• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.195-202
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• 2021

Concrete quality is mainly managed through the reliable prediction and control of compressive strength. Although related industries have established a relevant datasets based on the mixture proportions and compressive strength gain, whereas they have not been shared due to various reasons including technology leakage. Consequently, the costs and efforts for quality control have been wasted excessively. This study aimed to develop a web-based numerical model, which would present diverse optimal values including concrete strength prediction to the user, and to establish a sustainable database (DB) collection system by inducing the data entered by the user to be collected for the DB. The system handles the overall technology related to the concrete. Particularly, it predicts compressive strength at a mean accuracy of 89.2% by applying the artificial neural network method, modeled based on extensive DBs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.192-207
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• 2020

In industrial fields like big factories, efficient management of resources is critical in terms of time and expense. So, inefficient management of resources leads to additional costs. Nevertheless, in many cases, there is no proper system to manage resources. This study proposes a system to manage and track large-scale resources efficiently. We attached Bluetooth 5.0-based beacons to our target resources to track them in real time, and by saving their transportation data we can understand flows of resources. Also, we applied a diagonal survey method to estimate the location of beacons so we are able to build an efficient and accurate system. As a result, We achieve 47% more accurate results than traditional trilateration method.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1173-1181
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• 2020

Recently, as local heavy rains occur frequently in a short period of time, economic and social impacts are increasing beyond the simple primary damage. In advanced meteorologically advanced countries, realistic and reliable impact forecasts are conducted by analyzing socio-economic impacts, not information transmission as simple weather forecasts. In this paper, the degree of flooding was derived using the Spatial Runoff Assessment Tool (S-RAT) and FLO-2D models to calculate the threshold rainfall that can affect human walking, and the threshold rainfall of the concept of Grid to Grid (G2G) was calculated. In addition, although it was used a lot in the medical field in the past, a quantitative accuracy analysis was performed through the ROC analysis technique, which is widely used in natural phenomena such as drought or flood and machine learning. As a result of the analysis, the results of the time period similar to that of the actual and simulated immersion were obtained, and as a result of the ROC (Receiver Operating Characteristic) curve, the adequacy of the fair stage was secured with more than 0.7.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.2
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• pp.117-133
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• 2022

The archival content listed arbitrarily makes it difficult for users to efficiently access the records of major economic policies, especially given that they use it without understanding the required period and context. Using the text mining techniques in the 30-year economic policy direction from 1991 to 2021, this paper derives economic-related keywords and changes that the government mainly dealt with. It collects and preprocesses major economic policies' background, main content, and body text and conducts text frequency, term frequency-inverse document frequency (TF-IDF), network, and time series analyses. Based on these analyses, the following words are recorded in order of frequency: "job(일자리)," "competitive(경쟁력)," and "restructuring(구조조정)." In addition, the relative ratio of "job (일자리)," "real estate(부동산)," and "corporation(기업)," by year was analyzed in terms of chronological order while presenting major keywords mentioned by each government. Based on the results, this study presents implications for developing and broadening the area of archival information services related to economic policies.

• Progress in Medical Physics
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• v.14 no.1
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• pp.8-14
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• 2003

The spinal cord dose is the one of the limiting factor for the radiation treatment of the head ＆ neck or the thorax region. It is not an easy task to maintain the spinal cord dose below tolerance and to keep the clinically acceptable dose to the PTV in this region. To overcome this problem, the spinal cord partial block technique (PBT) with the dynamic Multi-Leaf Collimator (dMLC) has been developed. This technique is an extension of the conventional treatment planning. In the beginning the beam directions are selected as same as the conventional treatment planning to encompass the PTV, then the partial block are designed to shield the spinal cord. The plan comparisons between the conventional therapy plan and the PTB plan were performed to evaluate the validity of this technique. The mean dose and the dose volume histogram (DVH) were used as the plan comparison indices. A series of quality assurance (QA) was performed to guarantee the reliable treatment. The QA consisted of the film dosimetry for the verification of the dose distribution and the point measurements. The PBT plan generated better results than the conventional treatment plan and it was proved to be useful for the H＆N region.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.137-146
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• 2002

In VOD server, in order to guarantee playback of continuous media, such as video, without hiccups for multiusers, the server has to manage its buffer sophisticatedly by prefeching a part of the data into the buffer As the continuous media data buffered by one user can be used again by the others, the number of disk accesses is reduced and then the latency time far the users is also reduced. In this paper, we propose a new buffer management technique for continuous media in VOD server. Our basic algorithm partitions the buffer into groups and then a group of buffer which has the lowest utilization is chosen and partitioned again for a new user. The basic algorithm is extended for supporting multiple streams and clip data and for providing VCR functions. Our proposed technique is able to increase in the number of concurrent users as increasing the utilization of the buffer and to minimize the average waiting time for multiuser accesses as the bandwidth of storage is slowly reached to the limit. In the simulation study for comparing the performance of our technique with that of the existing techniques, we show that the average waiting time is reduced mere than 50% and the number of concurrent users increases by 1 ∼5% as compared with those of the exiting techniques.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.309-330
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• 2022

Intellectual structure analysis, which quantitatively identifies the structure, characteristics, and sub-domains of fields, has rapidly increased in recent years. Analysis techniques traditionally used to conduct intellectual structure analysis research include bibliographic coupling analysis, co-citation analysis, co-occurrence analysis, and author bibliographic coupling analysis. This study proposes a novel intellectual structure analysis method, Keyword Bibliographic Coupling Analysis (KBCA). The Keyword Bibliographic Coupling Analysis (KBCA) is a variation of the author bibliographic coupling analysis, which targets keywords instead of authors. It calculates the number of references shared by two keywords to the degree of coupling between the two keywords. A set of 1,366 articles in the field of 'Open Data' searched in the Web of Science were collected using the proposed KBCA technique. A total of 63 keywords that appeared more than 7 times, extracted from 1,366 article sets, were selected as core keywords in the open data field. The intellectual structure presented by the KBCA technique with 63 key keywords identified the main areas of open government and open science and 10 sub-areas. On the other hand, the intellectual structure network of co-occurrence word analysis was found to be insufficient in the overall structure and detailed domain structure. This result can be considered because the KBCA sufficiently measures the relationship between keywords using the degree of bibliographic coupling.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.54-68
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• 2011

As the prediction accuracy of conditional branch instruction is increased highly, the importance of prediction accuracy for unconditional branch instruction is also increased accordingly. Except the case of RAS(Return Address Stack) overflow, the prediction accuracy of function return address should be 100% theoretically. However, there exist some possibilities of miss-predictions for RAS return addresses, when miss-speculative execution paths are invalidated, in case of modern speculative microprocessor environments. In this paper, we propose the RAS rename technique to prevent RAS pollution, results in the reduction of RAS miss-prediction. We divide a RAS stack into a soft-stack and a hard-stack and we handle the instructions for speculative execution in the soft-stack. When some overwrites happen in the soft-stack, we move the soft-stack data into the hard-stack. In addition, we propose an enhanced version of RAS rename scheme. In simulation results, our solution provide 1/90 reduction of miss-prediction of function return address, results in up to 6.85% IPC improvement, compared to normal RAS method. Furthermore, it reduce miss-prediction ratio as 1/9, compared to previous technique.