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学者姓名：于乃功
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Abstract ：
Non-destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image-processing-based methods do not provide a detailed evaluation of the three-dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non-destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection.
Keyword ：
point cloud point cloud defect detection defect detection bonding wire bonding wire point cloud segmentation point cloud segmentation
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| GB/T 7714 | Yu, Naigong , Li, Hongzheng , Xu, Qiao et al. 3D reconstruction and defect pattern recognition of bonding wire based on stereo vision [J]. | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2023 . |
| MLA | Yu, Naigong et al. "3D reconstruction and defect pattern recognition of bonding wire based on stereo vision" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2023) . |
| APA | Yu, Naigong , Li, Hongzheng , Xu, Qiao , Sie, Ouattara , Firdaous, Essaf . 3D reconstruction and defect pattern recognition of bonding wire based on stereo vision . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2023 . |
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Abstract ：
Self-positioning and goal-oriented navigation behaviors in complex and variable environments are important capabilities that mammals rely on for survival in the natural world. To this end, this article proposes a robot navigation model with bioinspired environmental cognition and autonomous localization capabilities based on the spatial cognitive mechanism of the hippocampus in the rat brain. The model constructs a cognitive map by integrating its own motion cues and visual observation features, and uses the dynamic predictive relationship between each location cognitive node to achieve a goaloriented navigation process. The research approach in this article will provide inspirational implications for a robot navigation approach with brain-like cognitive mechanisms.
Keyword ：
Cognitive map Cognitive map Navigation Navigation Hippocampus Hippocampus
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| GB/T 7714 | Yu, Hejie , Yu, Naigong . A bio-inspired robot navigation model based on the environmental cognitive mechanism of the rat brain hippocampus [J]. | JOURNAL OF ENGINEERING RESEARCH , 2023 , 11 (2) . |
| MLA | Yu, Hejie et al. "A bio-inspired robot navigation model based on the environmental cognitive mechanism of the rat brain hippocampus" . | JOURNAL OF ENGINEERING RESEARCH 11 . 2 (2023) . |
| APA | Yu, Hejie , Yu, Naigong . A bio-inspired robot navigation model based on the environmental cognitive mechanism of the rat brain hippocampus . | JOURNAL OF ENGINEERING RESEARCH , 2023 , 11 (2) . |
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Abstract ：
Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.
Keyword ：
mobile robots mobile robots navigation path navigation path optimization optimization boundary vector cells boundary vector cells place cells place cells
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| GB/T 7714 | Liao, Yishen , Yu, Naigong , Yan, Jinhan . A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism [J]. | BIOMIMETICS , 2023 , 8 (5) . |
| MLA | Liao, Yishen et al. "A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism" . | BIOMIMETICS 8 . 5 (2023) . |
| APA | Liao, Yishen , Yu, Naigong , Yan, Jinhan . A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism . | BIOMIMETICS , 2023 , 8 (5) . |
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Abstract ：
The neuron model serves as the foundation for building a neural network. The goal of neuron modeling is to shoot a tradeoff between the biological meaningful and the implementation cost, so as to build a bridge between brain science knowledge and the brain-like neuromorphic computing. Unlike previous neuron models with linear static synapses, the focus of this research is to model neurons with relatively detailed nonlinear dynamic synapses. First, a universal soma-synapses neuron (SSN) is proposed. It contains a soma represented by a leaky integrate-and-fire neuron and multiple excitatory and inhibitory synapses based on ion channels dynamics. Short-term plasticity and spike-timing-dependent plasticity linked to biological microscopic mechanisms are also presented in the synaptic models. Then, SSN is implemented on field-programmable gate array (FPGA). The performance of each component in SSN is analyzed and evaluated. Finally, a neural network SSNN composed of SSNs is deployed on FPGA and used for testing. Experimental results show that the stimulus-response characteristics of SSN are consistent with the electrophysiological test findings of biological neurons, and the activities of SSNN exhibit a promising prospect. We provide a prototype for embedded neuromorphic computing with a small number of relatively detailed neuron models.
