• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Progress in Medical Physics
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• v.11 no.2
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• pp.157-165
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• 2000

Purpose: For estimation of yields of l)NA damages induced by radiation and enhanced by oxygen, a mathematical model was used and tested. Materials and Methods: Reactions of the products of water radiolysis were modeled as an ordinary time dependant equations. These reactions include formation of radicals, DNA damage, damage repair, restitution, and damage fixation by oxygen and H-radical. Several rate constants were obtained from literature while others were calculated by fitting an experimental data. Sensitivity studies were performed changing the chemical rate constant at a constant oxygen number density and varying the oxygen concentration. The effects of oxygen concentration as well as the damage fixation mechanism by oxygen were investigated. Oxygen enhancement ratio(OER) was calculated to compare the simulated data with experimental data. Results: Sensitivity studies with oxygen showed that DNA survival was a function of both oxygen concentration and the magnitude of chemical rate constants. There were no change in survival fraction as a function of dose while the oxygen concentration change from 0 to 1.0 x 10$^{7}$ . When the oxygen concentration change from 1.0 $\times$ 107 to 1.0 $\times$ 101o, there was significant decrease in cell survival. The OER values obtained from the simulation study were 2.32 at 10% cell survival level and 1.9 at 45% cell survival level. Conclusion: Sensitivity studies with oxygen demonstrated that the experimental data were reproduced with the effects being enhanced for the cases where the oxygen rate constants are largest and the oxygen concentration is increased. OER values obtained from the simulation study showed good agreement for a low level of cell survival. This indicated that the use of the semi-empirical model could predict the effect of oxygen in cell killing.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.4
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• pp.263-271
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• 2009

This study evaluated the flocs forming characteristics in the mixing zone to increase the coagulation effect in the drinking water plant. As a measuring tool of formed flocs, on-line particle dispersion analyzer (iPDA) was used in Y drinking water plant. To evaluate the forming flocs, many parameters such as poly amine, coagulant dosing amount, raw water turbidity, and pH was applied in this study. During the periods of field test, poly aluminium chloride (PACl) as a coagulant was used. With the increase of the raw water turbidities, poly amine was also added as one of aids for increasing in coagulation efficiency. The turbidity and pH of raw water was ranged from 7 to 9 and from 25 to 140 NTU, respectively. The increasing of raw water turbidity brought the bigger floc sizes accordingly. From a regression analysis, $R^2$ value was 0.8040 as a function of T, raw water turbidity. Floc size index (FSI) was obtained from a correlation equation as follows; FSI = 0.9388logT - 0.3214 Also, polyamine gave the bigger flocs the moment it is added to the coagulated water in the rapid mixing zone. One of parameters influencing the floc sizes was the addition of powdered active carbon(PAC) in the mixing zone. In case of higher turbidity of raw water, $R^2$ value was 0.9050 in the parameters of [PACl] and [PAC]; FSI = $0.0407[T]^{0.324}[PACI]^{0.769}[PAC]^{0.178}$ On-line floc monitor was beneficial to evaluate the flocs sizes depending on the many parameters consisting raw water properties, bring the profitable basic data to control the mixing zone more effectively.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.39-60
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• 2011

Since the introduction of web 2.0 technology, social network service has been recognized as the foundation of an important future information technology. The advent of web 2.0 has led to the change of content creators. In the existing web, content creators are service providers, whereas they have changed into service users in the recent web. Users share experiences with other users improving contents quality, thereby it has increased the importance of social network. As a result, diverse forms of social network service have been emerged from relations and experiences of users. Social network is a network to construct and express social relations among people who share interests and activities. Today's social network service has not merely confined itself to showing user interactions, but it has also developed into a level in which content generation and evaluation are interacting with each other. As the volume of contents generated from social network service and the number of connections between users have drastically increased, the social network extraction method becomes more complicated. Consequently the following problems for the social network extraction arise. First problem lies in insufficiency of representational power of object in the social network. Second problem is incapability of expressional power in the diverse connections among users. Third problem is the difficulty of creating dynamic change in the social network due to change in user interests. And lastly, lack of method capable of integrating and processing data efficiently in the heterogeneous distributed computing environment. The first and last problems can be solved by using FOAF, a tool for describing ontology-based user profiles for construction of social network. However, solving second and third problems require a novel technology to reflect dynamic change of user interests and relations. In this paper, we propose a novel method to overcome the above problems of existing social network extraction method by applying FOAF (a tool for describing user profiles) and RSS (a literary web work publishing mechanism) to OLAP system in order to dynamically innovate and manage FOAF. We employed data interoperability which is an important characteristic of FOAF in this paper. Next we used RSS to reflect such changes as time flow and user interests. RSS, a tool for literary web work, provides standard vocabulary for distribution at web sites and contents in the form of RDF/XML. In this paper, we collect personal information and relations of users by utilizing FOAF. We also collect user contents by utilizing RSS. Finally, collected data is inserted into the database by star schema. The system we proposed in this paper generates OLAP cube using data in the database. 'Dynamic FOAF Management Algorithm' processes generated OLAP cube. Dynamic FOAF Management Algorithm consists of two functions: one is find_id_interest() and the other is find_relation (). Find_id_interest() is used to extract user interests during the input period, and find-relation() extracts users matching user interests. Finally, the proposed system reconstructs FOAF by reflecting extracted relationships and interests of users. For the justification of the suggested idea, we showed the implemented result together with its analysis. We used C# language and MS-SQL database, and input FOAF and RSS as data collected from livejournal.com. The implemented result shows that foaf : interest of users has reached an average of 19 percent increase for four weeks. In proportion to the increased foaf : interest change, the number of foaf : knows of users has grown an average of 9 percent for four weeks. As we use FOAF and RSS as basic data which have a wide support in web 2.0 and social network service, we have a definite advantage in utilizing user data distributed in the diverse web sites and services regardless of language and types of computer. By using suggested method in this paper, we can provide better services coping with the rapid change of user interests with the automatic application of FOAF.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.67-81
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• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.321-322
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• 2012

