c) 2026 Jiajun He (Author) https://creativecommons.org/licenses/by/4.0
Multidimensional Performance Comparison and Evolutionary Trends of Typical Transmission Systems for Underactuated Robotic Hands
This review focuses on the transmission systems of underactuated robotic hands. It aims to compare the dynamic characteristics of three mainstream approaches—linkage, tendon-driven, and flexible structures—to reveal their performance bottlenecks and provide guidance for design optimization. Based on relevant theoretical and experimental data, the study comparatively analyzes key indicators such as force transmission efficiency, degrees of freedom, and environmental adaptability. The findings indicate that linkage transmission offers high stiffness and superior precision, making it suitable for heavy-load scenarios and widely used in aerospace applications. However, its bulky structure affects dynamic response speed. Tendon-driven transmission is lightweight and compliant, producing almost no rigid impact, which makes it ideal for biomimetic grasping and widely applied in medical rehabilitation due to its high safety. Nevertheless, it has insufficient lateral stiffness. Flexible structures exhibit strong adaptability to complex environments and are advantageous for handling fragile objects, such as in agricultural picking tasks, but suffer from low control precision and response lag. In summary, no universal solution exists. Future development should involve task-specific trade-offs in selection, exploration of hybrid mechanisms, or the integration of rigid-flexible coupled hybrid actuation systems.
underactuated robotic hand, transmission system, rigid-flexible coupling, nonlinear control
Copyright (c) 2026 Songyi Wang (Author) https://creativecommons.org/licenses/by/4.0
Empirical Research on the Influence of Image Enhancement Technology on Image Classification Performance Based on Deep Learning
To address the problem of insufficient systematic evaluation of image preprocessing technologies, this study adopts the control variable method and quantitative analysis to systematically evaluate the influence of three types of image enhancement technologies (destructive enhancement, conservative enhancement, and combined enhancement) on the performance of an image classification model based on ResNet-50 feature extraction and SVM classification. The CIFAR-10 dataset is used with a sample size of 5,000 images. The experimental results show that destructive enhancement brings an average accuracy decrease of 21.24%, Unsharp Mask in conservative enhancement brings an increase of 3.64%, and Wavelet→Unsharp in combined enhancement brings an increase of 2.26%. The impact of image enhancement on classification performance varies significantly: appropriate enhancement effectively improves model performance, while excessive enhancement impairs it. The findings provide a basis for technology selection in practical applications.
image enhancement, deep learning, image classification, ResNet-50, transfer learning
Copyright (c) 2026 Zhuofan Jia (Author) https://creativecommons.org/licenses/by/4.0
Development and Validation of a Scale for Assessing the Effectiveness of Generative AI-Enhanced English Writing Learning Among Chinese University Students
This study, grounded in the Technology Acceptance Model (TAM) and student engagement theory, aims to develop a psychometric instrument for assessing the effectiveness of generative AI-enhanced English writing learning among Chinese university students. Drawing upon established scales from domestic and international literature, the initial scale was adapted to the specific context of generative AI-assisted English writing, comprising three dimensions—AI Use Attitude, Learning Engagement, and Learning Outcomes—with 42 items. Following expert review, the scale was administered to university students across 28 provinces in China, yielding 282 valid responses. Data were analyzed using SPSS Statistics 27, employing item analysis, exploratory factor analysis (EFA), and reliability and validity testing. Item analysis, based on critical ratio (CR), corrected item-total correlation (CITC), communality, and factor loading, resulted in the deletion of six items (Q8, Q15, Q27, Q35, Q39, Q42), retaining 36 items in the final scale. Exploratory factor analysis supported a three-dimensional structure: “AI Use Attitude—Learning Engagement—Learning Outcomes.” Reliability analysis demonstrated Cronbach's α coefficients exceeding 0.87 for all dimensions and the overall scale, with a split-half reliability of 0.947. Validity testing confirmed satisfactory content validity and structural validity. The findings indicate that the scale possesses robust psychometric properties and practical feasibility, providing a valid instrument for evaluating the effectiveness of generative AI-enhanced English writing instruction.
generative AI, English writing, learning effectiveness, scale development, reliability and validity
Copyright (c) 2026 Jingyu Wang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Deep Learning-Based 15Visual Inspection Techniques for Chip Surface Defects
With the continuous improvement of semiconductor chip integration, chip surface defects have a serious impact on chip performance and reliability. Traditional detection methods such as manual inspection and traditional machine vision have problems such as strong subjectivity, reliance on manual feature design, and poor adaptability, which are difficult to meet the high-precision detection needs of advanced chips. Deep learning has shown obvious advantages in automatic feature extraction, small target detection and complex background adaptation, and has gradually become the core technology of chip surface defect visual inspection. This paper systematically combs the types and imaging characteristics of chip surface defects, classifies and summarizes deep learning-based detection methods from four aspects: classification, object detection, segmentation and Transformer architecture, and analyzes the key technical difficulties such as multi-scale defects, complex texture interference and data imbalance in the detection process. On this basis, the current challenges in detection accuracy, cross-process generalization, data annotation and industrial deployment are discussed, and the future development trends such as multi-modal fusion, weakly supervised learning, model lightweight and intelligent quality control closed-loop are prospected. The review aims to provide theoretical reference and technical support for the research and engineering application of chip surface defect detection based on deep learning.
