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第九届（2014）中国管理学年会——组织与战略分会场

作者： Song Zhu sunyoung byun Business School Beijing Normal University School of Business Pusan National University

Businesses have been acting not only as interest groups, but have also exercising their influence on society, in other words corporate social responsibility（CSR）, which will allow long-term continuous growth for the company. However, many companies do not regard CSR to be a part of corporate strategy, especially under the trend of globalization and current dramatic competitions. This paper empirically investigates the relationship between CSR and CFP（corporate financial performance） as the rapid globalization of Chinese firms. Furthermore, this paper explores the moderating effect of globalization on the relationship between CSR and CFP. By using 1,798 Chinese listed firms from 2008 to 2011 and the CSR score evaluated by a professional firm that is focusing on the CSR scoring for Chinese listed firms, we find that there is no relationship between CSR and CFP. However, when globalization（proxy by dual listed abroad） was used as a moderating effect and was incorporated into our analysis, a positive relationship exists between CSR and CFP, which suggests that the relationship between CSR and CFP is more complex than the simple linear relationship. Moreover, although the globalization of Chinese firms may bring some pressure on their performance, globalization is indeed helpful for their efforts of CSR being accepted by the market and investors, thus leading to better performance. Our results are robust for consideration of the endogeneity of CSR and the sample selection problem of globalization. Our findings suggest that if a learning effect takes place through a company’s active globalization and if Chinese companies put in the effort to match international regulation and standard, then eventually a positive relationship will develop between CSR and CFP. We believe that this study can be helpful to multinational enterprises in the Chinese market that utilize CSR as a strategy to enhance company performance.

关键词： Corporate social responsibility globalization moderating effect

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Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

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Journal of Magnesium and Alloys 2024年第1期12卷 186-196页

作者： Sujeong byun Jinyeong Yu Seho Cheon Seong Ho Lee Sung Hyuk Park Taekyung Lee School of Mechanical Engineering Pusan National UniversityBusan 46241South Korea School of Materials Science and Engineering Kyungpook National UniversityDaegu 41566South Korea

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

关键词： Plastic anisotropy Compression Annealing Machine learning Data augmentation

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Easy to Calibrate:Marker-Less Calibration of Multiview Azure Kinect

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 3083-3096页

作者： sunyoung Bu Suwon Lee Department of Liberal Arts Hongik UniversitySejong30016Korea Department of Computer Science and RINS Gyeongsang National UniversityJinju-si52828Korea

Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.

关键词： Azure Kinect Kinect calibration marker-less calibration 3D reconstruction iterative closest point

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Sex-specific Cutoff Values of Visceral Fat Area for Lean vs. Overweight/Obese Nonalcoholic Fatty Liver Disease in Asians

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Journal of Clinical and Translational Hepatology 2022年第4期10卷 595-599页

作者： sunyoung Lee Kyoung Won Kim Jeongjin Lee Department of Radiology and Research Institute of Radiological Science Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea Department of Radiology and Research Institute of Radiology Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea School of Computer Science and Engineering Soongsil UniversitySeoulRepublic of Korea

Background and Aims:Visceral obesity is a risk factor for nonalcoholic fatty liver disease(NAFLD).We investigated sex-specific optimal cutoff values for visceral fat area(VFA)associated with lean and overweight/obese NAFLD in an Asian population.Methods:This retrospective study includ-ed 678 potential living liver donors(mean age,30.8±9.4 years;434 men and 244 women)who had undergone ab-dominal computed tomography(CT)imaging and liver bi-opsy between November 2016 and October 2017.VFA was measured using single-slice abdominal CT.NAFLD was eval-uated by liver biopsy(≥5%hepatic steatosis).Receiver op-erating characteristic curve analysis was used to determine cutoff values for VFA associated with lean(body mass index[BMI]<23 kg/m2)and overweight/obese(BMI≥23 kg/m2)NAFLD.Results:Area under the curve(AUC)values with 95%confidence intervals(CI)for VFA were 0.82(95%CI,0.75-0.88)for lean and 0.74(95%CI,0.69-0.79)for over-weight/obese men with NAFLD.The AUC values were 0.67(95%CI,0.58-0.75)for lean and 0.71(95%CI,0.62-0.80)for overweight/obese women with NAFLD.The cutoff values for VFA associated with lean NAFLD were 50.2 cm2 in men and 40.5 cm2 in women.The optimal cutoff values for VFA associated with overweight/obese NAFLD were 100.6 cm2 in men and 68.0 cm2 in women.Conclusions:Sex-specific cutoff values for VFA may be useful for identifying subjects at risk of lean and overweight/obese NAFLD.

