• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.676-686
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• 2010

An URC (Ubiquitous Robot Companion) is aimed for providing the best service according to situational information that it recognizes. In order to offer human-friendly and intelligent services, a robot middleware requires the technique to automate and control URC service processes, which are based on context-awareness. In this paper, we propose a context-aware workflow system to provide web services based URC services according to situational information. The proposed system offers a platform-independent command object model to control heterogeneous URCs, and supports web services based context-aware URC services. Therefore, the proposed system can increase the reliability of URC services in ubiquitous network environment, on which the diverse URC robots and platforms exist. And it can enhance the flexibility and adaptability of the functional and structural changes of URC systems.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.69-76
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• 2002

In conducting studies related to the national base networks, it is very important to construct a simulation network. This research provides an algorithm to construct the simulation network from the digital transportation map which is constructed based on the National Geographic Information System(NGIS). The algorithm consists of three functions(extraction of networks from transportation digital map, transform of the derived network into one suitable to transportation simulation model, and inspection of errors in the network). The direct derivation of a simulation network from GIS enables to enhance the reliability of an analysis related to a transportation facility investment as well as to reduce cost and time. In this research, Emme/2 which is generally accepted transportation planning fields is adapted for the target system. However, this algorithm will be extended to other simulation models such as Satong-Paldal which is the only transportation simulation model developed in Korea, and Tranplan in the near future.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.127-134
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• 2021

This study aims to determine the role of innovative capabilities as a mediator in analyzing entrepreneurial marketing's effect on sustainable competitive advantage in food and beverage micro-, small-, and medium- enterprises (MSMEs). Data was obtained from a food and beverage store manager in Tangerang City, comprising 119 samples. Furthermore, the G⁎Power, a tool used to calculate statistical power analysis for various t-tests, F tests, χ2 tests, z tests, and several exact tests, was used to determine the number of research samples, the α error probability of 5%, and 3 variables. The data collection method used questionnaires with Likert Scale 1-5 to indicate strongly disagree to strongly agree. To analyze data, we used Path Analysis supported by SmartPLS statistics software. Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It aims to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. The data processing process took place in two stages, namely the estimation model testing with validity and reliability, and the structural model testing to decide the impact or correlation between variables utilizing the t-test. The result showed a positive and significant effect of entrepreneurial marketing to innovative capability and competitive advantage through the innovative capability of MSMEs.

• Steel and Composite Structures
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• v.31 no.4
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• pp.373-385
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• 2019

This paper presents a useful in-plane structural analysis of low-rise blind-bolted composite frames with semi-rigid joints. Analytical models were used to predict the moment-rotation relationship of the composite beam-to-column flush endplate joints that produced accurate and reliable results. The comparisons of the analytical model with test results in terms of the moment-rotation response verified the robustness and reliability of the model. Abaqus software was adopted to conduct frame analysis considering the material and geometrical non-linearities. The flexural behaviour of the composite frames was studied by applying the lateral loads incorporating wind and earthquake actions according to the Australian standards. A wide variety of frames with a varied number of bays and storeys was analysed to determine the bending moment envelopes under different load combinations. The design models were finalized that met the strength and serviceability limit state criteria. The results from the frame analysis suggest that among lateral loads, wind loads are more critical in Australia as compared to the earthquake loads. However, gravity loads alone govern the design as maximum sagging and hogging moments in the frames are produced as a result of the load combination with dead and live loads alone. This study provides a preliminary analysis and general understanding of the behaviour of low rise, semi-continuous frames subjected to lateral load characteristics of wind and earthquake conditions in Australia that can be applied in engineering practice.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of Distribution Science
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• v.13 no.11
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• pp.79-92
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• 2015

