• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.46-55
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• 2020

Ad Hoc network is a special wireless network, mainly because the nodes are no control center, the topology is flexible, and the networking could be established quickly, which results the transmission stability is lower than other types of networks. In order to guarantee the transmission of data packets in the network effectively, an improved Queue Ad Hoc On-demand Distance Vector Routing protocol (Q-AODV) for node detection by using blockchain technology is proposed. In the route search process. Firstly, according to the node's daily communication record the cluster is formed by the source node using the smart contract and gradually extends to the path detection. Then the best optional path nodes are chained in the form of Merkle tree. Finally, the best path is chosen on the blockchain. Simulation experiments show that the stability of Q-AODV protocol is higher than the AODV protocol or the Dynamic Source Routing (DSR) protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권2호
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

• 한국IT서비스학회지
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• 제18권1호
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• pp.27-44
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• 2019

The goal of this study is to find why the consumers are reacting slowly or negatively toward smartwatches. Even though, smartwatches provide useful information such as health care and text message, the market was not growing fast as expected, and seems to be stagnant at this point. Thus, the future market predictions are varied. To find out why this may have happened, a Q-method study using non-adopters was conducted. In order to find out depth explanations from each interviewers, the research team chose the Q method and Q sorting to classify the different reasons for non-adopters. Based on the interview, all participants were clustered with into groups with similar patterns of the answers. The research team classified the interview group to three categories 1) Technology Discontent 2) Service Discontent 3) Indifferent. The research team analyzed each category reasoning and logics. Also the team compared the result to the technology chasm as it was proposed by Rogers (1969) to measure the maturity of the consumers.

• 전기전자학회논문지
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• 제25권4호
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• pp.644-649
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• 2021

Self-Imitation Learning은 간단한 비활성 정책 actor-critic 알고리즘으로써 에이전트가 과거의 좋은 경험을 활용하여 최적의 정책을 찾을 수 있도록 해준다. 그리고 actor-critic 구조를 갖는 강화학습 알고리즘에 결합되어 다양한 환경들에서 알고리즘의 상당한 개선을 보여주었다. 하지만 Self-Imitation Learning이 강화학습에 큰 도움을 준다고 하더라도 그 적용 분야는 actor-critic architecture를 가지는 강화학습 알고리즘으로 제한되어 있다. 본 논문에서 Self-Imitation Learning의 알고리즘을 가치 기반 강화학습 알고리즘인 DQN에 적용하는 방법을 제안하고, Self-Imitation Learning이 적용된 DQN 알고리즘의 학습을 다양한 환경에서 진행한다. 아울러 그 결과를 기존의 결과와 비교함으로써 Self-Imitation Leaning이 DQN에도 적용될 수 있으며 DQN의 성능을 개선할 수 있음을 보인다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.538-540
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• 2021

본 논문에서는 드론의 표적탐지 임무 수행 시 상대운동 정보 제공을 위하여 지정된 표적을 탐지하고 인식하는 스마트 표적탐지 시스템을 제안하였다. 제안된 시스템은 적절한 정확도(i.e. mAP, IoU) 및 높은 실시간성을 동시에 확보할 수 있는 알고리즘을 개발하는데 중점을 두었다. 제안된 시스템은 Google Inception V2 딥러닝 모델의 100k 학습 후 test 결과가 1.0에 가까운 정확성을 보였고 실시간성도 Nvidia GTX 2070 Max-Q를 기반으로 한 고성능 노트북 활용 시에 추론 속도가 약 60-80[Hz]를 기록하였다. 제안된 스마트 표적탐지 시스템은 드론과 같이 운용되어 컴퓨터 영상처리를 활용하여 표적을 자동으로 인식하고 표적을 따라가면서 감시정찰 임무를 성공적으로 수행하는데 도움이 될 것이다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권10호
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• pp.269-276
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• 2021