chip surface defect, deep learning, visual inspection, defect detection, semiconductor manufacturing
Copyright (c) 2026 Borui Cui, Yuhuai Yin, Yujing Xiao, Sijia Wang, Xiao Xiao (Author) https://creativecommons.org/licenses/by/4.0
The Application and Future Advancement of Agricultural Harvesting Robot Technology
Against the trend of a reduction in agricultural labour and the scale of development of intelligent agricultural machinery, the traditional way of harvesting by hand is often ineffective and costly. The agricultural harvesting robot, endowed with technologies such as machine vision, deep learning, intelligent control, flexible manipulation, is the technical means of the intelligence automation harvesting of fruit and vegetables. In recent years, with the progress of multi-sensor fusion and three-dimensional visual perception technologies, harvesting robots' performance of the recognition of targets, positioning and grasping stability have been greatly improved. The harvesting robot has been preliminarily applied in harvesting crops such as tomatoes, strawberries and apples. In this paper, the development of agricultural harvesting robot is introduced and focused on its technical problems such as fruit recognition and fruit localization, path planning and flexible grasping. It also summarizes existing challenges, such as limited environmental flexibility and high cost, and provides insights into future trends of development.
agricultural harvesting robots, intelligent perception, deep learning, flexible manipulation, smart agriculture
Copyright (c) 2026 Yutian Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on Oracle Bone Inscription Detection and Recognition Algorithm Based on YOLO11-ViT
Oracle bone inscriptions are the earliest mature writing system discovered in China and constitute an important historical source for the origin of Chinese characters and traditional Chinese culture. However, oracle bone rubbing images are usually affected by manual carving variations, long-term underground burial, imbalanced sample distributions, severe noise interference and highly similar character structures, which considerably restrict recognition accuracy. To address these challenges, this paper proposes a two-stage oracle bone inscription detection and recognition model integrating YOLO11-ViT. First, the images are preprocessed using grayscale conversion, Otsu binarization, Gaussian denoising and morphological optimization. Second, YOLO11m is employed to accurately detect and localize oracle bone characters in the rubbing images. Finally, a Vision Transformer model is used to classify the cropped single-character regions. Experiments are conducted on the dataset provided by the 2024 14th MathorCup Mathematical Application Challenge. The results show that the proposed method achieves an mAP@0.5 of 0.92 in the detection stage and an accuracy of 97.60%, a recall of 0.96 and an F1 score of 0.97 in the recognition stage, outperforming the compared methods.
oracle bone inscription recognition, yolo11, vision transformer, object detection, image preprocessing
Copyright (c) 2026 Zhiyuan Lu (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-lingual Causal Representation Learning Based on Multi-dimensional Semantic Disentanglement
This paper focuses on several key challenges in cross-lingual natural language processing, including unstable semantic transfer, the entanglement of language-specific features, and insufficient interpretability of learned representations. To address these issues, the study introduces the necessity of combining multi-dimensional semantic disentanglement with causal representation learning. A cross-lingual causal representation learning framework is proposed, covering semantic dimension decomposition, causal factor modeling, language-invariant representation extraction, semantic intervention mechanisms, and cross-lingual transfer optimization. Finally, the paper summarizes the experimental findings and discusses the theoretical and practical significance of the proposed approach.
semantic disentanglement, causal representation learning, cross-lingual transfer, language-invariant representation, natural language processing
Copyright (c) 2026 Wenwen Zhao (Author) https://creativecommons.org/licenses/by/4.0
A Real-Time Multi-Target Capture Framework Based on a Single PTZ Camera
To address the problems of insufficient target details and low multi-target capture efficiency in large-scale surveillance scenes, this paper proposes a real-time multi-target capture framework based on a single PTZ (Pan-Tilt-Zoom) camera. The proposed method integrates object detection, PTZ parameter estimation, and path scheduling to achieve rapid sequential capture of multiple targets. First, an object detection algorithm is used to locate targets in the scene. Then, the corresponding PTZ control parameters are estimated according to the pixel coordinates of each target, and feasible capture regions are constructed. Finally, a scheduling strategy combining greedy path planning and local optimization is adopted to generate a low-cost capture sequence for rapid PTZ switching among multiple targets. Experimental results demonstrate that the proposed framework can effectively reduce PTZ motion time and improve multi-target capture efficiency while maintaining stable operation in real-world environments. The proposed framework requires no additional hardware and has good practical deployment value for intelligent surveillance applications.
