• Title/Summary/Keyword: 디지털 정보환경

Search Result 2,711, Processing Time 0.128 seconds

Hardware-Software Cosynthesis of Multitask Multicore SoC with Real-Time Constraints (실시간 제약조건을 갖는 다중태스크 다중코어 SoC의 하드웨어-소프트웨어 통합합성)

  • Lee Choon-Seung;Ha Soon-Hoi
    • Journal of KIISE:Computer Systems and Theory
    • /
    • v.33 no.9
    • /
    • pp.592-607
    • /
    • 2006
  • This paper proposes a technique to select processors and hardware IPs and to map the tasks into the selected processing elements, aming to achieve high performance with minimal system cost when multitask applications with real-time constraints are run on a multicore SoC. Such technique is called to 'Hardware-Software Cosynthesis Technique'. A cosynthesis technique was already presented in our early work [1] where we divide the complex cosynthesis problem into three subproblems and conquer each subproblem separately: selection of appropriate processing components, mapping and scheduling of function blocks to the selected processing component, and schedulability analysis. Despite good features, our previous technique has a serious limitation that a task monopolizes the entire system resource to get the minimum schedule length. But in general we may obtain higher performance in multitask multicore system if independent multiple tasks are running concurrently on different processor cores. In this paper, we present two mapping techniques, task mapping avoidance technique(TMA) and task mapping pinning technique(TMP), which are applicable for general cases with diverse operating policies in a multicore environment. We could obtain significant performance improvement for a multimedia real-time application, multi-channel Digital Video Recorder system and for randomly generated multitask graphs obtained from the related works.

Cooperative Multi-Agent Reinforcement Learning-Based Behavior Control of Grid Sortation Systems in Smart Factory (스마트 팩토리에서 그리드 분류 시스템의 협력적 다중 에이전트 강화 학습 기반 행동 제어)

  • Choi, HoBin;Kim, JuBong;Hwang, GyuYoung;Kim, KwiHoon;Hong, YongGeun;Han, YounHee
    • KIPS Transactions on Computer and Communication Systems
    • /
    • v.9 no.8
    • /
    • pp.171-180
    • /
    • 2020
  • Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

A Study on the Implementation of Eco-friendly Green IT-based Libraries (그린 IT기반 도서관의 친환경성 구현에 관한 연구)

  • Noh, Younghee
    • Journal of the Korean Society for Library and Information Science
    • /
    • v.54 no.2
    • /
    • pp.5-28
    • /
    • 2020
  • This study was conducted to discuss concrete ways to contribute to the sustainability of the planet by actively applying and using digital and IT resources in libraries. To this end, a survey was conducted for librarians of public libraries, and the results were as follows. First, librarians' awareness of the seriousness of environmental problems and the degree of eco-friendly activities were very high, but their awareness of library green IT and familiarity with eco-related terms were very low. Nevertheless, there was a high degree of agreement on the importance of applying green IT to libraries. Second, the areas evaluated as having the highest contribution to eco-friendliness and greening of the library were found in the equipment and product operation area, followed by the service area and the network and system area. Third, how do you think library green IT will have a social impact? The highest opinion was given that it could increase the awareness of the library's social responsibility as a social infrastructure. In conclusion, it seems that the library's green IT can contribute to improving library perception by making the public perceived as an institution that has a social responsibility as a social infrastructure.

Satellite Remote Sensing to Monitor Seasonal Horizontal Distribution of Resuspended Sediments in the East China Sea (위성원격탐사에 의한 동중국해 재부상 부유사의 계절적 수평분포 특성)

  • Lee, Na-Kyung;Suh, Young-Sang;Kim, Young-Seup
    • Journal of the Korean Association of Geographic Information Studies
    • /
    • v.6 no.3
    • /
    • pp.151-161
    • /
    • 2003
  • The spatiotemporal distribution of resuspended solid on the shelf of the southern Yellow Sea and the northern East China Sea was studied. The sea surface reflectance imageries obtained by remote sensing using satellite at channels of red (620~670nm), green(545~565nm) and blue(459~479nm) from Terra MODIS were used to explain the front of the high concentration suspended solid(SS) on the shelf in the East China Sea. The horizontal distribution of the resuspended solid was depended on the wind force, tidal current and stratification of water. The horizontal distribution areas of the resuspended solid in winter season during January~April, 2002 were three times wider than those in summer season during June~September, 2001.

  • PDF

Study on Fraud and SIM Box Fraud Detection Method in VoIP Networks (VoIP 네트워크 내의 Fraud와 SIM Box Fraud 검출 방법에 대한 연구)

  • Lee, Jung-won;Eom, Jong-hoon;Park, Ta-hum;Kim, Sung-ho
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.40 no.10
    • /
    • pp.1994-2005
    • /
    • 2015
  • Voice over IP (VoIP) is a technology for the delivery of voice communications and multimedia sessions over Internet Protocol (IP) networks. Instead of being transmitted over a circuit-switched network, however, the digital information is packetized, and transmission occurs in the form of IP packets over a packet-switched network which consist of several layers of computers. VoIP Service that used the various techniques has many advantages such as a voice Service, multimedia and additional service with cheap cost and so on. But the various frauds arises using VoIP because VoIP has the existing vulnerabilities at the Internet and based on complex technologies, which in turn, involve different components, protocols, and interfaces. According to research results, during in 2012, 46 % of fraud calls being made in VoIP. The revenue loss is considerable by fraud call. Among we will analyze for Toll Bypass Fraud by the SIM Box that occurs mainly on the international call, and propose the measures that can detect. Typically, proposed solutions to detect Toll Bypass fraud used DPI(Deep Packet Inspection) based on a variety of detection methods that using the Signature or statistical information, but Fraudster has used a number of countermeasures to avoid it as well. Particularly a Fraudster used countermeasure that encrypt VoIP Call Setup/Termination of SIP Signal or voice and both. This paper proposes the solution that is identifying equipment of Toll Bypass fraud using those countermeasures. Through feature of Voice traffic analysis, to detect involved equipment, and those behavior analysis to identifying SIM Box or Service Sever of VoIP Service Providers.

