• The Korean Journal of Archival Studies
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• no.13
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• pp.41-90
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• 2006

It's needed for the archivists to design a flexible and stable ERMS(Electronic Records Management System) which can ingest and store records through a consistent way and let users search and use records easily what they want. The basis of the design for ERMS are the conceptual composition and the logical and physical structure of the records when they are stored and managed in the ERMS. This paper explains the process of defining components and designing structure of electronic records using 3-layered approaches which consist of conceptual, logical and physical layer and shows advantages of this approaches. After benchmarking the information models of OAIS which is a reference model for the long-term preservation of digital information objects, this paper applies the model of AIP to a record as a 'Record AIP' and discusses the composition and structure of it. It's a critical task to identify mandatory or optional metadata groups which consists of the 'Record AIP's in the conceptual layer. This paper emphases that the metadata group related to services for the record information to users is required as a result of benchmarking OAIS information models. Various issues about the structure of 'Record AIP's are discussed according to the kind of preservation strategy such as migration or emulation and whether the encapsulation of records is required or not in the logical layer.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Management & Information Systems Review
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• v.36 no.1
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• pp.115-141
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• 2017

This study is to find the key factors of the Internet of Things for development in public sector. In previous studies, it is said that Internet of Things can work digital system without human operation and gives a lot of outputs(information) users. Generally, people are a subject of operating digital system in traditional way, while people are an object on the internet of things. In other words, it is possible to work digital system with only networking from things to things. After all, it is reported that these advantages of the Internet of Things make possible to reduce social costs significantly in public sector. However, despite the strengths of the Internet of Things, there is a specific user acceptance of the technology factor for the Internet of Things rarely. It means that developing of the Internet of Things only focuses on the final purpose. If the focus on development meet this purpose, the user is ignored for the specific reason that using a technique. As a result of this, many users gradually decrease the continuous using of the Internet of Things. Thus, in this study, we need to find what critical factors should reflect to the Internet of Things in public sector. To find this result, there is no choice to use Technology Acceptance Model(TAM). Many researchers have proved that Technology Acceptance Model is valid through the four process in model introduction, confirmation, expansion and refinement from 1986 to 2003. The results of this study showed that the result explanatory power of Internet of Things in public sector is the most important factor affecting only perceived social usefulness and ease of use. Finally, it can be seen that the user has a positive attitude toward use, which has a positive effect on the intention to use continuously. The implications of this study are summarized as follows: When the public Internet of Things service is provided, it means that the user can easily understand the result, and when the person and the object communicate the result to each other, they should be able to communicate with each other. This means that a lot of user effort is needed to understand the outcome of the public Internet of Things being provided.

• The Korean Journal of Archival Studies
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• no.25
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• pp.3-46
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• 2010

Digital records management is one whole system in which many social and technical elements are interacting. To maintain the trustworthiness, the repository needs periodical audit and certification. Thus, individual electronic records management agency needs toolkit that can be used to self-evaluate their trustworthiness continuously, and self-assess their atmosphere and system to recognize deficiencies. The purpose of this study is development of self-certification toolkit for repositories, which synthesized and analysed such four international standard and best practices as OAIS Reference Model(ISO 14721), TRAC, DRAMBORA, and the assessment report conducted and published by TNA/UKDA, as well as MoRe2 and current national laws and standards. As this paper describes and demonstrate the development process and the framework of this self-certification toolkit, other electronic records management agencies could follow the process and develop their own toolkit reflecting their situation, and utilize the self-assessment results in-house. As a result of this research, 12 areas for assessment were set, which include (organizational) operation management, classification system and master data management, acquisition, registration and description, storage and preservation, disposal, services, providing finding aids, system management, access control and security, monitoring/audit trail/statistics, and risk management. In each 12 area, the process map or functional charts were drawn and business functions were analyzed, and 54 'evaluation criteria', consisted of main business functional unit in each area were drawn. Under each 'evaluation criteria', 208 'specific evaluation criteria', which supposed to be implementable, measurable, and provable for self-evaluation in each area, were drawn. The audit and certification toolkit developed by this research could be used by digital repositories to conduct periodical self-assessment of the organization, which would be used to supplement any found deficiencies and be used to reflect the organizational development strategy.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.