• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.556-569
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• 2015

With about 75% of the population of Korea criticizing the government's disaster policy and a failure to respond to large-scale emergency like the Sewol ferry sinking means that there is a deep distrust in the government. In order to prevent dreadful disasters such as the Sewol ferry sinking, it is important to secure a prime time with respect to disaster safety. Improving crisis management skills and managerial role of police officers who are in close proximity to the people is necessary for the success of disaster management. With disaster management as one of the most essential missions of the police, as a part of a national crisis management, a step by step strengthening of the disaster safety management system of the police is necessary, as below. First, at the prevention phase, law enforcement officers were not injected into for profit large-scale assemblies or events, but in the future the involvement, injection should be based on the level of potential risk, rather than profitability. In the past and now, the priortiy was the priority was on traffic flow, traffic communication, however, the paradigm of traffic policy should be changed to a safety-centered policy. To prevent large-scale accidents, police investigators should root out improper routines and illegal construction subcontracting. The police (intelligence) should strengthen efforts to collect intelligence under the subject of "safety". Second, with respect to the preparatory phase, on a survey of police officers, the result showed that 72% of police officers responded that safety management was not related to the job descriptions of the police. This, along with other results, shows that the awareness of disaster safety must be adopted by, or rather changed in the police urgently. The training in disaster safety education should be strengthened. A network of experts (private, administrative, and police) in safety management should be established to take advantage of private resources with regard to crisis situtions. Third, with respect to the response phase, for rapid first responses to occur, a unified communication network should be established, and a real-time video information network should be adopted by the police and installed in the police situation room. Fourth, during the recovery phase, recovery teams should be injected, added and operated to minimize secondary damage.

• Journal of Korean Society of Archives and Records Management
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• v.3 no.1
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• pp.69-92
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• 2003

Registration and description of records are important elements of processing which provide with the background information of production of records and business-related information. They also enable to search and use the records. In this paper, I examined the Korean registration and description system defined in the Public Records Management Act which directs the records creating agency to register records in creating offices and directs the "professional archives" to make "basic registrations" and "detailed registrations" of the records. In the analysis and comparison of two different registration and description systems with the known international standards of records and archives management, such as ISO15489 and ISAD(G), I intended to evaluate the Korean records and archives management system and suggested recommendations for the renovation of the Korean recordskeeping system. Despite we have unique office business procedures and the culture of officialdom, and despite we have developed our system based on the established business procedures and office culture, it would be preferable to adopt or follow the international standards and established best practices. After the comparative analysis, I recommended some innovations in the filed of registration and description. For instance, in the basic registration. we would better to install an item of "simple contents summary." We may also need the multiple-level description. The fonds level description and the series level description should be introduced to our archival automated management system. We need to establish a Korean standard of description adopting the rules of the ISAD(G) and ISAAR(CPF). Essential requirements for electronic records management, such as contextual and structural information, should be incorporated in the new standard. Documentation of records disposition also should be reinforced to guarantee the authenticity of records and to ensure control of the records. To implement the recommendations for the standard, we need to amend the Public Records Management Act and its Regulations and Rules. Also it is imperative to redesign the GARS integrated archival automated management system.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.106-121
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• 2011

The number of World Natural Heritage Sites is smaller than that of World Cultural Heritage Sites. As of 2010, the total number of natural sites was 180, which is less than 1/3 of all cultural sites. The reason why the number of natural sites is smaller can be attributed to the evaluating criteria of OUV(outstanding universal value). Only 9 fossil related sites were designated as World Heritage Sites among 180 Natural Sites. This study compares their OUVs including the academic value and characteristics of the 9 World Heritage Sites to provide data and reference for KCDC(Korean Cretaceous Dinosaur Coast) to apply as a World Natural Heritage Site. This study was carried out to obtain information and data on the Wadi Al-Hitan of Egypt which was designated as a World Natural Heritage Site. The study includes field investigation for whale fossils, interviews of site paleontologists and staff, and inspections of facilities. Three factors can likely be attributed to its successful management and operating system. First, there is a system for comprehensive research and a monitoring plan. Secondly, experts have been recruited and hired and professional training for staff members has been done properly. Finally, the Wadi Al-Hitan has developed local resources with specialized techniques for conservation and construction design, which matched well with whale fossils and the environment at the site. The Wadi Al-Hitan put a master plan into practice and achieved goals for action plans. To designate a future World Natural Heritage Site in Korea, it is important to be recognized by international experts including IUCN specialists as the best in one's field with OUV. Full-time regular-status employees for a research position are necessary from the preparation stage for the UNESCO World Heritage Site. Local government and related organizations must do their best to control monitoring plans and to improve academic value after the UNESCO World Heritage Site designation. As we experienced during the designation process of Jeju Volcanic Island and Lava Tubes as the first Korean World Natural Heritage Site, participation by various scholars and specialists need to be in harmony with active endeavors from local governments and NGOs.

