• Journal of Audiology & Otology
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• v.25 no.2
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• pp.72-79
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• 2021

Background and Objectives: The speech-in-noise test is typically performed using an audiometer. The results of the digit-in-noise recognition (DIN) test may be influenced by the flat frequency response of free-field audiometry and frequency of the hearing aid fit based on fitting rationale. This study aims to investigate the DIN test in unaided and aided conditions. Subjects and Methods: Thirty four adults with moderate and severe sensorineural hearing loss (SNHL) participated in the study. The signal-to-noise ratio (SNR) for 50% of the DIN test was obtained in the following two conditions: 1) the unaided condition, performed using an audiometer in a free field; and 2) aided condition, performed using a hearing aid with an unvented individual earmold that was fitted based on NAL-NL2. Results: There was a statistically significant elevation in the mean SNR for the severe SNHL group in both test conditions when compared with that of the moderate SNHL group. In both groups, the SNR for the aided condition was significantly lower than that of the unaided condition. Conclusions: Speech recognition in hearing-impaired patients can be realized by fitting hearing aids based on evidence-based fitting rationale rather than by measuring it using free-field audiometry measurement that is utilized in a routine clinic setup.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.2061-2068
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• 1993

Condition monitoring technology is of great importance for the maintenance of complex machinery in view of its early monitoring of the abnormal condition and the protection against failure. Several methods have been used for the detection of failure of journal bearings, one of the main elements of mechanical system. The methods most frequently used are vibration and temperature monitoring, but these are unable to monitor the wear conditions exactly. In this study, an ultrasonic measument method, one of the non-destructive testing methods, was introduced as the monitoring technology. Furtermore a pattem recognition method was applied to analyze the ultrasonic signal. The monitoring system using the pattern recognition method is composed of digital signal processing units and uses Hamming net algorithm for the recognition of ultrasonic waves. From the journal bearing wear test, the occurrence of adhesive wear of the white metal in rubbing contact with the shaft was exactly detected by this system, and the wear status of the journal bearing was monitored by measuring the wear thickness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1601-1607
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• 2009

Vision system is an object recognition system analyzing image information captured through camera. Vision system can be applied to various fields, and automobile types recognition is one of them. There have been many research about algorithm of automobile types recognition. But have complex calculation processing. so they need long processing time. In this paper, we designed vision box based on embedded system. and suggested automobile types recognition system using the vision box. As a result of pretesting, this system achieves 100% rate of recognition at the optimal condition. But when condition is changed by lighting and angle, recognition is available but pattern score is lowered. Also, it is observed that the proposed system satisfy the criteria of processing time and recognition rate in industrial field.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.621-634
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• 2010

This study aimed to investigate the effects of dimensions of movie clip (2D vs. 3D), viewing orders (2D ${\to}$ 3D vs. 3D ${\to}$ 2D), and gender difference on participants' subjective fatigue, recognition for the elements in the clips, and arousal level. The results can be summarized as followings. First, subjective fatigue level was higher in the 3D condition than 2D condition, but this tendency was more clear in the 2D ${\to}$ 3D condition than in the 3D ${\to}$ 2D condition. Second, correct recognition rates were significantly higher for 3D than 2D only in the 3D ${\to}$ 2D condition. In particular, male participants showed higher correct recognition rates than female participants in the 3D clip condition, whereas female participants showed higher correct recognition rates than male participants in the 2D clip condition. Third, although 3D clips tended to induce higher level of arousal, this tendency was showed only in the 2D ${\to}$ 3D condition, which implied previous exposure to 2D clip increased the arousal level in following 3D clip than vice versa.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.191-197
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• 2007

Vision system is an object recognition system analyzing image information captured through camera. Vision system can be applied to various fields, and vehicle recognition is ole of them. There have been many proposals about algorithm of vehicle recognition. But have complex calculation processing. So they need long processing time and sometimes they make problems. In this research we suggested vehicle type recognition system using vision bpx based on embedded platform. As a result of testing this system achieves 100% rate of recognition at the optimal condition. But when condition is changed by lighting, noise and angle, rate of recognition is decreased as pattern score is lowered and recognition speed is slowed.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.8-13
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• 2019

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.136-142
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• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45 ${\mu}{\textrm}{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.54-62
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• 2018

According to evolution of technologies, many devices related to various applications were researched. The advanced driver assistance system is a famous technique effected from the evolution. The technique of driver assistance uses image recognition methods to collect exactly information around the vehicle. The computing power of driver assistance device has become more improved than in the past. However, it's difficult that processed various recognition methods at real-time. We propose new frame skip method to process various recognition methods at real-time in the limited hardware. In the previous researches, frame skip rate was set up static values, thus the number of processed frames through recognition methods was smaller. We set up the frame skip rate dynamically using a driving condition of vehicle through speed and acceleration value, in addition, the number of processed frames was maximized. The performance is improved more 32.5% than static frame skip method.

• MALSORI
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• no.45
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• pp.93-105
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• 2003

In this paper, we present a single framework based on GMM for speaker recognition. The proposed framework can simultaneously minimize environmental variations on mismatched conditions and adapt the bias free and speaker-dependent characteristics of claimant utterances to the background GMM to create a speaker model. We compare the closed-set speaker identification for conventional method and the proposed method both on TIMIT and NTIMIT. In the several sets of experiments we show the improved recognition rates on a simulated channel and a telephone channel condition by 7.2% and 27.4% respectively.