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Optimal Grading Policies in the Online Acquisition of Used Products

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Journal of Systems Science and Systems Engineering 2021年第1期30卷 29-43页

作者： xiang chu Zhong Wen Jian Chen School of Maritime Economics and Management Dalian Maritime UniversityDalian116026China International Business School Beijing Foreign Studies UniversityBeijing100089China School of Economics and Management Tsinghua UniversityBeijing100084China

Although online reverse commerce(recommerce)is convenient and efficient,it is not without *** limits recommerce firms’flexibility to offer personalized prices and may cause mismatched grading between the firms and sellers of used *** study examines a recommerce firm’s decision on grading criteria and *** find that the firm’s optimal policy exhibits two distinctly different patterns depending on the trade value of the *** demonstrate that sellers’overestimate and underestimate errors have qualitatively different effects on firm profitability,and the effects crucially rely on the type of optimal *** findings can apprise firms on how to preset sorting criteria and prices as well as reduce grading errors.

关键词： Closed-loop supply chains used-product acquisition management recommerce grading error grading policy

Multi-class classification method for steel surface defects with feature noise

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Journal of Iron and Steel Research International 2021年第3期28卷 303-315页

作者： Mao-xiang chu Yao Feng Yong-hui Yang Xin Deng School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051LiaoningChina

Defect classification is the key task of a steel surface defect detection *** current defect classification algorithms have not taken the feature noise into *** order to reduce the adverse impact of feature noise,an anti-noise multi-class classification method was proposed for steel surface *** the one hand,a novel anti-noise support vector hyper-spheres(ASVHs)classifier was *** N types of defects,the ASVHs classifier built N *** hyper-spheres were insensitive to feature and label *** the other hand,in order to reduce the costs of online time and storage space,the defect samples were pruned by support vector data description with parameter iteration adjustment *** the end,the ASVHs classifier was built with sparse defect samples set and auxiliary *** results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface.

关键词： Steel surface defect Multi-class classification Anti-noise support vector hyper-sphere Parameter iteration adjustment Feature noise

Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere

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Journal of Iron and Steel Research International 2018年第7期25卷 706-716页

作者： Mao-xiang chu Xiao-ping Liu Rong-fen Gong Jie Zhao School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan 114051 Liaoning China Department of Electrical Engineering Lakehead University Thunder Bay P7B 5E1 ON Canada State Key Laboratory of Robotics and System (HIT) Harbin 150001 Heilongjiang China

Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency.

关键词： Strip steel surface defect Multi-class classification Supporting vector machine Adjustable hyper-sphere

Unbalanced classification method using least squares support vector machine with sparse strategy for steel surface defects with label noise

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Journal of Iron and Steel Research International 2020年第12期27卷 1407-1419页

作者： Li-ming Liu Mao-xiang chu Rong-fen Gong Xin-yu Qi School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan 114051LiaoningChina

Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.

关键词： Steel surface defect Least squares support vector machine Anti-noise Sparseness Unbalanced data

DLF-YOLOF:an improved YOLOF-based surface defect detection for steel plate

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Journal of Iron and Steel Research International 2024年第2期31卷 442-451页

作者： Guang-hu Liu Mao-xiang chu Rong-fen Gong Ze-hao Zheng School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051LiaoningChina

Surface defects can affect the quality of steel *** methods based on computer vision are currently applied to surface defect detection of steel ***,their real-time performance and object detection of small defect are still *** improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called ***,the anchor-free detector is used to reduce the network ***,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature ***,the soft non-maximum suppression is used to improve detection accuracy ***,data augmentation is performed for small defect objects during training to improve detection *** show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,*** shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.

