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| 1 | Weighted average integration of sparse representation and collaborative representation for robust face recognition显示文摘Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification.As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition.The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10%improvement in recognition accuracy. | Shaoning Zeng Yang Xiong | 2016 | Computational Visual Media2016，2，4： | 1 |
| 2 | Integrating absolute distances in collaborative representation for robust image classification显示文摘 | Shaoning Zeng Xiong Yang Jianping Gou Jiajun Wen | 2016 | CAAI Transactions on Intelligence Technology2016，1，2： | 0 |
| 3 | B-PesNet: Smoothly Propagating Semantics for Robust and Reliable Multi-Scale Object Detection for Secure Systems显示文摘Multi-scale object detection is a research hotspot,and it has critical applications in many secure systems.Although the object detection algorithms have constantly been progressing recently,how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors,such as the deformation and occlusion of the object in the actual scene.The more interference factors,the more complicated the semantic information,so we need a deeper network to extract deep information.However,deep neural networks often suffer from network degradation.To prevent the occurrence of degradation on deep neural networks,we put forth a new model using a newly-designed Pre-ReLU,which inserts a ReLU layer before the convolution layer for the sake of preventing network degradation and ensuring the performance of deep networks.This structure can transfer the semantic information more smoothly from the shallow to the deep layer.However,the deep networks will encounter not only degradation,but also a decline in efficiency.Therefore,to speed up the two-stage detector,we divide the feature map into many groups so as to diminish the number of parameters.Correspondingly,calculation speed has been enhanced,achieving a balance between speed and accuracy.Through mathematical demonstration,a Balanced Loss(BL)is proposed by a balance factor to decrease the weight of the negative sample during the training phase to balance the positives and negatives.Finally,our detector demonstrates rosy results in a range of experiments and gains an mAP of 73.38 on PASCAL VOC2007,which approaches the requirement of many security systems. | Yunbo Rao Hongyu Mu Zeyu Yang Weibin Zheng Faxin Wang Jiansu Pu Shaoning Zeng | 2022 | Computer Modeling in Engineering & Sciences2022，，9： | 0 |
| 4 | Robust and Flexible Multimaterial Aerogel Fabric Toward Outdoor Passive Heating显示文摘Outdoor passive heating to maintain a constant human body temperature is critical for human activities.However,most traditional energy-exhausted heating systems and inefficient passive heating technologies are incapable of dealing with the cold outdoor environment.Developing fabrics with low thermal radiation and conduction to passively heat the human body is a viable way to overcome the constraints of existing passive heating strategies.Herein,a multimaterial aerogel fabric was developed to realize passive personal heating without any energy input.The multimaterial aerogel fabric was fabricated by coating an Ag layer on an aerogel composite fabric.The lightweight aerogel composite fabric,woven from aerogel composite fibers with multi-scale porous structure,exhibits excellent thermal insulation,self-cleaning,mechanical and thermal stability.Furthermore,by coating with an Ag layer,the multimaterial aerogel fabric exhibits both low thermal conductivity and low infrared emissivity at 7–14μm,demonstrating superior thermal insulating performance.As a result,the proposed multimaterial aerogel fabric with a thickness of only 1.29 mm is capable of improving the human body temperarure of 5.7℃ in a cold environment without energy input.This strategy offers a potential energy-saving alternative for future outdoor passive heating. | Jiawei Wu Manni Zhang Minyu Su Yuqi Zhang Jun Liang Shaoning Zeng Baishun Chen Li Cui Chong Hou Guangming Tao | 2022 | Advanced Fiber Materials2022，4，6： | 0 |
| 5 | The Simulation of L-Band Microwave Emission of Frozen Soil during the Thawing Period with the Community Microwave Emission Model(CMEM)显示文摘One-third of the Earth’s land surface experiences seasonal freezing and thawing.Freezing-thawing transitions strongly impact land-atmosphere interactions and,thus,also the lower atmosphere above such areas.Observations of two L-band satellites,the Soil Moisture Active Passive(SMAP)and Soil Moisture and Ocean Salinity(SMOS)missions,provide flags that characterize surfaces as either frozen or not frozen.However,both state transitions—freezing and thawing(FT)—are continuous and complex processes in space and time.Especially in the L-band,which has penetration depths of up to tens of centimeters,the brightness temperature(T_(B))may be generated by a vertically-mixed profile of different FT states,which cannot be described by the current version of the Community Microwave Emission Model(CMEM).To model such complex state transitions,we extended CMEM in Fresnel mode with an FT component by allowing for(1)a varying fraction of an open water surface on top of the soil,and(2)by implementing a temporal FT phase transition delay based on the difference between the soil surface temperature and the soil temperature at 2.5 cm depth.The extended CMEM(CMEM-FT)can capture the T_(B)progression from a completely frozen to a thawed state of the contributing layer as observed by the L-band microwave radiometer ELBARA-III installed at the Maqu station at the northeastern margin of the Tibetan Plateau.The extended model improves the correlation between the observations and CMEM simulations from 0.53/0.45 to 0.85/0.85 and its root-mean-square-error from 32/25 K to 20/15 K for H/V-polarization during thawing conditions.Yet,CMEM-FT does still not simulate the freezing transition sufficiently. | Shaoning Lv Clemens Simmer Yijian Zeng Jun Wen and Zhongbo Su | 2022 | Journal of Remote Sensing2022，，1： | 0 |
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