• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Multimedia Information System
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• 제9권1호
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• pp.11-20
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• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.33-42
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• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• Structural Engineering and Mechanics
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• 제84권1호
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• pp.101-111
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• 2022

It is widely known that axially loaded fiber-reinforced polymer (FRP) confined concrete presents significant and enhanced mechanical properties with reference to the unconfined concrete. Therefore, to predict the mechanical behavior of FRP-confined concrete two quantities-peak strength and ultimate strain are required. Despite the significant advances, the determination of the ultimate strain of FRP-confined concrete is one of the most challenging problems to be resolved. This is often attributed to our persistence in desiring the conventional methods as the sole technique to examine this phenomenon and the complex nature of the ultimate strain of FRP-confined concrete. To bridge the research gap, this study adopted two machine learning (ML) techniques-artificial neural network (ANN) and Gaussian process regression (GPR)-to analyze observations obtained from 627 datasets of FRP-confined concrete circular and non-circular sections under axial loading test. Besides, the techniques are also used to predict the ultimate strain of FRP-confined concrete. Seven parameters namely width/diameter of the specimens, corner radius ratio, the strength of concrete, FRP elastic modulus, FRP thickness, FRP tensile rupture strain, and the axial strain of unconfined concrete-are the input parameters used to predict the ultimate strain of FRP-confined concrete. The results of the current study highlight the merit of using AI techniques in structural engineering applications given their extraordinary ability to comprehend multidimensional phenomena of FRP-confined concrete structures with ease, low computational cost, and high performance over the existing empirical models.

• Smart Structures and Systems
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• 제29권4호
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.522-524
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• 2023

위치 기반 서비스(LBS)의 확산으로 다양한 분야에서 활용할 수 있는 많은 양의 경로 데이터가 생성되고 있다. 하지만 공격자가 경로 데이터를 통해 잠재적으로 사용자의 개인정보를 유추할 수 있다는 문제점이 존재한다. 따라서 경로 데이터의 프라이버시를 보존하며 유용성을 유지할 수 있는 GAN(Generative Adversarial Network)을 사용한 많은 연구가 진행되고 있다. 그러나 GAN은 생성된 결과물을 제어하지 못한다는 한계점을 가지고 있다. 본 논문에서는 ACGAN(Auxiliary classifier GAN)을 통해 생성된 결과물을 제어함으로써 경로 데이터의 민감한 정점을 숨기고, Attention mechanism을 결합하여 높은 유용성과 익명성을 제공하는 합성 경로 생성 모델인 TAP-GAN(Trajectory attention and protection-GAN)을 제안한다. 또한 모델의 성능을 입증하기 위해 유용성 및 익명성 실험을 진행하고, 선행 연구 모델과의 비교를 통해 TAP-GAN이 경로 데이터의 유용성을 보장하면서 사용자의 프라이버시를 효과적으로 보호할 수 있음을 확인하였다.

• 센서학회지
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• 제32권6호
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• pp.455-461
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• 2023

We investigated the electrical characteristics of ZnO nanoparticles (NPs) with air exposure that is a widely used electron transport layer for quantum dot light-emitting diodes (QLEDs). Upon air exposure, we observed changes in the density of states (DOS) of the trap levels of ZnO NPs. In particular, with air exposure, the concentration of deep trap energy levels in ZnO NPs decreased and electron mobility significantly improved. Consequently, the air-exposed ZnO reduced leakage current by approximately one order of magnitude and enhanced the external quantum efficiency at the low driving voltage region of the QLED. In addition, based on the excellent conductivity properties, high-brightness QLEDs could be achieved.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2023년도 제35회 한글 및 한국어 정보처리 학술대회
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• pp.525-530
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• 2023

최근 소셜 네트워크 서비스(SNS) 및 모바일 서비스가 증가함에 따라 사용자들은 다양한 종류의 위험에 직면하고 있다. 특히 온라인 그루밍과 온라인 루머 같은 위험은 한 개인의 삶을 완전히 망가뜨릴 수 있을 정도로 심각한 문제로 자리 잡았다. 그러나 많은 경우 이러한 위험들을 판단하는 시점은 사건이 일어난 이후이고, 주로 법적인 증거채택을 위한 위험성 판별이 대다수이다. 따라서 본 논문은 이러한 문제를 사전에 예방하는 것에 초점을 맞추었고, 계속적으로 발생하는 대화와 같은 event를 실시간으로 감지하고, 위험을 사전에 탐지할 수 있는 Real-Time Early Risk Detection(RERD) 문제를 정의하고자 한다. 온라인 그루밍과 루머를 실시간 조기 위험 감지(RERD) 문제로 정의하고 해당 데이터셋과 평가지표를 소개한다. 또한 RERD 문제를 정확하고 신속하게 해결할 수 있는 강화학습 기반 새로운 방법론인 RT-ERD 모델을 소개한다. 해당 방법론은 RERD 문제를 이루고 있는 온라인 그루밍, 루머 도메인에 대한 실험에서 각각 기존의 모델들을 뛰어넘는 state-of-the-art의 성능을 달성하였다.

• 한국수학교육학회지시리즈A:수학교육
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• 제63권2호
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• pp.233-254
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• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• Steel and Composite Structures
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• 제51권4호
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.