关键词： Steel surface defects detection YOLOF Anchor-free detector Small object detection Real-time detection

A new subfamily of the grasshopper （Orthoptera： Acridoidea： Gomphoceridae） from the Tibetan Plateau of China

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Insect Science 2012年第6期19卷 699-702页

作者： Dao-chuan Zhang Yu-Long Zhang xiang-chu Yin College of Life Sciences Hebei University Baoding College of Plant Protection Hebei Agricultural University Baoding College ofPlant Protection Shandong Agricultural University Taian China

This paper reports a new subfamily, a new genus and a new species, that is, Pacrinae subfam, nov., Pacris gen. nov and Pacris xizangensis sp. nov in Gomphoceridae. The new subfamily is allied to Orinhippinae of Gomphoeeridae and it differs from the latter by wings and tympanum absent. The new genus is similar to Orinhippus Uvarov, 1921 but differs from the latter in：（i） foveolae absent; （ii） tegmina absent; （iii） tympanum absent; （iv） hind margin of pronotum with incised in the middle. Type specimens are deposited in the Museum of Hebei University, Baoding, China.

关键词： Orthoptera Acridoidea Gomphoceridae Pacrinae new subfamily,grasshopper China

Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects

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Journal of Iron and Steel Research International 2014年第2期21卷 174-180页

作者： Mao-xiang chu An-na WANG Rong-fen GONG Mo SHA College of Information Science and Engineering Northeastern University School of Electronic and Information Engineering University of Science and Technology Liaoning

Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifierr s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples.

关键词： multi-class classification least squares twin support vector machine error variable contribution weight binary tree strip steel surface

Experimental investigation of nanoparticles precipitation in a rotating packed bed

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Particuology 2010年第4期8卷 372-378页

作者： Yang xiang Guangwen chu Lixiong Wen Kuang Yang Guangting Xiao Jianfeng Chen Key Laboratory for Nanomateriais Ministry of Education and Research Center of the Ministry of Education for High Gravity Engineering and Technology.Beijing University of Chemical Technology. Beijing 100029 China

Precipitation of BaSO4 nanoparticles was studied for the first time in a specially designed rotating packed bed （RPB）, which allowed sampling at different radial positions to provide better insight of the mechanism of precipitation in RPB. Particle size and morphology were characterized by TEM, while the quality of synthesized BaSO4 powders was analyzed by XRD and BET, and compared with those prepared in a stirred-tank reactor. The important role of the inlet region of the RPB in the whole precipitation process was experimentally confirmed, as a significant essence for the design of industrial RPB for the precipitation of sparingly soluble materials. The effects of different operating conditions on particle size were also investigated, showing that particle size decreases with increasing rotational speed and liquid flow rate, due to the enhancement of micromixing in the RPB.

关键词： Rotating packing bed BaSO4 Nanoparticle Precipitation Mixing

End-point dynamic control of basic oxygen furnace steelmaking based on improved unconstrained twin support vector regression

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Journal of Iron and Steel Research International 2020年第1期27卷 42-54页

作者： chuang Gao Ming-gang Shen Xiao-ping Liu Nan-nan Zhao Mao-xiang chu School of Materials and Metallurgy University of Science and Technology LiaoningAnshan 114051LiaoningChina School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan 114051LiaoningChina School of Information and Electrical Engineering Shandong Jianzhu UniversityJinan 250101ShandongChina

In order to improve the end-point hit rate of basic oxygen furnace steelmaking,a novel dynamic control model was proposed based on an improved twin support vector regression *** controlled objects were the end-point carbon content and *** proposed control model was established by using the low carbon steel samples collected from a steel plant,which consists of two prediction models,a preprocess model,two regulation units,a controller and a basic oxygen *** test results of 100 heats show that the prediction models can achieve a double hit rate of 90%within the error bound of 0.005 wt.%C and 15℃.The preprocess model was used to predict an initial end-blow oxygen ***,the double hit rate of the carbon con tent and temperature only achieves 65%.Then,the oxygen volume and coolant additi ons were adjusted by the regulation units to improve the hit ***,the double hit rate after the regulation is reached up to 90%.The results indicate that the proposed dynamic control model is efficient to guide the real production for low carbon steel,and the modeling method is also suitable for the applications of other steel grades.

关键词： Steelmaking Basic oxygen furnace End-point control Twin support vector regression Wavelet transform