의료용 방사선 장비는 초기의 아날로그 방식의 필름 및 카세트에서 진보되어 현재는 디지털 방식의 DR (Digital Radiography)이 널리 사용되며 그에 관한 연구개발이 활발히 진행되고 있다. DR은 크게 간접방식과 직접방식의 두 분류로 나눌 수 있는데, 간접방식은 X선을 흡수하면 가시광선으로 전환하는 형광체(Scintillator)를 사용하여 X선을 가시광선으로 전환하고, 이를 Photodiode와 같은 광소자로 전기적 신호로 변환하여 방사선을 검출하는 방식을 말하며, 직접 방식은 X선을 흡수하면 전기적 신호를 발생 시키는 광도전체(Photoconductor)를 사용하여 광도전체 양단 전극에 고전압을 인가한 형태를 취하고 있는 가운데, X선이 조사되면 일차적으로 광도전체 내부에서 전자-전공쌍(Electron-hole pair)이 생성된다. 이들은 광도전체 양단의 인가되어 있는 전기장에 의해 전자는 +극으로, 전공은 -극으로 이동하여 아래에 위치한 Active matrix array을 통해 방사선을 검출하는 방식이다. 본 연구에서는 직접방식 X-ray 검출기에서 활용되는 a-Se을 ITO (Indium Thin oxide) glass 상단에 Thermal evaporation증착을 이용하여 두께 $50{\mu}m$, 33 넓이로 증착 시킨 다음, a-Se상단에 Sputtering증착을 이용하여 ITO를 11 cm, 22 cm, $2.7{\times}2.7cm$ 넓이로 증착시켜 상하부의 ITO를 Electrode로 이용하여 직접방식의 X-ray검출기 샘플을 제작하였다. 제작 과정 중 a-Se의 Thermal evaporation증착 시, 저진공 $310^{-3}_{Torr}$, 고진공 $2.210^{-5}_{Torr}$에서 보트의 가열 온도를 두 번의 스텝으로 나누어 증착 시켰다. 첫 번째 스텝 $250^{\circ}C$, 두 번째 스텝은 $260^{\circ}C$의 조건으로 증착하여 보트 내의 a-Se을 남기지 않고 전량을 소모할 수 있었으며, 스텝간의 온도차를 $10^{\circ}C$로 제어하여 균일한 박막을 형성 할 수 있었다. Sputtering증착 시, 저진공 $2.510^{-3}$, 고진공 $310^{-5}$에서 Ar, $O_2$를 사용하여 100 Sec간 플라즈마를 생성시켜 ITO를 증착하였다. 제작된 방사선 각각의 검출기 샘플 양단의 ITO에 500V의 전압을 인가하고, 진단 방사선 범위의 70 kVp, 100 mA, 0.03 sec 조건으로 X-ray를 조사시켜 ITO넓이에 따른 민감도(Sensitivity)와 암전류(Dark current)를 측정하였다. 측정결과 민감도(Sensitivity)는 X-ray샘플의 두께에 따른 $1V/{\mu}m$ 기준 시, 증착된 ITO의 넓이가 11 cm부터 22 cm, $2.7{\times}2.7cm$까지 각각 $7.610nC/cm^2$, $8.169nC/cm^2$, $6.769nC/cm^2$로 22 cm 넓이의 샘플이 가장 높은 민감도를 나타내었으나, 암전류(Dark current)는 $1.68nA/cm^2$, $3.132nA/cm^2$, $5.117nA/cm^2$로 11 cm 넓이의 샘플이 가장 낮은 값을 나타내었다. 이러한 데이터를 SNR (Signal to Noise Ratio)로 합산 하였을 시 104.359 ($1{\times}1$), 60.376($2{\times}2$), 30.621 ($2.7{\times}2.7$)로 11 cm 샘플이 신호 대 별 가장 우수한 효율을 나타냄을 알 수 있었다. 따라서 ITO박막의 면적이 클수록 민감도는 우수하나 그에 따른 암전류의 증가로 효율이 떨어짐을 검증 할 수 있었으며, 이는 ITO면적이 넓어짐에 따른 저항의 증가로 암전류에 영향을 끼침을 할 수 있었다. 본 연구를 통해 a-Se의 ITO 박막 면적에 따른 전기적 특성을 검증할 수 있었다.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.109-115
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• 2015

