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RISS 인기검색어
- 검색키워드
- 저자 : Shuangbao Ma (검색결과 5 건)
- 무료
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- 유료
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A Defect Detection Algorithm of Denim Fabric Based on Cascading Feature Extraction Architecture
Shuangbao Ma,Renchao Zhang,Yujie Dong,Yuhui Feng,Guoqin Zhang 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.1
Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denimfabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extractionarchitecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the largedataset ImageNet and uses its portability to train the defect detection classifier and the defect recognitionclassifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layerwere retrained and adjusted from of these two training models on the high-definition fabric defect dataset. Thelast step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and otherfeature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show thatthe defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increasedby 1–3 percentage points.
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An Improved Defect Detection Algorithm of Jean Fabric Based on Optimized Gabor Filter
Shuangbao Ma,Wen Liu,Changli You,Shulin Jia,Yurong Wu 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.5
Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on theoptimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on themaximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter bankswith 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the onedimensionalimage entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly,these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, suchas normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has betterdetection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.
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An Improved Spin Echo Train De-noising Algorithm in NMRL
( Feng Liu ),( Shuangbao Ma ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.4
Since the amplitudes of spin echo train in nuclear magnetic resonance logging (NMRL) are small and the signal to noise ratio (SNR) is also very low, this paper puts forward an improved de-noising algorithm based on wavelet transformation. The steps of this improved algorithm are designed and realized based on the characteristics of spin echo train in NMRL. To test this improved de-noising algorithm, a 32 points forward model of big porosity is build, the signal of spin echo sequence with adjustable SNR are generated by this forward model in an experiment, then the median filtering, wavelet hard threshold de-noising, wavelet soft threshold de-noising and the improved de-noising algorithm are compared to de-noising these signals, the filtering effects of these four algorithms are analyzed while the SNR and the root mean square error (RMSE) are also calculated out. The results of this experiment show that the improved de-noising algorithm can improve SNR from 10 to 27.57, which is very useful to enhance signal and de-nosing noise for spin echo train in NMRL.
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An Improved Spin Echo Train De-noising Algorithm in NMRL
Liu, Feng,Ma, Shuangbao Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.4
Since the amplitudes of spin echo train in nuclear magnetic resonance logging (NMRL) are small and the signal to noise ratio (SNR) is also very low, this paper puts forward an improved de-noising algorithm based on wavelet transformation. The steps of this improved algorithm are designed and realized based on the characteristics of spin echo train in NMRL. To test this improved de-noising algorithm, a 32 points forward model of big porosity is build, the signal of spin echo sequence with adjustable SNR are generated by this forward model in an experiment, then the median filtering, wavelet hard threshold de-noising, wavelet soft threshold de-noising and the improved de-noising algorithm are compared to de-noising these signals, the filtering effects of these four algorithms are analyzed while the SNR and the root mean square error (RMSE) are also calculated out. The results of this experiment show that the improved de-noising algorithm can improve SNR from 10 to 27.57, which is very useful to enhance signal and de-nosing noise for spin echo train in NMRL.
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Yongqing Cao,Tao Zeng,Wei Han,Xueying Ma,Tiantian Gu,Li Chen,Yong Tian,Wenwu Xu,Jianmei Yin,Guohui Li,Lizhi Lu,Shuangbao Gun Asian Australasian Association of Animal Productio 2024 Animal Bioscience Vol.37 No.1
Objective: Tibetan chickens, which have unique adaptations to extreme high-altitude environments, exhibit phenotypic and physiological characteristics that are distinct from those of lowland chickens. However, the mechanisms underlying hypoxic adaptation in the liver of chickens remain unknown. Methods: RNA-sequencing (RNA-Seq) technology was used to assess the differentially expressed genes (DEGs) involved in hypoxia adaptation in highland chickens (native Tibetan chicken [HT]) and lowland chickens (Langshan chicken [LS], Beijing You chicken [BJ], Qingyuan Partridge chicken [QY], and Chahua chicken [CH]). Results: A total of 352 co-DEGs were specifically screened between HT and four native lowland chicken breeds. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses indicated that these co-DEGs were widely involved in lipid metabolism processes, such as the peroxisome proliferator-activated receptors (PPAR) signaling pathway, fatty acid degradation, fatty acid metabolism and fatty acid biosynthesis. To further determine the relationship from the 352 co-DEGs, protein-protein interaction network was carried out and identified eight genes (ACSL1, CPT1A, ACOX1, PPARC1A, SCD, ACSBG2, ACACA, and FASN) as the potential regulating genes that are responsible for the altitude difference between the HT and other four lowland chicken breeds. Conclusion: This study provides novel insights into the molecular mechanisms regulating hypoxia adaptation via lipid metabolism in Tibetan chickens and other highland animals.
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