• Journal of Service Research and Studies
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• v.14 no.1
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• pp.44-62
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• 2024

The development of online funeral services and social issues of eco-friendly funeral culture have raised awareness of the new need for online funeral culture. There have been several attempts to revitalize online funeral services in domestic institutions and companies, but the effect is weak. The purpose of this study is to propose a design that can improve the accessibility and usability of online memorial services by analyzing the usability problem factors through a First Time User Experience analysis of the online memorial platform. Therefore, in this study, in order to identify the problem factors of the online memorial platform, a literature review on the UX, OOBE, and FTUE theories was conducted. The subject of the study was the app 'Memorial'. Before analyzing the First-Time User-Experience, IA was compared and analyzed with other similar services to understand the characteristics of the UX service of the app 'Memorial', which is the subject of the study. In addition, tasks corresponding to the Unpack-Setup/Configure-First Use stage were performed on 10 subjects who had no experience using the online memorial platform. The experimental process was expressed as the UX Curve to identify factors that caused negative experiences. As a result, the major problem factors included unnecessary UI elements, the need for sensitive personal information at the membership stage, and lack of immersion in the service. The improvements included strengthening community functions to facilitate the sharing of emotions and promote smooth communication between users. We proposed UI/UX service developments that enhanced the app by incorporating these insights. In order to verify the effectiveness, serviceability, and value of the developed prototype, an interview with a expert was conducted. The interviewes consisted of three service design experts. This study was conducted to contribute to the quality improvement and activation of the recently emerging online funeral services. The study is significant as it aims to understand the current status of these services and identify the factors necessary to improve service accessibility and usability. Subsequent studies require in-depth user verification of how much the proposed improvement plan affects the actual user experience.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.749-753
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• 2008

Proprioception defined it as the ability to detect, the spatial position or movement of joints using balance, power of the muscle, agility in the internal parts of the body. In existing study for improvement of proprioception, reaching task training provided a feedback; the assessment was not provided a feedback. But, this has problem that it can not guide a proprioception from situation with visual feedback. Virtual reality technique can solve the problem of way providing feedback during training. In this study, we developed proprioception training program using virtual reality and pilot study is performed. VR task were composed three modes. In mode 1, real-time movement of the body was provided using visual feedback. In mode 2, body position was provided using visual feedback when participant have specific response. And in mode 3, body position was not provided. VR task is performed five sessions at each mode and one session performed one by one a three target. In the result of this study, the moving time toward the target from mode 3 was smaller than the moving time toward the target from mode 1 (p= 0.001). The correlation was statistically significant between mode 2 and mode 3 while be offering visual feedback position of mode 2 1session. But, the correlation was not statistically significant between mode 2 and mode 3 after be offered visual feedback position of mode2 1session (p = 0.012). Training environment of mode 1 shows which training used visual feedback than proprioception. Mode2 can execute training of proprioception because first session acquires visual feedback by proprioception. The next study will be verification of the system for training or assessment by clinical experiment.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of Life Science
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• v.22 no.10
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• pp.1359-1370
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• 2012

Arabidopsis AtERF71/HRE2, a transcription activator, is located in the nucleus and is involved in the signal transduction of low oxygen and osmotic stresses. In this study, microarray analysis using AtERF71/HRE2-overexpressing transgenic plants was performed to identify genes downstream of AtERF71/HRE2. A total of 161 different genes as well as AtERF71/HRE2 showed more than a twofold higher expression in AtERF71/HRE2-overexpressing transgenic plants compared with wild-type plants. Among the 161 genes, 24 genes were transcriptional regulators, such as transcription factors and DNA-binding proteins, based on gene ontology annotations, suggesting that AtERF71/HRE2 is an upstream transcription factor that regulates the activities of various downstream genes via these transcription regulators. RT-PCR analysis of 15 genes selected out of the 161 genes showed higher expression in AtERF71/HRE2-overexpressing transgenic plants, validating the microarray data. On the basis of Genevestigator database analysis, 51 genes among the 161 genes were highly expressed under low oxygen and/or osmotic stresses. RT-PCR analysis showed that the expression levels of three genes among the selected 15 genes increased under low oxygen stress and another three genes increased under high salt stress, suggesting that these genes might be downstream genes of AtERF71/HRE2 in low oxygen or high salt stress signal transduction. Microarray analysis results indicated that AtERF71/HRE2 might also be involved in the responses to other abiotic stresses and also in the regulation of plant developmental processes.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.1-10
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• 2022

Irrigation water supplied to the paddy field is consumed in the amount of evapotranspiration, underground infiltration, and natural and artificial drainage from the paddy field. Irrigation return flow is defined as the excess of irrigation water that is not consumed by evapotranspiration and crop, and which returns to an aquifer by infiltration or drainage. The research on estimating the return flow play an important part in water circulation management of agricultural watershed. However, the return flow rate calculations are needs because the result of calculating return flow is different depending on irrigation channel water loss, analysis methods, and local characteristics. In this study, the irrigation return flow rate of agricultural watershed was estimated using the monitoring and SWMM (Storm Water Management Model) modeling from 2017 to 2020 for the Heungeop reservoir located in Wonju, Gangwon-do. SWMM modeling was performed by weather data and observation data, water of supply and drainage were estimated as the result of SWMM model analysis. The applicability of the SWMM model was verified using RMSE and R-square values. The result of analysis from 2017 to 2020, the average annual quick return flow rate was 53.1%. Based on these results, the analysis of water circulation characteristics can perform, it can be provided as basic data for integrated water management.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.697-714
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• 2023

