• 한국안전학회지
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• 제37권5호
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• pp.1-6
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• 2022

In this study, to confirm the effects of the mixed load of normal and shear forces acting on a fixing bolt, fatigue design criteria were developed by varying the loading angle and conducting tensile and fatigue tests. After evaluating and comparing the test results under different loading angles, the evaluation criteria were selected. These evaluation criteria were then applied to develop the design criteria. An Arcan fixture was designed and manufactured to simultaneously apply a mixed load of normal and shear forces to the fixing bolt of a turntable, and a fatigue test was conducted. S-N diagrams for various loading angles were obtained, and a 1% P-S-N diagram of failure probability was determined using statistical processing techniques. Our results show that failures of the fixing bolt can be prevented using these diagrams as a basis for developing fatigue design criteria.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• 한국분말재료학회지
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• 제30권4호
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• pp.310-317
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• 2023

Small-film-type ion sensors are garnering considerable interest in the fields of wearable healthcare and home-based monitoring systems. The performance of these sensors primarily relies on electrode capacitance, often employing nanocomposite materials composed of nano- and sub-micrometer particles. Traditional techniques for enhancing capacitance involve the creation of nanoparticles on film electrodes, which require cost-intensive and complex chemical synthesis processes, followed by additional coating optimization. In this study, we introduce a simple one-step electrochemical method for fabricating gold nanoparticles on a carbon nanotube (Au NP-CNT) electrode surface through cyclic voltammetry deposition. Furthermore, we assess the improvement in capacitance by distinguishing between the electrical double-layer capacitance and diffusion-controlled capacitance, thereby clarifying the principles underpinning the material design. The Au NP-CNT electrode maintains its stability and sensitivity for up to 50 d, signifying its potential for advanced ion sensing. Additionally, integration with a mobile wireless data system highlights the versatility of the sensor for health applications.

• 한국컴퓨터정보학회논문지
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• 제28권9호
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• pp.121-128
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• 2023

무선 센서 네트워크에서 네트워크의 수명을 증가시키기 위해 주변 환경으로부터 에너지를 수집하거나, 무선 전력 전송으로 직접 에너지를 전달하는 방법이 사용되고 있다. 또한 에너지의 불균형을 줄이고 데이터의 수집량을 증가시키기 위해 직접 센서 노드를 방문해서 자료를 수집하는 모바일 싱크 노드를 활용한 방법이 사용되고 있다. 본 논문에서는 이러한 환경에서 각 센서 노드가 네트워크 환경과 에너지를 고려하여 Minimum Depth Tree (MDT)를 구성하고 자식 노드에 자료수집량을 할당해줌으로써 중계 노드의 부하를 줄이고 전체적으로 많은 데이터를 고르게 수집하는 기법을 제안한다. 시뮬레이션 결과, 본 기법은 기존의 다른 기법에 비해 에너지 고갈을 효과적으로 억제하고 더 많은 자료를 수집하는 것을 보여준다.

• 4차산업연구
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• 제3권2호
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.385-392
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• 2022

As energy-saving techniques based on human behavior patterns have recently become an issue, the occupant-centered control system is adopted for estimating personal preference of indoor environment and optimizing environmental comfort and energy consumption. Accordingly, IoT devices have been used to collect indoor environmental quality (IEQ) data and personal data. However, the need to safely collect and manage data has been emerged due to cybersecurity issues. Therefore, this paper aims to present a framework that can safely transmit occupant-centered data collected from IoT to a private blockchain server using Hyperledger fabric. In the case study, the minimum value product of the mobile application and smartwatch application was developed to evaluate the usability of the proposed blockchain-based occupant-centered data collection framework. The results showed that the proposed framework could collect data safely and hassle-free in the daily life of occupants. In addition, the performance of the blockchain server was evaluated in terms of latency and throughput when ten people in a single office participated in the proposed data collection framework. Future works will further apply the proposed data collection framework to the building management system to automatically collect occupant data and be used in the HVAC system to reduce building energy consumption without security issues.

