• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.3
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• pp.259-273
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• 1998

The hydroacoustic surveys to provide the essential information for the assessment, management and utilization of fishery resources in the southern waters of Korea were carried out during five research cruises between October 1996 and October 1997 by the training ship KAYA of Pukyong National University. These hydroacoustic investigations were designed to obtain more precise estimates of the geographic distribution, absolute abundance and biological characteristics of the fishery resources, and the vertically integrated densities of fish in terms of volume backscattering strength(SV) by survey region and depth bins, such as the entire water column and the 0~ 10 m from bottom fraction, were measured separately. Hydroacoustic data were collected by using a Simrad EK 500 Scientific echo sounder operating at two frequencies of 38kHz and 120kHz and the data stored in field were later processed on a HP PC using a Simrad EP 500 echo integration and target strength analysis system. The biological compositions of echo signal were identified and sampled using a demersal trawl during daylight hours. The mean target strength to scale the echo integration data for hydroacoustic surveys was derived from the relationship between the SV and the weight of trawl catch per unit volume of the water column sampled by demersal trawls. The results obtained can be summarized as follow : 1. The mean volume backscattering strength for the entire water column in the southern waters of Korea between 1996 and 1997 were -67.2 dB and -70.9 dB at two frequencies of 38 kHz and 120 kHz , respectively, and for the bottom layer of the 0-10 m from bottom friction were -68.8 dB, -70.2 dB, respectively. That is, the volume backscattering strength for the entire water column at low frequency was higher than that at high frequency. 2. The relationship between the mean backscattering strength (〈SV〉, dB) for the depth strata of trawl hauls and the weight (C, kg/m3) per cubic meter of the catch sampled by bottom trawling in the southern waters of Korea in January and July 1997 were expressed by the following equations: 38 kHz : 〈SV〉= -28.2 + 10 log(C), 120 kHz : 〈SV〉= -32.4 + 10 log(C). The mean weight -normalized target strengths derived from these equitions were -28.2 dB/ kg, -32.4 dB/ kg at 38 kHz and 120 kHz , respectively. That is, the mean weight -normalized target strength at 38 kHz was 4.2 dB higher than that at 120 kHz. 3. The distribution density of fish in terms of biomass per unit volume in the southern waters of Korea were estimated to be 125.9 $\times$ 10-6 kg/m3 and 141.3 $\times$ 10-6 kg/m3 at 38 kHz and 120 kHz , respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.210-218
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• 2002

This paper proposes an efficient separation method of the composite images by using independent component analysis(ICA) based on neural networks of the approximate learning algorithm. The Proposed learning algorithm is the fixed point(FP) algorithm based on Secant method which can be approximately computed by only the values of function for estimating the root of objective function for optimizing entropy. The secant method is an alternative of the Newton method which is essential to differentiate the function for estimating the root. It can achieve a superior property of the FP algorithm for ICA due to simplify the composite computation of differential process. The proposed algorithm has been applied to the composite signals and image generated by random mixing matrix in the 4 signal of 500-sample and the 10 images of $512{\times}512-pixel$, respectively The simulation results show that the proposed algorithm has better performance of the learning speed and the separation than those using the conventional algorithm based method. It also solved the training performances depending on initial points setting and the nonrealistic learning time for separating the large size image by using the conventional algorithm.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.101-114
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• 2023

The data augmentation technique is effectively used to solve the problem of overfitting the model by allowing the training dataset to be viewed from various perspectives. In addition to image augmentation techniques such as rotation, cropping, horizontal flip, and vertical flip, occlusion-based data augmentation methods such as Cutmix and Cutout have been proposed. For models based on speech data, it is possible to use an occlusion-based data-based augmentation technique after converting a 1D speech signal into a 2D spectrogram. In particular, SpecAugment is an occlusion-based augmentation technique for speech spectrograms. In this study, we intend to compare and study data augmentation techniques that can be used in the problem of false-voice detection. Using data from the ASVspoof2017 and ASVspoof2019 competitions held to detect fake audio, a dataset applied with Cutout, Cutmix, and SpecAugment, an occlusion-based data augmentation method, was trained through an LCNN model. All three augmentation techniques, Cutout, Cutmix, and SpecAugment, generally improved the performance of the model. In ASVspoof2017, Cutmix, in ASVspoof2019 LA, Mixup, and in ASVspoof2019 PA, SpecAugment showed the best performance. In addition, increasing the number of masks for SpecAugment helps to improve performance. In conclusion, it is understood that the appropriate augmentation technique differs depending on the situation and data.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.836-842
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• 2023

Under the Welfare of Disabled Persons Act, the national qualification test allows disabled and non-disabled people to appear for the same test, and the deaf do not fall under the reason for disqualification from obtaining a license for power-driven water leisure crafts; therefore, even the deaf can obtain a license. During a risk of collision at sea, ships and power-driven water leisure crafts notify the other party of dangerous signals such as maneuvering and warning signals through sound signals to prevent accidents that may occur because surrounding ships are not visible. However, a method is required to prevent marine accidents that may occur when the deaf cannot hear danger signals through sounds from nearby ships or power-driven water leisure crafts during leisure activities owing to hearing impairment. A sound reception system is a device installed on a ship where missionary work is completely deposed to display the amplification of external sounds and the direction of sound reception on the screen. Through visual display of sound signals such as dangerous signals that cannot be heard owing to hearing impairment, improvement measures were proposed to prevent marine accidents that can occur owing to the inability of the deaf to check sound signals during leisure activities.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.278-285
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• 2010

The difference between training and test environments is one of the major performance degradation factors in noisy speech recognition and many silence feature normalization methods were proposed to solve this inconsistency. Conventional silence feature normalization method represents higher classification performance in higher SNR, but it has a problem of performance degradation in low SNR due to the low accuracy of speech/silence classification. On the other hand, cepstral distance represents well the characteristic distribution of speech/silence (or noise) in low SNR. In this paper, we propose a Cepstral distance and Log-energy based Silence Feature Normalization (CLSFN) method which uses both log-energy and cepstral euclidean distance to classify speech/silence for better performance. Because the proposed method reflects both the merit of log energy being less affected with noise in high SNR and the merit of cepstral distance having high discrimination accuracy for speech/silence classification in low SNR, the classification accuracy will be considered to be improved. The experimental results showed that our proposed CLSFN presented the improved recognition performances comparing with the conventional SFN-I/II and CSFN methods in all kinds of noisy environments.

• Journal of the Korea Institute of Construction Safety
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• v.6 no.1
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• pp.27-32
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• 2024

In this study, based on accident cases of manned and unmanned tower cranes, the types and causes of accidents were analyzed and tower crane accident prevention measures were proposed accordingly. As a result, 'lack of communication' was commonly analyzed as the biggest cause of accidents. Therefore, in this study, we investigated domestic and international systems and laws regarding signalmen responsible for communication between tower crane operators and workers and proposed the following three improvement methods. First, the signalman system must be legally and institutionally reestablished. Second, national agencies and private organizations with expertise should create standardized training manuals and distribute them to signalman educators, and create checklists to manage educators. Third, in the case of a site using a tower crane, the employer must mandatorily deploy a signalman with professional qualifications. Lastly, the results of this study are expected to highlight the differences in accident types and causes of manned and unmanned tower cranes and the need for legal and institutional improvement in the signal system.