• The Journal of the Acoustical Society of Korea
• /
• v.16 no.5
• /
• pp.68-75
• /
• 1997

Spectral subtraction is widely-used preprocessing technique for speech recognition in additive noise environments, but it requires a good estimate of the noise power spectrum. In this paper, we employ the histogram technique for the estimation of noise spectrum. This technique has advantages over other noise estimation methods in that it does not requires speech/non-speech detection and can estimate slowly-varying noise spectra. According to the speaker-independent isolated word recognition in both colored Gaussian and car noise environments under various SNR conditions. Histogram-technique-based spectral subtraction method yields superier performance to the one with conventional noise estimation method using the spectral average of initial frames during non-speech period.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.16 no.2
• /
• pp.235-242
• /
• 1998

This study aims to detect and to reduce the multipath errors which are main errors source in high precious surveying such as GPS-aided aerial triangulation and Car Navigation. which reference receivers being fixed, when kinematic receivers move continuously, multipath is performed using smoothed code measurement and pure code measurement in the network. Through this methods, 3D RMS errors are reduced into about 30% in the single differential code solution to the kinematic receiver. This is based on the fact that the network adjustment are performed at multiple reference receivers, but positioning is carried out by the single differential methods between a reference receiver and a kinematic receiver. So it was supposed that this methods reduced the correlation errors including the atmospheric errors using the nearest receivers and can be mixed with another methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.6009-6027
• /
• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• Journal of Auto-vehicle Safety Association
• /
• v.9 no.2
• /
• pp.6-12
• /
• 2017

An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.815-818
• /
• 2016

Forward collision warning systems(FCWS) and lane departure warning systems(LDWS) need regions of interest for detecting lanes and objects as road regions. In general, the lane departure warning system using a vehicle front camera is tracking a lane curve using RANSAC or the like in the form of a straight line obtained image are compared with the center of the vehicle. This algorithm has weaknesses that requires a wide range of the lane being vulnerable to the curve. This paper presents an algorithm that checks whether the current lane departure by car from the Top-view space. The algorithm also can check whether the vehicle in the lane departure of the narrow range, and shows the result that is almost not affected by noise.

• Journal of Convergence for Information Technology
• /
• v.7 no.6
• /
• pp.229-235
• /
• 2017

In recent years, research has been actively carried out to recognize and recognize objects based on a large amount of data. In this paper, we propose a system that extracts objects that are thought to be obstacles in road driving images and recognizes them by car, man, and motorcycle. The objects were extracted using Optical Flow in consideration of the direction and size of the moving objects. The extracted objects were recognized using Alexnet, one of CNN (Convolutional Neural Network) recognition models. For the experiment, various images on the road were collected and experimented with black box. The result of the experiment showed that the object extraction accuracy was 92% and the object recognition accuracy was 96%.

• International Journal of Safety
• /
• v.9 no.2
• /
• pp.29-34
• /
• 2010

When doing maintenance works at the trackside of railway, the method which delivers information on approaching of train to maintenance workers through alarm devices such as the flag or indication light, etc., is being used by locating persons in charge of safety alarm in addition to the maintenance workers at fixed distances in the front and rear of the workplace. Workers maintaining at the trackside may collide with the train since they cannot recognize the approach of train although it approaches to the vicinity of maintenance workplace because of the sensory block phenomenon occurred due to their long hours of continued monotonous maintenance work. The clash or rear-end collision accidents between many maintenance trains called motor-cars can be occurred since there are cases where the signal systems for safe operation of motor-car such as track circuit etc. are blocked or not operated normally. We developed the new safety equipment for protection of trackside maintenance workers using radio frequency signals and bidirectional detection mechanism. The developed safety equipment must analyze the several operational mechanism for each different operation situations. In this paper the analysis results are represented.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.5
• /
• pp.1141-1146
• /
• 2011

In this paper, an adaptive RFID tag antenna in 900MHz for using in inadequate electromagnetic environment is designed and implemented. The dimension of the proposed passive tag is $102mm{\times}102mm{\times}9mm$, the operating frequency band is 908~914MHz and the detection range is 5.1m. This paper describes the development of the thermal, water and impact resisting tag. The implemented tag can be used for the inadequate environment such as in-built tag on the ground for the car management service.

• Journal of Korea Multimedia Society
• /
• v.21 no.11
• /
• pp.1342-1352
• /
• 2018

Voice Command systems are important means of ensuring accessibility to digital devices for use in situations where both hands are not free or for people with disabilities. Interests in services using speech recognition technology have been increasing. In this study, we developed a mobile writing application using voice recognition and voice command technology which helps people create and edit documents easily. This application is characterized by the minimization of the touch on the screen and the writing of memo by voice. We have systematically designed a mode to distinguish voice writing and voice command so that the writing and execution system can be used simultaneously in one voice interface. It provides a shortcut function that can control the cursor by voice, which makes document editing as convenient as possible. This allows people to conveniently access writing applications by voice under both physical and environmental constraints.

• Journal of the Korea Safety Management & Science
• /
• v.22 no.1
• /
• pp.9-14
• /
• 2020

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.