• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1063-1068
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.3-9
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• 1998

Since the introduction of hands-free phone set and teleconferencing system, acoustic echo cancellation has been a challenge for engineers. Recently many researches have shown that the best solution for the acoustic echo compensation problem is represented by an adaptive filter which iteratively tries to identify the unknown impulse response of the system from loudspeaker to microphone. In this paper, we apply the delayless subband adaptive filters and fast affine projection algorithm for the identification of room impulse response. Simulation results show 3∼8 dB more enhanced performance than conventional fullband adaptive filters or subband adaptive filters. In addition, fast affine projection algorithm shows better convergence speed at the expense of the low computational complexity than conventional LMS algorithm.

• Computers and Concrete
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• v.20 no.1
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• pp.49-56
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• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

• Smart Structures and Systems
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• v.17 no.1
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• pp.59-72
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• 2016

The Impact Echo method can be used to measure the thickness of concrete plate like structures. Measurements are based on the identification of a clear thickness resonance frequency which can be difficult in very thick or highly attenuative plates. In this study the detectability of the measured resonant frequency is enhanced by time domain summation of signals with different source receiver spacing. The proposed method is based on the spatial and temporal properties of the first higher symmetric zero group velocity Lamb mode (S1-ZGV) which are described in detail. No application dependent tuning or filtering is needed which makes the method robust and suitable for implementation in automatic IE thickness measurements. The proposed technique is exemplified with numerical data and field data from a thick concrete wall and a highly attenuative asphalt concrete layer.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.1-9
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• 2021

The aim of this study was to provide a new assessment of rotator cuff muscle activity. Eight male subjects (24.7 ± 3.2 years old,171.2 ± 9.8 cm tall, and weighing 63.8 ± 11.9 kg) performed the study exercises. The subjects performed 10 sets of the exercise while fixing the elbow at 90 degrees flexure and lying supine on a bed. One exercise set consisted of the subject performing external shoulder rotation 50 times using training equipment. Two imaging protocols were employed: (a) true fast imaging with steady precession (TrueFISP) at an acquisition time of 12 seconds and (b) multi-shot spin-echo echo-planar imaging (MSSE-EPI) at an acquisition time of 30 seconds for one echo. The main method of assessing rotator cuff muscle activity was functional T2 mapping using ultrafast imaging (fast-acquired muscle functional MRI [fast-mfMRI]). Fast-mfMRI enabled real-time imaging for the identification and evaluation of the degree of muscle activity induced by the exercise. Regions of interest were set at several places in the musculus subscapularis (sub), musculus supraspinatus (sup), musculus teres minor (ter), and deltoid muscle (del). We used the MR signal of the images and transverse relaxation time (T2) for comparison. Most of the TrueFISP signal was not changed by exercise and there was no significant difference from the resting values. Only the T2 in the musculus teres minor was increased after one set and the change were seen on the T2 images. Additionally, except for those after one and two sets, the changes in T2 were significant compared to those at rest (P < 0.01). We also demonstrated identify and visualize the extent to which muscles involved in muscle activity by exercise. In addition, we showed that muscle activity in a region such as a shoulder, which is susceptible to B0 inhomogeneity, could be easily detected using this technique.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.224-233
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• 2016

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.

• The Journal of the Acoustical Society of Korea
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• v.33 no.3
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• pp.210-215
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• 2014

In spite of distortion caused by noise and echo, the audio fingerprinting technique must identify successfully an audio source. This audio fingerprinting technique is applying for TV commercial advertisement identification. In this paper, we propose a robust audio fingerprinting method for TV commercial advertisement identification. In the proposed method, a prominent audio peak pair fingerprint based on constant Q transform improves the accuracy of the audio fingerprinting system in real noisy environments. Experimental results confirm that the proposed method is quite robust than previous audio fingerprinting method in different noise conditions and achieves promising accurate results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.605-614
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• 2015

Drone imaging, which is more cost-effective and controllable compared to airborne LiDAR, requires a low-cost camera and is used for capturing color images. From the overlapped color images, we produced two high-resolution digital surface models over different test areas. After segmentation, we performed tree identification according to the method proposed by , and computed the tree height and the canopy crown size. Compared with the field measurements, the computed results for the tree height in test area 1 (coniferous trees) were found to be accurate, while the results in test area 2 (deciduous coniferous trees) were found to be underestimated. The RMSE of the tree height was 0.84 m, and the width of the canopy crown was 1.51 m in test area 1. Further, the RMSE of the tree height was 2.45 m, and the width of the canopy crown was 1.53 m in test area 2. The experiment results validated the use of drone images for the extraction of a tree structure.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.1
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• pp.52-61
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• 1998

We studied behaviour pattern of anchovy (Engraulis japonicus) shoal by a method of shoal echo integration and tested species identification by a method of artificial neural network using the acoustic data collected in the East China Sea in March 1994 and in the southern coastal waters of the East Sea of Korea in March 1995. Between areas, frequency distribution of 10 shoal descriptors was different, which showed characteristics of shoal behaviour in size, bathymetric position and acoustic strength. The range and mean of shoal size distribution in length and height was wider and bigger in the southern coastal waters of the East Sea than in the East China Sea. Relative shoal size of China Sea. Fractal dimension of shoal was almost same in both areas. Mean volume reverbration index of shoal was 3 dB higher in the southern coastal waters of the East Sea than in the East China Sea. The depth layer of shoal distribution was related to bottom depth in the southern coastal waters of the East Sea, while it was between near surface and central layer in the East China Sea. Principal component analysis of shoal descriptors showed the correlation between shoal size and acoustic strength which was higher in the southern coastal waters of the East Sea, than in the East China Sea. Correlation was also found among the bathymetric positions of shoal to some degree higher in the southern coastal waters of the East Sea than in the East China Sea. The anchovy shoal of two areas was identified by artificial neural network. The contribution factor index (Cio) of the shoal descriptors between two areas were almost identical feature. The shoal volume reverberation index (Rv) was showed the highest contribution to the species identification, while shoal length and shoal height showed relatively high negative contribution to the species identification.