A Case Study of Producing Infographics Using Tableau Public (Tableau Public을 이용한 인포그래픽 제작 사례연구)

  • Kim, Dong Hwan
    • Spatial Information Research
    • /
    • v.23 no.2
    • /
    • pp.21-29
    • /
    • 2015
  • Recently, according to the increasingly populated data, many media and organizations focus on big data, data visualization, information visualization and infographics. Domestically, Chosun.com and Hankyoreh online have improved on the data visualization field and internationally, the Guardian, Wall Street Journal, and New York Times are the leading companies on that area. Until now, many people have recognized infographics as a design-oriented product in Korea. However, one of significant data visualization programs, Tableau Public, can visualize data more efficiently. In this paper, Data Visualization Methods Quadrant for Policy Making is defined, and data analysis and producing infographics are executed. As used data, World Bank open source was adopted and using the number of passenger cars per 1,000 people, two analysis results are extracted. First, in high income group, the more GNI per capita, the lesser Slope is represented and in mid income group, the more GNI per capita positively affects to Slope. Second, in the global finance crisis, the car ownership rate was about 1.7 times than the usual state in the global economy. Through the case study, this paper suggests that the direction of producing infographics should be changed from design-oriented to data-oriented. Moreover, the data-oriented infographics should be propagated as means of scientific research and policy making.

A Study on the Policy Proposal and Model B2B2C for Safe Open Banking (안전한 오픈뱅킹 구축을 위한 정책 및 B2B2C 모델에 관한 연구)

  • Choi, Dae-Hyun;Kim, In-Seok
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.29 no.6
    • /
    • pp.1271-1283
    • /
    • 2019
  • The fourth industrial revolution and digital transformation are also bringing major changes to the financial ecosystem in Korea. Already, global financial firms overseas are opening their financial markets and exploring new financial businesses by seeking ways to co-prosperity with fintech firms. However, it is also true that the domestic financial environment has failed to respond to the changes due to its monopolistic and closed structure. In response, the government began pushing for the introduction of open banking in December 2019 with the aim of fully opening the financial settlement system. However, unlike the existing simple financial transaction structure, open banking still has an unresolved part due to the unclear relationship of responsibilities between interested parties in the event of financial accidents due to the complex linkage structure of transactions such as financial firms, fintech firms and customers. This study analyzed the security threat of open banking in depth. By doing so, the government and financial firms want to present policy proposals that need to be improved to enhance the safety of open banking in korea and protect financial consumers, as well as new financial models that have improved the vulnerable parts of existing models.

Highly Reliable Fault Detection and Classification Algorithm for Induction Motors (유도전동기를 위한 고 신뢰성 고장 검출 및 분류 알고리즘 연구)

  • Hwang, Chul-Hee;Kang, Myeong-Su;Jung, Yong-Bum;Kim, Jong-Myon
    • The KIPS Transactions:PartB
    • /
    • v.18B no.3
    • /
    • pp.147-156
    • /
    • 2011
  • This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

A Study on the Improvement Measures for the Management and Utilization of Korea's Fiscal Government Data: Focusing on Fiscal Data Governance (재정데이터의 관리 및 활용을 위한 개선방안 연구: 재정데이터 거버넌스를 중심으로)

  • Song, Seok-Hyun
    • Informatization Policy
    • /
    • v.28 no.3
    • /
    • pp.95-111
    • /
    • 2021
  • To achieve a data-driven policy decision-making system, the Ministry of Strategy and Finance has formed a marketing team and is actively building upon it. This system, currently under construction, will enable data-driven financial tasks beyond simple financial administration. The U.S. has already enacted The Foundations for Evidence-Based Policymaking Act in the process of similar pursuits. Since last year, the data-driven system administrative law has been enacted in Korea, and a legal framework has been established for data-driven administrative work. The next-generation budget accounting system to fulfill its role as a data-driven system needs public policy support to operate. Innovation and transformation are needed in various areas such as data management, legal system, and installation of related systems. Accordingly, it is very timely to analyze the financial systems and policies of advanced countries such as the U.S. and U.K., which already have established and operates such a financial system. By benchmarking and applying existing financial information systems to the next-generation budget accounting system, a better system will result. In this study, major developed countries, including the U.S., U.K., France, and Canada were benchmarked and analyzed in terms of the main elements of data governance: public policy, systems, legal framework, promotion system, and service level. It was discovered that the role and direction of the national fiscal policy system that the people favor should be able to respond quickly to the recent difficult economic crisis environment such as the digital transformation trend and COVID-19.

Comparative Analysis of CNN Deep Learning Model Performance Based on Quantification Application for High-Speed Marine Object Classification (고속 해상 객체 분류를 위한 양자화 적용 기반 CNN 딥러닝 모델 성능 비교 분석)

  • Lee, Seong-Ju;Lee, Hyo-Chan;Song, Hyun-Hak;Jeon, Ho-Seok;Im, Tae-ho
    • Journal of Internet Computing and Services
    • /
    • v.22 no.2
    • /
    • pp.59-68
    • /
    • 2021
  • As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.