• ETRI Journal
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• 제45권2호
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• pp.318-328
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• 2023

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep-learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep-learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep-learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator-scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type-based operator-scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling-based operator-scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• 한국IT서비스학회지
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• 제1권1호
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• pp.57-66
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• 2002

지금까지 VoIP에 음성인식을 접목했을 때 만들 수 있는 서비스를 동향에 맞춰 살펴보았다. 최근 들어 음성인식 기술을 이용한 서비스나 상품들이 홍수처럼 쏟아져 나오고 있으며, 이제는 음성인식 기술이 GUI나 일반 DTMF를 이용하는 사용자 인터페이스(User Interfaces)를 대신할 수 있을 정도로 발전되었고 또 앞으로도 지속적인 발전이 있을 것이라 예상되므로 이제 시작된 VoIP와 음성인식의 접목은 수많은 다양한 종류의 새로운 서비스를 창출해낼 것으로 예상된다. 본 논문의 그림들에서 유선전화, 무선전화, 무선 인터넷 등 세 종류의 서비스가 계속 등장한다. 이렇게 세 종류의 서비스를 유지하는 이유는 현재 유무선 전화 및 인터넷 서비스 사업자에 관련되어 각각 다른 비즈니스 모델이 다음과 같이 형성될 수 있기 때문이다. 유선 전화망을 통한 인터넷 서비스는 유선망과 인터넷망을 연동시켜 주는 하드웨어를 개발하는 제조업체와 인터넷 정보를 제공해 주는 정보제공 업자 및 서비스를 제공하는 통신사업자가 협력하여 부를 창출하는 비즈니스 형태이다. 무선 전화망을 통한 인터넷 서비스는 무선 전화 사업자들이 이미 무선 인터넷이라는 이름으로 다양한 정보 제공 서비스를 지속해왔다는 점에서 인터넷 정보를 제공해 주는 정보제공업자 및 무선전 화 사업자 그리고 서비스 제공업자가 협력하여 매 출을 올라는 비즈니스 형태이다. 무선 인터넷 서비스는 일반 인터넷 서비스와의 차이는 없으며, 특별히 이동성을 강조한 서비스를 제공한다면 일반적인 인터넷 기반 정보제공자보다 경쟁력을 가질 수 있다. VoIP는 단독으로 쓰이기보다는 다른 다양한 기술과 서비스와 합쳐졌을 때 그 효과가 커진다. 이 제 음성처리기술, 특히 음성인식기술과 함께 사용되는 VoIP 기술의 응용 범위가 어디까지 확대될지 사뭇 기대되는 바이다.

• 한국산학기술학회논문지
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• 제17권9호
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• pp.12-20
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• 2016

기존의 주력산업의 고도화 및 고부가가치 산업이 창출되고 있는 가운데 클라우드 컴퓨팅 기반의 융합서비스가 등장하였다. 사용자 개인의 밀착서비스부터 산업용 서비스까지 다양한 융합서비스가 제공되고 있으며 국내에서는 클라우드 서비스 기반의 금융, 모바일, 소셜 컴퓨팅, 홈서비스를 중심으로 경제 전반에 걸쳐 기존 산업시장의 원동력이 되고 있다. 그러나 클라우드 스토리지 환경에서 Dos, DDos공격뿐만 아니라 스토리지 서버의 중요 데이터를 타깃으로 한 공격기법들이 발생하고 있으며, APT, 백도어 침투, 특정 대상에 대한 다단계 공격과 같은 감지하기 어려운 보안위협들이 발생하고 있다. 이를 보완하기 위해서 본 논문에서는 사용자들로 하여금 안전한 스토리지 서비스를 제공하는 권한 관리 프로토콜에 관하여 설계하였으며, 클라우드 환경과 빅데이터 기반 기술의 융합사례와 보안위협 및 요구사항에 대해서 연구하였고, 클라우드 컴퓨팅 환경과 빅 데이터 기술의 융합사례와 보안위협 및 보안 요구사항에 대해서 관련연구를 수행하였다. 이를 기반으로 제안된 프로토콜은 기존의 클라우드 환경과 빅데이터 기반 기술에서 발생하는 공격기법에 대해서 안전성을 분석하였고, 세션키 생성부분에서 대략 55%의 향상성을 확인 할 수 있었다.