• Title/Summary/Keyword: Feature extraction algorithm

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Induction Motor Diagnosis System by Effective Frequency Selection and Linear Discriminant Analysis (유효 주파수 선택과 선형판별분석기법을 이용한 유도전동기 고장진단 시스템)

  • Lee, Dae-Jong;Cho, Jae-Hoon;Yun, Jong-Hwan;Chun, Myung-Geun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.3
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    • pp.380-387
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    • 2010
  • For the fault diagnosis of three-phase induction motors, we propose a diagnosis algorithm based on mutual information and linear discriminant analysis (LDA). The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by mutual information As the next step, feature extraction is performed by LDA, and then diagnosis is evaluated by k-NN classifier. The results to verify the usability of the proposed algorithm showed better performance than various conventional methods.

Real-time comprehensive image processing system for detecting concrete bridges crack

  • Lin, Weiguo;Sun, Yichao;Yang, Qiaoning;Lin, Yaru
    • Computers and Concrete
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    • v.23 no.6
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    • pp.445-457
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    • 2019
  • Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

Network Traffic Measurement Analysis using Machine Learning

  • Hae-Duck Joshua Jeong
    • Korean Journal of Artificial Intelligence
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    • v.11 no.2
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    • pp.19-27
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    • 2023
  • In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

Hardware Design of SURF-based Feature extraction and description for Object Tracking (객체 추적을 위한 SURF 기반 특이점 추출 및 서술자 생성의 하드웨어 설계)

  • Do, Yong-Sig;Jeong, Yong-Jin
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.5
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    • pp.83-93
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    • 2013
  • Recently, the SURF algorithm, which is conjugated for object tracking system as part of many computer vision applications, is a well-known scale- and rotation-invariant feature detection algorithm. The SURF, due to its high computational complexity, there is essential to develop a hardware accelerator in order to be used on an IP in embedded environment. However, the SURF requires a huge local memory, causing many problems that increase the chip size and decrease the value of IP in ASIC and SoC system design. In this paper, we proposed a way to design a SURF algorithm in hardware with greatly reduced local memory by partitioning the algorithms into several Sub-IPs using external memory and a DMA. To justify validity of the proposed method, we developed an example of simplified object tracking algorithm. The execution speed of the hardware IP was about 31 frame/sec, the logic size was about 74Kgate in the 30nm technology with 81Kbytes local memory in the embedded system platform consisting of ARM Cortex-M0 processor, AMBA bus(AHB-lite and APB), DMA and a SDRAM controller. Hence, it can be used to the hardware IP of SoC Chip. If the image processing algorithm akin to SURF is applied to the method proposed in this paper, it is expected that it can implement an efficient hardware design for target application.

An Algorithm for Filtering False Minutiae in Fingerprint Recognition and its Performance Evaluation (지문의 의사 특징점 제거 알고리즘 및 성능 분석)

  • Yang, Ji-Seong;An, Do-Seong;Kim, Hak-Il
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.3
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    • pp.12-26
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    • 2000
  • In this paper, we propose a post-processing algorithm to remove false minutiae which decrease the overall performance of an automatic fingerprint identification system by increasing computational complexity, FAR(False Acceptance Rate), and FRR(False Rejection Rate) in matching process. The proposed algorithm extracts candidate minutiae from thinned fingerprint image. Considering characteristics of the thinned fingerprint image, the algorithm selects the minutiae that may be false and located in recoverable area. If the area where the selected minutiae reside is thinned incorrectly due to noise and loss of information, the algorithm recovers the area and the selected minutiae are removed from the candidate minutiae list. By examining the ridge pattern of the block where the candidate minutiae are found, true minutiae are recovered and in contrast, false minutiae are filtered out. In an experiment, Fingerprint images from NIST special database 14 are tested and the result shows that the proposed algorithm reduces the false minutiae extraction rate remarkably and increases the overall performance of an automatic fingerprint identification system.

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Reinforcement Post-Processing and Feedback Algorithm for Optimal Combination in Bottom-Up Hierarchical Classification (상향식 계층분류의 최적화 된 병합을 위한 후처리분석과 피드백 알고리즘)

  • Choi, Yun-Jeong;Park, Seung-Soo
    • The KIPS Transactions:PartB
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    • v.17B no.2
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    • pp.139-148
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    • 2010
  • This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.

A Real Time Lane Detection Algorithm Using LRF for Autonomous Navigation of a Mobile Robot (LRF 를 이용한 이동로봇의 실시간 차선 인식 및 자율주행)

  • Kim, Hyun Woo;Hawng, Yo-Seup;Kim, Yun-Ki;Lee, Dong-Hyuk;Lee, Jang-Myung
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.11
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    • pp.1029-1035
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    • 2013
  • This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.

A block-based face detection algorithm for the efficient video coding of a videophone (효율적인 화상회의 동영상 압축을 위한 블록기반 얼굴 검출 방식)

  • Kim, Ki-Ju;Bang, Kyoung-Gu;Moon, Jeong-Mee;Kim, Jae-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.9C
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    • pp.1258-1268
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    • 2004
  • We propose a new fast, algorithm which is used for detecting frontal face in the frequency domain based on human skin-color using OCT coefficient of dynamic image compression and skin color information. The region where each pixel has a value of skin-color were extracted from U and V value based on DCT coefficient obtained in the process of Image compression using skin-color map in the Y, U, V color space A morphological filter and labeling method are used to eliminate noise in the resulting image We propose the algorithm to detect fastly human face that estimate the directional feature and variance of luminance block of human skin-color Then Extraction of face was completed adaptively on both background have the object analogous to skin-color and background is simple in the proposed algorithm The performance of face detection algorithm is illustrated by some simulation results earned out on various races We confined that a success rate of 94 % was achieved from the experimental results.

A Study of Music Recommendation System in P2P Network using Collaborative Filtering (P2P 환경에서 협업 필터링을 이용한 음악 추천 시스템에 대한 연구)

  • Won, Hee-Jae;Park, Kyu-Sik
    • Journal of Korea Multimedia Society
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    • v.11 no.10
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    • pp.1338-1346
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    • 2008
  • In this paper, we propose a new P2P-based music recommendation system. In comparison with previous system in client-server environment, the proposed system shows higher quality of music recommendation through real-time sharing of music preference information between peers. A collaborative filtering is implemented as a recommendation algorithm. As a user preference profile, we use the inherit KID music genre index contained in all legitimate music file instead of music feature vectors as in previous research so that the proposed system can mitigate the performance degradation and high computational load caused by feature inaccuracy and feature extraction. The performance of the proposed system is evaluated in various ways with real 16-weeks transaction data provided by Korean music portal, 5 company and it shows comparative quality of recommendation with only small amount of computational load.

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Region Decision Using Modified ICM Method (변형된 ICM 방식에 의한 영역판별)

  • Hwang Jae-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.5 s.311
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    • pp.37-44
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    • 2006
  • In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.