• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.4
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• pp.163-172
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• 2012

A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle's features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.291-305
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• 2013

Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.

• Journal of Microbiology and Biotechnology
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• v.27 no.4
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• pp.816-824
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• 2017

Human noroviruses are widespread and contagious viruses causing nonbacterial gastroenteritis. Real-time reverse transcription quantitative PCR (real-time RT-qPCR) is currently the gold standard for the sensitive and accurate detection of these pathogens and serves as a critical tool in outbreak prevention and control. Different surveillance teams, however, may use different assays, and variability in specimen conditions may lead to disagreement in results. Furthermore, the norovirus genome is highly variable and continuously evolving. These issues necessitate the re-examination of the real-time RT-qPCR's robustness in the context of accurate detection as well as the investigation of practical strategies to enhance assay performance. Four widely referenced real-time RT-qPCR assays (Assays A-D) were simultaneously performed to evaluate characteristics such as PCR efficiency, detection limit, and sensitivity and specificity with RT-PCR, and to assess the most accurate method for detecting norovirus genogroups I and II. Overall, Assay D was evaluated to be the most precise and accurate assay in this study. A ZEN internal quencher, which decreases nonspecific fluorescence during the PCR, was added to Assay D's probe, which further improved the assay performance. This study compared several detection assays for noroviruses, and an improvement strategy based on such comparisons provided useful characterizations of a highly optimized real-time RT-qPCR assay for norovirus detection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.129-136
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• 2012

In the US, Japan and Germany the Aerial-Based Vehicle Detection System, which collects real-time traffic data using the Unmanned Aerial Vehicle (UAV), helicopters or fixed-wing aircraft has been developed for the last several years. Therefore, this study was done to find out whether the Aerial-Based Vehicle Detection System could be used for real-time traffic data collection. For this purpose the study was divided into two parts. In the first part the possibility of retrieving real-time traffic data such as travel speed from the aerial photographic image using the image processing technique was examined. In the second part the quality of the retrieved real-time traffic data was examined to find out whether the data are good enough to be used as traffic information source. Based on the results of examinations we could conclude that it would not be easy for the Aerial- Based Vehicle Detection System to replace the present Vehicle Detection System due to technological difficulties and high cost. However, the system could be effectively used to make the emergency traffic management plan in case of incidents such as abrupt heavy rain, heavy snow, multiple pile-up, etc.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.933-940
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• 2009

This paper proposes a robust detection scheme for quasi-orthogonal space-time block code over time-selective fading channels. The proposed detector performs interference cancellation and decision feedback equalization to remove the inter-antenna interference and inter-symbol interference when the channel varies from symbol to symbol. Cholesky factorization is used on the channel Gram matrix after performing interference cancellation to obtain feed forward equalizer and feedback equalizer. It is shown by simulations that the proposed detection scheme outperforms the conventional detection schemes and the exiting detection schemes to time-selectivity.

• Proceedings of the IEEK Conference
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• 2009.05a
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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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.11
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• pp.918-925
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• 2007

In this paper, different criteria fur determining hue change time in the hue detection based transient liquid crystals technique were compared. Results showed that methods utilizing threshold of intensity or saturation gave many missing points and quality of the calculated results were strongly depends on the value of threshold. Wider bandwidth in the hue bandwidth method showed better distribution of calculated hue change time, but induced ambiguity in the hue change time. In the time-hue curve fitting method, the distribution of evaluated hue change time was smooth and reasonable, and, by the nature of curve fitting, the noise effect on the hue was successfully considered in calculating of the hue change time. Compared to other methods, it is expected that the time-hue curve fitting method would provide better and accurate hue change time in the hue detection based transient liquid crystals technique.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.52-58
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• 2007

We propose a new contact detection algorithm for fine pitch wire bonding. Fast and stable contact detection of the z-axis in wire bonding is extremely important to maintain the quality of fine pitch gold wire bonding processes, which use a small pad less than $70{\mu}m$ in diameter. A small perturbation in the contact detection time causes a large difference in the size of the formed squashed ball. The new detection method is based on a statistical approach and designed for a discrete Kalman filter. It is faster and has smaller detection time variations than conventional detection methods. Experimental results are presented to demonstrate the advantages of the proposed algorithm.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.