최근 스마트 센서는 다양한 환경에서 사용되고 있으며, 애드혹 센서 네트워크 (ASN) 구현에 대한 연구가 활발하게 진행되고 있다. 그러나 기존 센서 네트워크 라우팅 알고리즘은 특정 제어 문제에 초점을 맞추며 ASN 작업에 직접 적용할 수 없는 문제점이 있다. 본 논문에서는 Q-learning 기술을 이용한 새로운 라우팅 프로토콜을 제안하는데, 제안된 접근 방식의 주요 과제는 균형 잡힌 시스템 성능을 확보하면서 효율적인 에너지 할당을 통해 ASN의 수명을 연장하는 것이다. 제안된 방법의 특징은 다양한 환경적 요인을 고려하여 Q-learning 효과를 높이며, 특히 각 노드는 인접 노드의 Q 값을 자체 Q 테이블에 저장하여 데이터 전송이 실행될 때마다 Q 값이 업데이트되고 누적되어 최적의 라우팅 경로를 선택하는 것이다. 시뮬레이션 결과 제안된 방법이 에너지 효율적인 라우팅 경로를 선택할 수 있으며 기존 ASN 라우팅 프로토콜에 비해 우수한 네트워크 성능을 얻을 수 있음을 확인하였다.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2005년도 춘계학술발표회
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• pp.675-676
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• 2005

• Smart Structures and Systems
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• 제2권3호
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• pp.225-235
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• 2006

With the discovery in plants of the proteinaceous forisome crystalloid (Knoblauch, et al. 2003), a novel, non-living, ATP-independent biological material became available to the designer of smart materials for advanced actuating and sensing. The in vitro studies of Knoblauch, et al. show that forisomes (2-4 micron wide and 10-40 micron long) can be repeatedly stimulated to contract and expand anisotropically by shifting either the ambient pH or the ambient calcium ion concentration. Because of their unique abilities to develop and reverse strains greater than 20% in time periods less than one second, forisomes have the potential to outperform current smart materials as advanced, biomimetic, multi-functional, smart sensors or actuators. Probing forisome material properties is an immediate need to lay the foundation for synthesizing forisomebased smart materials for health monitoring of structural integrity in civil infrastructure and for aerospace hardware. Microfluidics is a growing, vibrant technology with increasingly diverse applications. Here, we use microfluidics to study the surface interaction between forisome and substrate and the conformational dynamics of forisomes within a confined geometry to lay the foundation for forisome-based smart materials synthesis in controlled and repeatable environment.

• 디지털산업정보학회논문지
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• 제16권1호
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• pp.11-19
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• 2020

IoT has been consistently used in various fields such as smart home, wearables, and healthcare. Since IoT devices are small terminals, relatively simple wireless communication protocols such as IEEE 802.15.4 and ISO 18000 series are used. In this paper, we designed the 802.15.4q 2.4 GHz TASK physical layer. Physical protocol data unit of TASK supports bit-level interleaving and shortened BCH encoding. It is spread by unique ternary sequences. There are four spreading factors to choose the data rate according to the communication channel environment. The TASK physical layer was designed using verilog-HDL and verified through the loop-back test of the transceiver. The designed TASK physical layer was implemented in a fpga and tested using MAXIM RFICs. The PER was about 0% at 10 dB SNR. It is expected to be used in small, low power IoT applications.

• Smart Structures and Systems
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• 제32권4호
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• pp.219-233
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• 2023

Crack width is an important indicator to evaluate the health condition of the concrete structure. The crack width is measured by manual using crack width gauge commonly, which is time-consuming and laborious. In this paper, we have proposed a fast and simplified crack width quantification method via deep Q learning and geometric calculation. Firstly, the crack edge is extracted by using U-Net network and edge detection operator. Then, the intelligent decision of is made by the deep Q learning model. Further, the geometric calculation method based on endpoint and curvature extreme point detection is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method, achieving high precision in the real crack width quantification.