• The Korean Journal of Archival Studies
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• no.8
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• pp.137-180
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• 2003

The term 'Business Archives' is not familiar with us in our society. Some cases can be found that materials are collected for publishing the history of a firm on commemoration of some decades of its foundation. However, the appropriate management of these collected materials doesn't seem to be followed in most of companies. The Records and archives management is inevitable in order to maximize the utility of Information and knowledge in the business world. The interest in records management has been grown, especially in the fields of business management and information technology. However, the importance of business archives hasn't been conceived yet. And also no attention has been paid to the business archives as social resources and the responsibility of the society as a whole for their preservation. The company archives doesn't have a long history in Germany although the archives of the nation, the aristocracy, communes and churches have a long tradition. However the company archives of Krupps which was established in 1905, is regarded as the first business archives in the world, It means that Germany has taken a key role to lead the culture of business archives. This paper focuses on the process of the establishment of business archives in Germany and its characteristics. The business archives in Germany can be categorized in three types: company archives, regional business archives and branch archives. It must be noted here that each type of these was generated in the context of the accumulation of the social resources and its effective use. A company archives is established by an individual company for the preservation of and use of the archives that originated in the company. The holdings in the company archives can be used as materials for decision making of policies, reporting, advertising, training of employees etc. They function not only as sources inside the company, but also as raw sources for the scholars, contributing to the study of the social-economic history. Some archives of German companies are known as a center of research. A regional business archives manages materials which originated m commerce chambers, associations and companies in a certain region. There are 6 regional business archives in Germany. They collect business archives which aren't kept in a proper way or are under pressure of damage in the region for which they are responsible. They are also open to the public offering the sources for the study of economic history, social history like company archives, so that they also play a central role as a research center. Branch business archives appeared relatively late in Germany. The first one is established in Bochum in 1969. Its general duties and goals are almost similar with ones of other two types of archives. It has differences in two aspects. One is that the responsibility of the branch business archives covers all the country, while regional business archives collects archives in a particular region. The other is that a branch business archives collects materials from a single industry. For example, the holdings of Bochum archives are related with the mining industry. The mining industry-specialized Bochum archives is run as an organization in combination with a museum, which is called as German mine museum, so that it plays a role as a cultural center with the functions of exhibition and research. The three types of German business archives have their own functions but they are also closely related each other under the German Association of Business Archivists. They are sharing aims to preserve primary materials with historical values in the field of economy and also contribute to keeping the archives as a social resources by having feed back with the public, which leads the archives to be a center of information and research. The German case shows that business archives in a society should be preserved not only for the interest of the companies, but also for the utilities of social resources. It also shows us how business archives could be preserved as a social resource. It is expected that some studies which approach more deeply on this topic will be followed based on the considerations from the German case.

• Korean Journal of Heritage: History & Science
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• v.52 no.1
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• pp.190-211
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• 2019

In March 2015, The Agency for Cultural Affairs of Japan implemented a project called "Japan Heritage," which aims to promote the unique narratives of cultural properties of the region by branding the locality for revitalization in preparation for the 2020 Tokyo Olympics and Paralympics. This bottom-up approach of cultural policy has been called a "Cool Japan Strategy of Cultural Heritage" in the 21st century, which effectively incorporates local cultural heritage and tourism. However, although a total of 67 Japan Heritage projects have been designated as of December 2018, almost none has been introduced in the academic forum in Korea. On the basis of this background and a lack of academic awareness in Korea on Japan's recent cultural policies, this research aims to focus on the three cases of Gunma Prefecture implemented in local, global, and glocal aspects. To specify, the cases are the "Gunma Silk Heritage" project, implemented in 2011, the "Tomioka Silk Mill and Related Sites" project that was included on the UNESCO World Heritage List in 2014, and "The Best Wife in the World - Silk Story of Gunma," case certified as the first project of "Japan Heritage" launched in 2015. Based on the questionnaire method conducted with the World Heritage Registration Promotion Division in Gunma Prefectural Government, as well as a literature view, the research revealed that the consecutive implementation of a series of cultural heritage projects in Gunma is not coincidental, but rather a strategy aiming to create a synergism where each project complements the others. Moreover, this paper demonstrates that Gunma Prefecture has been utilizing the local silk industry as a tangible and intangible cultural resource in multi-layered heritage projects, resulting in a "spiral synergy effect" and a "chain of the recognition process." In conclusion, it illustrates the recent trend of utilizing cultural heritage in the context of the Cool Japan strategy, which seeks to move away from the administration of maintaining the status quo cultural heritage protection to a proactive one with greater potential growth. This research may thus provide meaningful insight into the utilization of domestic historical and cultural resources as well as related policy-making, in that it will ultimately promote the chain effect of linking the multiple heritage policies and projects at the local, global, and glocal levels.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.34-42
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• 2019