PTZ camera, multi-target capture, path planning, object detection, intelligent surveillance
Copyright (c) 2026 Yi Tian, Yang Yang (Author) https://creativecommons.org/licenses/by/4.0
Stochastic Gradient Descent and the Law of Large Numbers: A Probabilistic Analysis of Convergence
Stochastic Gradient Descent (SGD) is one of the most fundamental optimization algorithms in modern large-scale machine learning. However, by relying on only a minuscule number of random samples to estimate gradients at each iteration, it inevitably introduces stochastic noise. This paper aims to delve into the mathematical essence of how this highly randomized algorithm achieves stable convergence from a probabilistic perspective. The paper first rigorously reviews the theoretical foundations and mathematical proofs of the Weak and Strong Laws of Large Numbers. Subsequently, it constructs a probabilistic model for stochastic gradients and thoroughly analyzes the update mechanism of the SGD algorithm. The study demonstrates that the stochastic gradients in single iterations of SGD form a sequence of independent and identically distributed random vectors, whose mathematical expectation is the true full-batch gradient. Based on the Law of Large Numbers, the sample mean of these stochastic gradients converges asymptotically to the true gradient in probability (or almost surely), thereby effectively averaging out and dissipating the random noise during long-term iterations. The conclusion indicates that the effectiveness of SGD is mathematically equivalent to “unbiased estimation combined with the Law of Large Numbers,” and its overall optimization behavior asymptotically approximates standard, noise-free gradient descent.
stochastic gradient descent, law of large numbers, convergence analysis, unbiased estimation, machine learning optimization
Copyright (c) 2026 Haoting Chai (Author) https://creativecommons.org/licenses/by/4.0
Advances in Deep Learning-Based Face Deblurring Methods
The task of face deblurring is to restore a clear, realistic and identity-preserving high-quality face image from a given blurred face image. This paper systematically expounds the methods of face deblurring based on deep learning from five stages: traditional methods based on physical models, early deep learning methods based on end-to-end regression, methods based on generative adversarial networks (GAN) and prior embedding, methods based on the Transformer architecture, and methods based on diffusion models, following the development of technology. By sorting out the core ideas, key technologies and representative works of each stage, it reveals the clear evolution of face deblurring research from “pixel-level image restoration” to “identity-constrained generative modeling”. In addition, it introduces the commonly used datasets for face deblurring, and makes predictions and prospects for the problems to be solved and future research directions in face deblurring research. Face deblurring is a research hotspot in the field of computer vision, and more high-quality algorithms will be proposed in the future, and it is developing in a more diversified direction.
face deblurring, deep learning, identity preservation, diffusion model
Copyright (c) 2026 Haochen Wang (Author) https://creativecommons.org/licenses/by/4.0
An Integrated Learning-Based Model for Intelligent Classification of Logistics Claims Risk and Prediction of Payout Amounts
In recent years, the rapid development of the express and logistics industry has led to a continuous increase in shipment volume, accompanied by a growing number of claims disputes and payment risks. The intelligent automation of the claims settlement process has become a crucial direction for enterprises to reduce costs and improve efficiency. For Problem 1, this paper proposes a claims risk labeling model based on quantile distribution analysis and monotonicity constraints. The method employs a combined strategy of equal-frequency binning, quantile estimation, PAVA monotonic regression, and PCHIP continuous interpolation to ensure the classification boundaries are smooth and align with business logic. The results show that the proportions for the three classified risk groups are: Reasonable Claims at 85.12%, Elevated Claims at 12.19%, and Severe Overclaims at 2.70%. To address problem 2, this paper proposes a Stacking ensemble learning-based model for predicting the actual claim payment amount in logistics compensation. By performing feature selection, enhanced feature engineering, and target variable transformation on historical waybill data, we employed multiple machine learning algorithms, including CatBoost, MLP neural networks, XGBoost, and LightGBM, and combined them using a Stacking ensemble strategy to optimize prediction performance. For problem 3, this paper proposes a risk labeling classification model based on an MLP model to determine the risk level category of shipping orders. To address the severe class imbalance in the dataset, the SMOTE oversampling method was employed to balance the sample distribution. At the model level, a CatBoost classifier, a neural network (MLP), and a Support Vector Machine (SVM) were trained separately, and their performance was compared via cross-validation. The results indicate that the MLP model performed best, achieving a weighted F1-Score of 0.9098 and a training accuracy of 95.55%, significantly outperforming CatBoost and SVM. This demonstrates the model's strong discriminative capability.
logistics claims risk identification, stacking ensemble learning, MLP model, classification evaluation
Copyright (c) 2026 Jiali Du (Author) https://creativecommons.org/licenses/by/4.0
Research on Multi-Sensor Fusion Technology, Application Scenarios, and Future Prospects Review
To better acquire information and understand actual situations, multi-sensor technology has been widely applied in various industries. It represents a significant advancement over single sensors and is currently a core method for perceiving complex environments. However, issues such as inconvenient information collection, improper data processing, and insufficient environmental adaptability persist. This paper systematically elaborates on the technical background of multi-sensor fusion technology, its core algorithms including Kalman filtering and Bayesian estimation, as well as its applications in robotics, intelligent monitoring, and healthcare. It also discusses existing problems and corresponding solutions, and provides an outlook on the future application of sensors in fields like quantum technology and bionics, as well as potential emerging industries.
