• Journal of Information Processing Systems
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• 제19권6호
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• pp.767-777
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• 2023

In modern society, user privacy is emerging as an important issue as closed-circuit television (CCTV) systems increase rapidly in various public and private spaces. If CCTV cameras monitor sensitive areas or personal spaces, they can infringe on personal privacy. Someone's behavior patterns, sensitive information, residence, etc. can be exposed, and if the image data collected from CCTV is not properly protected, there can be a risk of data leakage by hackers or illegal accessors. This paper presents an innovative approach to "machine learning based reversible chaotic masking method for user privacy protection in CCTV environment." The proposed method was developed to protect an individual's identity within CCTV images while maintaining the usefulness of the data for surveillance and analysis purposes. This method utilizes a two-step process for user privacy. First, machine learning models are trained to accurately detect and locate human subjects within the CCTV frame. This model is designed to identify individuals accurately and robustly by leveraging state-of-the-art object detection techniques. When an individual is detected, reversible chaos masking technology is applied. This masking technique uses chaos maps to create complex patterns to hide individual facial features and identifiable characteristics. Above all, the generated mask can be reversibly applied and removed, allowing authorized users to access the original unmasking image.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.1301-1304
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• 1998

Several neural networks have been successfully used to classify complex patterns such as handwritten numerals or words. This paper describes the discrimination of totally unconstrained handwritten numerals using the proposed chaotic neural network (CNN) to improve the recognition rate. The recognition system in the paper consists of the preprocessing stage to extract features using Kirsch mask and the classification stage to recognize numerals using the CNN. In order to evaluate the performance of the proposed network, we performed the recognition with unconstrained handwritten numeral database of Concordia university, Canada. Experimental results show that the CNN based recognizer performs higher recognition rate than other neural network-based methods reported using same database.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.70-73
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• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.

• 융합신호처리학회논문지
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• 제2권1호
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• pp.44-52
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• 2001

본 논문은 수정된 카오틱 신경망(MCNN)을 이용하여 완전 무제약 서체 숫자 인식을 다루고 있다. 카오틱 신경망(CNN)의 동적 특성과 학습과정을 강화함으로써 복잡한 패턴인식 문제를 해결할 수 있는 유용한 신경망으로 수정하였다. MCNN은 신경망 구조와 뉴런 자체가 높은 차수의 비선형 동적특성을 갖고 있으므로 복잡한 서체 숫자를 분류할 수 있는 적합한 신경망이다. 숫자 확인은 원래의 숫자 이미지로부터 특징을 추출하고 MCNN에 근거한 분류기를 이용하여 숫자를 인식한다. MCNN 분류기의 성능은 Canada, Montreal의 Concordia 대학의 숫자 데이터 베이스로 평가하였다. 인식성능의 상대적인 비교를 위해 MCNN 분류기는 리커런트 신경망(RNN) 분류기와 비교하였다. 실험결과에 의하면 인식율은 98.0%이었으며, 이는 MCNN 분류기가 같은 데이터 베이스에 대해 발표되었던 다른 분류기와 RNN 분류기보다 성능이 우수함을 나타낸다.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2003년도 The Fifth Asian Computational Fluid Dynamics Conference
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• pp.268-269
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• 2003

The Boltzmann kinetic equation is solved directly by means of the conservative splitting method. Underexpanded supersonic free jet flows with small Knudsen numbers are studied. In this numerical simulation features intrinsic to appropriate experiments are observed. Streamwise vortices in a mixing layer and chaotic downstream temporal-spatial fluctuations of microscopic quantities with large amplitude are obtained.

• 대한전기학회논문지:전력기술부문A
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• 제48권12호
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• pp.1486-1491
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• 1999

This paper presents a method of recognizing high impedance fault(HIF) of electrical power systems and classifying fault patterns based on chaos attractors. Two dimensional chaos attractors are reconstructed from neutral point current waveforms. Reliable features for HIF pattern classification are obtained from the chaos attractors. Radial basis function network, trained with two types of HIF data generated by the electromagnetic transient program and measured form actual faults. The RBFN successfully classifies normal and the three types of fault patterns according to the features generated from the chaos attractors.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.37-39
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. Since the background noise presenting to record will introduce disturbance while we just want to get the stationary parameters to represent the corresponding speech section, in particular, a major source of error in automatic recognition system of isolated words is the inaccurate detection of beginning and ending boundaries of test and reference templates, thus we must find potent method to remove the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two simple time-domain measurements - short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Comparing with the conventional methods based on short-time energy and short-time zero-crossing rate, the novel approach based on time-domain Lyapunov Exponents(LEs) is low complexity and suitable for Digital Isolated Word Recognition System.

• International Journal of Concrete Structures and Materials
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• 제18권1E호
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• pp.23-28
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• 2006

In this study, the general theory of fractality is discussed to provide a fundamental understanding of fractal geometry applied to heterogeneous material surfaces like pavement surface and rock surface. It is well known that many physical phenomena and systems are chaotic, random and that the features of roughness are found at a wide spectrum of length scales from the length of the sample to the atomic scales. Studying the mechanics of these physical phenomena, it is absolutely necessary to characterize such multi scaled rough surfaces and to know the structural property of such surfaces at all length scales relevant to the phenomenon. This study emphasizes the role of fractal geometry to characterize the roughness of various surfaces. Pavement roughness and rock surface roughness were examined to correlate their roughness property to fractality.

• 한국전자통신학회논문지
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• 제7권5호
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• pp.1073-1078
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• 2012

본 논문에서는 MEMS에서 비선형적인 특성을 확인하기 위하여 Duffing 방정식을 가지는 MEMS 시스템을 제안하고 여기에 다른 종류의 비선형 항을 삽입하였을 때의 비선형 현상을 분석하였다. 검증 방법으로 파라미터 변화에 의한 카오스 운동이 있음을 시계열 데이터, 위상 공간, 전력 스펙트럼을 통하여 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2497-2517
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• 2020

For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.