• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.2
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• pp.147-156
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• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.355-364
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• 2011

In most applications requiring high-confidential images, reversible watermarking is an effective way to ensure the integrity of images. Many watermarking researches which have been adapted to authenticate contents cannot recover the original image after authentication. However, reversible watermarking inserts the watermark signal into digital contents in such a way that the original contents can be restored without any quality loss while preserving visual quality. To detect malicious tampering, this paper presents a new block-based image authentication algorithm using differential histogram-based reversible watermarking. To generate an authentication code, the DCT-based authentication feature from each image block is extracted and combined with user-specific code. Then, the authentication code is embedded into image itself with reversible watermarking. The image can be authenticated by comparing the extracted code and the newly generated code and restored into the original image. Through experiments using multiple images, we prove that the presented algorithm has achieved over 97% authentication rate with high visual quality and complete reversibility.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.155-164
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• 2018

Many malicious programs have been compressed or encrypted using various commercial packers to prevent reverse engineering, So malicious code analysts must decompress or decrypt them first. The OEP (Original Entry Point) is the address of the first instruction executed after returning the encrypted or compressed executable file back to the original binary state. Several unpackers, including PinDemonium, execute the packed file and keep tracks of the addresses until the OEP appears and find the OEP among the addresses. However, instead of finding exact one OEP, unpackers provide a relatively large set of OEP candidates and sometimes OEP is missing among candidates. In other words, existing unpackers have difficulty in finding the correct OEP. We have developed new tool which provides fewer OEP candidate sets by adding two methods based on the property of the OEP. In this paper, we propose two methods to provide fewer OEP candidate sets by using the property that the function call sequence and parameters are same between packed program and original program. First way is based on a function call. Programs written in the C/C++ language are compiled to translate languages into binary code. Compiler-specific system functions are added to the compiled program. After examining these functions, we have added a method that we suggest to PinDemonium to detect the unpacking work by matching the patterns of system functions that are called in packed programs and unpacked programs. Second way is based on parameters. The parameters include not only the user-entered inputs, but also the system inputs. We have added a method that we suggest to PinDemonium to find the OEP using the system parameters of a particular function in stack memory. OEP detection experiments were performed on sample programs packed by 16 commercial packers. We can reduce the OEP candidate by more than 40% on average compared to PinDemonium except 2 commercial packers which are can not be executed due to the anti-debugging technique.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Satellite, Information and Communications
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• v.4 no.2
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• pp.12-19
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• 2009

In the modern UAV systems, the role of radar payload has been increasing with its unique performance of day-and-night operation and see-through capability over hidden obstacles. Contrary to the satellite reconnaissance, UAV is expected to provide high resolution target detection and recognition capability while frequent flight missions would deliver enhanced SAR image and local information over the target area. STK(Satellite Tool Kit) is a professional space-analysis software widely used in all phases of a space system's life cycle. The simulation of STK is efficient and accurate relatively. In this paper, the author attempt to model the UAV operation and measure the expected SAR image quality. STK(Satellite Tool Kit) is employed to analyze UAV operation and produce SAR raw data. A SAR simulator is developed to produce high resolution SAR image for various ground targets.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.1
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• pp.57-63
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• 2007

The systematic method to construct equivalent static load from a given dynamic load is proposed in the present study. Previously reported works to construct equivalent static load were based on ad hoc methods. Due to improper selection of loading position, they may results in unreliable structural design. The present study proposes the employment of primary degrees of freedom for imposing the equivalent static loads. The degrees of freedom are selected by two-level condensation scheme with reliability and efficiency. In several numerical examples, the efficiency and reliability of the proposed scheme is verified by comparison displacement for equivalent static loading and dynamic loading at the critical time.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.257-261
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• 2002

We have carried out integrated research including field survey and numerical modeling to appraise the applicability of SP method to the leakage problems of irrigation facilities. The leakage pattern of the dike studied here can be classified into the three categories: leakage through the abutment, leakage by piping through dike, and leakage due to the composite effects of landslide and distortion of the dike structure. for the numerical modeling to interpret quantitatively SP survey results acquired at dike, we have modified the computer code proposed by Sill (1983) to apply to the leakage problems. The numerical studies match the characteristic patterns of SP anomalies according to the leakage types and appear to be very useful to interpret the leakage zone and path. The SP monitoring results were also well coincided with tidal variations observed at every embankment so we found the SP method is quite effective not only to detect the leakage zone but also to determine the leakage trend. The numerical modeling results also reproduced the SP anomalies due to seawater leakage in the embankment.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.75-87
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• 2011

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.