multi-sensor fusion, fusion algorithm, robot, intelligent recognition, healthcare
Copyright (c) 2026 Feiyang Zhao (Author) https://creativecommons.org/licenses/by/4.0
Mesh Simplification Method Based on Implicit Geometric Constraints
To address the lack of continuous geometric constraints in traditional quadric error metrics (QEM)-based mesh simplification under high simplification ratios, this paper proposes a 3D mesh simplification method with implicit geometric constraints. First, TetWeave is used to perform implicit modeling on the original high-resolution mesh, from which a continuous implicit reference field is constructed, together with query modules for implicit deviation and implicit normal evaluation at candidate collapse points. Then, within the classical edge-collapse framework, the implicit geometric deviation is introduced as an additional constraint term in a normalized composite cost, while implicit normal consistency is employed as a legality criterion for collapse execution. Finally, experiments on multiple models are conducted to analyze the influence of different weight settings on simplification error and computational cost. The results show that the constructed implicit query module can reliably reflect the geometry of the original continuous surface, and that the normalized implicit constraint has a significant influence on candidate edge ranking and the final Hausdorff distance. With an appropriate weight setting, the proposed method improves the preservation of the original surface geometry, although it also introduces additional computational overhead. The proposed framework provides a feasible way to incorporate continuous implicit geometric supervision into classical edge-collapse simplification.
mesh simplification, quadric error metrics, implicit geometric constraint, edge collapse, Hausdorff distance
Copyright (c) 2026 Yangyang Qiu (Author) https://creativecommons.org/licenses/by/4.0
A Review of Three Types of Neural Networks: Convolutional, Recurrent, and Recursive
Artificial neural networks are a core technology in the field of nonlinear system modeling and intelligentization. Convolutional, Recurrent, and Recurrent Neural Networks, as their three core branches, each have unique structural and performance characteristics, and related research urgently needs systematic review and integration. This article adopts the methods of literature combing and comparative analysis to comprehensively sort out the development vein of the three types of neural networks, analyze their structural characteristics and applicable scenarios, explore the technical pain points and optimization paths of various networks, and look forward to the future development direction. The innovation of this research lies in the research results of the development and application of the three types of neural networks in the system, clearly defining the technical characteristics, differences between advantages and disadvantages and integration application modes of each network, and clarifying their respective optimization directions. The study found that the three types of neural networks have their own advantages and disadvantages, complementary adaptation, and have irreplaceable application value in computer vision, natural language processing and other fields. In the future, its development needs to focus on optimizing its own technical defects, improving the ability of model generalization, further expand the application boundaries through deep integration with other network models and learning mechanisms, and promote the application of intelligent technology in complex scenarios.
convolutional neural networks, recurrent neural networks, deep learning, network structure, feature extraction, temporal data, model fusion
Copyright (c) 2026 Zheng Tang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Meta-Learning-Based Automatic Hyperparameter Optimization Algorithms for Neural Networks
The setting of hyperparameters directly determines the performance of neural networks. Neural network hyperparameters are characterized by a large number, significant mutual influence, and the need for a complete model training process during adjustment, resulting in relatively high parameter-tuning costs. Although traditional methods can achieve hyperparameter tuning, each new task requires starting from scratch and cannot leverage historical experience. Meta-learning can learn “how to tune parameters” from historical tasks, providing a new approach for hyperparameter optimization. This paper systematically reviews the automatic hyperparameter optimization methods based on meta-learning. From the perspective of “how to utilize historical experience”, the existing methods are classified into three categories: configuration recommendation methods based on task similarity, search guidance methods based on meta-knowledge, and end-to-end optimization methods based on learning-based optimizers. The paper elaborates in detail on the core ideas, representative works, and advantages and disadvantages of each category of methods. The research findings indicate that the similarity-based recommendation methods (such as KGLasso) have fast inference speed and strong interpretability, and are suitable for small to medium-scale scenarios with relatively fixed task types; the meta-knowledge-based search methods (such as MetaLLMIX) strike a good balance between effectiveness and efficiency, and can usually obtain better configurations within 5 to 10 evaluations; the learning-based optimizer methods (such as VeLO) enable hyperparameter-free optimization and have obvious advantages in large-scale tasks, but the cost of training the optimizer itself is relatively high. Finally, this paper discusses the current key challenges, including issues such as meta-feature design, high meta-training costs, and task distribution shift, and looks forward to future development directions, such as adaptive meta-feature learning, large-scale meta-pre-training, and online meta-learning.