Keyword ：
FPGA FPGA ion channel ion channel spike-timing-dependent plasticity spike-timing-dependent plasticity LIF LIF short-term plasticity short-term plasticity neuromorphic computing neuromorphic computing
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| GB/T 7714 | Wang, Zongxia , Yu, Naigong , Essaf, Firdaous . A soma-synapses neuron model and FPGA implementation [J]. | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2023 , 35 (27) . |
| MLA | Wang, Zongxia et al. "A soma-synapses neuron model and FPGA implementation" . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 35 . 27 (2023) . |
| APA | Wang, Zongxia , Yu, Naigong , Essaf, Firdaous . A soma-synapses neuron model and FPGA implementation . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2023 , 35 (27) . |
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Abstract ：
The semiconductor manufacturing industry relies heavily on wafer surface defect detection for yield enhancement. Machine learning and digital image processing technologies have been used in the development of various detection algorithms. However, most wafer surface inspection algorithms are not be applied in industrial environments due to the difficulty in obtaining training samples, high computational requirements, and poor generalization. In order to overcome these difficulties, this paper introduces a full-flow inspection method based on machine vision to detect wafer surface defects. Starting with the die image segmentation stage, where a die segmentation algorithm based on candidate frame fitting and coordinate interpolation is proposed for die sample missing matching segmentation. The method can segment all the dies in the wafer, avoiding the problem of missing dies splitting. After that, in the defect detection stage, we propose a die defect anomaly detection method based on defect feature clustering by region, which can reduce the impact of noise in other regions when extracting defect features in a single region. The experiments show that the proposed inspection method can precisely position and segment die images, and find defective dies with an accuracy of more than 97%. The defect detection method proposed in this paper can be applied to inspect wafer manufacturing.
Keyword ：
image segmentation image segmentation feature extraction feature extraction wafer surface defect detection wafer surface defect detection machine vision machine vision machine learning machine learning
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| GB/T 7714 | Yu, Naigong , Li, Hongzheng , Xu, Qiao . A full-flow inspection method based on machine vision to detect wafer surface defects [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2023 , 20 (7) : 11821-11846 . |
| MLA | Yu, Naigong et al. "A full-flow inspection method based on machine vision to detect wafer surface defects" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 20 . 7 (2023) : 11821-11846 . |
| APA | Yu, Naigong , Li, Hongzheng , Xu, Qiao . A full-flow inspection method based on machine vision to detect wafer surface defects . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2023 , 20 (7) , 11821-11846 . |
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Abstract ：
In view of the fact that Dempster-Shafer (D-S) evidence theory is unable to fuse data of multiple different kinds of sensors, an improved D-S evidence theory method based on the fusion of support and confidence entropy is proposed. Firstly, the identification framework of evidence theory is improved; secondly, Spearman correlation coefficient is introduced to represent the correlation between evidences; thirdly, a new confidence entropy is defined to describe the inconsistent uncertainty and nonspecific uncertainty between evidences; then, the evidence set is modified by the combination of correlation and confidence entropy; finally, Dempster combination rule is used for information fusion. The simulation results confirm that the improved method of D-S evidence theory is feasible and more effective than the traditional algorithm.
Keyword ：
confidence entropy confidence entropy evidence theory evidence theory information fusion information fusion support support
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| GB/T 7714 | Yu, Naigong , Yang, Kang , Gan, Mengzhe . Research on The Improved Method of D-S Evidence Theory Based on The Fusion of Support and Confidence Entropy [J]. | 2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) , 2022 : 1610-1615 . |
| MLA | Yu, Naigong et al. "Research on The Improved Method of D-S Evidence Theory Based on The Fusion of Support and Confidence Entropy" . | 2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) (2022) : 1610-1615 . |
| APA | Yu, Naigong , Yang, Kang , Gan, Mengzhe . Research on The Improved Method of D-S Evidence Theory Based on The Fusion of Support and Confidence Entropy . | 2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) , 2022 , 1610-1615 . |
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Abstract ：
One key challenge in object pose estimation is learn small object features under poor quality conditions while preventing false positives for object boundaries. The current study rarely focuses on small objects, and emphasizes specificity while ignores sensitivity. In this paper, we propose a multi-stage learning framework-SO-PERM to address these problems, which includes multiple modules like sub-pixel detection, 3D view reconstruction and multi-scale unsupervised network. Instead of using single object features, we choose to combine the noise distribution and boundary artifacts around the interference region, then refine the sub-pixels to clearly extract the target boundary. The 3D view reconstruction allows us to correlate the constructed 2D features with the real scene. The further obtained 3D heat map of simulated objects is not negligible for the pose estimation of tiny targets. In addition, considering the feature sensitivity of small targets, we propose a multi-scale unsupervised network and an iterative refinement scheme to improve performance, which combines global and local methods to quickly locate objects and extract specific features respectively. Extensive experiments on four benchmark sets not only demonstrate the robustness of SO-PERM, but also the feasibility of low-dimensional features to extend the idea of high-dimensional features.
