• Journal of Sensor Science and Technology
• /
• v.5 no.6
• /
• pp.43-50
• /
• 1996

It is very important to improve the combustion efficiency and reduce pollutant emission in order to save energy and environment. Especially, thermal NOx has been reduced through monitoring burner flame, because the thermal NOx is strongly related to flame characteristics. In this work, a flame-monitoring system was fabricated with photodiode, optical fiber, interference filter and data acquisition system, and it was applied to a lab-scale methane combustion system and a testing facility. Flame intensity and mean frequency increased with increasing turbulent intensity and fuel loading. The sensor signal from flame fluctuations differed from that without flame, which showed the availability af the flame scanner to find the presence of flame. NOx emissions increased with flame intensity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.996-997
• /
• 2010

This paper is designed for disinfection and sterilization devices, devices based on the monitoring operation to detect the internal fault diagnosis and design a disinfection device Sterilization device monitored the internal state of the TCP / IP-based communications using Ethernet can be managed remotely, the system presented.

• Journal of Platform Technology
• /
• v.10 no.1
• /
• pp.20-28
• /
• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.18 no.3
• /
• pp.143-152
• /
• 2014

In this study, the sensor response and sensitivity is experimented and analyzed quantitatively by the line numbers of chlorine ion reaction type corrosion sensor that is developed. The sensor response of the developed corrosion sensor is verified with properties of chlorine ion. The multilineal sensor is shown a large resistance change more than the single line sensor by damage of the sensor. And, the resistance change of sensor is as large as high concentration of NaCl aqueous solution, the sensitivity of multilineal sensor is higher than single line sensor's, and the depth of sensor's location is as large as the increasing of resistance change time (cycle). These results suggest that, the developed corrosion sensor could sense corrosion reaction, sensor sensitivity and change of resistance for chloride ion. Especially, It was judged that 7 line sensor was the most superior for monitoring chloride ion immigration into concrete.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.230-233
• /
• 2016

In this paper, we developed the ice machine conditions monitoring system that confirms conditions of the ice machine. The developed system is composed of Communication Board, Server Program, and Web-based User Application. Communication Board which is connected to the ice machine periodically sends various data, such as current, voltage, the refrigerant pressure and temperature, the external temperature and humidity. Server Program stores the data received from Communication Board into database. The manager or the ice machine operator can see the state of the own machine through User Application based on Web. When a symptom is detected on the ice machine, the manager and the operator can checks the current condition of the ice machine by using the data obtained in real time and also prevents the machine troubles by taking proper actions.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.1
• /
• pp.163-169
• /
• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of Korean Society of Environmental Engineers
• /
• v.27 no.2
• /
• pp.191-197
• /
• 2005

A new method, ToxTemp (TOXcity test based on TEMPerature control) using Ceridaphnia dubia was applied to evaluate the toxicity of insecticide materials and compared with the standard 48 hr acute bioassay. BPMC, diazinon and fenitrothion may cause the inhibition to the biological process in sewage treatment plant and need to detect toxicity within short contact time. The ToxTemp method showed sensitive detection with more shorter contact of 1-1.5 hr time than that of the standard 48 hr acute bioassay. To evaluate toxicity of real wastewater/sewage, the inhibition rate of nitrification and oxygen uptake rate (OUR) using activated sludge, the standard 48hr acute bioassay and ToxTemp method using C. dubia were compared, respectively. On the basis of the inhibition rate of nitrification, the OUR test showed the less sensitive results at the relatively strong toxic sewage. On the other hands, the standard 48hr acute bioassay and ToxTemp method using C. dubia represented the toxicity of each wastewater/sewage with high sensitivity. Even the slightly low (about 1.5%) sensitivity, the ToxTemp method showed the high applicability to the real site of sewage treatment plant.

• Journal of Navigation and Port Research
• /
• v.46 no.4
• /
• pp.367-374
• /
• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.71-84
• /
• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Journal of Periodontal and Implant Science
• /
• v.31 no.4
• /
• pp.777-785
• /
• 2001

치주질환이 진행되는 동안이나 치주처치후 치유되는 과정에서 치조골의 변화가 야기되는데 방사선 사진은 치조골 변화를 감지하는 유일한 비외과적인 방법이다. 미묘한 치조골 변화의 진단은 치료시나 유지관리기 환자의 평가시 중요한 바, 최근에는 규격화시킨 디지털 영상을 이용하여 정량적인 골변화 측정이 가능하게 되었다. 본 연구의 목적은 중등도의 치주질환을 지닌 환자에서 국소마취하에 초기 치주처치를 시행한후 참조체와 함께 구내 방사선 사진을 촬영하고 디지털화 한 다음 참조체 당량치를 이용하여 치조골의 밀도변화를 평가하기 위한 것이다. 이 연구를 위하여 치주질환에 이환된 환자 5명(남자 3명, 여자 2명 : 평균 47.4세)에서 탐침깊이가 5㎜ 이상이고 골내낭이 있는 제 $1{\cdot}2$ 소구치, 제 $1{\cdot}2$ 대구치 40개(상악 24개, 하악 16개)를 대상으로 구강위생교육과 치석제거술, 치근면활택술을 시행하였다. 임상지수는 술전과 술후 8주째에 측정하였고, 방사선 사진은 술전, 술후 2주, 4주, 6주, 8주째에 촬영하였고, 구리 스텝웨지를 사용하여 규격화 하였다. 촬영된 영상은 NIH image program(U.S.A)에 의해 분석되어졌고 이들 자료를 통해 다음과 같은 결과를 얻었다. 1. 치조골의 밀도는 초기치료후 2주째 까지는 감소된 양상을 보이다가 4주 이후로는 점차적으로 증가하는 양상을 보였다. 2. 치조골의 밀도는 초기치료전과 비교시 초기치료수 6주째와 8주째에서 유의한 차이를 보였다.(p<0.01) 3. 상하악 간의 치조골 밀도는 유의한 차이를 보이지 않았다(p>0.05). 이상과 같은 결과를 통하여 볼때 초기 치주처치만으로도 치조골의 밀도가 증가됨을 확인할 수 있었다.