• The Korean Journal of Archival Studies
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• no.22
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• pp.157-199
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• 2009

The organization for managing records and archives did not emerge together with the founding of the Chinese Communist Party. Such management became active with the establishment of the Department of Documents (文書科) and its affiliated offices overseeing reading and safekeeping of official papers, after the formation of the Central Secretariat(中央秘書處) in 1926. Improving the work of the Secretariat's organization became the focus of critical discussions in the early 1930s. The main criticism was that the Secretariat had failed to be cognizant of its political role and degenerated into a mere "functional organization." The solution to this was the "politicization of the Secretariat's work." Moreover, influenced by the "Rectification Movement" in the 1940s, the party emphasized the responsibility of the Resources Department (材料科) that extended beyond managing documents to collecting, organizing and providing various kinds of important information data. In the mean time, maintaining security with regard to composing documents continued to be emphasized through such methods as using different names for figures and organizations or employing special inks for document production. In addition, communications between the central political organs and regional offices were emphasized through regular reports on work activities and situations of the local areas. The General Secretary not only composed the drafts of the major official documents but also handled the reading and examination of all documents, and thus played a central role in record processing. The records, called archives after undergoing document processing, were placed in safekeeping. This function was handled by the "Document Safekeeping Office(文件保管處)" of the Central Secretariat's Department of Documents. Although the Document Safekeeping Office, also called the "Central Repository(中央文庫)", could no longer accept, beginning in the early 1930s, additional archive transfers, the Resources Department continued to strengthen throughout the 1940s its role of safekeeping and providing documents and publication materials. In particular, collections of materials for research and study were carried out, and with the recovery of regions which had been under the Japanese rule, massive amounts of archive and document materials were collected. After being stipulated by rules in 1931, the archive classification and cataloguing methods became actively systematized, especially in the 1940s. Basically, "subject" classification methods and fundamental cataloguing techniques were adopted. The principle of assuming "importance" and "confidentiality" as the criteria of management emerged from a relatively early period, but the concept or process of evaluation that differentiated preservation and discarding of documents was not clear. While implementing a system of secure management and restricted access for confidential information, the critical view on providing use of archive materials was very strong, as can be seen in the slogan, "the unification of preservation and use." Even during the revolutionary movement and wars, the Chinese Communist Party continued their efforts to strengthen management and preservation of records & archives. The results were not always desirable nor were there any reasons for such experiences to lead to stable development. The historical conditions in which the Chinese Communist Party found itself probably made it inevitable. The most pronounced characteristics of this process can be found in the fact that they not only pursued efficiency of records & archives management at the functional level but, while strengthening their self-awareness of the political significance impacting the Chinese Communist Party's revolution movement, they also paid attention to the value possessed by archive materials as actual evidence for revolutionary policy research and as historical evidence of the Chinese Communist Party.

• The Journal of Korean Medical History
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• v.24 no.1
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• pp.43-56
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• 2011

Everyone once in a life experience headaches as symptoms are very common. According to a study in a country of more than a week and as many as those who have experienced a headache amounts to 69.4%. In addition, the high reported prevalence of migraine in 30s for 80% of all migraine sufferers daily life interfere with work or was affected. In Western medicine, the cause of headaches is traction or deformation of pain induced tissue like scalp, subcutaneous tissue, muscle, fascia, extracranial arteriovenous, nerves, periosteum. But it turns out there are not cause why pain induced tissue is being tracted or deformated. Therefore, most of the western-therapy is mainly conducted with regimen for a temporary symptom reduction. Therefore, I examined how it has been developed in Chinese Ming Dynasty, the perception of headache, change in disease stage and an etiological cause. Oriental medicine in the treatment of headache is a more fundamental way to have an excellent treatment. The recognition of head in "素問($s{\grave{u}}$ $w{\grave{e}}n$)" and "靈樞($l{\acute{i}}ng$ $sh{\bar{u}}$)" began to appear in 'Soul-神($sh{\acute{e}}n$) dwelling place' and 'where to gather all the Yang-'諸陽之會($zh{\bar{u}}$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $hu{\grave{i}}$)'. Also, head was recognized as '六腑($li{\grave{u}}f{\check{u}}$) 淸陽之氣($q{\bar{i}}ng$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $q{\grave{i}}$) and 五臟($w{\check{u}}$ $z{\grave{a}}ng$) 精血($j{\bar{i}}ng$ $xu{\grave{e}}$) gathering place'. More specific structures such as the brain is considered a sea of marrow(髓海-$su{\check{i}}$ $h{\check{a}}i$) in "內經($n{\grave{e}}i$ $j{\bar{i}}ng$)" and came to recognized place where a stroke occurs. Accompanying development of the recognition about head, there had been changed about the perception of headache and the recognition of the cause and mechanism of headache. And the recognition of headache began to be completed in Ming Dynasty through Jin, Yuan Dynasty. Chinese Ming Dynasty, specially 樓英($l{\acute{o}}u$ $y{\bar{i}}ng$), in "醫學綱目($y{\bar{i}}xu{\acute{e}}$ $g{\bar{a}}ngm{\grave{u}}$)", first enumerated prescription in detail by separating postpartum headache. and proposed treatment of headache especially due to postpartum sepsis(敗血-$b{\grave{a}}i$ $xu{\grave{e}}$). 許浚($x{\check{u}}$ $j{\grave{u}}n$) accepted a variety of views without impartial opinion in explaining one kind of headache in "東醫寶鑑($d{\bar{o}}ng-y{\bar{i}}$ $b{\check{a}}oji{\grave{a}}n)$" 張景岳($zh{\bar{a}}ng$ $j{\check{i}}ng$ $yu{\grave{e}}$), in "景岳全書($j{\check{i}}ng$ $yu{\grave{e}}$ $qu{\acute{a}}nsh{\bar{u}}$)", established his own unique classification system-新舊表裏($x{\bar{i}}nji{\grave{u}}$ $bi{\check{a}}ol{\check{i}}$)-, and offered a clear way even in treatment. Acupuncture treatment of headache in the choice of meridian has been developed as a single acupuncture point. Using the classification of headache to come for future generation as a way of locating acupoints were developed. Chinese Ming Dynasty, there are special treatments like 導引按蹻法($d{\check{a}}o$ y ${\check{i}}n$ ${\grave{a}}n$ $ji{\check{a}}o$ $f{\check{a}}$), 搐鼻法($ch{\grave{u}}$ $b{\acute{i}}$ $f{\check{a}})$, 吐法($t{\check{u}}$ $f{\check{a}}$), 外貼法($w{\grave{a}}i$ $ti{\bar{e}}$ $f{\check{a}}$), 熨法($y{\grave{u}}n$ $f{\check{a}}$), 點眼法($di{\check{a}}n$ $y{\check{a}}n$ $f{\check{a}}$), 熏蒸法($x{\bar{u}}nzh{\bar{e}}ng$ $f{\check{a}}$), 香氣療法($xi{\bar{a}}ngq{\grave{i}}$ $li{\acute{a}}of{\check{a}}$). Most of this therapy in the treatment of headache, it is not used here, but if you use a good fit for today's environment can make a difference.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.