The new medical device technologies for bio-signal information and medical information which developed in various forms have been increasing. Information gathering techniques and the increasing of the bio-signal information device are being used as the main information of the medical service in everyday life. Hence, there is increasing in utilization of the various bio-signals, but it has a problem that does not account for security reasons. Furthermore, the medical image information and bio-signal of the patient in medical field is generated by the individual device, that make the situation cannot be managed and integrated. In order to solve that problem, in this paper we integrated the QR code signal associated with the medial image information including the finding of the doctor and the bio-signal information. bio-signal. System implementation environment for medical imaging devices and bio-signal acquisition was configured through bio-signal measurement, smart device and PC. For the ROI extraction of bio-signal and the receiving of image information that transfer from the medical equipment or bio-signal measurement, .NET Framework was used to operate the QR server module on Window Server 2008 operating system. The main function of the QR server module is to parse the DICOM file generated from the medical imaging device and extract the identified ROI information to store and manage in the database. Additionally, EMR, patient health information such as OCS, extracted ROI information needed for basic information and emergency situation is managed by QR code. QR code and ROI management and the bio-signal information file also store and manage depending on the size of receiving the bio-singnal information case with a PID (patient identification) to be used by the bio-signal device. If the receiving of information is not less than the maximum size to be converted into a QR code, the QR code and the URL information can access the bio-signal information through the server. Likewise, .Net Framework is installed to provide the information in the form of the QR code, so the client can check and find the relevant information through PC and android-based smart device. Finally, the existing medical imaging information, bio-signal information and the health information of the patient are integrated over the result of executing the application service in order to provide a medical information service which is suitable in medical field.

• Progress in Medical Physics
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• v.23 no.2
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• pp.81-90
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• 2012

The effect of setup uncertainties on CTV dose and the correlation between setup uncertainties and setup margin were evaluated by Monte Carlo based numerical simulation. Patient specific information of IMRT treatment plan for rectal cancer designed on the VARIAN Eclipse planning system was utilized for the Monte Carlo simulation program including the planned dose distribution and tumor volume information of a rectal cancer patient. The simulation program was developed for the purpose of the study on Linux environment using open source packages, GNU C++ and ROOT data analysis framework. All misalignments of patient setup were assumed to follow the central limit theorem. Thus systematic and random errors were generated according to the gaussian statistics with a given standard deviation as simulation input parameter. After the setup error simulations, the change of dose in CTV volume was analyzed with the simulation result. In order to verify the conventional margin recipe, the correlation between setup error and setup margin was compared with the margin formula developed on three dimensional conformal radiation therapy. The simulation was performed total 2,000 times for each simulation input of systematic and random errors independently. The size of standard deviation for generating patient setup errors was changed from 1 mm to 10 mm with 1 mm step. In case for the systematic error the minimum dose on CTV $D_{min}^{stat{\cdot}}$ was decreased from 100.4 to 72.50% and the mean dose $\bar{D}_{syst{\cdot}}$ was decreased from 100.45% to 97.88%. However the standard deviation of dose distribution in CTV volume was increased from 0.02% to 3.33%. The effect of random error gave the same result of a reduction of mean and minimum dose to CTV volume. It was found that the minimum dose on CTV volume $D_{min}^{rand{\cdot}}$ was reduced from 100.45% to 94.80% and the mean dose to CTV $\bar{D}_{rand{\cdot}}$ was decreased from 100.46% to 97.87%. Like systematic error, the standard deviation of CTV dose ${\Delta}D_{rand}$ was increased from 0.01% to 0.63%. After calculating a size of margin for each systematic and random error the "population ratio" was introduced and applied to verify margin recipe. It was found that the conventional margin formula satisfy margin object on IMRT treatment for rectal cancer. It is considered that the developed Monte-carlo based simulation program might be useful to study for patient setup error and dose coverage in CTV volume due to variations of margin size and setup error.