meta-learning, hyperparameter optimization, neural network, deep learning
Copyright (c) 2026 Zihan Zhao (Author) https://creativecommons.org/licenses/by/4.0
KBT-YOLO: A Lightweight and Efficient Small Object Detector for UAV Aerial Imagery
Object detection in Unmanned Aerial Vehicle (UAV) aerial imagery frequently suffers from missed detections and false alarms due to the extremely small scale of objects, dense distributions, and highly complex backgrounds. To mitigate these challenges, this paper proposes a novel lightweight small object detection method tailored for UAV aerial images. First, a dynamic convolution module is introduced to replace the standard convolutions within the C3k2 module, which simultaneously enhances feature extraction capabilities and effectively reduces computational redundancy. Second, a Bidirectional Feature Pyramid Network (BiFPN) architecture is incorporated to dynamically balance feature interactions via learnable weights, thereby improving local feature capturing and the effectiveness of multi-scale fusion. Finally, a task-aligned dynamic detection head based on a dual-branch structure is designed. By integrating task decomposition and alignment mechanisms with dynamic convolutions, this detection head significantly boosts both detection accuracy and stability. Extensive experiments on the DIOR UAV aerial dataset demonstrate that the proposed method achieves leading detection accuracy while substantially minimizing the parameter count to merely 1.85M. This research offers a promising new technical paradigm for lightweight object detection in UAV applications.
small-object detection, dynamic convolution, bidirectional feature pyramid network, task align dynamic detect head
Copyright (c) 2026 Fanghao Shi , Manli Wang (Author) https://creativecommons.org/licenses/by/4.0
BA-YOLO: A Lightweight Multi-scale Detection Model for Longitudinal Tear of Conveyor Belts
To enhance the multi-scale adaptability and real-time performance of the longitudinal tear detection network for conveyor belts, this paper proposes a lightweight tear detection algorithm based on laser line assistance, which is an improved version of the YOLOv11n model. Firstly, a bidirectional feature pyramid network (BiFPN) is introduced into the neck network to strengthen the network's ability to fuse features of different scales, enabling it to detect tear faults of different sizes on conveyor belts; Secondly, the downsample convolution layer of the main backbone network is replaced by an ADown layer, which reduces the size of the feature map and computational cost through a more efficient feature compression method, thereby effectively reducing the model complexity. Finally, the network is trained on the conveyor belt tear fault dataset. Experimental results show that the precision and recall rate of the BA-YOLO model are 92.4% and 82.8% respectively, and the mean average precision (mAP@0.5) is 90.3%. Compared with the original model, the model parameters have been reduced by 38.9% and the computational cost has been reduced by 14.3%. The improved model not only demonstrates the best detection performance but also shows stronger adaptability.
conveyor belt tearing, multi-scale, yolov11, BiFPN, adown
Copyright (c) 2026 Jianguang Yin, Manli Wang (Author) https://creativecommons.org/licenses/by/4.0
Current Status and Challenges of IoT Device Identity Authentication Technology
The Internet of Things (IoT) has witnessed exponential growth, integrating billions of devices into our daily lives. A critical challenge in realizing the full potential of IoT lies in ensuring the secure and reliable authentication of these devices. This review paper provides a comprehensive overview of the current status and challenges of IoT device identity authentication technology. We begin by outlining the fundamental requirements for IoT device authentication, considering the resource-constrained nature of many IoT devices and the diverse application scenarios. We explore the historical evolution of authentication methods, from traditional password-based schemes to more sophisticated cryptographic protocols, highlighting their strengths and weaknesses in the context of IoT. The core of this review focuses on analyzing prominent authentication technologies, including symmetric-key based approaches, public-key infrastructure (PKI), and lightweight authentication protocols designed specifically for IoT. Furthermore, we delve into emerging authentication techniques based on physical unclonable functions (PUFs) and blockchain technology. We critically compare the performance, security, and scalability of these different approaches, considering factors such as computational overhead, communication costs, and resilience against various attacks. We address key challenges, including the need for interoperability, the management of device identities throughout their lifecycle, and the mitigation of risks associated with compromised devices. Finally, we offer perspectives on future research directions, emphasizing the importance of developing adaptive authentication mechanisms, enhancing privacy preservation, and addressing the evolving threat landscape in the IoT ecosystem. This review aims to provide researchers, developers, and policymakers with a valuable resource for understanding the current state-of-the-art and the open challenges in IoT device identity authentication.
IoT, device authentication, security, identity management, cryptography, PUF, blockchain
Copyright (c) 2026 Yixuan Dou (Author) https://creativecommons.org/licenses/by/4.0
Research Review on Federated Learning Technology for Fault Diagnosis
Federated learning (FL) is a distributed machine learning (ML) method. This technology only needs to exchange model parameters without sharing private data and plays an important role in industrial fault diagnosis. This paper focuses on analysing four mainstream technical schemes: fault diagnosis methods based on traditional federated learning, fault diagnosis methods based on federated deep learning, optimized federated fault diagnosis methods for nonindependent and identically distributed (non-IID) data, and lightweight federated fault diagnosis methods for edge device deployment. This paper systematically sorts out the federated learning technologies for fault diagnosis, summarizes the practical challenges existing in these technical schemes, and proposes corresponding future research prospects, providing specific references for the innovation and implementation of federated learning technologies for fault diagnosis.
