• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.49-60
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• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.401-406
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• 2008

Fall event detection is one of the most common problems for elderly people, especially those living alone because falls result in serious injuries such as joint dislocations, fractures, severe head injuries or even death. In order to prevent falls or fall-related injuries, several previous methods based on video sensor showed low fall detection rates in recent years. To improve this problem and outperform the system performance, this paper presented a novel approach for fall event detection in the elderly using a subtraction between successive difference images and temporal templates in real time environment. The proposed algorithm obtained the successful detection rate of 96.43% and the low false positive rate of 3.125% even though the low-quality video sequences are obtained by a USB PC camera sensor. The experimental results have shown very promising performance in terms of high detection rate and low false positive rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1954-1960
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• 2008

Modem Robot is used in all industries. Previous Robot was used by simplicity work, at recent times, robot is developed in form that can do action such as a person. Robot's action runs according to command repeat or in the every moment according to sensor's output value, achieve other action. In this raper, we studied about self-regulation transfer robot that follow Object autonomously. This robot can be used by purpose that carry heavy burden instead of human. Robot's composition is drive part which run object's position awareness Sensor, Processor that control action and Motor part. After robot is connects with Network, we did robot remote control and monitor the action situation of robot. For the methode to reduce drive error, we developed algorithm for outside environment. For an experiment we made the self-regulation robot. We showed the directivity of sensor, error of directivity and soft moving of robot. We showed the monitoring system and the execution screen for communication between robot and PC.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.551-558
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• 2021

In this paper, we made the integrated intelligent air ventilation of the actuator module that can be remotely controlled based on IoT and sensors. we implemented a ventilation window system by configuring an algorithm design and a driving circuit to control the operation of the actuator to open and close the ventilation port based on a predetermined number of data that detects indoor gas/CO2/humidity temperature and outdoor fine dust related indoor/outdoor environment. It is difficult to store, manage, and analyze data due to the large number of sensors and conditions for the transmission data of indoor air circulation module. The remote monitoring and remote wireless control screens were constructed to automate the separation and operation conditions by extracting and managing the state. We apply MQTT to enhance big data transmission and construct the system using Rocket MQ to ensure safe transmission of operational big data against system errors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.251-256
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• 2021

Unlike general cameras, a high-speed camera capable of capturing a large number of frames per second can enable the advancement of some image processing technologies that have been limited so far. This paper proposes a method of removing undesirable noise from an high-speed input color image, and then detecting a human face from the noise-free image. In this paper, noise pixels included in the ultrafast input image are first removed by applying a bidirectional filter. Then, using RetinaFace, a region representing the person's personal information is robustly detected from the image where noise was removed. The experimental results show that the described algorithm removes noise from the input image and then robustly detects a human face using the generated model. The model-based face-detection method presented in this paper is expected to be used as basic technology for many practical application fields related to image processing and pattern recognition, such as indoor and outdoor building monitoring, door opening and closing management, and mobile biometric authentication.

• The Korean journal of internal medicine
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• v.33 no.6
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• pp.1203-1209
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• 2018

Background/Aims: Adverse drug reaction (ADR) is an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product. The present study was conducted in order to monitor the frequency and severity of ADR during antimicrobial therapy of septicemia. Methods: A prospective, observational, and noncomparative study was conducted over a period of 6 months on patients of septicemia admitted at a university hospital. Naranjo algorithm scale was used for causality assessment. Severity assessment was done by Hartwig severity scale. Results: ADRs in selected hospitalized patients of septicemia was found to be in 26.5% of the study population. During the study period, 12 ADRs were confirmed occurring in 9, out of 34 admitted patients. Pediatric patients experienced maximum ADRs, 44.4%. Females experienced a significantly higher incidence of ADRs, 66.7%. According to Naranjo's probability scale, 8.3% of ADRs were found to be definite, 58.3% as probable, and 33.3% as possible. A higher proportion of these ADRs, 66.7% were preventable in nature. Severity assessment showed that more than half of ADRs were moderate. Teicoplanin was found to be the commonest antimicrobial agent associated with ADRs, followed by gemifloxacin and ofloxacin. Conclusions: The incidence and severity of ADRs observed in the present study was substantially high indicating the need of extra vigilant during the antimicrobial therapy of septicemia.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.90-96
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• 2019

Storage cabinet in a lab in these days measures various environmental factors in real-time with IoT sensors. Preexisting system collects sensor data, analyze a risk and then command other equipment. Such centralized control system tends to have an issue with of speed slowing down. It's because when there are more storage cabinets, there are more data to process. In order to solve this issue, this report addresses decentralized IoT sensor based lab safety control system. It can analyze internal state of storage cabinet to identify any hazardous situations and effectively control them. Such decentralized control system using sensor modules for internal environment of the cabinet storage and automated control algorithm based on administrator's log history can manage any hazardous situations by automated control of environment factors of inside a lab. It would allow users to deal with a hazard if it happens. Even better, it can prevent it to happen from the beginning.