Flow ripple reduction of an axial piston pump by a combination of cross-angle and pressure relief grooves: Analysis and optimization

Bing Xu,Shaogan Ye,Junhui Zhang,Chunfeng Zhang 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.6

This paper investigates the potential of flow ripple reduction of an axial piston pump by a combination of cross-angle and pressure relief grooves. A dynamic model is developed to analyze the pumping dynamics of the pump and validated by experimental results. The effects of cross-angle on the flow ripples in the outlet and inlet ports, and the piston chamber pressure are investigated. The effects of pressure relief grooves on the optimal solutions obtained by a multi-objective optimization method are identified. A sensitivity analysis is performed to investigate the sensitivity of cross-angle to different working conditions. The results reveal that the flow ripples from the optimal solutions are smaller using the cross-angle and pressure relief grooves than those using the cross-angle and ordinary precompression and decompression angles and the cross-angle can be smaller. In addition, when the optimal design is used, the outlet flow ripples sensitivity can be reduced significantly.

Single Low-Light Ghost-Free Image Enhancement via Deep Retinex Model

( Yan Liu ),( Bingxue Lv ),( Jingwen Wang ),( Wei Huang ),( Tiantian Qiu ),( Yunzhong Chen ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.5

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

Gas detonation cell width prediction model based on support vector regression

Jiyang Yu,Bingxu Hou,Alexander Lelyakin,Zhanjie Xu,Thomas Jordan 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.7

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, λ, is highly correlated with the characteristic reaction zone width, δ. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of λ/δ. The obtained correlations formulate the dependency of the ratio λ/δ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

Physical systems based on Intrinsic Transistors Characteristics and Having collective Computational Abilities

Yang Wang,Zhijian Li,Bingxue Shi 대한전자공학회 1994 ICONIP : International Conference On Neural Inform Vol.3 No.1

Enzymatic Synthesis of Lipophilic Caffeoyl Lipids Using Soybean Oil as the Novel Acceptor

Shangde Sun,Liya Tian,Bingxue Hu,Cong Jiang 한국생물공학회 2018 Biotechnology and Bioprocess Engineering Vol.23 No.5

Soybean oil-based caffeoyl lipids are the novel lipophilic derivatives of caffeic acid, which can be used as UV absorbers and antioxidants in the food and cosmetic industries. In the work, the novel lipophilic structured lipids were prepared using soybean oil as the novel caffeoyl acceptor by enzymatic transesterification. The effects of the reaction variables on the transesterification were investigated, and response surface methodology was used to optimize the reaction variables. Reactions were monitored by HPLC-UV. Different enzymes (Novozym 435, Lipozyme RMIM, and Lipozyme TLIM) were used as biocatalysts, and Novozym 435 showed the best performance for the reaction. The results showed that a high lipophilic soybean oil-based caffeoyl lipids yield (73.5 ± 1.2%) was achieved under the optimal conditions (reaction temperature 85°C, substrate molar ratio 1:6 (ethyl caffeate (EC)/soybean oil), enzyme load 25% (w/w), and 60 h at atmosphere pressure). The activation energies of EC conversion, hydrophilic glyceryl caffeates (GC) and lipophilic caffeoylated acylglycerol (CAG) formations were 32.92 kJ/mol, 17.21 kJ/mol and 57.36 kJ/mol, respectively. Km and Vm were 0.022 mol/L and 0.033 × 10-3 mol/(Lmin), respectively.

A Current-Mode Hamming Neural Network

Wei Lu,Zhijian Li,Bingxue Shi 대한전자공학회 1994 ICONIP : International Conference On Neural Inform Vol.1 No.3

Research on the Sequence Planning of Manufacturing Feature Based on the Node Importance of Complex Network

Bin Cheng,Dingjie Guan,Bingxue Jing 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.23 No.2

Small and medium-sized manufacturing enterprises involve a lot of customized products. The degree of adaptability should be noted while improving product design and manufacturing digital and intelligent levels. This paper presents a process sequencing method of manufacturing features based on the node importance of a complex network. The method is based on the adjacency matrix and connected graph to analyze the process constraint semantics of the product model. The adjacency matrix expresses the positioning dimensions between features. The connected graph is applied to define the constraint relationships between features and aggregate the multi-dimensional process dimension chain in all directions. Based on the processing sequence of node importance in a complex network, most of process planning can be realized. The method also can make adaptive decisions for different structural parts and monitor the machining of key features. Examples verify the validity and feasibility of the proposed method.

