• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.84-91
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• 2011

Recently, WBAN(Wireless Body Area Network) which has progressed standardization based on IEEE 802.15.6 standardization is a network for the purpose of the short-range wireless communications within around 3 meters from the inner or outer human body. Effective QoS control technique and data efficient management in limited bandwidth such as audio and video are important elements in terms of users and loads in short-range wireless networks. In this paper, for high-speed WBAN IEEE 802.15.6 standard, the dynamic allocation to give an efficient bandwidth management and weighted fair queueing algorithm have been proposed through the adjustment of the super-frame about limited data and Quality of Service (QoS) based on the queuing algorithm. Weighted Fair Queueing(WFQ) Algorithm represents the robust performance about elements to qualitative aspects as well as maintaining fairness and maximization of system performance. The performance results show that the dynamic allocation expanded transmission bandwidth five times and the weighted fair queueing increased maximum 24.3 % throughput and also resolved delay bound problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.495-503
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• 2014

In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.599-605
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• 2004

기존의 구축된 GIS 컴포넌트 혹은 개발 중이거나 향후개발을 목표로 설계단계에 있는 컴포넌트들의 최종 목표는 재사용성과 상호운용성의 가능성 여부이다. 하지만 컴포넌트 개발에 있어 시스템 개발환경의 다양성으로 인하여 그 재활용성은 생각만큼 쉬운 작업이 아니며, 특히 공간정보를 다루고 있는 GIS(Geographic Information System)분야에서의 GIS 컴포넌트 재활용은 전 세계의 산재한 각 데이터형의 포맷, 개발 환경, 운영환경을 고려하여 볼 때 시급한 일임에도 불구하고 그에 대한 노력이 상당히 미진한 실 정 이 다. 본 논문에서는 GIS 애플리케이션을 보다 효율적이고 유용하게 개발하기 위하여 GIS 컴포넌트의 개발과 관리에 이르는 전 과정을 관리 감독할 수 있는 COGIS(Component Oriented Geographic Information System)을 제안하고, COGIS 프로세스의 가이드라인이며 GIS 컴포넌트의 기능적인 면을 정의하기 위한 GCA(GIS based Component Architecture) 아키텍처를 제안하였다. 아울러 GIS 컴포넌트의 메타데이터를 분류 및 정의하여 GIS 컴포넌트의 비 기능적면을 제시하고 이를 이용하여 웹 기반 GIS 컴포넌트 등록/검색 에이전트 시스템을 개발하였으며 기존 GIS 컴포넌트 재사용 및 확장, 신규 컴포넌트의 등록, 검색이 가능하도록 한다. 사례연구로 웹 상에서 산불 발생 위험지수 표출을 위한 GIS 공간 분포도 작성이 쉽게 이루어지도록 2FDRV.avx와 2FDRC.exe 컴포넌트를 개발하였으며, COGIS 프로세스의 컴포넌트 관리방법을 통하여 여러 관련 컴포넌트를 조합함으로써 웹 기반 산불위험지수예보시스템을 구축하였다.입력 근거의 확보’, ‘갱신주체별 역할의 정의 및 유지관리 기준의 설정’, ‘분야별업무 특성을 고려한 관련 기준의 마련 및 타 시스템과 연계되는 항목을 고려한 절차 정의’ 등에 대한 다양한 접근을 시도하였다. 본 연구에서 제시하는 유지관리 모델을 기반으로 각 지자체별로 적절한 컨설팅이 진행되고 이에 따라 담당자의 실천이 이루어진다면 지자체 GIS의 투자대비 효과에 대한 기대는 이상이 아닌 현실로 다가오게 될 것이다.가오게 될 것이다. 동일하게 25%의 소유권을 가지고 있다. ？스굴 시추사업은 2008년까지 수행될 계획이며, 시추작업은 2005년까지 완료될 계획이다. 연구 진행과 관련하여, 공동연구의 명분을 높이고 분석의 효율성을 높이기 위해서 시료채취 및 기초자료 획득은 4개국의 연구원이 모여 공동으로 수행한 후의 결과물을 서로 공유하고, 자세한 전문분야 연구는 각 국의 대표기관이 독립적으로 수행하는 방식을 택하였다 ？스굴에 대한 제1차 시추작업은 2004년 3월 말에 실시하였다. 시추작업 결과, 약 80m의 시추 코아가 성공적으로 회수되어 현재 러시아 이르쿠츠크 지구화학연구소에 보관중이다. 이 시추코아는 2004년 8월 중순경에 4개국 연구팀원들에 의해 공동으로 기재된 후에 분할될 계획이다. 분할된 시료는 국내로 운반되어 다양한 전문분야별 연구에 이용될 것이다. 한편, 제2차 시추작업은 2004년 12월에서 2005년 2월 사이에 실시될 계획이다. 수백만년에 이르는 장기간에 걸쳐 지구환경변화 기록이 보존되어 있는 ？스굴호에 대한 시추사업은 후기 신생대 동안 유라시아 대륙 중부에서 일어난 지구환경 및 기후변화를 이해함과 동시에 이러한 변화가 육상생태계 및 지표지질환경에 미친 영향을 이해하는데 크게 기여할 것이다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.99-112
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• 2015

Datum point for displaying location and altitude of point has being advantage usefully in various measurement parts. However, datum point has been increasing loss cases owing to weather changes and stratum changes and neglecting meaninglessly. In this paper, we design and implement a multi electronic measurement system monitoring software with functions such as include maximize utilization of existing measurement datum system as well as collected various environment data and detection stratum changes of surround area. Proposed software is implemented to support that reusability and extensibility of software using object oriented modeling method. Our software supports a GUI for electronic measurement datum point administrator as well as for web user and mobile user. Our system can support a graph GUI for various data analysis and reposition in realtime to database that measured location information and various sensing information to prevent loss of electronic measurement datum point and to detected stratum changes. In addition, we include a QR code and RFID recognition function. Finally, we suggest performance evaluation result to confirm stratum changes detection and GPS location error rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.59-69
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• 2011

Since power analysis attack which uses a characteristic that power consumed by crypto device depends on processed data has been proposed, many logics that can block these correlation originally have been developed. DRP logic has been adopted by most of logics maintains power consumption balanced and reduces correlation between processed data and power consumption. However, semi-custom design is necessary because recently design circuits become more complex than before. This design method causes unbalanced design pattern that makes DRP logic consumes unbalanced power consumption which is vulnerable to power analysis attack. In this paper, we have developed new logic style which adds another discharge phase to discharge two output nodes at the same time based on DyCML to remove this unbalanced power consumption. Also, we simulated 1bit fulladder to compare proposed logic with other logics to prove improved performance. As a result, proposed logic is improved NED and NSD to 60% and power consumption reduces about 55% than any other logics.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.