• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.3
• /
• pp.547-552
• /
• 2019

Recently, the massive growth in the scale of data has been observed as a major issue in the Big Data. Furthermore, the Big Data should be preprocessed for normalization to get a high performance of the Machine learning since the Big Data is also an input of Machine Learning. The performance varies by many factors such as the scope of the columns in a Big Data or the methods of normalization preprocessing. In this paper, the various types of normalization preprocessing methods and the scopes of the Big Data columns will be applied to the SVM(: Support Vector Machine) as a Machine Learning method to get the efficient environment for the normalization preprocessing. The Machine Learning experiment has been programmed in Python and the Jupyter Notebook.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.45-48
• /
• 2022

건축물 안전 점검은 대부분 전문가의 현장 방문을 통한 육안검사다. 그중 균열 검사는 건물 위험도를 나타내는 중요한 지표로써 발생 위치, 진행성, 크기를 조사하는데, 최근 균열 조사 방식에 대해 객관성과 체계성을 보완할 딥러닝 개발이 활발하다. 그러나 균열 이미지는 외부 현장에 모양, 규모도 많은 종류라 도메인이 다양해야 하는데 대부분 제한된 환경과 실제적인 균열 검사와는 무관한 데이터로 구성되어 실효적이지 않다. 본 연구에서는 균열 조사에 적합하고 Wild 환경에 적용 가능한 POC 데이터셋을 소개한다. 기존 균열 공인 데이터셋 4종의 특징과 한계점을 분석을 토대로 고해상도 이미지로써 균열의 세부 특징을 담았고 균열 유사 환경과 조건들을 추가 촬영해 균열 검출에 강인하게 학습되도록 지향하였다. 정제 및 라벨링 작업을 거친 POC 데이터 셋은 균열 검출모델인 YOLO-v5으로 성능을 실험하였고, mAP(mean Average Precision) 75.5%로 높은 검출률을 보였다. POC 데이터셋으로 더욱 도메인에 적응적(Domain-adapted)인 인공지능 모델을 개발하여 건물, 댐, 교량 등 각종 대형 건축물에 대한 안전하고 효과적인 안전 관리 도구로써 활용할 것을 기대한다.

• Journal of Platform Technology
• /
• v.10 no.4
• /
• pp.47-55
• /
• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• TTA Journal
• /
• s.99
• /
• pp.70-75
• /
• 2005

지금까지의 홈네트워크 서비스는 주로 홈 오토메이션 기반의 서비스나 간단한 데이터통신 기반의 원격검침 및 보안서비스 등이 주류를 이루어 왔으나 FTTH 기술이 본격 개발되면서 홈네트워크 서비스는 기존의 서비스 이외에 통방융합 기반의 광대역 서비스로 그 영역을 확장하게 되었다. 본 고에서는 FTTH 및 홈네트워크의 주요기술을 열거하고 통방융합 기반의 광대역 홈네트워크 서비스의 종류와 KT가 제공하고 있는 광주광역시 FTTH 시험서비스의 내용에 관해 기술하였다.

• Digital Contents
• /
• no.4 s.95
• /
• pp.30-33
• /
• 2001

과거 기업들의 정보기반은 제품을 대량생산하던 시기에 맞는 체제였으며, 당시와 같이 제품의 종류가 적고 시장환경을 세밀하게 분류할 필요가 없었던 시기에는 구매패턴이 반복적이고 제품수명주기가 일정했다. 그러나 현대는 다품종 소량생산의 시대일뿐만 아니라 서비스와 정보 또한 상품화되었으며, 고객의 눈높이는 더욱 높아만 가고 있는 실정이다.

• Review of KIISC
• /
• v.5 no.3
• /
• pp.69-85
• /
• 1995

ITU-T X.435 EDI 시스템에서의 정보보호 서비스는 크게 MHS 정보보호 서비스와 Pedi 정보보호 서비스로 나눌 수 있다. 본 논문에서는 X.435 EDI 정보보호 서비스의 종류를 살펴보고, 이들의 데이타 구조의 분석뿐만 아니라 정보보호 서비스를 제공하기 위해 사용되는 각 필드들이 의미하는 바를 분석, 정리하였다.

• Journal of Digital Convergence
• /
• v.12 no.6
• /
• pp.311-316
• /
• 2014

Social network server due to the popularity of smart phones, and data stored in a big usable access data services are increasing. Big Data Big Data processing technology is one of the most important technologies in the service, but a solution to this minor security state. In this paper, the data services provided by the big -sized data is distributed using a double hash user to easily access to data of multiple distributed hash chain based data processing technique is proposed. The proposed method is a kind of big data data, a function, characteristics of the hash chain tied to a high-throughput data are supported. Further, the token and the data node to an eavesdropper that occurs when the security vulnerability to the data attribute information to the connection information by utilizing hash chain of big data access control in a distributed processing.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.235-242
• /
• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• Journal of Digital Convergence
• /
• v.13 no.1
• /
• pp.263-268
• /
• 2015

Put your information in the object-based sensors and mobile networks has been developed that correlate with ubiquitous information technology as the development of IT technology. However, a security solution is to have the data stored in the server, what minimal conditions. In this paper, we propose a data management method is applied to a hash chain of the properties of the multiple techniques to the data used by the big user and the data services to ensure safe handling large amounts of data being provided in the big data services. Improves the safety of the data tied to the hash chain for the classification to classify the attributes of the data attribute information according to the type of data used for the big data services, functions and characteristics of the proposed method. Also, the distributed processing of big data by utilizing the access control information of the hash chain to connect the data attribute information to a geographically dispersed data easily accessible techniques are proposed.

• Journal of The Korean Association of Information Education
• /
• v.26 no.5
• /
• pp.439-448
• /
• 2022

This study aims to search for education-related datasets provided by public data portals and examine what data types are constructed through classification using topic modeling methods. Regarding the data of the public data portal, 3,072 cases of file data in the education field were collected based on the classification system. Text mining analysis was performed using the LDA-based topic modeling method with stopword processing and data pre-processing for each dataset. Program information and student-supporting notifications were usually provided in the pre-classified dataset for education from the data portal. On the other hand, the characteristics of educational programs and supporting information for the disabled, parents, the elderly, and children through the perspective of lifelong education were generally indicated in the dataset collected by searching for education. The results of data analysis through this study show that providing sufficient educational information through the public data portal would be better to help the students' data science-based decision-making and problem-solving skills.