• Korean Journal of Construction Engineering and Management
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• v.25 no.3
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• pp.58-67
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• 2024

In the era of the fourth industrial revolution, data plays a vital role in enhancing the productivity of industries. To advance digitalization in the construction industry, which suffers from a lack of available data, this study proposes a model that classifies construction site photos by work types. Unlike traditional image classification models that solely rely on visual data, the model in this study includes semantic analysis of construction work types. This is achieved by extracting the significance of relationships between objects and work types from the standard construction specification. These relationships are then used to enhance the classification process by correlating them with objects detected in photos. This model improves the interpretability and reliability of classification results, offering convenience to field operators in photo categorization tasks. Additionally, the model's practical utility has been validated through integration into a classification program. As a result, this study is expected to contribute to the digitalization of the construction industry.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.144-153
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• 2024

Recent research is increasingly focused on utilizing seismic waves for structure health monitoring (SHM). Specifically, seismic interferometry, a technique applied in geophysical surveys using ambient noise, is widely applied in SHM. This method involves analyzing the response of buildings to propagating seismic waves. This enables the estimation of changes in structural stiffness and the evaluation of the location and presence of damage. Analysis of seismic interferometry applied to SHM, along with case studies, indicates its highly effective application for assessing structural stability and monitoring building conditions. Seismic interferometry is thus recognized as an efficient approach for evaluating building integrity and damage detection in SHM and monitoring applications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

• The Journal of Bigdata
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• v.9 no.1
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• pp.61-73
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• 2024

Class imbalance is one of the significant challenges in deep learning tasks, particularly pronounced in areas with limited data. This study proposes a new approach that utilizes minimal labeled data for effectively classifying tomato leaf diseases. We introduced a recursive learning method using the YOLOv8 model. By utilizing the detection predictions of images on the training data as additional training data, the number of labeled data is progressively increased. Unlike conventional data augmentation and up-down sampling techniques, this method seeks to fundamentally solve the class imbalance problem by maximizing the utility of actual data. Based on the secured labeled data, tomato leaves were extracted, and diseases were classified using the EfficientNet model. This process achieved a high accuracy of 98.92%. Notably, a 12.9% improvement compared to the baseline was observed in the detection of Late blight diseases, which has the least amount of data. This research presents a methodology that addresses data imbalance issues while offering high-precision disease classification, with the expectation of application to other crops.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.651-666
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• 2024

Android devices employ lock screens with various authentication methods to protect user data. However, even with the lock screen active, the device can be accessed via the Android Debug Bridge(ADB), a powerful development tool that controls devices connected through USB. In this paper, we explore methods to bypass the lock screen security mechanism by leveraging the characteristics of ADB. To achieve this, we analyze ADB commands to categorize those that can severely impact the Android system and propose LockPickFuzzer, a fuzzing test tool that automatically explores ways to combine these commands to disable lock screen security. To demonstrate LockPickFuzzer's ability to detect security vulnerabilities using ADB, we conducted experiments on the Galaxy S23 and Pixel 8, both running Android 14. The results revealed two ADB command combinations that could either steal authentication information or bypass the lock screen. We submitted a report on these discovered vulnerabilities to the Samsung security team and received official acknowledgment (SVE-2023-1344) from Samsung Electronics for one ADB command combination that can be reproduced on user devices. LockPickFuzzer is a practical tool that operates automatically without user intervention and is expected to contribute to the effective detection of security vulnerabilities caused by ADB command combinations on Android devices.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.468-473
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• 2024

Excavation work in urban areas can induce ground deformation, which may damage nearby infrastructure. Such ground deformation can result in displacement of paving blocks near the construction site. Accurate measurement of these displacements can serve as an indicator for assessing the potential risks associated with ground deformation. This paper proposes a robust and efficient method for paving block displacement measurement using a 3D laser line sensor mounted on an Unmanned Ground Vehicle (UGV). The proposed method consists of two stages: 2D projection based object detection and measurement through the CPLF algorithm. Experimental results demonstrate that the CPLF algorithm is more efficient compared to the PLF algorithm, achieving an error of 1.36 mm and a processing time of 10.76 ms, confirming that the proposed method ensures robust online measurements with high accuracy in real-world environments with various types of paving blocks and environmental factors using a 3D laser line sensor on a UGV.

• Journal of IKEEE
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• v.28 no.3
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• pp.342-351
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• 2024

Effective pest control in the agricultural field is essential for improving crop productivity. To do so, information on the type and timing of pests, as well as the amount of pests generated, is required. Mada-CenterNet, a prior study on pest counting, which is a method of identifying the amount of pest occurrence, has improved the accuracy of pest counting by utilizing transformable convolution and multiscale attention fusion and is reported to be the best in the field. In this study, a new transformer structure with a dual block was applied instead of multiscale attention, which is the transformer structure of Mada-CenterNet. More sophisticated feature maps were extracted through cross-attention of pixel path and semantic path. As a result of the experiment, the proposed model has improved the accuracy of pest counting. It is better than the existing Mada-CenterNet and effectively alleviates obstruction problems, damage to pests' bodies, and detection difficulties caused by various appearances. Unlike conventional pest counting methods, it can secure the advantage of reducing manpower and time costs, and it is expected that it can be used in other agricultural fields that require counting of objects.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.643-653
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• 2018

Xamarin is a representative cross-platform development framework that allows developers to write mobile apps in C# for multiple mobile platforms, such as Android, iOS, or Windows Phone. Using Xamarin, mobile app developers can reuse existing C# code and share significant code across multiple platforms, reducing development time and maintenance costs. Meanwhile, malware authors can also use Xamarin to spread malicious apps on more platforms, minimizing the time and cost of malicious app creation. In order to cope with this problem, it is necessary to analyze and detect malware written with Xamarin. However, little studies have been conducted on static analysis methods of the apps written in Xamarin. In this paper, we examine the structure of Android apps written with Xamarin and propose a static analysis technique for the apps. We also demonstrate how to statically reverse-engineer apps that have been transformed using code obfuscation. Because the Android apps written with Xamarin consists of Java bytecode, C# based DLL libraries, and C/C++ based native libraries, we have studied static reverse engineering techniques for these different types of code.