This study quantitatively analyzed the target strength (TS) characteristics of the dotted gizzard shad (Konosirus punctatus) across various fork lengths (FL) and frequency conditions. In July 2023, TS measurements were conducted on six size groups (FL: 14.4–23.5 cm) under free-swimming conditions in a seawater acoustic tank at the Fisheries Resources Research Center in Tongyeong, Korea. A scientific echosounder (EK80, SIMRAD) was used to collect TS data at three frequencies: 38, 70, and 120 kHz. The results showed that TS values increased with fork length, and the 120 kHz frequency exhibited the widest distribution range and distinct bi- or multi-modal patterns. The TS–FL relationships for each frequency were as follows: TS38 kHz = 20·log(FL) ‒ 68.41, TS70 kHz = 20·log(FL) ‒ 70.76, and TS120 kHz = 20·log(FL) ‒ 70.90. Unlike traditional tethered measurement methods, this study obtained TS data under free-swimming conditions, providing values more representative of real-world acoustic survey environments. The findings are expected to serve as foundational data for improving the accuracy of monitoring the distribution and biomass estimation of K. punctatus using hydroacoustic methods.

A compact vibratory bowl feeder system is proposed to transport lightweight annular film components. Vibration analysis was conducted to calculate its natural frequencies, and the motion characteristics of the bowl and transported parts were analyzed under resonance excitation at varying supply voltage levels. The natural frequencies of the proposed system were found to be 157Hz, 249Hz, and 505Hz. At these resonance frequencies, significant rotational vibrations occurred, while vertical vibrations were relatively small. Especially at 505Hz, bending of the leaf spring caused large rotational motion of the bowl. The part feeding speed increased linearly with the applied voltage, reaching 4mm/sec at 100V and 18mm/sec at 200V. At 157Hz and 249Hz excitation frequencies, large rotational and vertical vibrations were observed, respectively. Under rotational vibration, the parts moved forward via jumping motion when the bowl's velocity amplitude was relatively large, or via slipping when smaller. Minor backward slipping was also observed. Under vertical vibration, parts exhibited forward jumping motion without back-and-forth slipping.

본 연구는 신속보기 기반 안구운동 과제에서 시선추적 장비의 샘플링 주파수가 시선의 이동 및 고정 지표와 반응 분류 성능에 미치는 영향을 규명하고자 하였다. 고령 성인 30명을 대상으로 정방향과 역방향의 혼합형 신속보기 과 제를 수행하는 동안 기준 주파수인 300Hz로 수집된 시선 데이터를 30∼200Hz 범위로 다운샘플링하여 분석하였다. 조건별 안구운동 주요 지표 분석 결과, 샘플링 주파수가 감소함에 따라 잠복기는 27ms 이상 증가하였고, 이동크기는 2∼3deg, 최대속도는 90∼120deg/s 감소하였다. 시선고정 지속시간은 최대 397ms 증가하였으며, 위치 분산은 정방향에서 약 3배, 역방향에서 최대 10배 증가하였다. 반면, 시표적과의 위치 오차는 큰 변동 없이 유지되었다. 안구운동 반응 분류 성능 비교 결과, 샘플링 주파수 90Hz 이상에서는 정확도 .98, 정밀도 .99, F1 점수 .99, 일치도 계수 .95 이상을 유지하였으나, 60Hz 이하에서는 F1 점수가 .91 이하, 일치도 계수는 .63 이하로 급격히 저하되었다. 본 연구 결과는 신속보기 안구운동 연구에서 시선추적 장비의 샘플링 주파수가 분석 정확도와 신뢰도에 실질적인 영향을 미 친다는 점을 시사하며, 300Hz 기준의 시선 데이터 분석 수준을 안정적으로 유지하기 위해서는 최소 90Hz 이상의 샘플링 주파수 확보가 필요함을 제안한다.

This study aimed to measure the in-situ target strength (TS) of the moon jellyfish (Aurelia aurita) using a 200 kHz scientific echo sounder in a natural coastal environment. The acoustic survey was conducted in the coastal waters of Soho Port, Yeosu, Korea, from June 4 to 5, 2024. TS measurements were performed by installing the transducer in both horizontal and vertical orientations, and the TS distribution characteristics were analyzed. The measured TS values ranged from -89.8 to -64.8 dB in the horizontal direction and from -90.0 to -59.1 dB in the vertical direction, showing no significant difference between detection orientations (p<0.05). Additionally, a bell diameter-wet weight relationship for Aurelia coerulea was derived based on the specimens collected at the survey site. The empirical TS model proposed by Mutlu (1996) was applied to estimate TS values using the measured morphological data, and the results exhibited a similar trend to the field-measured TS distribution. These findings provide fundamental data for acoustic monitoring and stock assessment of jellyfish populations in natural environments.

일반적으로 전기 패널은 용접이나 앵커링을 통해 기초에 설치된다. 콘크리트 기초-앵커 시스템에서 고려해야 할 열화 요인에 는 콘크리트 기초의 균열이 포함된다. 콘크리트 균열은 전기 패널의 앵커에 영향을 미치는 열화 현상 중 하나로 간주될 수 있다. 또한 독립반 및 열반된 전기 패널의 동적 특성은 상당히 다를 수 있다. 그러나 많은 연구자들이 하나의 전기 캐비닛 시편으로 진동대실험을 수행하였다. 따라서 열반 구성을 고려하여 동적 특성을 평가할 필요가 있다. 본 연구에서는 0.5 mm 및 1.0 mm 균열 폭을 고려하여 콘크리트 기초-앵커 시스템을 설계하였다. 콘크리트 기초-앵커 시스템을 진동대에 고정하고 1∼3개의 열반으로 구성된 단순화된 캐비 닛 모델을 설치하였다. 열반 수와 콘크리트 균열을 매개변수로 고려하여 진동대에 의한 공진주파수 검색 실험을 수행했으며 각 실험편 의 공진 주파수를 비교하였다.

