• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.293-298
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• 1999

본 논문은 수중음의 안정적 실시간 인식을 위한 새로운 음원 인식 알고리즘을 다루고 있다 본 논문에서 이용된 주파수인식 알고리즘은 크게 네 부분으로 구성되어 있는데 1)입력된 음파 신호를 duty cycle $50\%$의 디지털 신호로 바꾸고 기준 주파수의 음원을 duty cycle $50\%$, 위상차 0도 90도 180도 270도의 디지털 신호를 생성하는 부분, 2)입력된 음파신호를 4가지 위상의 각 기준신호와 배타적 논리합을 구하는 부분, 3)두 번째에서 만들어진 각 신호를 적분회로에 통과시키는 부분, 4)세 번째에서 발생한 각 신호중 최대값을 추출하여 입력된 음파신호의 주파수를 인식하는 부분으로 이루어져 있다. 이 회로에 대한 수치 해석을 통하여 각 부분의 특성치에 대한 최적 값 및 성능을 검증하였으며, 이의 결과를 각각 computer 수치 시험, 실제 회로 실험과 비교하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.33-38
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• 2004

In this paper we presents a underwater sound recognition algorithm by which we can identify the sound signal without the influence of disturbances due to underwater environmental changes. The proposed method provides a means suitable for acoustic releaser which require low power dissipation and long-time underwater operation. We demonstrate its ability of securing stability and fast sound recognition through both numerical and experimental methods.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.93-103
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• 2007

Ship identification systems currently employed provide the underwater sound analysis, analyzed data saving and user interface with comparison function. But final analysis and identification depend only on experts. Therefore, the reliability of these identification systems relies on user's ability on information recognition. This paper presents the method of recognition for the purpose of providing the basic data for an automatic ship class identification. we get the underwater sounds using the PC. We use Matlab in order to reduce ambient noises, take out an acoustic characteristics using the pattern recognition, and classify the ships.

• Journal of Auto-vehicle Safety Association
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• v.8 no.1
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• pp.19-25
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• 2016

As new car technology is developing, temporal interaction is needed in automotive. Rhythmic pattern is one of the practical examples of temporal interaction in vehicle. To recognize rhythmic pattern and its input medium, dictionary learning is applicable algorithm. In this paper, performance and memory requirement of the learning algorithm is tested and is sufficiently good for use this acoustic sound.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.506-508
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• 2023

비접촉 음파 센서와 기계학습을 결합하여 도로 표면의 투명한 블랙아이스 감지 및 노면 분류 97%의 정확도를 달성한 새로운 접근 방법을 제안한다. 개발된 시스템은 블랙아이스를 포함한 다양한 물질의 반사 특성을 분석하여 미끄러운 도로 상황을 실시간 감지 및 예측이 가능하여 도로 안정성을 향상한다. 본 연구에서는 테스트 벤치와 투명하고 미끄러운 물질을 이용하여 블랙아이스를 감지할 수 있는 기술의 정확도를 비교하며, 실험 결과를 통해 제안된 블랙아이스 감지 방법의 타당성을 입증하고자 한다.

• Journal of the Korean Geophysical Society
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• v.4 no.2
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• pp.113-120
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• 2001

The discrete wavelet transform(DWT) has potential as a tool for supplying discriminatory attributes with which to distinguish seismic events. The wavelet transform has the great advantage over the Fourier transform in being able to localize changes. In this study, a discrete wavelet transform is applied to seismic traces for identifying seismic events and picking of arrival times for first breaks and S-wave arrivals. The precise determination of arrival times can greatly improve the quality of a number of geophysical studies, such as velocity analysis, refraction seismic survey, seismic tomography, down-hole and cross-hole survey, and sonic logging, etc. provide precise determination of seismic velocities. Tests for picking of P- and S- wave arrival times with the wavelet transform method is conducted with synthetic seismic traces which have or do not have noises. The results show that this picking algorithm can be successfully applied to noisy traces. The first arrival can be precisely determined with the field data, too.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.17-26
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• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.5
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• pp.436-442
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• 2008

Graphite material has been recognized as a very competitive candidate for reflector, moderator, and structural material for very high temperature reactor (VHTR). Since VHTR is operated up to $900-950^{\circ}C$, small amount of impurity may accelerate the oxidation and degradation of carbon graphite, which results in increased porosity and lowered fracture toughness. In this study, ultrasonic wave propagation properties were investigated for both as-received and degradated material, and the feasibility of ultrasonic testing (UT) was estimated based on the result of ultrasonic property measurements. The ultrasonic properties of carbon graphite were half, more than 5 times, and 1/3 for velocity, attenuation, and signal-to-noise (S/N) ratio respectively. Degradation reduces the ultrasonic velocity slightly by 100 m/s, however the attenuation is about 2 times of as-receive state. The results of probability of detection (POD) estimation based on S/N ratio for side-drilled-hole (SDHs) of which depths were less than 100 mm were merely affected by oxidation and degradation. This result suggests that UT would be reliable method for nondestructive testing of carbon graphite material of which thickness is not over 100 mm. In accordance with the result produced by commercial automated ultrasonic testing (AUT) system, human error of ultrasonic testing is barely expected for the material of which thickness is not over 80 mm.