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学者姓名：于乃功
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Abstract ：
The firing of spatial cells in the entorhinal-hippocampal structure is believed to enable the formation of a cognitive map for the environment. Inspired by the spatial cognitive mechanism of the rat's brain, the authors proposed a real-time cognitive map construction method based on the entorhinal-hippocampal working mechanism. Firstly, based on the physiological properties of the rat's brain, the authors constructed an entorhinal-hippocampal CA3 neurocomputational model for path integration. Then, the transformation relationship between the cell plate and the real environment is used to solve the robot's position. Path integration inevitably generates cumulative errors, which require loop-closure detection and pose optimisation to eliminate errors. To solve the problem that the RatSLAM algorithm is slow in pose optimisation, the authors proposed a pose optimisation method based on a multi-layer CA1 place cell to improve the speed of pose optimisation. To validate the method, the authors designed simulation experiments, dataset experiments, and physical experiments. The experimental results showed that compared to other brain-like SLAM algorithms, the authors' method possesses outstanding performance in path integration accuracy and map construction speed. As a result, the authors' method can endow mobile robots with the ability to quickly and accurately construct cognitive maps in complex and unknown environments.
Keyword ：
intelligent robots intelligent robots artificial intelligence artificial intelligence cognitive systems cognitive systems
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| GB/T 7714 | Liao, Yishen , Yu, Naigong . A real-time cognitive map construction method based on the entorhinal-hippocampal working mechanism of the rat's brain [J]. | COGNITIVE COMPUTATION AND SYSTEMS , 2024 , 6 (4) : 49-64 . |
| MLA | Liao, Yishen 等. "A real-time cognitive map construction method based on the entorhinal-hippocampal working mechanism of the rat's brain" . | COGNITIVE COMPUTATION AND SYSTEMS 6 . 4 (2024) : 49-64 . |
| APA | Liao, Yishen , Yu, Naigong . A real-time cognitive map construction method based on the entorhinal-hippocampal working mechanism of the rat's brain . | COGNITIVE COMPUTATION AND SYSTEMS , 2024 , 6 (4) , 49-64 . |
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Abstract ：
This paper analyzes the operating environment and actual use requirements of the photovoltaic glass curtain wall cavity in high-rise buildings, designs a cross-frame motion mechanism that combines the obstacle-crossing lifting and adsorption modules into an obstacle-crossing adsorption device and integrates the motion mechanism of the robot into one. In terms of the design of the robot's control system, all working conditions of the robot are defined according to the motion state of the robot in the cleaning test of photovoltaic curtain wall cavities and combined with indicators such as coverage and cleaning speed of the photovoltaic cleaning test of photovoltaic curtain wall cavities, a set of automatic control system suitable for cross-frame wall-climbing robots. In the experimental section, we tested and analyzed the robot's motion performance, obstacle-crossing ability, and control system, proving the rationality of the robot structure and control system.
Keyword ：
Cross frame structure Cross frame structure Photovoltaic curtain wall Photovoltaic curtain wall Climbing robot Climbing robot
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| GB/T 7714 | Yu, Naigong , Tian, Zheng . Design and Analysis of a Cross-Frame Wall-Climbing Robot [J]. | 2024 43RD CHINESE CONTROL CONFERENCE, CCC 2024 , 2024 : 4555-4560 . |
| MLA | Yu, Naigong 等. "Design and Analysis of a Cross-Frame Wall-Climbing Robot" . | 2024 43RD CHINESE CONTROL CONFERENCE, CCC 2024 (2024) : 4555-4560 . |
| APA | Yu, Naigong , Tian, Zheng . Design and Analysis of a Cross-Frame Wall-Climbing Robot . | 2024 43RD CHINESE CONTROL CONFERENCE, CCC 2024 , 2024 , 4555-4560 . |
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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 等. "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 等. "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 等. "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 ：
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 , 9 (2) : 348-364 . |
| MLA | Yu, Naigong et al. "3D reconstruction and defect pattern recognition of bonding wire based on stereo vision" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 9 . 2 (2023) : 348-364 . |
| 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 , 9 (2) , 348-364 . |
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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 ：
Wafer map inspection is essential for semiconductor manufacturing quality control and analysis. The deep convolutional neural network (DCNN) is the most effective algorithm in wafer defect pattern analysis. Traditional DCNNs rely heavily on high quality datasets for training. However, obtaining balanced and sufficient labeled data is difficult in practice. This paper reconsiders the causes of the imbalance and proposes a deep learning method that can learn robust knowledge from an imbalanced dataset using the attention mechanism and cosine normalization. We interpret the dataset imbalance as both a feature and a quantity distribution imbalance. To compensate for feature distribution imbalance, we add an improved convolutional attention module to the DCNN to enhance representation. In particular, a feature-map-specific direction mapping module is developed to amplify the positional information of defect clusters. For quantity distribution imbalance, the cosine normalization algorithm is proposed to replace the fully connected layer, and classifier fine-tuning is realized through a small amount of iterative training, which decreases the sensitivity to the quantitative distribution. The experimental results on real-world datasets demonstrate that the proposed method significantly improves the robustness of wafer map inspection and outperforms existing algorithms when trained on imbalanced datasets.
Keyword ：
wafer map classification wafer map classification attention mechanism attention mechanism convolutional neural network convolutional neural network imbalanced dataset imbalanced dataset cosine normalization cosine normalization
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| GB/T 7714 | Xu, Qiao , Yu, Naigong , Essaf, Firdaous . Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization [J]. | MACHINES , 2022 , 10 (2) . |
| MLA | Xu, Qiao et al. "Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization" . | MACHINES 10 . 2 (2022) . |
| APA | Xu, Qiao , Yu, Naigong , Essaf, Firdaous . Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization . | MACHINES , 2022 , 10 (2) . |
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Abstract ：
The entorhinal-hippocampus structure in the mammalian brain is the core area for realizing spatial cognition. However, the visual perception and loop detection methods in the current biomimetic robot navigation model still rely on traditional visual SLAM schemes and lack the process of exploring and applying biological visual methods. Based on this, we propose a map construction method that mimics the entorhinal-hippocampal cognitive mechanism of the rat brain according to the response of entorhinal cortex neurons to eye saccades in recent related studies. That is, when mammals are free to watch the scene, the entorhinal cortex neurons will encode the saccade position of the eyeball to realize the episodic memory function. The characteristics of this model are as follows: 1) A scene memory algorithm that relies on visual saccade vectors is constructed to imitate the biological brain's memory of environmental situation information matches the current scene information with the memory; 2) According to the information transmission mechanism formed by the hippocampus and the activation theory of spatial cells, a localization model based on the grid cells of the entorhinal cortex and the place cells of the hippocampus was constructed; 3) Finally, the scene memory algorithm is used to correct the errors of the positioning model and complete the process of constructing the cognitive map. The model was subjected to simulation experiments on publicly available datasets and physical experiments using a mobile robot platform to verify the environmental adaptability and robustness of the algorithm. The algorithm will provide a basis for further research into bionic robot navigation.
Keyword ：
Entorhinal-Hippocampus Entorhinal-Hippocampus cognitive map cognitive map episodic memory episodic memory visual SLAM visual SLAM spatial cell spatial cell
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| GB/T 7714 | Yu, Naigong , Yu, Hejie . A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus [J]. | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES , 2022 , 131 (2) : 1147-1169 . |
| MLA | Yu, Naigong et al. "A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus" . | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 131 . 2 (2022) : 1147-1169 . |
| APA | Yu, Naigong , Yu, Hejie . A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus . | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES , 2022 , 131 (2) , 1147-1169 . |
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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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