This study aims to analyze the natural frequency characteristics of multi-cracked extensible beams. The model and governing equations of the multi-cracked beam were derived using Hamilton's principle while considering crack energy. The eigenmode functions were obtained through eigenvalue analysis by applying the patching conditions of the cracks, and the equations for the discretized cracked beam were formulated and solved. The displacement responses from nonlinear system analysis were used to calculate frequencies via Fast Fourier Transform (FFT), and the frequency characteristics were systematically analyzed with respect to the number of cracks, crack depth, and cross-sectional loss. Additionally, the natural frequencies and orthonormal bases of the linear system were derived by exploring the solutions of the characteristic equation reflecting the cracks. Numerical analyses showed that the natural frequency of a cracked extensible beam was higher than that of a cracked EB beam. However, as the number or depth of cracks increased, the natural frequency gradually decreased. Notably, in extensible beams with large deflections, the dynamic changes caused by cracks demonstrated results that could not be obtained through the EB beam model.

The performance of various types of silencers used to reduce the micropressure waves radiated from ventilation holes and inclined shafts, which are being studied as measures to reduce micropressure waves in railway tunnels, was evaluated to find an effective silencer. In order to find the optimal silencer, the magnitude and frequency characteristics of the pressure waves emitted from the inclined shaft were analyzed to find an excellent silencer. The evaluation showed that the model with a porous cylinder and a small diameter outer tube was the simplest but performed the best.

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f2 trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R2 (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.

PURPOSES : The tire-pavement interaction noise (TPIN) comprises four sources, among which the tire tread vibration noise (TTVN) and air pumping noise (APN) are known to be the most influential. However, when evaluating TPIN, the noise level is estimated based on the overall noise, because general noise measurement methods cannot separate TTVN and APN. Therefore, this study aims to develop a method to separate TTVN and APN in TPIN for quantitative assessment of pavement noise. METHODS : Based on the results of our literature review and frequency band noise data measured in our study, we identified the dominant frequency ranges for TTVN and APN. Additionally, we evaluated TTVN and APN across various pavement types. RESULTS : TTVN was found to be dominant in frequency bands below 800 Hz, while APN was dominant in frequency bands above 800 Hz. Additionally, regardless of the vehicle type, vehicle speed, or pavement type, APN exhibited higher levels compared to TTVN. This result shows that APN has a more significant impact on TPIN than TTVN. CONCLUSIONS : The separation method of TTVN and APN proposed in this study can be utilized to quantitatively assess the relationship between the primary noise sources in TPIN and the characteristics of pavement texture in future research. Furthermore, it is anticipated that characteristics of low TPIN and optimal texture conditions can be proposed to mitigate TPIN, thus contributing to the development of lownoise pavements.

Recently, domestic fishing production of Japanese horse mackerel has been continuously decreasing. To achieve sustainable fishing of this species, it is essential to acquire its target strength (TS) for accurate biomass estimation and to study its ecological characteristics. To date, there has been no TS research using a broadband echosounder targeting Japanese horse mackerel. In this study, for the first time, we synchronized an underwater camera with a broadband frequency (nominal center frequency of 200 kHz, range: 160-260 kHz) to measure the TS according to the body size (16.8-35.5 cm) and swimming angle of the species. The relationship between Japanese horse mackerel length and body weight showed a general tendency for body weight to increase as length increased. The pattern of the frequency spectra (average values) by body length exhibited a similar trend regardless of body length, with no significant fluctuations in frequency observed. The lowest TS value was observed at 243 kHz while the highest TS values were recorded at 180 and 257.5 kHz. The frequency spectra for the swimming angles appeared to be flat at angles of –5, 0, 30, 60, 75, and 80° while detecting more general trends of frequency spectra for swimming angle proved challenging. The results of this study can serve as fundamental data for Japanese horse mackerel biomass estimation and ecological research.

본 논문에서는 볼트로 체결된 구조체에 대하여 초기 볼트풀림 상태에서의 볼트 체결력 예측 합성곱 신경망 훈련 방법을 제시한다. 8개의 볼트의 체결력이 변경된 상태에서 계산한 주파수응답들을 완전 체결된 상태의 초기 모델과의 크기 및 모양 유사성을 표현하는 유사성 지도로 생성한다. 주파수응답 데이터들의 생성에는 크리로프 부공간법 기반의 모델차수축소법을 적용하여 효율적인 방법으 로 수행할 수 있도록 한다. 합성곱 신경망 모델은 회귀 출력 계층을 사용하여 볼트의 체결력을 예측하도록 하였으며, 훈련 데이터의 개 수와 합성곱 신경망 계층의 개수를 다르게 준비하여 훈련시킨 네트워크들을 비교하여 그 성능을 평가하였다. 주파수응답에서 파생되 는 유사성 지도를 입력 데이터로 사용하여 초기 볼트풀림 영역에서 볼트 체결력의 진단 가능성과 유효성을 제시하였다.

Controller modeling is essential for the design. It allows various control techniques to be simulated in advance, and various interpretations can be performed. If this is not the case, we need to reverse engineering in the real system developed by others. In this paper, controller modeling was reversely designed using the frequency test results of the target system. First, the characteristic equation of the target equipment was based on and a block diagram was assumed. Thereafter, controller variables were estimated using the frequency test results for each of the four control loops. In addition, time response simulations were performed using the estimated controller modeling. This method is thought to be of great help to reverse engineering in situations where there is completed equipment but no controller modeling.