关键词： Hepatic steatosis Adipose tissue Liver biopsy Computed tomography

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Blockchain and Machine Learning for Intelligent Multiple Factor-Based Ride-Hailing Services

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Computers, Materials & Continua 2022年第3期70卷 4429-4446页

作者： Zeinab Shahbazi Yung-Cheol byun IIST Department of Computer EngineeringJeju National UniversityJeju-siJeju Special Self-Governing Province63243Kore

One of the common transportation systems in Korea is calling taxis through online applications,which is more convenient for passengers and drivers in the modern area.However,the driver’s passenger taxi request can be rejected based on the driver’s location and distance.Therefore,there is a need to specify driver’s acceptance and rejection of the received request.The security of this systemis anothermain core to save the transaction information and safety of passengers and drivers.In this study,the origin and destination of the Jeju island SouthKorea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques.The blockchain framework is implemented in the Hyperledger Fabric platform.The experimental results represent the features of socio-economic.The cross-validation was accomplished.Distance is another factor for the taxi trip,which in total trip in midnight is quite shorter.This process presents the successful matching of ride-hailing taxi services with the specialty of distance,the trip request,and safety based on the total city measurement.

关键词： Taxi trip blockchain machine learning ride-hailing prediction trip distance

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Recent advances in 3D printed electrode materials for electrochemical energy storage devices

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Journal of Energy Chemistry 2023年第6期81卷 272-312,I0008页

作者： Suhail Mubarak Duraisami Dhamodharan Hun-Soo byun Department of Chemical and Biomolecular Engineering Chonnam National UniversityYeosuJeonnam 59626Republic of Korea Greenlab Department of prosthodonticsSaveetha Dental College and HospitalsSaveetha Institute of Medical and Technical SciencesChennaiTamil Nadu 600077India

Electrochemical energy storage(EES)systems like batteries and supercapacitors are becoming the key power sources for attempts to change the energy dependency from inadequate fossil fuels to sustainable and renewable resources.Electrochemical energy storage devices(EESDs)operate efficiently as a result of the construction and assemblage of electrodes and electrolytes with appropriate structures and effective materials.Conventional manufacturing procedures have restrictions on regulating the morphology and architecture of the electrodes,which would influence the performance of the devices.3D printing(3DP)is an advanced manufacturing technology combining computer-aided design and has been recognised as an artistic method of fabricating different fragments of energy storage devices with its ability to precisely control the geometry,porosity,and morphology with improved specific energy and power densities.The capacity to create mathematically challenging shape or configuration designs and high-aspect-ratio 3D architectures makes 3D printing technology unique in its benefits.Nevertheless,the control settings,interactive manufacturing processes,and protracted post-treatments will affect the reproducibility of the printed components.More intelligent software,sophisticated control systems,high-grade industrial equipment,and post-treatment-free methods are necessary to develop.3D printed(3DPd)EESDs necessitate dynamic printable materials and composites that are influenced by performance criteria and fundamental electrochemistry.Herein,we review the recent advances in 3DPd electrodes for EES applications.The emphasis is on printable material synthesis,3DP techniques,and the electrochemical performance of printed electrodes.For the fabrication of electrodes,we concentrate on major 3DP technologies such as direct ink writing(DIW),inkjet printing(IJP),fused deposition modelling(FDM),and stereolithography3DP(SLA).The benefits and drawbacks of each 3DP technology are extensively discussed.We provide an outlook on the integration

