• Title/Summary/Keyword: Intelligent Data Analysis

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Performance Analysis of Cognitive Radio Cooperative Spectrum Sensing for Intelligent Transport System (지능형 교통 시스템을 위한 인지무선 협력 스펙트럼 센싱의 성능 분석)

  • Kim, Jin-Young;Baek, Myung-Kie
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.7 no.6
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    • pp.110-120
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    • 2008
  • Cognitive Radio (CR) technology is proposed for using the unused spectrum band efficiently because of the spectrum scarcity problems. Spectrum sensing technology is one of the key challenge issues in cognitive radio technologies, which enables unlicensed users to identify and utilize vacant spectrum resource allocated to primary users. In this paper, the cooperative spectrum sensing technologies apply the ITS(Intelligent Transport System) and performance of signal detection analyzes. Then, we utilize the OR-rule and AND-rule for the cooperative signal detection. These data fusion rules improve the performance and reliability of the signal detection.

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Efficient hardware implementation and analysis of true random-number generator based on beta source

  • Park, Seongmo;Choi, Byoung Gun;Kang, Taewook;Park, Kyunghwan;Kwon, Youngsu;Kim, Jongbum
    • ETRI Journal
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    • v.42 no.4
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    • pp.518-526
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    • 2020
  • This paper presents an efficient hardware random-number generator based on a beta source. The proposed generator counts the values of "0" and "1" and provides a method to distinguish between pseudo-random and true random numbers by comparing them using simple cumulative operations. The random-number generator produces labeled data indicating whether the count value is a pseudo- or true random number according to its bit value based on the generated labeling data. The proposed method is verified using a system based on Verilog RTL coding and LabVIEW for hardware implementation. The generated random numbers were tested according to the NIST SP 800-22 and SP 800-90B standards, and they satisfied the test items specified in the standard. Furthermore, the hardware is efficient and can be used for security, artificial intelligence, and Internet of Things applications in real time.

Development of Social Map Prototype for Intelligent Crime Prevention based on Geospatial Information

  • Kwon, Hoe-Yun;Song, Ki-Sung;Seok, Sang-Muk;Jang, Hyun-Jin;Hwang, Jung-Rae
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.8
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    • pp.49-55
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    • 2016
  • In this study, we proposed the social map system prototype for intelligent crime prevention. For developing the social map system prototype, functional requirements were derived through the analysis of related cases and preceding studies. Derived requirements are providing a variety of map-based safety information, using crowdsourcing data such as SNS, connecting to intelligent CCTV. To satisfy these requirements, the prototype is developed with four main menus: the integrated search menu including social media data, the safety map menu providing a variety of safety and danger information, the community map menu to collect safety and danger information from users, and the CCTV menu providing the link to intelligent CCTV. The social map for intelligent crime prevention in this study is expected to greatly enhance the safety of local community with the provision of prompt response to risk information, safe route, etc. through actual service and user participation.

In-silico inferences for expression data using IGAM: Applied to Fuzzy-Clustering & Regulatory Network Modeling (연판 지식을 이용한 유전자 발현 데이터 분석: 퍼지 플러스링과 조절 네트웍 모델링에의 응용)

  • Lee, Philhyone;Hojeong Nam;Lee, Doheon;Lee, Kwang H.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.273-276
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    • 2004
  • Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

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Intelligent adaptive controller for a process control

  • Kim, Jin-Hwan;Lee, Bong-Guk;Huh, Uk-Youl
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10b
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    • pp.378-384
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    • 1993
  • In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

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FUZZY REGRESSION ANALYSIS WITH NON-SYMMETRIC FUZZY COEFFICIENTS BASED ON QUADRATIC PROGRAMMING APPROACH

  • Lee, Haekwan;Hideo Tanaka
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.63-68
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    • 1998
  • This paper proposes fuzzy regression analysis with non-symmetric fuzzy coefficients. By assuming non-symmetric triangular fuzzy coefficients and applying the quadratic programming fomulation, the center of the obtained fuzzy regression model attains more central tendency compared to the one with symmetric triangular fuzzy coefficients. For a data set composed of crisp inputs-fuzzy outputs, two approximation models called an upper approximation model and a lower approximation model are considered as the regression models. Thus, we also propose an integrated quadratic programming problem by which the upper approximation model always includes the lower approximation model at any threshold level under the assumption of the same centers in the two approximation models. Sensitivities of Weight coefficients in the proposed quadratic programming approaches are investigated through real data.

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The Optimum Fuzzy Vector Quantizer for Speech Synthesis

  • Lee, Jin-Rhee-;Kim, Hyung-Seuk-;Ko, Nam-kon;Lee, Kwang-Hyung-
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1321-1325
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    • 1993
  • This paper investigates the use of Fuzzy vector quantizer(FVQ) in speech synthesis. To compress speech data, we employ K-means algorithm to design codebook and then FVQ technique is used to analysize input speech vectors based on the codebook in an analysis part. In FVQ synthesis part, analysis data vectors generated in FVQ analysis is used to synthesize the speech. We have fined that synthesized speech quality depends on Fuzziness values in FVQ, and the optimum fuzziness values maximized synthesized speech SQNR are related with variance values of input speech vectors. This approach is tested on a sentence, and we compare synthesized speech by a convensional VQ with synthesized speech by a FVQ with optimum Fuzziness values.

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Visualized Preference Transition Network Based on Recency and Frequency

  • Masruri, Farid;Tsuji, Hiroshi;Saga, Ryosuke
    • Industrial Engineering and Management Systems
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    • v.10 no.4
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    • pp.238-246
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    • 2011
  • Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

Usability Evaluation Scale for Product of Intelligent Homecare based on Retail Consumer

  • KWON, Jieun;LEE, Jin-Suk
    • Journal of Distribution Science
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    • v.17 no.12
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    • pp.55-62
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    • 2019
  • Purpose: The number intelligent homecare products are focused on the development of technology, resulting in a lack of realistic environments or requirements for consumers. The purpose of this paper is to define the consumer and context for intelligent homecare products and to develop a usability evaluation scale. Research design, data and methodology: For this study, first, consumer and contexts related to intelligent homecare products were analyzed through literature review. Second, the primary usability evaluation factors were derived for intelligent homecare products by collecting the factors related to usability evaluation and conducting in-depth interviews with experts. Third, the second usability evaluation factors were derived through survey and statistical analysis based on the derived usability evaluation factors. Results: As a result, users of intelligent homecare products were classified as primary users and secondary consumers and six related contexts. The usability evaluation scale was established with four factors - Functionality, Error, Convenience, and Emotion - and 13 items. Conclusions: This study can be the basis for developing and distributing products that meet the consumer environment and requirements related to intelligent homecare products that will contribute to securing the competitiveness of companies and developing the technology and service value of related industries.

PPNC: Privacy Preserving Scheme for Random Linear Network Coding in Smart Grid

  • He, Shiming;Zeng, Weini;Xie, Kun;Yang, Hongming;Lai, Mingyong;Su, Xin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.3
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    • pp.1510-1532
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    • 2017
  • In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.