Keyword ：
robust measurement robust measurement sub-pixel sub-pixel small objects small objects pose estimation pose estimation 3D view reconstruction 3D view reconstruction multi-scale unsupervised learning multi-scale unsupervised learning
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| GB/T 7714 | Wang, Jingyao , Yu, Naigong . SO-PERM : Pose Estimation and Robust Measurement for Small Objects [J]. | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
| MLA | Wang, Jingyao et al. "SO-PERM : Pose Estimation and Robust Measurement for Small Objects" . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2022) . |
| APA | Wang, Jingyao , Yu, Naigong . SO-PERM : Pose Estimation and Robust Measurement for Small Objects . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
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Abstract ：
Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines. During the wafer testing stage, deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capabilities. However, most of the current wafer defect patterns classification models have high complexity and slow detection speed, which are difficult to apply in the actual wafer production process. In addition, there is a data imbalance in the wafer dataset that seriously affects the training results of the model. To reduce the complexity of the deep model without affecting the wafer feature expression, this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM-PeleeNet based on the PeleeNet module. In addition, to reduce the impact of data imbalance on model training, this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer. The method proposed in this paper has an average accuracy of 95.4% on the WM-811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs, and takes only 22.99 s to detect 1000 wafer pictures. Compared with the original PeleeNet network without optimization, the number of parameters and FLOPs are reduced by 92.68% and 58.85%, respectively. Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8% on the WM-811K dataset. At the same time, the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.
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| GB/T 7714 | Yu, Naigong , Chen, Huaisheng , Xu, Qiao et al. Wafer map defect patterns classification based on a lightweight network and data augmentation [J]. | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2022 , 8 (3) : 1029-1042 . |
| MLA | Yu, Naigong et al. "Wafer map defect patterns classification based on a lightweight network and data augmentation" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 8 . 3 (2022) : 1029-1042 . |
| APA | Yu, Naigong , Chen, Huaisheng , Xu, Qiao , Hasan, Mohammad Mehedi , Sie, Ouattara . Wafer map defect patterns classification based on a lightweight network and data augmentation . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2022 , 8 (3) , 1029-1042 . |
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Abstract ：
Physiological studies have shown that rats in a dark environment rely on the limbs and vestibule for their self-motion information, which can maintain the specific firing patterns of grid cells and hippocampal CA3 place cells. In the development stage of rats, grid cells are considered to come from place cells, and place cells can be encoded by hippocampal theta cells. Based on these, the quadruped robot is used as a platform in this paper. Firstly, the sensing information of the robot's limbs and inertial measurement unit is obtained to solve its position in the environment. Then the position information is encoded by theta cells and mapped to place cells through a neural network. After obtaining the place cells with single-peak firing fields, Hebb learning is used to adjust the connection weight of the neural network between place cells and grid cells. In order to verify the model, 3-D simulation experiments are designed in this paper. The experiment results show that with the robot exploring in space, the spatial cells firing effects obtained by the model are consistent with the physiological research facts, which lay the foundation for the bionic environmental cognition model.
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| GB/T 7714 | Yu, Naigong , Liao, Yishen , Yu, Hejie et al. Construction of the rat brain spatial cell firing model on a quadruped robot [J]. | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2022 , 7 (4) : 732-743 . |
| MLA | Yu, Naigong et al. "Construction of the rat brain spatial cell firing model on a quadruped robot" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 7 . 4 (2022) : 732-743 . |
| APA | Yu, Naigong , Liao, Yishen , Yu, Hejie , Sie, Ouattara . Construction of the rat brain spatial cell firing model on a quadruped robot . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2022 , 7 (4) , 732-743 . |
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Abstract ：
As the basis of robot kinematics, path planning occupies an important position in artificial intelligence and other fields. However, not many researches have focus on the importance of generating waypoints in path planning. In order to accurately find the most efficient moving path, we proposes a path planning method based on Naive Bayes Classifier and CNN to improve A* algorithm, which is a search-based algorithm. It first constructs a cost map of the space where the target object is located, and obtains the starting point, ending point with path contour. Next, we obtain the contours of obstacles to calculate the size, use Naive Bayes to realize the mapping, and update the cost map faster instead of the classifers with network structure that need supervised training. After that, we use CNN to find key waypoints, eliminate redundant waypoints and calculate the robot gait. Finally the path planning of the robot is realized. Experiments were carried out in the simulation environment Gazebo and the real space respectively, and the results showed the effectiveness and feasibility of the method.
Keyword ：
Path Planning Path Planning Machine ion Machine ion CNN CNN ROS ROS Naive Bayes Classifier Naive Bayes Classifier
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| GB/T 7714 | Wang Jingyao , Yu Naigong . Universal Path Planning Based on Naive Bayes Classifier and Improved A* Algorithm using CNN [J]. | 2022 41ST CHINESE CONTROL CONFERENCE (CCC) , 2022 : 7030-7035 . |
| MLA | Wang Jingyao et al. "Universal Path Planning Based on Naive Bayes Classifier and Improved A* Algorithm using CNN" . | 2022 41ST CHINESE CONTROL CONFERENCE (CCC) (2022) : 7030-7035 . |
| APA | Wang Jingyao , Yu Naigong . Universal Path Planning Based on Naive Bayes Classifier and Improved A* Algorithm using CNN . | 2022 41ST CHINESE CONTROL CONFERENCE (CCC) , 2022 , 7030-7035 . |
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