Baseflow which is one of the unmeasurable components of streamflow and slowly flows through underground is important for water resource management. Despite various separation methods from researches preceded, it is difficult to find a significant separation method for baseflow separation. This study applied the MRC method and developed the improved approach to separate baseflow from total streamflow hydrograph. Previous researchers utilized the whole streamflow data of study period at once to derive synthetic MRCs causing unreliable results. This study has been proceeded with total nine areas with gauging stations. Each three areas are selected from 3 domestic major watersheds. Tool for drawing MRC had been used to draw MRCs of each area. First, synthetic MRC for whole period and two other MRCs were drawn following two different criteria. Two criteria were set by different conditions, one is flow condition and the other is seasonality. The whole streamflow was classified according to seasonality and flow conditions, and MRCs had been drawn with a specialized program. The MRCs for flow conditions had low R2 and similar trend to recession segments. On the other hand, the seasonal MRCs were eligible for the baseflow separation that properly reflects the seasonal variability of baseflow. Comparing two methods of assuming MRC for baseflow separation, seasonal MRC was more effective for relieving overestimating tendency of synthetic MRC. Flow condition MRCs had a large distribution of the flow and this means accurate MRC could not be found. Baseflow separation using seasonal MRC is showing more reliability than the other one, however if certain technique added up to the flow condition MRC method to stabilize distribution of the streamflow, the flow conditions method could secure reliability as much as seasonal MRC method.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Music and Human Behavior
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• v.5 no.1
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• pp.67-90
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• 2008

This case used MI techniques that give an imagery experience to depressed client's mental resource, and that makes in to verbalism. Also those images are supportive level therapy examples that apply to positive variation. MI is simple word of 'Music and Imagery' with one of psychology cure called GIM(Guided Imagery and Music). It makes client can through to the inner world and search, confront, discern and solve with suitable music. Supportive Level MI is only used from safety level music. Introduction of private session can associate specification feeling, subject, word or image. And those images are guide to positive experience. The First session step of MI program is a prelude that makes concrete goal like first interview. The Second step is a transition that can concretely express about client's story. The third step is induction and music listening. And it helps to associate imagery more easily by used tension relaxation. Also it can search and associate about various imagery from the music. The last step is process that process drawing imagery, talking about personal imagery experience in common with therapist that bring the power by expansion the positive experience. Client A case targets rapport forming(empathy, understanding and support), searching positive recourse(child hood, family), client's emotion and positive support. Music must be used simple tone, repetition melody, steady rhythm and organized by harmony music of what therapist and client's preference. The client used defense mechanism and couldn't control emotion by depression in 1 & 2 sessions. But the result was client A could experience about support and understanding after 3 sessions. After session 4 the client had stable, changed to positive emotion from the negative emotion and found her spontaneous. Therefore, at the session 6, the client recognized that she will have step of positive time at the future. About client B, she established rapport forming(empathy, understanding and support) and searching issues and positive recognition(child hood, family), expression and insight(present, future). The music was comfortable, organizational at the session 1 & 2, but after session 3, its development was getting bigger and the main melody changed variation with high and low of tune. Also it used the classic and romantic music. The client avoids bad personal relations to religious relationship. But at the session 1 & 2, client had supportive experience and empathy because of her favorite, supportive music. After session 3, client B recognized and face to face the present issue. But she had avoidance and face to face of ambivalence. The client B had a experience about emotion change according depression and face to face client's issues After session 4. At the session 5 & 6, client tried to have will power of healthy life and fairly attitude, train mental power and solution attitude in the future. On this wise, MI program had actuality and clients' issues solution more than GIM program. MI can solute the issue by client's based issue without approach to unconsciousness like GIM. Especially it can use variety music and listening time is shorter than GIM and structuralize. Also can express client's emotion very well. So it can use corrective and complement MI program to children, adolescent and adult.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.