• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.9
• /
• pp.1694-1701
• /
• 2007

Recently The biggest three mobile telecommunication companies of our country still using independence wireless internet platform. And, It can so many difficulties to the phone company and content provide company. Such as the timing of the development or the fee of the development. Because even they develop one product and Hey must make it prepare for some platform of every mobile telecommunication companies. And this make the development more longer and more expensive. For this reason, SKT, LG telecom and KTF develop the new wireless internet platform named WIPI with ETRI. and the working is still go on and go ahead with propulsion. And if it come to reality, the WIPI will attached from much of attack such as hacking or virus. But some data exchange between mobile phone is so important as to flow. Thus, in this paper, we consideration the XML using in the wireless environment and we are design and implementation the XML encryption system working at the WIPI in order to protect the data, we want to protect.

• Journal of the Korea Society for Simulation
• /
• v.30 no.4
• /
• pp.1-8
• /
• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.7
• /
• pp.307-314
• /
• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.13 no.2
• /
• pp.102-110
• /
• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.492-492
• /
• 2022

최근 농업용 담수호 내에서 발생하는 녹조 및 수질오염으로 인한 민원이 증가하고 있다. 농업용수로 이용하고 있는 농업용 담수호의 수질관리는 상류유역에서 유입되는 오염원관리가 중요하나 장기적인 유역계획이 수립되어야하므로 즉각적인 수질개선효과를 기대하기 어렵다. 또한 호 내 수질관리는 광범위한 수면적으로 인해 인력 운영 및 시간적 소모가 크며, 일시적인 수질관리만 기대할 수 있다. 장치형 시설을 설치할 경우 막대한 시설비가 소요되며, 지속적으로 체계적인 유지관리가 필요하다. 따라서 담수호 내 수환경 특성을 고려하여 자율감시 및 수처리 장치를 이용한 지속가능한 수질관리가 필요한 실정이다. 본 연구에서는 담수호의 자율적인 수질관리를 위해 무인항법장비와 자동 수처리 장치를 융합한 제품을 제작하여 현장적용을 통해 수질개선 효과를 분석하였다. 무인 자율 이동장치에 설치된 자동 수처리 장치는 녹조발생에 대응하기 위해 환경부에서 고시한 수처리체(황산알루미늄, Alum)를 이용한 약품 살포 장치를 제작하였다. 자율항법장치의 운행 구역을 지정하고, 총 5회 지정된 구역내에서 약품을 살포하고, 미살포 구역을 대조군으로 하여 살포 구역과 수질개선효과를 비교하였다. 비교 결과 수질 항목별 자동 수처리 장치에 의한 수질 저감효율은 ○ COD 13.8%, TOC 18.6%, SS 23.3%, T-N 8.4%, T-P 58.9%, Chl-a 74.4%로 나타났다.

• Journal of The Korean Institute of Defense Technology
• /
• v.5 no.4
• /
• pp.16-21
• /
• 2023

Although the global number of landmines is on a declining trend, the damages caused by previously buried landmines persist. In light of this, the present study contemplates solutions to issues and constraints that may arise due to the improvement of mine detection equipment and the reduction in the number of future soldiers. Current mine detectors lack data storage capabilities, posing limitations on data collection for research purposes. Additionally, practical data collection in real-world environments demands substantial time and manpower. Therefore, in this study, gprMax simulation was utilized to generate data. The lightweight CNN-based model, MobileNet, was trained and validated with real data, achieving a high identification rate of 97.35%. Consequently, the potential integration of technologies such as deep learning and simulation into geographical detection equipment is highlighted, offering a pathway to address potential future challenges. The study aims to somewhat alleviate these issues and anticipates contributing to the development of our military capabilities in becoming a future scientific and technological force.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.123-139
• /
• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.320-323
• /
• 2007

