• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.93-101
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• 2009

This paper suggests a new algorithm automatically searching for Region-of-Interest(ROI) with high speed, using the edge information of high frequency subband transformed with wavelet. The proposed method executes a searching algorithm of 4-direction object boundary by the unit of block using the edge information, and detects ROIs. The whole image is splitted by $64{\times}64$ or $32{\times}32$ sized blocks and the blocks can be ROI block or background block according to taking the edges or not. The 4-directions searche the image from the outside to the center and the algorithm uses a feature that the low-DOF image has some edges as one goes to center. After searching all the edges, the method regards the inner blocks of the edges as ROI, and makes the ROI masks and sends them to server. This is one of the dynamic ROI method. The existing methods have had some problems of complicated filtering and region merge, but this method improved considerably the problems. Also, it was possible to apply to an application requiring real-time processing caused by the process of the unit of block.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.687-693
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea. showed: (1) average RMSE error of the approach was 0.435 pixel; (2) the average number of matching points was over 25,573; (3) the average processing time was 4.2 min per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.85-92
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• 2023

In this paper, we propose a method for automatically generating story-based abbreviated summaries from long-form dramas and movies. From the shooting stage, the basic premise was to compose a frame with illusion of depth considering the golden division as well as focus on the object of interest to focus the viewer's attention in terms of content delivery. To consider how to extract the appropriate frames for this purpose, we utilized elemental techniques that have been utilized in previous work on scene and shot detection, as well as work on identifying focus-related blur. After converting the videos shared on YouTube to frame-by-frame, we divided them into a entire frame and three partial regions for feature extraction, and calculated the results of applying Laplacian operator and FFT to each region to choose the FFT with relative consistency and robustness. By comparing the calculated values for the entire frame with the calculated values for the three regions, the target frames were selected based on the condition that relatively sharp regions could be identified. Based on the selected results, the final frames were extracted by combining the results of an offline change point detection method to ensure the continuity of the frames within the shot, and an edit decision list was constructed to produce an abbreviated summary of 62.77% of the footage with F1-Score of 75.9%

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.

• Economic and Environmental Geology
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• v.38 no.2 s.171
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• pp.101-111
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• 2005

The objectives of this study we to assess the extent and degree of environmental contamination and to draw general conclusions on the fate of toxic elements derived from mining activities in Korea. 인t abandoned mines with four base-metal mines and four Au-Ag mines were selected and the results of environmental surveys in those areas were discussed. In the base-metal mining areas, the Sambo Pb-Zn-barite, the Shinyemi Pb-Zn-Fe, the Geodo Cu-Fe and the Shiheung Cu-Pb-Zn mine, significant levels of Cd, Cu, Pb and Zn were found in mine dump soils developed over mine waste materials, tailings and slag. Furthermore, agricultural soils, stream sediments and stream water near the mines were severely contaminated by the metals mainly due to the continuing dispersion downstream and downslope from the sites, which was controlled by the feature of geography, prevailing wind directions and the distance from the mine. In e Au-Ag mining areas, the Kubong, the Samkwang, the Keumwang and the Kilkok mines, elevated levels of As, Cd, Cu, Pb and Zn were found in tailings and mine dump soils. These levels may have caused increased concentrations of those elements in stream sediments and waters due to direct dis-charge downstream from tailings and mine dumps. In the Au-Ag mines, As would be the most characteristic contaminant in the nearby environment. Arsenic and heavy metals were found to be mainly associated with sulfide gangue minerals, and mobility of these metals would be enhanced by the effect of oxidation. According to sequential extraction of metals in soils, most heavy metals were identified as non-residual chemical forms, and those are very susceptible to the change of ambient conditions of a nearby environment. As application of pollution index (PI), giving data on multi-element contamination in soils, over 1.0 value of the PI was found in soils sampled at and around the mining areas.