• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.63-69
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• 2022

In this paper, we propose a new GPU-based framework for quickly calculating adaptive and continuous SDF(Signed distance fields), and examine cases related to rendering/collision processing using them. The quadtree constructed from the triangle mesh is transferred to the GPU memory, and the Euclidean distance to the triangle is processed in parallel for each thread by using it to find the shortest continuous distance without discontinuity in the adaptive grid space. In this process, it is shown through experiments that the cut-off view of the adaptive distance field, the distance value inquiry at a specific location, real-time raytracing, and collision handling can be performed quickly and efficiently. Using the proposed method, the adaptive sign distance field can be calculated quickly in about 1 second even on a high polygon mesh, so it is a method that can be fully utilized not only for rigid bodies but also for deformable bodies. It shows the stability of the algorithm through various experimental results whether it can accurately sample and represent distance values in various models.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Journal of IKEEE
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• v.26 no.4
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• pp.614-621
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• 2022

This paper proposes RIDS (Random Forest-Based Intrusion Detection), which is an intrusion detection system to detect hacking attack based on random forest. RIDS detects three typical attacks i.e. DoS (Denial of service) attack, fuzzing attack, and spoofing attack. It detects hacking attack based on four parameters, i.e. time interval between data frames, its deviation, Hamming distance between payloads, and its diviation. RIDS was designed in memory-centric architecture and node information is stored in memories. It was designed in scalable architecture where DoS attack, fuzzing attack, and spoofing attack can be all detected by adjusting number and depth of trees. Simulation results show that RIDS has 0.9835 accuracy and 0.9545 F1 score and it can detect three attack types effectively.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.6
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• pp.1-11
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• 2021

To develop an efficient procedure related to the flamelet library, the machine learning process based on artificial neural network(ANN) is applied for the gaseous hydrogen/liquid oxygen combustor under a supercritical pressure condition. For hidden layers, 25 combinations based on Rectified Linear Unit(ReLU) and hyperbolic tangent are adopted to find an optimum architecture in terms of the computational efficiency and the training performance. For activation functions, the hyperbolic tangent is proper to get the high learning performance for accurate properties. A transformation learning data is proposed to improve the training performance. When the optimal node is arranged for the 4 hidden layers, it is found to be the most efficient in terms of training performance and computational cost. Compared to the interpolation procedure, the ANN procedure reduces computational time and system memory by 37% and 99.98%, respectively.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.76-84
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• 2022

In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

• 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.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.4
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• pp.71-81
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• 2022

Purpose : This study aimed to present evidence by analyzing the characteristics and effectiveness of group art therapy interventions through an examination of domestic studies on group art therapy for older people with dementia. Methods : The database used DBpia, Riss, and Google Scholar, and the research period was from 2016 to November 2021. For the selected studies, the level of evidence was analyzed, bias evaluation was performed, and patient, intervention, comparison, and outcome were analyzed. For the evaluation of bias, the risk of bias assessment tool for non-randomized study (RoBANS) and Cochrane's risk of bias (RoB) were used. Results : As for the level of evidence of the included studies, level I consisted of five studies, and levels II and III each had one article. As a result of the bias evaluation of five studies through RoB, a "low risk of bias" was found for incomplete result data, selective result reporting, and others, except for four unclear evaluation areas. The "low risk of bias" ratio was 0~25 % in the evaluation of bias in two studies through RoBANS. For the evaluation tool, cognitive evaluation tool was used the most while mini-mental state examination-Korea was used the most frequently. For the intervention method, the most frequently used was group art therapy that employed recall in three studies, while collage, Korean painting, use of paper media, and procedural memory were used in each of the other studies. Each intervention was found to be significantly effective overall. Conclusion : This study provided clinical evidence by systematically reporting research on group art therapy for older people with dementia. In the future, it is necessary to check the effect of group art therapy on various areas other than cognition for older people with dementia. Moreover, the study should be conducted with the risk of bias sufficiently taken into consideration.

• 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.