• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.3
• /
• pp.361-370
• /
• 2017

In this paper, correlation analysis was performed between questionnaire and machine learning based aggressive tendency measurements. this study is part of a aggressive driver detection using machine learning and questionnaire. To collect two types tendency from questionnaire and measurements system, we constructed experiments environments and acquired the data from 30 drivers. In experiment, the machine learning based aggressive tendency measurements system was designed using a driver behavior detection model. And the model was constructed using accelerate and brake position data and hidden markov model method through supervised learning. We performed a correlation analysis between two types tendency using Pearson method. The result was represented to high correlation. The results will be utilize for fusing questionnaire and machine learning. Furthermore, It is verified that the machine learning based aggressive tendency is unique to each driver. The aggressive tendency of driver will be utilized as measurements for advanced driver assistance system such as attention assist, driver identification and anti-theft system.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.3
• /
• pp.450-456
• /
• 2016

STDR (sequence time domain reflectometry) to detect a cable fault using a pseudo noise sequence as a reference signal, and time correlation analysis between the reference signal and reflection signal is robust to noisy environments and can detect intermittent faults including open faults and short circuits. On the other hand, if the distance of the fault location is far away or the fault type is a soft fault, attenuation of the reflected signal becomes larger; hence the correlation coefficient in the STDR becomes smaller, which makes fault detection difficult and the measurement error larger. In addition, automation of the fault location by detection of phase and peak value becomes difficult. Therefore, to improve the cable fault detection of a conventional STDR, this paper proposes the algorithm in that the peak value of the correlation coefficient of the reference signal is detected, and a peak value of the correlation coefficient of the reflected signal is then detected after removing the reference signal. The performance of the proposed method was validated experimentally in low-voltage power cables. The performance evaluation showed that the proposed method can identify whether a fault occurred more accurately and can track the fault locations better than conventional STDR despite the signal attenuation. In addition, there was no error of an automatic fault type and its location by the detection of the phase and peak value through the elimination of the reference signal and normalization of the correlation coefficient.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1992.06a
• /
• pp.23-28
• /
• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.99-108
• /
• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• Journal of Biomedical Engineering Research
• /
• v.18 no.4
• /
• pp.357-364
• /
• 1997

The early detection of breast cancer is clearly a key ingredient for reducing breast cancer mortality. Microcalcification is the only visible feature of the DCIS's(ductal carcinoma in situ) which consist 15 ~ 20% of screening-detected breast cancer. Therefore, the analysis of the shapes and distributions of microcalcifications is very significant for the early detection. The automatic detection procedures have b(:on the concern of digital image processing for many years. We proposed here one efficient method which is essentially statistical pattern classification accelerated by one representative feature, correlation coefficient. We compared the results by this additional feature with results by a simple gray level thresholding. The average detection rate was increased from 48% by gray level feature only to 83% by the proposed method The performances were evaluated with TP rates and FP counts, and also with Bayes errors.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.10 no.6
• /
• pp.25-44
• /
• 1973

The please relation between motive force and result is reviewed in view point of the correlation function as well as the redundancy in a continuous signal which permits the sampled treatment. A new correlation function (to be named Time Dependent Correlation Function) which is a functon of time, is defined in order to indicate the variation of the correlation between two signals. As application a phase looked loop is analysed which shows the increase of correlation between input signal and output signal of the loop after the application of the input signal. Finally again the T.D.Correlation Function method is used to show how the polyphase envelope detection-method is justifiable by this method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.7C
• /
• pp.715-723
• /
• 2009

In this paper, we propose a new detection method called dual-cell combining (DCC) detection for the acquisition in time of spread spectrum codes in the presence of residual frequency offset (RFO). When the RFO exists, the correlation peak used for detection during the acquisition process is split into two neighboring peaks with smaller amplitudes, resulting in considerable degradation in the overall acquisition performance of conventional methods. In the DCC detection method, the decision variable for detection is formed by combining two consecutive correlator outputs so that the influence of the reduction in the correlation peak due to the RFO can be alleviated. Numerical results show that the DCC detection method can offer better mean-time-to-synchrouization performance than the conventional method based on the cell-by-cell detection.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.1
• /
• pp.459-466
• /
• 2011

In this paper, we proposed a data mining framework for the management of alerts in order to improve the performance of the intrusion detection systems. The proposed alert data mining framework performs alert correlation analysis by using mining tasks such as axis-based association rule, axis-based frequent episodes and order-based clustering. It also provides the capability of classify false alarms in order to reduce false alarms. We also analyzed the characteristics of the proposed system through the implementation and evaluation of the proposed system. The proposed alert data mining framework performs not only the alert correlation analysis but also the false alarm classification. The alert data mining framework can find out the unknown patterns of the alerts. It also can be applied to predict attacks in progress and to understand logical steps and strategies behind series of attacks using sequences of clusters and to classify false alerts from intrusion detection system. The final rules that were generated by alert data mining framework can be used to the real time response of the intrusion detection system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.6
• /
• pp.464-470
• /
• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2736-2754
• /
• 2015

Android dominates the mobile operating system market, which stimulates the rapid spread of mobile malware. It is quite challenging to detect mobile malware. System call sequence analysis is widely used to identify malware. However, the malware detection accuracy of existing approaches is not satisfactory since they do not consider correlation of system calls in the sequence. In this paper, we propose a new scheme called Artificial Neural Networks (ANNs) on Co-occurrence Matrices Droid (ANNCMDroid), using co-occurrence matrices to mine correlation of system calls. Our key observation is that correlation of system calls is significantly different between malware and benign software, which can be accurately expressed by co-occurrence matrices, and ANNs can effectively identify anomaly in the co-occurrence matrices. Thus at first we calculate co-occurrence matrices from the system call sequences and then convert them into vectors. Finally, these vectors are fed into ANN to detect malware. We demonstrate the effectiveness of ANNCMDroid by real experiments. Experimental results show that only 4 applications among 594 evaluated benign applications are falsely detected as malware, and only 18 applications among 614 evaluated malicious applications are not detected. As a result, ANNCMDroid achieved an F-Score of 0.981878, which is much higher than other methods.