웹 서비스와 같은 분산된 환경에서, 특정 서비스를 수행하기 위해서는 원격의 컴퓨터나 사이트상에서 다중 에이전트들의 협업을 통해 이루어진다. 이때 서비스는 여러 에이전트들의 복잡한 행위들에 의해 구성된다. 또한 지능적인 서비스를 위해서는 에이전트들의 상태정보, 목적정보, 그리고 계획정보 등을 이용한다. 특히 계획정보는 에이전트들이 일련의 행위들로 구성된다. 하지만 계획수립을 위한, 기존 연구들 대부분은 정적으로 기술된 서비스 명세와 초기상태 정보를 이용하여 특정 목표를 만족시키는 단일 계획 생성 방법을 연구해왔다. 따라서 계획수립이 실행 도중에 기대하지 않은 네트워크 장애나 방해 등으로 서비스 수행을 실패하는 경우, 그 계획은 무효가 되고 다시 계획을 생성 해야만 한다. 그러나 다시 계획을 생성하기 위해서는 많은 시간을 소비하게 될 뿐만 아니라 태스크 중복이 불가피하므로 매우 비효율적이다. 이 논문에서는 강건한 계획수립과 그 계획을 실행하기 위한 효과적인 방법을 제안한다. 즉, 계획수립의 재생성을 피하기 위한 방법으로 단일 계획수립 대신에 실행 가능한 다중 계획들로 표현된 강건한 계획을 생성하는 것이다. 강건한 계획의 행위들이 실행되는 동안, 각 단계마다 실행 가능한 행위를 선택한 후, 그 행위를 실행한다. 그러나 선택된 행위가 실행결과를 낼 수 없을 경우, 대체 가능한 서브 계획 경로를 선택하여 실행한다. 강건한 계획을 표현하기 위해 페트리 넷 기반의 방법을 제안한다. 강건한 계획 생성 방법에서는 이용 가능한 모든 계획들을 입력으로 사용한다. 그 계획수립 방법은 HTN 계획수립기로 잘 알려진 JSHOP2 계획수립기내에 구현하였다. 계획 실행 방법으로는 주어진 강건한 계획에 대하여 행위들이 직접 실행하수 있도록 한다.며 용량에 의존하는 양상을 보였다. $H_2O_2$에 의해 유발(誘發)된 DNA의 손상은 catalase와 deferoxamine에 의해 억제되었지만 DPPD는 억제시키지 못했다. 배기음(排氣飮)은 $H_2O_2$에 의해 유발(誘發)된 ATP의 소실을 회복시켰다. 이러한 실험결과 $H_2O_2$에 의해 유발(誘發)된 세포(細胞)의 손상(損傷)은 지질(脂質)의 과산화(過酸化)와는 다른 독립적인 기전에 의해 일어남을 나타낸다. 결론 : 이러한 결과들로 볼 때 Caco-2 세포(細胞)에서 배기음(排氣飮)이 항산화작용(亢酸化作用)보다는 다른 기전을 통하여 Caco-2 세포안에서 산화제(酸化劑)에 의해 유발(誘發)된 세포(細胞)의 사망(死亡)와 DNA의 손상(損傷)을 방지할 수 있다는 것을 가리킨다. 따라서 본 연구(硏究)는 배기음(排氣飮)이 반응성산소기(反應性酸素基)에 의해 매개된 인체(人體) 위장관질환(胃腸管疾患)의 치료(治療)에 사용할 수 있을 가능성(可能性)이 있음을 제시하고 있다.에 이를 이용하여 유가배양시 기질을 공급하는 공정변수로 사용하였다 [8]. 생물학적인 폐수처리장치인 활성 슬러지법에서 미생물의 활성을 측정하는 방법은 아직 그다지 개발되어있지 않다. 본 연구에서는 슬러지의 주 구성원이 미생물인 점에 착안하여 침전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.enin과 Rhaponticin의 작용(作用)에 의(依)한 것이며, 이는 한의학(韓醫學) 방제(方劑) 원리(原理)인 군신좌사(君臣佐使) 이론(理論)에서 군약(君藥)이 주증(主症)에 주(主)로 작용(作用)하는 약물(藥物)이라는 것을 밝혀주는

• Journal of Korea Soil Environment Society
• /
• v.4 no.3
• /
• pp.57-65
• /
• 1999

Generation of gaseous ammonia has been a major problem in composting facilities. Microbial inocula. KMT-199(brand name: CompoBac$^{TM}$). was developed in INBI0NET CORPORATION and tested in the field for its ammonia reducing capability. When KMT-199 was applied. a ten-fold increase of mesophilic and thermophilic microorganisms was observed during the early stage of composting process. Also. the temperature and pH of early stage compost increased at a higher rate when compared to control. KMT-199 treated compost reached highest temperature of $75^{\circ}C$at day 9, indicating treatment could shift the maximum composting temperature to 3 days earlier The highest temperature also reached $3^{\circ}C$ higher than the control. The pH of compost gradually increased during composting. KMT-199 treated compost reached a plateau of pH 9.32 at day 15 after treatment, and then slowly decreased thereafter. On the other hand. pH of the control steadily increased until day 38 of composting. 29% reduction of gaseous ammonia generation during composting was observed compared to that of the control. KMT-199 amended compost resulted in a higher germination rate of radish seeds than the control. These results indicate that application of microbial inocula facilitates degradation of organic materials, including ammonia during the composting process.

• Journal of KIISE:Software and Applications
• /
• v.37 no.1
• /
• pp.60-73
• /
• 2010

This paper aims to describe KorLexClas 1.5 which provides us with a very large list of Korean numeral classifiers, and with the co-occurring noun categories that select each numeral classifier. Differently from KorLex of other POS, of which the structure depends largely on their reference model (Princeton WordNet), KorLexClas 1.0 and its extended version 1.5 adopt a direct building method. They demand a considerable time and expert knowledge to establish the hierarchies of numeral classifiers and the relationships between lexical items. For the efficiency of construction as well as the reliability of KorLexClas 1.5, we use following processes: (1) to use various language resources while their cross-checking for the selection of classifier candidates; (2) to extend the list of numeral classifiers by using a shallow parsing techniques; (3) to set up the hierarchies of the numeral classifiers based on the previous linguistic studies; and (4) to determine LUB(Least Upper Bound) of the numeral classifiers in KorLexNoun 1.5. The last process provides the open list of the co-occurring nouns for KorLexClas 1.5 with the extensibility. KorLexClas 1.5 is expected to be used in a variety of NLP applications, including MT.