• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.273-275
• /
• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.100-103
• /
• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.1
• /
• pp.31-38
• /
• 2008

Ad-Hoc network is a network architecture which has no backbone network and is deployed temporarily and rapidly in emergency or war without fixed mobile infrastructures. All communications between network entities are carried in ad-hoc networks over the wireless medium. Due to the radio communications being extremely vulnerable to propagation impairments, connectivity between network nodes is not guaranteed. Therefore, many new algorithms have been studied recently. This study proposes the secondary header approach to the cluster based routing protocol (CBRP). The primary header becomes abnormal status so that the primary header can not participate in the communications between network entities, the secondary header immediately replaces the primary header without selecting process of the new primary header. This improves the routing interruption problem that occurs when a header is moving out from a cluster or in the abnormal status. The performances of proposed algorithm ACBRP(Advanced Cluster Based Routing Protocol) are compared with CBRP. The cost of the primary header reelection of ACBRP is simulated. And results are presented in order to show the effectiveness of the algorithm.

• Science of Emotion and Sensibility
• /
• v.20 no.3
• /
• pp.13-22
• /
• 2017

Emotion has a direct influence such as decision-making, perception, etc. and plays an important role in human life. For the convenient and accurate recognition of high-arousal negative emotion, the purpose of this paper is to design an algorithm for analysis using the bio-signal. In this study, after two emotional induction using the 'normal' / 'fear' emotion types of videos, we measured the Galvanic Skin Response (GSR) signal which is the simple of bio-signals. Then, by decomposing Tonic component and Phasic component in the measured GSR and decomposing Skin Conductance Very Slow Response (SCVSR) and Skin Conductance Slow Response (SCSR) in the Phasic component associated with emotional stimulation, extracting the major features of the components for an accurate analysis, we used a discrete wavelet transform with excellent time-frequency localization characteristics, not the method used previously. The extracted features are maximum value of Phasic component, amplitude of Phasic component, zero crossing rate of SCVSR and zero crossing rate of SCSR for distinguishing high-arousal negative emotion. As results, the case of high-arousal negative emotion exhibited higher value than the case of low-arousal normal emotion in all 4 of the features, and the more significant difference between the two emotion was found statistically than the previous analysis method. Accordingly, the results of this study indicate that the GSR may be a useful indicator for a high-arousal negative emotion measurement and contribute to the development of the emotional real-time rating system using the GSR.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.2
• /
• pp.221-231
• /
• 2013

Exchanging personal health information(PHI) is an essential process of healthcare services using information and communication technology. But the process have the inherent risk of information disclosure, so the PHI should be protected to ensure the reliability of healthcare services. In this paper, we designed encryption module for wearable personal health devices(PHD). A main goal is to guarantee that the real-time encoded and transmitted PHI cannot be allowed to be read, revised and utilized without user's permission. To achieve this, encryption algorithms as DES and 3DES were implemented in modules operating in Telos Rev B(16bit RISC, 8Mhz). And the experiments were performed in order to evaluate the performance of encryption and decryption using vital-sign measured by PHD. As experimental results, an block encryption was measured the followings: DES required 1.802 ms and 3DES required 6.683 ms. Also, we verified the interoperability among heterogeneous devices by testing that the encrypted data in Telos could be decoded in other machines without errors. In conclusion, the encryption module is the method that a PHD user is given the powerful right to decide for authority of accessing his PHI, so it is expected to contribute the trusted healthcare service distribution.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.808-814
• /
• 2010

In this paper we executed a SBL(Short Baseline) underwater acoustic positioning system that is a kind of underwater position estimation system to estimates the 3-dimensional position of ROV(Remotely Operated Vehicle) using hydrophones and DAQ(Data Acquisition) system in the basin which dimensions are $3{\times}3{\times}1.7(m)$. For this experiment, we let 4 hydrophones in different positions of the basin for receiver and 1 hydrophone is fixed on the underwater vehicle for transmitting sensor(pinger). These five hydrophones are communicated with each other to find the 3-D positions of the moving ROV in the basin. The measured signals are collected by DAQ system and the positions of the ROV are plotted by LabView program in real-time. To estimate the position of the ROV we used a trigonometric method. In X and Y plane the estimated data has a small errors but in Z plane the estimated data has large errors so we cannot use this data for position control. One solution of this problem is using depth sensor that implemented of the underwater vehicle. Hereafter, we will test in the ocean using designed SBL system.

• The KIPS Transactions:PartD
• /
• v.16D no.1
• /
• pp.133-138
• /
• 2009

This paper introduces a pilot CDMA network-based wireless lap time measurement system set up on a ski slope of Yongpyong Ski Resort. The wireless lap time measurement system is one output of U-Sports Project of Gangwon Province, which is intendended for promoting local strategic business and preparation for hosting 2018 Winter Olympic Games at Pyeongchang. A pair of laser sensors is installed at the entry and exit points of a section requiring lap time measurement on a ski slope. Each laser sensor is connected to a sensor node via wire so that the sensor node can detect the time when a skier enters or exits the section. Also each sensor node is connected to a CDMA network via a modem and receives a standard time from a NTP server. Each node executes the NTP algorithm to synchronize its local time to the received server time. As a result of the time synchronization, the sensor nodes maintain its local time within a resolution of at least 10 miliseconds and transmit the time of detection to a central control center. While the wireless lap time measurement system introduced in the paper does not need expensive measurement equipment, the system allows the central control center to provide lap time records in a more convenient manner compared to conventional manual lap time measuremnt methods.

• Journal of the Korean Institute of Gas
• /
• v.2 no.1
• /
• pp.28-39
• /
• 1998

This study investigated the natural convection and radiation in a rectangular enclosure with ceiling vent experimentally and numerically. A heat source is located on the center of the bottom surface. The analysis was peformed a pure convection and is combination of natural convection and radiation. The shape of the considered two dimensional model is a square whose center of ceiling($30\%$) is opened. The numerical simulations are carried out for the pure natural convection case and the combined heat transfer case by using the SIMPLE algorithm. For the turbulent flow, Reynolds stresses are closed by the standard $k-{\epsilon}$ model and the wall function is used to determine the wall boundary conditions. The experiment was performed on the same geometrical shape as the computations. The radiative heat transfer is analized by the S-N discrete ordinates method. The results of pure natural convection are compared with those of combined heat transfer by the velocity vectors, stream lines, isothermal lines. The results obtained are as follows 1. Comparing the results of pure convection with those of the combined convection-radiation through the shape of stream lines, isothermal lines are similar to each other. 2. The temperature fields obtained by numerical method are compared to those obtained by experimental one, and it is found that they are showed mean relative error $8.5\%$. 3. Visualization bt smoke is similar to computational results.

• Journal of Life Science
• /
• v.33 no.6
• /
• pp.490-497
• /
• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.

• Convergence Security Journal
• /
• v.23 no.2
• /
• pp.47-54
• /
• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.