• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.597-599
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• 2018

최근 IOT(Internet of Things)환경에서 위치기반 서비스를 제공하기 위해 실내 위치인식 연구가 활발히 진행되고 있으며 실내 위치인식 기술은 주로 WiFi, 블루투스, RFID 등으로 구현되고 있다. 본 논문은 Arduino를 이용해 WiFi 측정 및 통신이 가능한 Smart Watch를 제작하였다. 실내위치 측위를 위해 WiFi Fingerprinting기법 Radiomap을 구축한 다음 Arduino Smart Watch에서 측정한 AP신호 값을 Radiomap과 비교하여 실내위치 측정 및 데이터 수집하였다. 향후 수집된 다수의 사용자 데이터를 군집도 분석하거나 실내공간에서의 IOT(Internet of Things)분야에 활용 가능 할 것으로 예상된다.

• Smart Media Journal
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• v.8 no.2
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• pp.94-98
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• 2019

In this paper, we developed the tools enabling to manage the IoT systems owned by managers. Since equipment agents consists based on open-source block-chain framework, we can secure the invariance on data and furthermore can locate the resources by searching the AP connected to the equipments. Also the manager can trace the connecting details on equipments from their block-chain accounts. In addition, we work on the possibility of protecting ARP poisoning attacks by removing the credibility on additional ARP requests being generated during the process of network creation.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.213-221
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• 2022

Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.161-161
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• 2017

스마트 시설환경의 제어 요소는 난방기, 창 개폐, 수분/양액 밸브 개폐, 환풍기, 제습기 등 직접적으로 시설환경의 조절에 관여하는 인자와 정보 교환을 위한 통신, 사용자 인터페이스 등 간접적으로 제어에 관련된 요소들이 복합적으로 존재한다. PID 제어와 같이 하는 수학적 논리를 바탕으로 한 제어와 전문 관리자의 지식을 기반으로 한 비선형 학습 모델에 의한 제어 등이 공존할 수 있다. 이러한 다양한 요소들을 복합적으로 연동시키기 위해선 기존의 시퀀스 기반 제어 방식에는 한계가 있을 수 있다. 관행의 방식과 같이 시계열 상에서 획득한 충분한 데이터를 이용하여 제어의 양과 시점을 결정하는 방식은 예외 상황에 충분히 대처하기 어려운 단점이 있을 수 있다. 이러한 예외 상황은 자연적인 조건의 변화에 따라 불가피하게 발생하는 경우와 시스템의 오류에 기인하는 경우로 나뉠 수 있다. 본 연구에서는 실시간으로 변하는 시설환경 내의 다양한 환경요소를 실시간으로 분석하고 상응하는 제어를 수행하여 수학적이며 예측 가능한 논리에 의해 준비된 제어시스템을 보완할 방법을 연구하였다. 과거의 고성능 컴퓨팅(HPC; High Performance Computing)은 다수의 컴퓨터를 고속 네트워크로 연동하여 집적적으로 연산능력을 향상시킨 기술로 비용과 규모의 측면에서 많은 투자를 필요로 하는 첨단 고급 기술이었다. 핸드폰과 모바일 장비의 발달로 인해 소형 마이크로프로세서가 발달하여 근래 2 Ghz의 클럭 속도에 이르는 어플리케이션 프로세서(AP: Application Processor)가 등장하기도 하였다. 상대적으로 낮은 성능에도 불구하고 저전력 소모와 플랫폼의 소형화를 장점으로 한 AP를 시설환경의 실시간 제어에 응용하기 위한 방안을 연구하였다. CPU의 클럭, 메모리의 양, 코어의 수량을 다음과 같이 달리한 3가지 시스템을 비교하여 AP를 이용한 마이크로 클러스터링 기술의 성능을 비교하였다.1) 1.5 Ghz, 8 Processors, 32 Cores, 1GByte/Processor, 32Bit Linux(ARMv71). 2) 2.0 Ghz, 4 Processors, 32 Cores, 2GByte/Processor, 32Bit Linux(ARMv71). 3) 1.5 Ghz, 8 Processors, 32 Cores, 2GByte/Processor, 64Bit Linux(Arch64). 병렬 컴퓨팅을 위한 개발 라이브러리로 MPICH(www.mpich.org)와 Open-MP(www.openmp.org)를 이용하였다. 2,500,000,000에 이르는 정수 중 소수를 구하는 연산에 소요된 시간은 1)17초, 2)13초, 3)3초 이었으며, $12800{\times}12800$ 크기의 행렬에 대한 2차원 FFT 연산 소요시간은 각각 1)10초, 2)8초, 3)2초 이었다. 3번 경우는 클럭속도가 3Gh에 이르는 상용 데스크탑의 연산 속도보다 빠르다고 평가할 수 있다. 라이브러리의 따른 결과는 근사적으로 동일하였다. 선행 연구에서 획득한 3차원 계측 데이터를 1초 단위로 3차원 선형 보간법을 수행한 경우 코어의 수를 4개 이하로 한 경우 근소한 차이로 동일한 결과를 보였으나, 코어의 수를 8개 이상으로 한 경우 앞선 결과와 유사한 경향을 보였다. 현장 보급 가능성, 구축비용 및 전력 소모 등을 종합적으로 고려한 AP 활용 마이크로 클러스터링 기술을 지속적으로 연구할 것이다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.334-348
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• 2015