federated learning, fault diagnosis, non-IID, privacy protection, lightweight model
Copyright (c) 2026 Xuebin Han (Author) https://creativecommons.org/licenses/by/4.0
Research on a Weakly Supervised Algorithm Based on Few-Shot Learning
To address the issues of weakly supervised learning (WSL) such as limited feature diversity, poor adaptability to new scenarios, and the incomplete intra-class feature coverage and low accuracy in complex scenarios inherent to few-shot learning (FSL), this paper proposes a weakly supervised algorithm based on few-shot learning. The algorithm first constructs a rule-based system through five core modules to generate approximately accurate pseudo-labels for large-scale unlabeled data. Subsequently, an intra-class feature expansion optimization strategy is designed to mine effective features using methods such as weighted distance metrics, optimizing the feature space and reducing the risk of model overfitting. By fully exploiting the effective information in data and optimizing the structure of the feature space, the algorithm compensates for the lack of intra-class feature diversity in FSL, effectively alleviates overfitting, and improves classification accuracy and generalization in complex scenarios. Experimental results demonstrate that, compared with existing literature, the proposed few-shot feature expansion algorithm under weak supervision achieves at least a 1.18% improvement in accuracy, 2.30% in precision, 1.19% in recall, and 1.76% in F1 score. For strongly supervised algorithms, the improvements are at least 4.55%, 4.49%, 4.60%, and 4.55%, respectively. The proposed approach provides a novel solution for model training under data scarcity conditions.
few-shot learning, weakly supervised learning, feature expansion, joint training
Copyright (c) 2026 Yufan Wu (Author) https://creativecommons.org/licenses/by/4.0
Thermal Runaway Mechanism and Prevention Methods of Lithium-ion Batteries
With the development of lithium-ion batteries, it has been found that lithium-ion batteries exhibit advantages such as high energy density and long cycle life. Thus, people began to apply lithium-ion batteries to the invention of electric vehicles. However, in recent years, there have been numerous cases where lithium-ion batteries experience thermal runaway and lead to explosions. Aiming at the thermal runaway problem of lithium-ion batteries, this paper explains the mechanism of thermal runaway in lithium-ion batteries. Subsequently, it proposes corresponding prevention and control strategies: the method involves coating the separator with a ceramic layer to protect it, or using a highly heat-resistant polymer substrate to prepare a composite separator as a replacement for the original separator; optimizing the electrolyte by substituting solid electrolytes, flame-retardant electrolytes, etc. for the original liquid electrolyte, or adopting high-concentration electrolytes or local high-concentration designs to inhibit side reactions; and improving the positive and negative electrodes by using alternative electrode materials, or coating the surface of the positive electrode, or optimizing the electrolyte formula for the negative electrode. Finally, the challenges and prospects for the development of high-safety lithium-ion batteries are presented.
Lithium-ion battery, thermal runaway, electrolyte, electrode, separator
Copyright (c) 2026 Xinjun Yu (Author) https://creativecommons.org/licenses/by/4.0
Responsibility Definition and Risk Management in the Clinical Application of Medical Artificial Intelligence:A Review Based on Four-Level Classification
The rapid penetration of medical artificial intelligence (MAI) into clinical diagnosis and treatment scenarios has reshaped the traditional medical service model. However, issues such as ambiguous responsibility definition and lagging risk management have severely constrained its safe and compliant development. This article uses the four-level AI classification system (tool-type, advisor-type, collaborative-type, autonomous-type) as the analytical framework to systematically review the responsibility definition logic and full-lifecycle risk management mechanisms for MAI clinical applications. The study finds that a multi-stakeholder responsibility system covering manufacturers, medical institutions, doctors, and regulatory authorities has been formed, along with a “prevention-control-remediation” risk management closed loop. However, deficiencies still exist in empirical validation, standard unification, adaptation to special scenarios, and coordination between technology and institutions. Future research should focus on directions such as dynamic responsibility quantification and matching, practical design of insurance pools, and development of lightweight management tools for primary care, providing theoretical support and practical references for the systematic governance of MAI clinical applications.
medical artificial intelligence, responsibility definition, risk management, four-level classification system, dynamic responsibility matrix, full-lifecycle governance
Copyright (c) 2026 Shihan Yin (Author) https://creativecommons.org/licenses/by/4.0
Construction of a Dense Mapping System for Stereo Vision SLAM Based on Point-Line Fusion
To address issues such as insufficient feature extraction, inaccurate localization, and sparse point clouds encountered by visual SLAM systems in low-texture, dynamic environments, this paper proposes improvements to PLCD-SLAM. It introduces dual hardware-software constraints for depth estimation and presents a novel dense mapping architecture for stereo visual SLAM based on point-line fusion. Leveraging the robust Superpoint feature extractor and the strong structural capabilities of end-to-end wireframe parsing, the system compensates for feature information loss and enhances pose estimation accuracy. During depth computation, stereo matching combined with dual soft-hard constraints optimizes depth value precision, enabling high-accuracy dense mapping. To validate the improved algorithm's performance, comparative experiments were conducted on the KITTI, EUROC, and TUM (converted to stereo) public datasets. Results demonstrate that the proposed method achieves significantly lower absolute trajectory error (RMSE) than mainstream SLAM algorithms. It substantially enhances the system's robustness and positioning accuracy in dynamic and complex environments while producing high-quality dense maps.
