• Wind and Structures
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• v.35 no.6
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• pp.405-418
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• 2022

The aerodynamic behaviour of a CRH high-speed train under three infrastructure scenarios (flat ground, embankment, and viaduct) in the presence of a crosswind was simulated using a 1/8th scaled train model with three cars and the IDDES framework. The time-averaged and instantaneous flow field around the model were examined. The employed numerical algorithm was verified through a wind tunnel test, and the grid and timestep resolution analyses were conducted to ensure the reliability of the data. It was noted that the flow around the rail line was different under different infrastructure scenarios, especially in the case of the embankment, which degraded the aerodynamic performance of the train under the crosswind. The flow around the train on the flat ground and viaduct was different, although the aerodynamic performance of the train was similar in both cases. Moreover, the viaduct accidents were noted to have the most critical consequences, thereby requiring the most attention. The aerodynamic performance of the train on the windward track of the embankment under the crosswind was worse than that of the train on the leeward track. But for the other two infrastructure scenarios, the aerodynamic performance of the train on the windward track is relatively dangerous, which is mainly caused by the head car. These observations suggest that the aerodynamic behaviour of the train on an embankment under a crosswind must be carefully considered and that certain wind protection measures must be adopted around rail lines in windy areas.

• Smart Structures and Systems
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• v.29 no.1
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.79-85
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• 2022

Internet of Things (IoT) connects devices with various platforms, computing power, and functions. Due to the diversity of networks and the ubiquity of IoT devices, demands for security and privacy are increasing. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, while at the same time effective enough to be implemented in devices with long-range specifications. In this paper, we present the performance and memory limitations of modern cryptographic primitives and schemes for different types of devices that can be used in IoT. In addition, detailed performance evaluation of the performance of the most commonly used encryption algorithms in low-spec devices frequently used in IoT networks is performed. To provide data protection, the binary ring uses encryption asymmetric fully homomorphic encryption and symmetric encryption AES 128-bit. As a result of the experiment, it can be seen that the IoT device had sufficient performance to implement a symmetric encryption, but the performance deteriorated in the asymmetric encryption implementation.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.845-853
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• 2022

As the memory capacity of smartphone increases, the type and amount of privacy stored in the smartphone is also increasing. but recently there is an increasing possibility that various personal information such as photos and videos of smartphones may be leaked due to malicious apps by malicious attackers or other people such as repair technicians. This paper analyzed and studied the security and vulnerability of these vault apps by analyzing the cryptography algorithm and data protection function. We analyzed 5.3.7(June 13, 2022) and 3.3.2(December 30, 2020) versions of AppLock, the most downloaded information-hidding apps registered with Google Play, and found various vulnerabilities. In the case of access control, there was a vulnerability in that values for encrypting patterns entered by users were hardcoded into plain text in the source code, and encrypted pattern values were stored in xml files. In addition, in the case of the vault function, there was a vulnerability in that the files and log files for storing in the vault were not encrypted.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.311-319
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• 2017

Recently invasion of privacy problem in medical information have been issued following the interest in secondary use of large medical information. These large medical information is very useful information that can be used in various fields such as disease research and prevention. However, due to the privacy laws such as Privacy Act and Medical Law, these informations including patients or health professionals' personal information are difficult to utilize secondary. Accordingly, various methods such as k-anonymity, l-diversity and differential-privacy that can be utilized while protecting privacy have been developed and utilized in this field. In this paper, we study differential privacy processing procedure, one of various methods, and find out about the differential privacy problem using Laplace noise. Finally, we propose a new method using the Shamir's secret sharing method and symemetric key encryption algorithm such as AES for this problem.

• Korean Journal of Forensic Psychology
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• v.13 no.2
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• pp.121-145
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• 2022

The current study applied the random forest algorithm to Korean crime victim survey data collected biennially between 2010 and 2018 to explore the relationship between crime/victim characteristics and the victim's criminal damages. A total of 3,080 cases including gender, age (life cycle stage), type of crime, perpetrator acquisition, repeated victimization, psychological damage (depression, isolation, extreme fear, somatic symptoms, interpersonal problems, moving out to avoid people, suicidal impulses, suicide attempts), and emotional changes after victimization (changes in self-protection confidence, self-esteem, confidence in others, confidence in legal institutions, and respect for Korean legal system/law) were analyzed. Considering the features of data that are difficult to apply traditional statistical techniques, this study implemented random forest algorithms to predict crime and victim characteristics using the victim's criminal damages (psychological damage and emotional change) and selected good predictors using VSURF function in VSURF package for R. As a result of the analysis, it was confirmed that the relationship between the type of crime and depression, extreme fear, somatic symptoms, and interpersonal problems, between perpetrator acquisition and somatic symptoms and interpersonal problems, and between repeated victimization and changes in respect for Korean legal system/law. Gender and life cycle stage (youth/adult/elderly) were found to be related to extreme fear and changes in self-protection confidence, respectively. However, more empirical evidence should be aggregated to explain the results as meaningful. The results of this study suggest that it is necessary to enhance the experts' knowledge and educate them on cases about the relationship between crime/victim characteristics and criminal damage. Strengthening their interview strategy and knowledge about law/rules were also needed to increase the effectiveness of the Korean victim assessment system.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Journal of Radiation Protection and Research
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• v.27 no.1
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• pp.37-49
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• 2002

The accuracy of radiation dose delivery to target volume is one of the most important factors for good local control and less treatment complication. In vivo dosimetry is an essential QA procedure to confirm the radiation dose delivered to the patients. Transmission dose measurement is a useful method of in vivo dosimetry and it's advantages are non-invasiveness, simplicity and no additional efforts needed for dosimetry. In our department, in vivo dosimetry system using measurement of transmission dose was manufactured and algorithms for estimation of transmission dose were developed and tested with phantom in various conditions successfully. This system was applied in clinic to test stability, reproducibility and applicability to daily treatment and the accuracy of the algorithm. Transmission dose measurement was performed over three weeks. To test the reproducibility of this system, X-tay output was measured before daily treatment and then every hour during treatment time in reference condition(field size; $10 cm{\times} 10 cm$, 100 MU). Data of 11 patients whose pelvis were treated more than three times were analyzed. The reproducibility of the dosimetry system was acceptable with variations of measurement during each day and over 3 week period within ${\pm}2.0%$. On anterior- posterior and posterior fields, mean errors were between -5.20% and +2.20% without bone correction and between -0.62% and +3.32% with bone correction. On right and left lateral fields, mean errors were between -10.80% and +3.46% without bone correction and between -0.55% and +3.50% with bone correction. As the results, we could confirm the reproducibility and stability of our dosimetry system and its applicability in daily radiation treatment. We could also find that inhomogeneity correction for bone is essential and the estimated transmission doses are relatively accurate.

• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.