关键词： 3D printing 3D printed electrodes Electrochemical energy storage Lithium-ion battery Zinc-ion battery Supercapacitor

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Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

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Computers, Materials & Continua 2023年第3期74卷 5507-5525页

作者： Subhajit Chatterjee Yung-Cheol byun Department of Computer Engineering Jeju National UniversityJeju-si63243Korea Department of Computer Engineering Major of Electronic EngineeringJeju National UniversityInstitute of Information Science&TechnologyJeju63243Korea

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to

关键词： Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression

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Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

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Computers, Materials & Continua 2022年第2期70卷 3151-3167页

作者： Debapriya Hazra Wafa Shafqat Yung-Cheol byun Department of Computer Engineering Jeju National UniversityJeju-siKorea

Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing.Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies.The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data.Another critical factor is balancing the data for enhanced prediction.Data Augmentation is a technique used for increasing the data available for training.Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models.In this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction error.We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data.We modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training TGAN-skip.We used various evaluation metrics and visual representation to compare the performance of our proposed model.We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data.The mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation.The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the p

关键词： Energy consumption generative adversarial networks synthetic data time series data TGAN WGAN-GP TGAN-skip prediction error augmentation

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Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

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Nano-Micro Letters 2023年第5期15卷 236-253页

作者： Ji Hyun Baek Kyung Ju Kwak Seung Ju Kim Jaehyun Kim Jae Young Kim In Hyuk Im sunyoung Lee Kisuk Kang Ho Won Jang Department of Materials Science and Engineering Research Institute of Advanced MaterialsSeoul National UniversitySeoul 08826Republic of Korea Advanced Institute of Convergence Technology Seoul National UniversitySuwon 16229Korea

Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of nondistributed weight updates owing to stable ion migrations.However,the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems.Meanwhile,achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging.Here,two-terminal Au/LixCoO_(2)/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks.The Au/LixCoO_(2)/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO_(2)films.The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike,resulting in improved weight control functionality.Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity,symmetricity,and dynamic range.Notably,the LixCoO_(2)-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error.These impressive performances suggest the vertical two-terminal Au/LixCoO_(2)/Pt artificial synapses as promising candidates for hardware neural networks.

关键词： Artificial synapse Neuromorphic Li-based Two-terminal Synaptic plasticity

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Pleckstrin Homology-Like Domain, Family A, Member 1 (PHLDA1): A Multifaceted Cell Survival Factor that Drives Metabolic Disease

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Engineering 2023年第1期20卷 9-18页

作者： Tamana Yousof Jae Hyun byun Jack Chen Richard C.Austin Department of Medicine Division of NephrologyMcMaster UniversityThe Research Institute of St.Joe’s Hamilton and the Hamilton Centre for Kidney ResearchHamiltonOntarioL8N 4A6Canada

Pleckstrin homology-like domain, family A, member 1 (PHLDA1) is a multifaceted intracellular protein belonging to the evolutionarily conserved pleckstrin homology-related domain family. Its murine homologue, T-cell death-associated 51 (TDAG51) gene, was initially discovered for its role in activation-induced apoptosis in T-cell hybridomas. In recent years, PHLDA1 has received increased attention due to its association with obesity, fatty liver disease, diabetes, atherosclerosis, and cancer. Accumulating evidence also supports its role in endoplasmic reticulum stress signaling pathways as a crucial mediator of apoptosis, autophagy, and cell proliferation. In this review, the current knowledge of PHLDA1 gene and protein regulation, localization, and function is summarized. This review highlights the pro- and anti-apoptotic roles of PHLDA1 that contribute to vast array of metabolic diseases.

关键词： ER-stress Metabolism Apoptosis Cell survival PHLDA1

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