Widespread use of smart devices accompanies increase of use of access point (AP), which enables the connection to the wireless network. If the appropriate security is not served when a user tries to connect the wireless network through an AP, various security problems can arise due to the rogue APs. In this paper, we are going to examine the threat by evil-twin, which is a kind of rogue APs. Most of recent researches for detecting rogue APs utilize the measured time difference, such as round trip time (RTT), between the evil-twin and authorized APs. These methods, however, suffer from the low detection rate in the network congestion. Due to these reasons, in this paper, we suggest a new factor, packet inter-arrival time (PIAT), in order to detect evil-twins. By using both RTT and PIAT as the learning factors for the support vector machine (SVM), we determine the non-linear metric to classify evil-twins and authorized APs. As a result, we can detect evil-twins with the probability of up to 96.5% and at least 89.75% even when the network is congested.

• Nutrition Research and Practice
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• v.7 no.6
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• pp.423-429
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• 2013

Luteolin is a flavonoid found in abundance in celery, green pepper, and dandelions. Previous studies have shown that luteolin is an anti-inflammatory and anti-oxidative agent. In this study, the anti-inflammatory capacity of luteolin and one of its glycosidic forms, luteolin-7-O-glucoside, were compared and their molecular mechanisms of action were analyzed. In lipopolysaccharide (LPS)-activated RAW 264.7 cells, luteolin more potently inhibited the production of nitric oxide (NO) and prostaglandin E2 as well as the expression of their corresponding enzymes (inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) than luteolin-7-O-glucoside. The molecular mechanisms underlying these effects were investigated to determine whether the inflammatory response was related to the transcription factors, nuclear factor (NF)-${\kappa}B$ and activator protein (AP)-1, or their upstream signaling molecules, mitogen-activated protein kinases (MAPKs) and phosphoinositide 3-kinase (PI3K). Luteolin attenuated the activation of both transcription factors, NF-${\kappa}B$ and AP-1, while luteolin-7-O-glucoside only impeded NF-${\kappa}B$ activation. However, both flavonoids inhibited Akt phosphorylation in a dose-dependent manner. Consequently, luteolin more potently ameliorated LPS-induced inflammation than luteolin-7-O-glucoside, which might be attributed to the differentially activated NF-${\kappa}B$/AP-1/PI3K-Akt pathway in RAW 264.7 cells.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.4_1
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• pp.397-409
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• 2021

This study proposes a new approach to implimentation of robust control and torque control response analysis based on monitoring simulator for smart factory. According to the physical properties of a flexible manipulator, a two time-scale approach, namely, singular perturbation ap proach, is further utilized for thorough analysis and general controller design. It is shown that asymptotic motional tracking can be effectively achieved, whereas the force regulation errors can be made arbitrarily small. For demonstration of the proposed technology performance, experiments of a eight joint flexible manipulator are performed for the proposed control method, and the reliability of proposed control results are illustrated based on monitoring simulator.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.583-591
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• 2018

With the development of ICT(Information and Communication Technology), the number of smart devices is rapidly increasing. LBS(Location Based Service) applications that provide user's location based service are used in various fields. There is also a growing demand for indoor precision positioning technology to provide seamless services. In this paper, we propose an indoor positioning system that estimates the location of a smartphone user. The proposed algorithm determines whether the received signal is LOS(Line-of-Sight) or NLOS(Non-Line of-Sight) in order to decrease multipath effect by the indoor environment. The proposed positioning algorithm is very simple and requires only the AP(Access Point) coordinates. In addition, it requires only two APs for estimating the location of a smartphone user. The proposed algorithm is a practically applicable technology without any additional hardware and kernel modification in the smartphone. In the experiment results, the reliability of the positioning system was found to be within 0.83 m.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1890-1897
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• 2021

People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.