• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.559-571
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• 2021

Along with the increase prevalence of computers, the number of malware distributions by attackers to ordinary users has also increased. Research to detect malware continues to this day, and in recent years, research on malware detection and analysis using AI is focused. However, the AI algorithm has a disadvantage that it cannot explain why it detects and classifies malware. XAI techniques have emerged to overcome these limitations of AI and make it practical. With XAI, it is possible to provide a basis for judgment on the final outcome of the AI. In this paper, we conducted malware group classification using XGBoost and Random Forest, and interpreted the results through SHAP. Both classification models showed a high classification accuracy of about 99%, and when comparing the top 20 API features derived through XAI with the main APIs of malware, it was possible to interpret and understand more than a certain level. In the future, based on this, a direct AI reliability improvement study will be conducted.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.261-268
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• 2022

Recently, the number of elderly living alone suffering from loneliness and depression is also increasing significantly due to the rapid aging of the population and nuclear families. In this paper, we propose a smart aging system that increases life satisfaction by providing the elderly with the optimal service tailored to the elderly with the help of IT according to their residential environment and health status. It is possible to provide an advanced customized support system for the elderly by fully utilizing IoT, AI, and Metaverse techniques not only for the elderly who want to live an active life in society but also for the elderly who need care in a nursing hospital. The proposed system provides human satisfaction by providing social connection in real space and virtual space in accordance with the residential environment and health status to the elderly suffering from loneliness in hospital (hospital care) facilities and at home. This paper proposes a new path for future-oriented welfare policy for the elderly by providing a user-customized smart aging system by combining AI and Metaverse technology with a rapidly changing social environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.905-912
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• 2022

Virtual reality, one of the core technologies of the 4th industrial revolution, is entering a new phase with the spread of low-cost wearable devices represented by Oculus. In the case of disaster evacuation drills, where practical training is almost impossible due to the risk of accidents, virtual reality is becoming a new alternative that enables effective training. In this paper, we propose a smoke modeling method that can be applied to fire evacuation drills implemented with virtual reality technology. In the event of a fire, smoke spreads along the aisle, and the density of the smoke changes over time. The proposed method models the smoke by applying a procedural function that can reflect the density of smoke calculated through simulation to the model in real-time. Implementation results in the background of the factory show that the proposed method produces models that can express the smoke according to the user's movement.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.124-128
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• 2023

Semiconductor engineers have long sought to enhance the energy efficiency of mobile semiconductors by reducing their voltage. During the final stages of the semiconductor manufacturing process, the screening and evaluation of voltage is crucial. However, determining the optimal test start voltage presents a significant challenge as it can increase testing time. In the semiconductor manufacturing process, a wealth of test element group information is collected. If this information can be controlled to predict the test voltage, it could lead to a reduction in testing time and increase the probability of identifying the optimal voltage. To achieve this, this paper is exploring machine learning techniques, such as linear regression and ensemble models, that can leverage large amounts of information for voltage prediction. The outcomes of these machine learning methods not only demonstrate high consistency but can also be used for feature engineering to enhance accuracy in future processes.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.89-100
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• 2023

Recently, on-device artificial intelligence (AI) solutions using mobile devices and embedded edge devices have emerged in various fields, such as computer vision, to address network traffic burdens, low-energy operations, and security problems. Although vision transformer deep learning models have outperformed conventional convolutional neural network (CNN) models in computer vision, they require more computations and parameters than CNN models. Thus, they are not directly applicable to embedded edge devices with limited hardware resources. Many researchers have proposed various model compression methods or lightweight architectures for vision transformers; however, there are only a few studies evaluating the effects of model compression techniques of vision transformers on performance. Regarding this problem, this paper presents a performance evaluation of vision transformers on embedded platforms. We investigated the behaviors of three vision transformers: DeiT, LeViT, and MobileViT. Each model performance was evaluated by accuracy and inference time on edge devices using the ImageNet dataset. We assessed the effects of the quantization method applied to the models on latency enhancement and accuracy degradation by profiling the proportion of response time occupied by major operations. In addition, we evaluated the performance of each model on GPU and EdgeTPU-based edge devices. In our experimental results, LeViT showed the best performance in CPU-based edge devices, and DeiT-small showed the highest performance improvement in GPU-based edge devices. In addition, only MobileViT models showed performance improvement on EdgeTPU. Summarizing the analysis results through profiling, the degree of performance improvement of each vision transformer model was highly dependent on the proportion of parts that could be optimized in the target edge device. In summary, to apply vision transformers to on-device AI solutions, either proper operation composition and optimizations specific to target edge devices must be considered.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.1-8
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• 2010

With recent development of computer hardware and 3D graphic technique, a lot of people have concern for something to express as the 3D graphic that look the real environment. Because the request of users have increased, the 3D simulation is developed and popularized in the many field. In this paper, we design and implement the simulation system that humans evacuate a building fires using the 3D graphic techniques. In this paper, we use the A* algorithm to humans have the artificial intelligence at evacuating a building fires, calculate the evacuation speed of each human considering temperature damage and smoke damage. In this paper, we applied the real building to demonstrate the effect of proposed evacuation simulation. Experimental results showed that the evacuation speed is affected by the temperature condition and the smoke density.