• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.322-331
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• 2013

The purpose of the present study was to develop a convergent education contents for dementia prevention, operating on the web network applying modern information technology(IT). At the preparation stage, local and worldwide literatures related to dementia were analyzed followed by surveying industry demands, based on which the program was designed and developed. In the following enhancement stage, the program was modified as much as possible by advices obtained from experts in various fields. Development results of the present program are summarized as follows. Firstly, 645 intellect development model to prevent dementia was established through peer review and verification of convergent education theories by expert groups. This model was named as "Garisani" meaning "cognition capable of judging objects" in the Korean language. Secondly, 'Find a way' and 'Connect a line' modules were developed in the numeric field as well as 'Identify a letter(I, II)' modules, in the language field for web-based left brain training program. Thirdly, 'Find my car' and 'Vision training' modules in the attention field and 'Object inference' and 'Compare pictures' modules in the cognition field were developed for web-based right brain training program. Fourth, 'Pentomino' and 'BQmaze'(Brain Quotient and maze) modules in the space perception field and 'Visual training' in the memory field were developed for web-based left and right brains training. Fifth, all results were integrated leading to a 52 week Garisani convergent education program for dementia prevention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.702-708
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• 2018

Volatile organic compounds (VOCs), as a significant air pollutant, is generated mainly from the burning of fossil fuels, building materials using painting, etc. The inhalation of a certain amount of VOCs can be deleterious to human health, e.g., headaches, nausea and vomiting. In addition, it can also cause memory loss and even increase the rate of leukemia. Therefore, as one of the methods for reducing VOCs in air, polyacrylonitrile/fly ash (PAN/FA) composite nanofibrous membranes were fabricated by electrospinning. To observe their VOCs adsorption capacity, the morphological structure of PAN/FA nanofibrous mats was investigated by field emission scanning electron microscopy (FE-SEM), and the VOCs (chloroform, benzene, toluene, and xylene) adsorption capacity of PAN/FA membranes were tested by gas chromatography/mass spectrometry (GC/MS). The results indicated that the PAN nanofiber containing 40 wt. % FA powder had the smallest fiber diameter of 283 nm; they also showed the highest VOCs adsorption capacity compared to other composite membranes.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.5
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• pp.92-102
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• 2016

While there have many completions of large parks recently under development, there has been a dearth of quality assessments. Studies focused on post-evaluation have been made to resolve this, but most of these are biased toward user satisfaction after completion and therefore behaviour analysis has limitations on solving problemsduring the actual design implementation processes. Therefore, this study examined the internal phenomenon and structure of the implementation process of design competition through the ground theory and microscopic independent perspective. As a result, maintaining the identity and differentiation of parks derived from the preserved design competition scheme contributed greatly to completeness and satisfaction. Outcomes were mainly caused by the trust of public officials as the ordering organization, the will of policy decision-makers, and the competence of operational enterprises, etc. Negative factors such as undermining the whole concept of the park and landscape occurred as external pressure and related subjects intruded on change design factors due to variations in social conditions. Additionally, illogical construction processes occurred, such as a reinvestigation of the budget for restoration after damaging on original landscape. There have been needs for the improvement of the work processing system. On balance, an interventional role is very important in the park construction process, especially the PA and operation committee in terms of maintaining the basic direction, landscape design supervision for detailed designs, and expert construction management on LA in terms of rational work management in the field. The study, using the microscopic perspective of the designer and ground theory, deliver significant meaning as an early study by suggesting alternative methods for the after-evaluation of large parks and structurally looking into main influence factors driven during the construction process.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.463-476
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• 2006

R-trees have been traditionally optimized for the I/O performance with the disk page as the tree node. Recently, researchers have proposed cache-conscious variations of R-trees optimized for the CPU cache performance in main memory environments, where the node size is several cache lines wide and more entries are packed in a node by compressing MBR keys. However, because there is a big difference between the node sizes of two types of R-trees, disk-optimized R-trees show poor cache performance while cache-optimized R-trees exhibit poor disk performance. In this paper, we propose a cache and disk optimized R-tree, called the PR-tree (Prefetching R-tree). For the cache performance, the node size of the PR-tree is wider than a cache line, and the prefetch instruction is used to reduce the number of cache misses. For the I/O performance, the nodes of the PR-tree are fitted into one disk page. We represent the detailed analysis of cache misses for range queries, and enumerate all the reasonable in-page leaf and nonleaf node sizes, and heights of in-page trees to figure out tree parameters for best cache and I/O performance. The PR-tree that we propose achieves better cache performance than the disk-optimized R-tree: a factor of 3.5-15.1 improvement for one-by-one insertions, 6.5-15.1 improvement for deletions, 1.3-1.9 improvement for range queries, and 2.7-9.7 improvement for k-nearest neighbor queries. All experimental results do not show notable declines of the I/O performance.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.1
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• pp.13-24
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• 2003

MPEG-4 is a standard for multimedia applications and provides a set of technologies to satisfy the needs of authors, service providers and end users alike. In this paper, we suggest a Real-time Multiple Circular Buffer (M4RM Buffer) model, which is suitable for streaming these MPEG-4 contents efficiently. M4RM buffer generates each structure of the buffer, which matches well with each object composing an MPEG-4 content, according to the transferred information, and manipulates multiple read/write operations only by its reference. It divides the decoder buffer and the composition buffer, which are described in the standard, by the unit of frame allocated to minimize the range of access. This buffer unit of a frame is allocated according to the object description. Also, it processes the objects synchronization within the buffer and provides APIs for an efficient buffer management to process the real-time user events. Based on the performance evaluation, we show that M4RM buffer model decreases the waiting time in a buffer frame, and so allows the real-time streaming of an MPEG-4 content using the smaller size of the memory block than IM1-2D and Window Media Player.

• The Korean Journal of Archival Studies
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• no.64
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• pp.27-62
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• 2020

The article has the characteristics of a preliminary writing about how to look at the trend of new archives 'fever' and 'impulsion' emerging around the domestic and foreign art world, which have not been paid much attention yet in the 'mainstream' archive research, and how to accept it independently. Specifically, this study aims to examine how archival art is involved in history and memory with aesthetic attitudes and methods through observation of recent tendency of domestic archive art, and what implications or influence the 'archival impulse' phenomenon in the art world can have on the research trend of 'archival studies.' First, I would like to look at the meaningful movement to reinterpret and actively accept archival impulses in concrete overseas cases, that is, the archive system of a public archive in the United States. This is followed by an attempt to explore the characteristics and characteristics of creative works that are carried out through the medium of archives, that has not yet reached the level of organization of specific archive methods but are sporadically attempted in the domestic art world. It examines how so-called 'archive artists' record unrecorded in a way that is not observed in the existing archival world, and how they summon and include excluded history in aesthetic language. In conclusion, this study explores the possibility of pulling the historical records of tradition out from archival boxes and reinterpreting them as living archives within the contemporary emotional structure from this new artistic trend called 'archival art'.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.