Purpose - The aim of this study is to develop an empirical model of the effects of traditional market service quality factors on customer value, relationship quality, and behavior. The specific objectives of the study are as follows: 1) to classify study objects into cultural tourism markets and non-cultural tourism markets as well as to verify the differences in service quality among the two markets and, 2) to present practical service marketing methods that fit with the characteristics of the traditional markets by amending the five quality evaluation items of SERVQUAL (a multiple-item scale for measuring service quality)to suit the characteristics of the traditional markets and establish the relationship among customer value, relationship quality, and behavior intention. Research design, data, and methodology - The study methods of empirical investigation are as follows. First, this study selected for a study object the Suwon Paldalmun Gate Market to represent the cultural tourism market, and general traditional markets to represent the non-cultural tourism market. This study also conducted personal interviews in order to increase the response rate and collected a total of 418 responses between March 18, 2014 and April 05, 2014. The total of 418 responses used for this study excluded 14 responses that had either misleading information or missing values. Results - This study verified the perceived differences of service quality based on traditional market specialization through an independent sample t-test. It appeared that the perceived service quality of the cultural tourism market was generally higher than that of the non-cultural tourism market. This study executed a path analysis in order to examine the effects of service quality factors on customer value, relationship quality, and behavior intention. This study also comprehensively analyzed the specialized market and non-specialized market separately. Although there were some differences among the results, the overall results were uniform. It appeared that convenience, reliability, and empathy, among the service quality factors, exerted meaningful effects on customer value. On the other hand, convenience, reliability, responsiveness, and empathy, excluding the tangibles, exerted meaningful effects on the relationship quality. In addition, it appeared that all service quality factors exerted meaningful effects on the customer value, relationship quality, and behavior intention. Therefore, the study verified that all of the hypotheses formulated in the study were generally adopted. Conclusions - The implication of this study may be classified into academic and practical implication as follows. With respect to the academic implication, it seems that this study is among the early studies to verify the differences between the cultural tourism market and the non-cultural tourism market. The practical implication of this study is that the perceived service quality, such as convenience, reliability, responsiveness, and tangibles, excluding empathy, was higher in the cultural tourism market than in the non-cultural tourism market. This means that customer satisfaction is enhanced by governmental aid such as hardware, software, and information and communications technology.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.345-357
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• 2023

Nuclear forensics has been understood as a mendatory component in the international society for nuclear material control and non-proliferation verification. Radiochronometry of nuclear activities for nuclear forensics are decay series characteristics of nuclear materials and the Bateman equation to estimate when nuclear materials were purified and produced. Radiochronometry values have uncertainty of measurement due to the uncertainty factors in the estimation process. These uncertainties should be calculated using appropriate evaluation methods that are representative of the accuracy and reliability. The IAEA, US, and EU have been researched on radiochronometry and uncertainty of measurement, although the uncertainty calculation method using the Bateman equation is limited by the underestimation of the decay constant and the impossibility of estimating the age of more than one generation, so it is necessary to conduct uncertainty calculation research using computer simulation such as Monte Carlo method. This highlights the need for research using computational simulations, such as the Monte Carlo method, to overcome these limitations. In this study, we have analyzed mathematical models and the LHS (Latin Hypercube Sampling) methods to enhance the reliability of radiochronometry which is to develop an uncertainty algorithm for nuclear material radiochronometry using Bateman Equation. We analyzed the LHS method, which can obtain effective statistical results with a small number of samples, and applied it to algorithms that are Monte Carlo methods for uncertainty calculation by computer simulation. This was implemented through the MATLAB computational software. The uncertainty calculation model using mathematical models demonstrated characteristics based on the relationship between sensitivity coefficients and radiative equilibrium. Computational simulation random sampling showed characteristics dependent on random sampling methods, sampling iteration counts, and the probability distribution of uncertainty factors. For validation, we compared models from various international organizations, mathematical models, and the Monte Carlo method. The developed algorithm was found to perform calculations at an equivalent level of accuracy compared to overseas institutions and mathematical model-based methods. To enhance usability, future research and comparisons·validations need to incorporate more complex decay chains and non-homogeneous conditions. The results of this study can serve as foundational technology in the nuclear forensics field, providing tools for the identification of signature nuclides and aiding in the research, development, comparison, and validation of related technologies.