The Tracksite of Cretaceous Dinosaurs and Pterosaurs in Jeongchon, Jinju was discovered in late 2017 during the construction of the Ppuri industry complex. This site is a natural heritage site with a high paleontological value, as it preserves fossils of various types of dinosaurs, pterosaurs, and animal traces at a dense concentration. In this study, we surveyed that physical weathering such as joint, crack, scaling, exfoliation, and fragmentation occurred through field research in the fossil site, and conducted basic research on conservation science to reduce the damage. To this end, among the eight levels identified after excavation, the rocks of Level 3, which yielded a large number of theropod footprint fossils, and Level 4, which yielded pterosaur footprint fossils, were analyzed for material characteristics and evaluation of the effectiveness of consolidation and adhesion. This results showed that the rocks in the Level 3 stratum were dark gray siltstone and the rocks in the Level 4 stratum were dark gray shale, which contained a large amount of calcite and were composed of quartz, plagioclase, mica, alkali feldspar, and other clay minerals, which are likely to be damaged by rainfall under external conditions. As a result of conducting an artificial weathering experiment by dividing the probationary sample into four groups: untreated, consolidation treatment, anti-swelling treatment, and adhesive treatment, the consolidation and the swelling inhibitor showed an effect immediately after treatment, but did not show a blocking effect under a freezing-thawing environment. The adhesive showed that the adhesive effect was maintained even under freezing-thawing conditions. In order to preserve the fossil sites at Jeongchon in the future, in addition to temporary measures to block the inflow of moisture, practical measures such as the construction of protective facilities should be prepared.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.203-209
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• 2020

The purpose of this study is to assess the mechanical stability and alignment of the patient positioning system (PPS) of Leksell Gamma Knife Perfexion(LGK PFX). The alignment of the PPS of the LGK PFX was evaluated through measurements of the deviation of the coincidence of the Radiological Focus Point(RFP) and the PPS Calibration Center Point(CCP) applying different weights on the couch(0, 50, 60, 70, 80, and 90 kg). In measurements, a service diode test tool with three diode detectors being used biannually at the time of the routine preventive maintenance was used. The test conducted with varying weights on the PPS using the service diode test tool measured the radial deviations for all three collimators 4, 8, and 16 mm and also for three different positions of the PPS. In order to evaluate the alignment of the PPS, the radial deviations of the correspondence of the radiation focus and the LGK calibration center point of multiple beams were averaged using the calibrated service diode test tool at three university hospitals in Busan and Gyeongnam. Looking at the center diode for all collimators 4, 8, and 16 mm without weight on the PPS, and examining the short and long diodes for the 4 mm collimator, the means of the validation difference, i.e., the radial deviation for the setting of 4, 8, and 16 mm collimators for the center diode were respectively measured to 0.058 ± 0.023, 0.079 ± 0.023, and 0.097 ± 0.049 mm, and when the 4 mm collimator was applied to the center diode, the short diode, and the long diode, the average of the radial deviation was respectively 0.058 ± 0.023, 0.078 ± 0.01 and 0.070 ± 0.023 mm. The average of the radial deviations when irradiating 8 and 16 mm collimators on short and long diodes without weight are measured to 0.07 ± 0.003(8 mm sd), 0.153 ± 0.002 mm(16 mm sd) and 0.031 ± 0.014(8 mm ld), 0.175 ± 0.01 mm(16 mm ld) respectively. When various weights of 50 to 90 kg are placed on the PPS, the average of radial deviation when irradiated to the center diode for 4, 8, and 16 mm is 0.061 ± 0.041 to 0.075 ± 0.015, 0.023 ± 0.004 to 0.034 ± 0.003, and 0.158 ± 0.08 to 0.17 ± 0.043 mm, respectively. In addition, in the same situation, when the short diode for 4, 8, and 16 mm was irradiated, the averages of radial deviations were 0.063 ± 0.024 to 0.07 ± 0.017, 0.037 ± 0.006 to 0.059 ± 0.001, and 0.154 ± 0.03 to 0.165 ± 0.07 mm, respectively. In addition, when irradiated on long diode for 4, 8, and 16 mm, the averages of radial deviations were measured to be 0.102 ± 0.029 to 0.124 ± 0.036, 0.035 ± 0.004 to 0.054 ± 0.02, and 0.183 ± 0.092 to 0.202 ± 0.012 mm, respectively. It was confirmed that all the verification results performed were in accordance with the manufacturer's allowable deviation criteria. It was found that weight dependence was negligible as a result of measuring the alignment according to various weights placed on the PPS that mimics the actual treatment environment. In particular, no further adjustment or recalibration of the PPS was required during the verification. It has been confirmed that the verification test of the PPS according to various weights is suitable for normal Quality Assurance of LGK PFX.