• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.63-84
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• 2023

Purpose - The purpose of this study is to examine the effect of CSR activities on innovation performance and to identify the mediating role of knowledge sharing and innovation behaviors between CSR activities and innovation performance. In addition, the purpose of this study is to verify the role of sequential mediating effect of knowledge sharing and innovation behavior between CSR activities and innovation performance. Design/methodology/approach - This study collected survey data from 293 organizational members working in Chinese companies. The exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability analysis, correlation analysis and process macro were used in order to analyze the data. Findings - First, this study verified that CSR activities had a significant positive effect on knowledge sharing, innovation behavior, and innovation performance. Second, it was found that knowledge sharing had a significant positive effect on innovation behavior and innovation performance. Third, it was verified that innovation behavior had a significant positive effect on innovation performance. Fourth, knowledge sharing and innovation behavior had a sequential mediating effect in the relationship between CSR activities and innovation performance. Research implications or Originality - With the uncertainty of the environment and the intensification of competition among companies, more and more companies begin to pay attention to innovation. Different from existing studies, this study focuses on CSR activities, identifies the role of CSR activities, explores ways to guide innovation performance, and verifies the sequential mediating role of knowledge sharing and innovation behavior. Through this measure, the importance of knowledge sharing and innovative behavior among organizational members is emphasized, solutions to strengthen innovation are explored, and theoretical and practical implications are provided for companies.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.5 s.142
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• pp.637-648
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• 2005

The purpose of this study was to investigate the differences among internet and TV home-shopping perceived risk segments in regard to clothing shopping orientations and purchase intention. The subjects used for the study were 290 female consumers aged from 20 to 40 living in Seoul. The study used factor analysis, cluster analysis, ANOVA, Duncan test, and $\chi^2-test$. The results showed that the Internet and TV home-shopping perceived risks consisted of 9 factors: Products uncertainty risk, Internet shopping mall trust risk, account-related risk, delivery risk, social risk, size risk, exchange/return risk, TV watching-related risk, and price risk. The cluster analysis showed that there were five groups segmented: Size risk/TV watching risk group, Social risk/Internet trust risk group, Return risk/TV watching low-risk group, Delivery risk/product trust group, and Product risk group. The clothing shopping orientations were classified by 5 factors: Planned shopping, pleasure shopping, sales/fashion oriented shopping, time saving shopping, and credit card preference/in-store oriented shopping. The results showed that the five segmented perceived risk groups differed in regard to clothing shopping orientations, purchase intention, and demographics. Further group differences and implications of the results were discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.638-643
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• 2012

This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.803-811
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• 2002

Machine vision systems are usually used to identify traffic lanes and then determine the steering angle of an autonomous vehicle in real time. The steering angle is calculated using a geometric model of various parameters including the orientation, position, and hardware specification of a camera in the machine vision system. To find the accurate values of the parameters, camera calibration is required. This paper presents a new camera-calibration algorithm using known traffic lane features, line thickness and lane width. The camera parameters considered are divided into two groups: Group I (the camera orientation, the uncertainty image scale factor, and the focal length) and Group II(the camera position). First, six control points are extracted from an image of two traffic lines and then eight nonlinear equations are generated based on the points. The least square method is used to find the estimates for the Group I parameters. Finally, values of the Group II parameters are determined using point correspondences between the image and its corresponding real world. Experimental results prove the feasibility of the proposed algorithm.

• Geomechanics and Engineering
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• v.22 no.3
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• pp.207-217
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• 2020

Slope reliability analysis and risk assessment for spatially variable soils under rainfall infiltration are important subjects but they have not been well addressed. This lack of study may in part be due to the multiple and diverse evaluation indexes and the low computational efficiency of Monte-Carlo simulations. To remedy this, this paper proposes a highly efficient computational method for investigating random field problems for slopes. First, the probability density evolution method (PDEM) is introduced. This method has high computational efficiency and does not need the tens of thousands of numerical simulation samples required by other methods. Second, the influence of rainfall on slope reliability is investigated, where the reliability is calculated from based on the safety factor curves during the rainfall. Finally, the uncertainty of the sliding mass for the slope random field problem is analyzed. Slope failure consequences are considered to be directly correlated with the sliding mass. Calculations showed that the mass that slides is smaller than the potential sliding mass (shallow surface sliding in rainfall). Sliding mass-based risk assessment is both needed and feasible for engineered slope design. The efficient PDEM is recommended for problems requiring lengthy calculations such as random field problems coupled with rainfall infiltration.

• Earthquakes and Structures
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• v.12 no.3
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• pp.271-284
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• 2017

This paper presents a multimodal adaptive nonlinear static method of analysis that, differently from the nonlinear static methods suggested in seismic codes, does not require the definition of the equivalent Single-Degree-Of-Freedom (SDOF) system to evaluate the seismic response of structures. First, the proposed method is formulated for the assessment of r.c. plane frames and then it is extended to 3D framed structures. Furthermore, the proposed nonlinear static approach is re-elaborated as a displacement-based design method that does not require the use of the behaviour factor and takes into account explicitly the plastic deformation capacity of the structure. Numerical applications to r.c. plane frames and to a 3D framed structure with inplan irregularity are carried out to illustrate the attractive features as well as the limitations of the proposed method. Furthermore, the numerical applications evidence the uncertainty about the suitability of the displacement demand prediction obtained by the nonlinear static methods commonly adopted.