A fast and simplified crack width quantification method via deep Q learning

Xingu Zhong,Xiong Peng,Kun Zhou,Bingxu Duan,Chao Zhao,Tianyu Zhang 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.4

Crack width is an important indicator to evaluate the health condition of the concrete structure. The crack width is measured by manual using crack width gauge commonly, which is time-consuming and laborious. In this paper, we have proposed a fast and simplified crack width quantification method via deep Q learning and geometric calculation. Firstly, the crack edge is extracted by using U-Net network and edge detection operator. Then, the intelligent decision of is made by the deep Q learning model. Further, the geometric calculation method based on endpoint and curvature extreme point detection is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method, achieving high precision in the real crack width quantification.

Enhancing the adsorption function of F⁻ by iron and zirconium doped zeolite: Characterization and parameter optimization

Bo Yang,Chaomin Jia,Guirong Sun,Bingxu Quan,Chunhui Zhang,Qianqian Huo,Peidong Su 대한환경공학회 2023 Environmental Engineering Research Vol.28 No.2

This study presented an original study on the F⁻ removal by Fe and Zr doped Gismondine-dominated Zeolite. Various modified zeolites are prepared by systematically adjusting the synthesis variables, namely pH, mass ratio of zeolite to modify agent (m<SUB>Zeolite:m(Fe+Zr)</SUB>), mass ratio of Fe to Zr (Fe:Zr) to investigate their effects on the F⁻ adsorption. The performance of prepared Fe-Zr-Zeolite on F⁻ removal was examined through both statistic adsorption and dynamic adsorption. Results indicated that when the modified pH was 7, the Fe:Zr ratio was 1:1 and m<SUB>Zeolite:m(Fe+Zr)</SUB> was 1:2, the modified natural zeolites which was named Fe-Zr-Zeolite showed the best removal efficiency on F⁻. Fe-Zr-Zeolite could remove over 80% when the initial concentration was less than 20 mg/L and F⁻ adsorption followed pseudo-second-order adsorption kinetic and Langmuir adsorption isotherm, indicating that F⁻ adsorption by Fe-Zr-Zeolite was chemically dominated. Besides, Fe-Zr-Zeolite had better removal efficiency of F⁻ under acidic conditions than that under alkaline conditions, and the competing anions, typically, Cl-, NO₃⁻ and SO₄<SUP>2-</SUP> had negligible effect on F⁻ removal by Fe-Zr-Zeolite. The dynamic adsorption test demonstrated that in order to maintain the high removal efficiency of F⁻, the filling thickness of Fe-Zr-Zeolite should be at least 30 ㎝ and the small the flow rate is, the higher the F⁻ removal efficiency will be.

The Impact of Corporate Governance on CSR in Chinese Manufacturing Industries

이병희(Byung Hee Lee),상빙설(Bingxue Chang),진굉(Hong Chen),장유진(Yujin Chang) 연세대학교 동서문제연구원 2020 동서연구 Vol.32 No.4

본 연구는 이해 관계자 이론과 기업 지배 구조에 관한 선행연구를 바탕으로 중국 제조업에서 기업 지배 구조가 기업의 사회적 책임(CSR)에 미치는 영향을 실증적으로 탐구하였다. 1,268개 기업의 설문 조사 데이터를 활용하여 국가 소유, 사외 이사 수 및 감사위원회의 존재는 CSR 활동과 긍정적인 관계가 있는 반면, CEO 듀얼리티는 CSR 활동에 부정적인 영향을 미친다는 것을 보여주었다. 조사결과는 CSR에 대한 기업 지배 구조의 중요한 역할을 반영하므로 중국 기업에 내부 지배 구조 메커니즘과 CSR 참여를 개선하는 방법에 대한 지침을 제공할 수 있다. Based on stakeholder theory and the literature on corporate governance, this study empirically investigates how corporate governance influences corporate social responsibility(CSR) in Chinese manufacturing industries. By utilizing survey data from 1,268 firms, we demonstrate that while state ownership, the number of independent directors and the presence of an audit committee are positively related to CSR, CEO duality has a negative impact on CSR. Our findings reflect the significant role of corporate governance on CSR, which, therefore, provides guidance for Chinese enterprises on how to improve the internal governance mechanisms and CSR engagement.