• The Korean Journal of Archival Studies
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• no.21
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• pp.355-383
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• 2009

The greatest mission of archival community lies in collecting records to document past and present Korean society and in safeguarding them to pass over to the next generation. For the last decade, Korean archival community has actively pursued this mission and accomplished it to the certain extent. A series of events occurred during the last year urged the community to regretfully accept that the so-called accomplishment was by no means satisfactory. The present study aims to review what has been achieved against what should be if the community is to be truthful to the fundamental mission. It begins with addressing attributes of electronic records. To be specific, attributes of electronic records as 'records' are compared against those as 'digital objects.' Current state of long-term preservation of electronic records is reviewed. Suggestions follow in terms of four directions: establishing a policy framework based on principles of electronic records management, building integrated electronic records management system, researching and developing functionalities for long-term preservation, and finally, knowledge transfer and coordination.

• Applied Chemistry for Engineering
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• v.30 no.1
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• pp.23-28
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• 2019

We report a supercapacitor cable, which consists of three-dimensional (3D) porous graphene coated onto the surface of carbon thread. The 3D porous framework of graphene was constructed by microwave-activated process using a graphene oxide-coated carbon thread. The use of microwave irradiation enabled to convert graphene oxide into reduced graphene oxide without any reducing agents and activate graphene sheets into exfoliated and porous graphene sheets. Combining two wire electrodes with a polymer gel electrolyte successfully completed supercapacitor device in a form of cable construction. The supercapacitor cables were highly flexible, and thus can be transformed into various shapes of devices and be integrated into textile items. A high area-capacitance of 38.1 mF/cm was obtained at a scan rate of 10 mV/s. This capacitance was retained 88% of its original value at 500 mV/s. The cycle life was also demonstrated by repeating a charge/discharge process during 10,000 cycles even under bent states, showing a high capacitance retention of 96.5%.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.41-47
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• 2019

Defense simulation is actively used to test various weapon systems and evaluate their effectiveness. The AddSIM environment is a simulation framework designed to support the weapon systems dealt with in defense simulation from an integrated point of view and is designed for reuse and scalability. Models used in AddSIM require base model structure fidelity and verification of user code area. Therefore, this paper describes the dynamic verification method used for completeness of models used in AddSIM. For the dynamic verification of user code, the specification method and the verification algorithm are described. Also, we introduce the prototype of the dynamic verifier implemented based on verification specification method and algorithm. The case study analyzes the verification results based on the simulation example implemented in AddSIM environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.93-98
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• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• Informatization Policy
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• v.27 no.4
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• pp.47-66
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• 2020

The rapid development of artificial intelligence technologies such as machine learning and deep learning is expanding its impact in the public administrative and public policy sphere. This paper is an exploratory study on policy decision-making in the age of artificial intelligence to design automated configuration and operation through data analysis and algorithm development. The theoretical framework was composed of the types of policy problems according to the degree of problem structuring, and the success and failure cases were classified and analyzed to derive implications. In other words, when the problem structuring is more difficult than others, the greater the possibility of failure or side effects of decision-making using artificial intelligence. Also, concerns about the neutrality of the algorithm were presented. As a policy suggestion, a subcommittee was proposed in which experts in technical and social aspects play a professional role in establishing the AI promotion system in Korea. Although the subcommittee works independently, it suggests that it is necessary to establish governance in which the results of activities can be synthesized and integrated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.85-100
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• 2022

Recently, with the widespread expansion of convergence based on digital connectivity, the transportation and mobility fields are rapidly changing, and research related to this is also diversifying. This study aims to analyze the research trends in the mobility field and identify key research areas and topics. Topic modeling analysis has been proved as a useful approach for analyzing the research trends. The abstracts of 142 research papers concerning mobility from the Korean academic citation index were analyzed, derived 9 research topics and linked to 6 key elements of research framework. The result showed that 'Advanced vehicle and transportaion technology' and 'Linkage and integrated services among means for mobility' were most actively studied research fields. It also found that research on insurance, law, regulation for securing user's safety and conflict-resolving with the existing industry has been conducted.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.74-82
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• 2022

The purpose of this paper is to provide a multi-modular account of double accusative constructions in Korean in the framework of Autolexical Grammar. The grammar views syntactic, semantic, and morphological structures of sentences as modules which are generated simultaneously and independently. Unlike syntactocentric theories, this paper analyzes semantic characteristics of double accusatives through function-argument (F/A) structure along with roles structure (RS) and information structure (IS). In F/A structure of double accusatives, the first accusative becomes an argument of a predicate, unlike the possessive, which is an argument of a relational noun. Furthermore, the first accusative of double accusatives takes the role of patient in RS, which allows it to become the subject of a passive sentence. On the other hand, the second accusative, which is originally the possessee, becomes a focal area in IS. Therefore, the purpose of double accusatives is twofold: one is to turn the possessor into an independent argument of a predicate which takes patient role, and the other is to turn the possessee into a focus. Such semantic characteristics of double accusatives can be expressed by means of multi-dimensional structures of F/A structure, RS, and IS of Autolexical Grammar, which allows an integrated account of the phenomenon.