• The Journal of Information Systems
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• v.21 no.3
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• pp.187-214
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• 2012

Supply chain management(SCM) helps firms in integrating their business by collaborating with other value chain partners to meet the unpredictable demand of the end user. Agility is the fundamental characteristic of a supply chain needed for survival in turbulent and volatile markets, which are becoming norms as product life cycles shorten and environmental forces create additional uncertainty resulting in higher risk in the supply chain management. Literature reviews reveals various factors that could affect the agility. The objective of this study is to derive interdependence, partnership, quality of information, joint problem solving, modelize the mutual influential relationship between them, and look into the influential relationship influencing the agility. To test the proposed model, we used a data set generated from survey. Date analysis was conducted using 122 respondents. We used structural equation model (SEM) implemented in partial least square(PLS). The results of this study can be summarized as follows. Interdependence had a significant impact on partnership. Partnership had a significant impact on quality of information and joint problem solving. Quality of information had a significant impact on joint problem solving. Quality of information and joint problem solving had a significant impact on agility. This study diagnoses that Inter-organizational relationship characteristics are important for supply chain agility. Inter-organizational relationship characteristics are considered as an essential factor for supply chain agility.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.365-372
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• 2000

현장상황에 대한 불충분한 자료와 파괴 메커니즘에 대한 불완전한 이해로 인해 발생하는 가변성(variability)과 불확실성(uncertainty)은 암반사면공학뿐만 아니라 지반공학에서 흔히 접하게 되는 문제점이다. 특히 암반사면공학에서는 이러한 가변성과 불확실성이 불연속면의 방향 및 기하학적 특성, 그리고 실내실험 결과의 분산으로 나타난다. 그러나 안전율(factor of safety)의 개념을 기초로 하는 전통적인 결정론적 해석방법(deterministic analysis)은 이러한 분산을 고려하지 않은 채 단일 대표 값만을 이용하여 구조물의 안정성을 판단하여 왔다. 확률론적 해석방법(probabilistic analysis)은 이러한 가변성과 불확실성을 효과적으로 정량화하여 해석에 이용할 수 있는 방법 중의 하나로 제안되었다. 이러한 해석방법은 불연속면의 기하학적 특성과 강도 특성을 확률변수(random variable)로 취급하여 신뢰성이론(reliability theory)과 확률이론(probability theory)을 근거로 분석하였으며 이를 기초로 하여 Monte Carlo Simulation과 같은 해석법을 이용, 구조물의 붕괴가능성을 확률로 표현하였다. 확률론적 해석 방법은 기존의 안전율을 대체하여 구조물의 안정성을 붕괴확률(probability of failure)로 제안하였으며 이 붕괴확률은 안전율의 확률분포함수 (probability density function)에서 안전율이 1보다 작을 가능성을 확률로 나타낸 수치이다. 본 논문에서는 확률론적 해석방법을 이용하여 불연속면 특성들의 확률특성을 고찰하였으며 이를 기초로 하여 암반사면의 안정성 해석에 응용했다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.469-473
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• 2006

In general, a classification process between ground data and non-ground data, which include building objects, is required prior to producing a DEM for a certain surface reconstruction from LiDAR data in which the DEM can be produced from the ground data, and certain objects like buildings can be reconstructed using non-ground data. Thus, an exact classification between ground and non-ground data from LiDAR data is the most important factor in the ground reconstruction process using LiDAR data. In particular, building objects can be largely used as digital maps, orthophotos, and urban planning regarding the object in the ground and become an essential to providing three dimensional information for certain urban areas. In this study, an entropy theory, which has been used as a standard of disorder or uncertainty for data used in the information theory, is used to apply a more objective and generalized method in the recognition and segmentation of buildings from raw LiDAR data. In particular, a method that directly uses the raw LiDAR data, which is a type of point shape vector data, without any changes, to a type of normal lattices was proposed, and the existing algorithm that segments LiDAR data into ground and non-ground data as a binarization manner was improved. In addition, this study proposes a generalized building extraction method that excludes precedent information for buildings and topographies and subsidiary materials, which have different data sources.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.488-493
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• 2006

Accurate river discharge is the most important factor in managing river basins and for successfully maintaining total maximum daily loads in Korea. It is not easy to measure the discharge directly in large rivers owing to physical and environmental constraints, even after investing much time and money. Recently, to overcome these historical drawbacks in river discharge measurement, we have developed the Automatic Discharge Acquisition & Management System (ADAMS) that scans the river cross-section and measures each cell $(1m{\times}1m)$ velocity using HADCP. The hardware system is composed of an HADCP sensor and winch, as well as a PC and software system for the discharge calculation module and hardware control module. It is controlled remotely via the internet and uses the velocity-depth integration method and the velocity-contour method for calculating river discharges. The characteristics of ADAMS are a ubiquitously accessible system, featuring real time automatic discharge measurement, remote control via the internet. The results using ADAMS at the Jindong stage site show less than 5% uncertainty and are 4 times more efficient than the ADCP & Q-boat system. This system can be used to measure any large river, river mouth or tributary river affected by backwater, all of which have a very difficult measuring real time discharge. The next generation of ADAMS will feature an upgrade to increase portability and GPS integration.