visual SLAM, point-line fusion, wireframe parsing, desnse mapping
Copyright (c) 2026 Lanqing Zhang, Lili Yuan (Author) https://creativecommons.org/licenses/by/4.0
Visual SLAM Algorithm Optimized by Dynamic Feature Elimination and Keyframe Selection
Traditional visual SLAM algorithms struggle to maintain stability in dynamic scenes, often resulting in tracking failures and low positioning accuracy. This paper proposes the YDK-SLAM algorithm, which is suitable for dynamic environments. Building upon ORB-SLAM3, a dynamic feature detection thread has been added. It employs the YOLOv8 object detection network to identify potential moving objects, precisely removing dynamic feature points through depth information and multi-view geometry methods, and utilizing the remaining static feature points for pose estimation. Simultaneously, by employing optical flow methods and adaptive keyframe insertion strategies in the tracking thread based on camera motion characteristics, thereby enhancing the operational speed of YDK-SLAM. Experimental results on the public TUM dataset demonstrate that compared to ORB-SLAM3, YDK-SLAM achieves an average reduction of 70.73% in absolute trajectory error. In high-dynamic scenes, the error is reduced by an average of 93.73%, effectively enhancing the system's positioning accuracy and robustness.
visual slam, dynamic feature point removal, keyframe optimization, dynamic environment
Copyright (c) 2026 Xue Zhao, Na Gao, Qingqing Zhang, Yao Zhao, Lili Yuan, Jianye Wang (Author) https://creativecommons.org/licenses/by/4.0
Design and Evaluation of an Intelligent Agent-Based Home Healthcare System for Clinical Decision Support
The rapid development of large language models (LLMs) has stimulated growing interest in medical intelligent agents for clinical decision support. However, existing systems often suffer from limited grounding in authoritative medical knowledge, potential safety risks, and a tendency to generate definitive diagnostic conclusions without sufficient clinical context. In this work, we present the design of a medical intelligent agent aimed at supporting clinical decision-making through evidence-grounded information retrieval and safety-aware interaction. The proposed system focuses on two primary functions: (i) providing drug usage guidance, dosage information, and food–drug interaction warnings based on authoritative medical knowledge sources, and (ii) retrieving relevant clinical guidelines in response to patient-reported symptoms to assist clinicians with differential diagnostic considerations rather than definitive diagnoses. To mitigate safety risks, the agent is explicitly constrained to avoid diagnostic claims and instead emphasizes guideline-based recommendations and referral suggestions when appropriate. The agent integrates structured medical knowledge retrieval with natural language interaction, enabling users to obtain context-aware, interpretable and clinically relevant responses. By grounding outputs in curated medical references and enforcing non-diagnostic constraints, the system aims to reduce hallucinations and enhance reliability in medical consultations. This work highlights the potential of retrieval-augmented medical intelligent agents as supportive tools for clinical decision support, medical education, and patient-facing health information services, while underscoring the importance of safety, transparency, and scope limitation in medical AI deployment.
medical intelligent agent, clinical decision support, retrieval-augmented generation, medication counseling, clinical guideline retrieval, differential diagnosis, medical AI safety
Copyright (c) 2026 Zhanyan Zhu, Yifan Peng, Leyi Xu, Weitong Hu, Jiale Feng, Yuqing Liu, Ke Zhou, Yang Zhang (Author) https://creativecommons.org/licenses/by/4.0
Ethical Issues of Data Rights in the Big Data Era
The ethical issues of data rights have presented unprecedented complexity in the era of big data, and traditional rights frameworks are difficult to effectively respond to the inherent conflict between data liquidity and ownership certainty. The essence of data rights is not simply a matter of legal recognition, but rather involves the dynamic balance between individual dignity, social equity, and technological power. The informed consent during the data collection stage often becomes a formality, and the data processing process leads to a gradual dilution of ownership. The distribution of benefits in the data trading process is clearly tilted towards the platform, and these ethical dilemmas collectively point to the failure of the data subject rights protection mechanism. Starting from the perspective of the data lifecycle, this study analyzes the generation logic of ethical conflicts in each stage, proposes a governance path for constructing a full cycle ethical review mechanism and platform responsibility list, in order to promote the transformation of data rights from formal rights confirmation to substantive protection.
