• Journal of Korea Water Resources Association
• /
• v.56 no.7
• /
• pp.439-449
• /
• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Journal of the Korean Geotechnical Society
• /
• v.38 no.12
• /
• pp.19-28
• /
• 2022

Cavities behind concrete walls can adversely affect the stability of structures. Thus study aims to detect cavities behind concrete structures using a microphone in a laboratory model test. A small-scale concrete wall is constructed in a chamber, which is composed of a reinforced concrete plate and dry soil. A plastic bowl is then placed between the plate and soil to simulate a cavity behind the concrete structure. Leaky surface acoustic waves are generated by impacting the concrete plate using a hammer and are measured using a microphone. The measured signals are analyzed using natural frequencies, and cavity-free sections are evaluated. The test results show that the first natural frequency decreases at the cavity section due to the flexural vibration behavior of the plate. In addition, the amplitude corresponding to the first natural frequency decreases as the measurement location becomes farther from the cavity center and significantly decreases at the measurement locations near the rebars. This study demonstrates that a microphone may be useful to detect cavities behind concrete walls.

• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.1-13
• /
• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Journal of Digital Contents Society
• /
• v.16 no.3
• /
• pp.389-395
• /
• 2015

Digital image provides many conveniences at the internet environment recently. A great number of applications, like Digital Library, Stock Image, Personal Image and Important Information, require the use of digital image. However it has fatal defect which is easy to be modified because digital image is only electronic file. Numerous digital image forgeries have become a serious problem due to the sophistication and accessibility of image editing software. Copy-Move forgery is the simplest type of forgery that involves copying portion of an image and paste it on different location within the image. There are many approaches to detect Copy-Move forgery, but all of them have their own limitations. In this paper, visual and invisible feature based forgery detection techniques are tested and analyzed. The analysis shows that pros and cons of these two techniques compensate each other. Therefore, a hybrid of visual based and invisible feature based forgery detection that combine the merits of both techniques is proposed. The experimental results show that the proposed algorithm has enhanced performance compared to individual techniques. Moreover, it provides more information about the forgery, like identifying copy and duplicate regions.