• Journal of Hospice and Palliative Care
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• v.22 no.1
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• pp.39-47
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• 2019

Purpose: This study was performed to investigate the current status of pediatric palliative care provision and how it is perceived by the palliative care experts. Methods: A descriptive study was conducted with 61 hospice institutions. From September through October 2017, a questionnaire was completed by experts from the participating institutions. Data were analyzed using SPSS 21.0. Results: Among 61 institutions, palliative care is currently provided for pediatric cancer patients by 11 institutions (18.0%), all of which are concentrated in Seoul, Incheon and Gyeonggi and Gyengsang provinces; 85.2% of all do not plan to provide specialized pediatric palliative care in the future. According to the experts, the main barriers in providing pediatric palliative care were the insufficient number of trained specialists regardless of the delivery type. Experts said that it was appropriate to intervene when children were diagnosed with cancer that was less likely to be cured (33.7%) and to move to palliative care institutions when their conditions worsened (38.2%); and it was necessary to establish a specialized pediatric palliative care system, independent from the existing institutions for adult patients (73.8%). Conclusion: It is necessary to develop an education program to establish a nationwide pediatric palliative care centers. Pediatric palliative care intervention should be provided upon diagnosis rather than at the point of death. Patients should be transferred to palliative care institutions after intervention by their existing pediatric palliative care team at the hospital is started.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.45-54
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• 2021

This study was written to create educational content in non-destructive fields based on Mixed Reality. Currently, in the field of radiation, there is almost no content for educational Mixed Reality-based educational content. And in the field of non-destructive inspection, the working environment is poor, the number of employees is often 10 or less for each manufacturer, and the educational infrastructure is not built. There is no practical training, only practical training and safety education to convey information. To solve this, it was decided to develop non-destructive worker education content based on Mixed Reality. This content was developed based on Microsoft's HoloLens 2 HMD device. It is manufactured based on the resolution of 1280 ⁎ 720, and the resolution is different for each device, and the Side is created by aligning the Left, Right, Bottom, and TOP positions of Anchor, and the large image affects the size of Atlas. The large volume like the wallpaper and the upper part was made by replacing it with UITexture. For UI Widget Wizard, I made Label, Buttom, ScrollView, and Sprite. In this study, it is possible to provide workers with realistic educational content, enable self-directed education, and educate with 3D stereoscopic images based on reality to provide interesting and immersive education. Through the images provided in Mixed Reality, the learner can directly operate things through the interaction between the real world and the Virtual Reality, and the learner's learning efficiency can be improved. In addition, mixed reality education can play a major role in non-face-to-face learning content in the corona era, where time and place are not disturbed.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.1-18
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• 1997

This study was carried out to reveal the status of health examination among farmers and to attract more attention to the health care system for farmers. Ten pre-trained medical students interviewed the rural residents 18 years of age and older in eight villages which were randomly selected from a county near Taegu city in Korea, in August 1996. Finally 751 persons were interviewed of whom the percentages of male and female were 41.8%, 58.2% respectively. Among the subjects, 361(48.3%) were fully engaged in farming, 184(24.4%) were partly engaged, and the remaining 206(27.3%) were not engaged in farming at all. The overall prevalence of farmer's disease was 23.0% and there was no significant difference between the group of fully engaged in farming(23.3%) and the group of not-fully engaged(22.9%). But the prevalence of farmer's disease in female subjects(27.8%) was significantly higher than that in male(16.2%)(p<0.01). Among the 288 farmer engaged in spraying pesticide, 113(39.2%) had experienced one or more pesticide related symptoms during last one year, but only 18(15.9%) of them had visited medical facilities due to their symptoms. The experience of receiving education about pesticide was significantly correlated with the degree of wearing protectors during pesticide spraying(p<0.001). Among the 736 persons excluding non-respondents, 281(38.2%) received health examination during last one year ; 176(62.6%) of them received free health examination, and 105(37.4%) received charged one. Among the 533 persons 40 years age and older, only 124(23.3%) had received the 'health examination for the elderly' during last one year, which is provided for the 40 years age and older by Korea medical insurance corporation and medical insurance societies. Most of all beneficiaries of self-employed medical insurance thought the imposed contributions as very expensive(77.4%) or moderately expensive(13.2%). The great majority of farmers are exposed to various health risk factors including pesticide, high temperature, overwork etc. comparable to industrial workers. But farmers are excluded from the regular yearly worker's health examination because of not belonging to a company despite they pay relatively more medical insurance contributions compared with the industrial workers and the urban self-employed medical insureds. It is necessary to develop special health management program for farmers such as the special health examination for the industrial workers exposed harmful agents.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Distribution Research
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• v.15 no.3
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• pp.1-34
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• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.