big data era, data rights, ethical issues, data lifecycle
Copyright (c) 2026 Kuangzhi Qin (Author) https://creativecommons.org/licenses/by/4.0
How Artificial Intelligence Helps SMEs Make Better Decisions under Sudden Global Shocks: A Resilience Path from Sensing to Simulation, Optimization, and Continuous Correction
This essay examines how artificial intelligence (AI) can improve the decision quality and resilience of small and medium-sized enterprises (SMEs) during sudden global shocks, including tariff changes, sanctions, logistics disruptions, exchange-rate volatility, and compliance rule shifts. It argues that the primary value of AI for SMEs is not perfect prediction, but the creation of a resilience-oriented decision system that helps firms respond faster and more systematically under uncertainty. The paper proposes a four-stage decision chain—risk sensing, scenario simulation, constrained optimization, and continuous monitoring/correction—to explain how AI can support SMEs in detecting credible signals, comparing alternative actions, allocating limited resources under real constraints, and adapting decisions as conditions change. The analysis further applies this framework to three high-impact SME contexts: supply-chain disruption, pricing and foreign-exchange shocks, and cross-border compliance volatility. In addition, the essay outlines a practical implementation roadmap for SMEs that emphasizes incremental adoption, minimal data governance, workflow integration, and trigger-based decision rules. Finally, it discusses major risks and limitations, including poor data quality, structural breaks, generative AI hallucinations, privacy and cross-border data governance concerns, and weak organizational accountability. The essay concludes that AI can materially strengthen SME resilience when deployed as a governed, human-supervised decision chain rather than a one-time predictive tool.
artificial intelligence (AI), SMEs, global shocks, business resilience, decision-making
Copyright (c) 2026 Yuchang Lin (Author) https://creativecommons.org/licenses/by/4.0
How Artificial Intelligence Can Empower the Robotic Recycling Industry: Toward an Adaptive, Data-Driven, and Circular Material Recovery System
This paper examines how artificial intelligence (AI) can strengthen the robotic recycling industry by improving material identification, adaptive sorting, process optimization, and data-driven decision-making across recycling systems. As waste streams become more heterogeneous and contaminated, conventional recycling methods—particularly manual sorting and rigid automation—face growing limitations in throughput, consistency, and economic viability. Drawing on recent institutional reports and academic research, this paper argues that AI-enabled robotics offers a socio-technical pathway for transforming recycling into a more adaptive and resilient material recovery system. Specifically, AI improves robotic recycling through enhanced perception and classification, real-time response to variable feedstocks, higher-speed sorting with quality control, and cumulative operational learning. The paper also analyzes barriers to large-scale adoption, including capital intensity, infrastructure readiness, model reliability under domain shift, workforce transition challenges, and governance requirements. It further argues that the long-term effectiveness of AI-powered robotic recycling depends on integration with policy incentives, standards, and circular economy objectives rather than technology deployment alone. Overall, the study concludes that AI can serve as a powerful enabler of robotic recycling when implemented as part of a broader socio-technical system that combines technical innovation, institutional support, and responsible governance.
artificial intelligence, robotic recycling, waste sorting, circular economy, material recovery
Copyright (c) 2026 Chengyao Cai (Author) https://creativecommons.org/licenses/by/4.0
HAGAN: A Lightweight Compressed Sensing Framework for Motor Bearing Fault Diagnosis
Motor bearings are the most failure-prone components in industrial motors, and the accuracy of their fault diagnosis is highly dependent on the effective acquisition and analysis of vibration signals. However, traditional Compressed Sensing (CS) methods face an inherent trade-off between compression ratio and reconstruction accuracy, while deep learning-enhanced CS models generally suffer from complex architectures and insufficient real-time performance. These drawbacks severely restrict their practical application in industrial fault diagnosis scenarios. To address the aforementioned challenges, this study proposes a lightweight compressed sensing framework tailored for motor bearing fault diagnosis-Hybrid Autoencoder Generative Adversarial Network (HAGAN). This framework integrates the core advantages of Autoencoders (AE) and Generative Adversarial Networks (GAN). The AE is responsible for extracting key fault features and compressing high-dimensional vibration signals, while the GAN improves the fidelity of reconstructed signals through an adversarial training mechanism. Meanwhile, a streamlined network structure design is adopted to remove redundant nonlinear modules, thereby reducing computational overhead and ensuring real-time deployment capabilities in industrial settings. In the experiments, this method achieves an ultra-high data compression ratio of 100:1 for motor bearing fault data. Three metrics-Root Mean Square Error (RMSE), Percentage Root Mean Square Deviation (PRD), and Signal-to-Noise Ratio (SNR)-are employed to comprehensively verify the compression and reconstruction performance. The results demonstrate that the HAGAN framework can effectively preserve the characteristic information of bearings in both healthy states and various fault states (including inner race faults, outer race faults, rolling element faults, and combination faults) even under high compression ratios. Its compression and reconstruction performance, quantified and validated by the three metrics, is excellent. The framework can be successfully applied to data compression and reconstruction tasks in motor bearing fault diagnosis, providing an efficient and reliable fault diagnosis solution for resource-constrained industrial environments.
motor bearing fault diagnosis, compressed sensing, HAGAN, autoencoder, generative adversarial network (GAN), signal compression and reconstruction
Copyright (c) 2026 Weibing Tang, Hang Liu (Author) https://creativecommons.org/licenses/by/4.0