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| 1 | Ranking Birdstrike risk : A case study at Huanghua International Airpor, Changsha, China 显示文摘 | Yang Daode Zhang Zhiqiang Hu Maowang | 2010 | Acta Ecologica Sinica2010，30，： | 1 |
| 2 | Preparation, characterization, and in vitro release investigation of lutein/zein nanoparticles via solution enhanced dispersion by supercritical fluids显示文摘 | Daode Hu Changchun Lin etc | 2012 | Journal of Food Engineering2012，，109： | 1 |
| 3 | Ranking Birdstrike risk:A case study at Huanghua International Airpor, Changsha, China 显示文摘 | Daode Yang Zhiqiang Zhang Maowang Hu | 2010 | Acta Ecologica Siniea2010，，30： | 1 |
| 4 | Preparation, characterization, and in vitro release investigation of lutein/zein nanoparticles via solution enhanced dispersion by supercritical fluids显示文摘 | Daode Hu Changchun Lin Liang Liu Sining Li Yaping Zhao | 2011 | Journal of Food Engineering2011，，3： | 1 |
| 5 | Research and Measures on the Security of Internet of Things显示文摘With the development of intelligent perception,recognition technology and pervasive computing,Internet of things( IoT) is widely used,the security problem is also concerned by more and more researchers. IoT is a double-edged sword. On the one hand,it has great potential in simplifying the business process and provides an effective way for the enterprise to interact with the customers. But on the other hand,it also provides convenience for cyber crimes and hackers.First of all,Three layers logic architecture of IoT is introduced,and the security problems at each level and the key points of the research are expounded,and then the security requirements are analyzed. The main causes of security problems are summarized and analyzed: physical attack and the threat of equipment and malware,file attack and hacker attack. Finally,through the application of the existing technology in IoT environment,the exploration of new technology and the security of the hardware related to IoT,the security of the software and the security team,the future research direction of the security of IoT is pointed out. | HU Liangjun WEI Daode LI Xiaofeng WANG Hongmei SONG Rui | 2018 | International Journal of Plant Engineering and Management2018，23，3： | 0 |
| 6 | Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism显示文摘Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images. | Xinyu Hu Defeng Kong Xiyang Liu Junwei Zhang Daode Zhang | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
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