• Korean Journal of Clinical Pharmacy
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• v.32 no.2
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• pp.116-124
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• 2022

Background: High-alert medications (HAMs) are medications that bear a heightened risk of causing significant patient harm if used in error. To facilitate safe use of HAMs, identifying specific HAM lists for clinical setting is necessary. We aimed to develop the national level HAM list for acute care setting. Methods: We used three-step process. First, we compiled the pre-existing lists referring HAMs. Second, we analyzed medication related incidents reported from national patient safety incident report data and adverse events indicating medication errors from the Korea Adverse Event Reporting System (KAERS). We also surveyed the assistant staffs to support patient safety tasks and pharmacist in charge of medication safety in acute care hospital. From findings from analysis and survey results we created additional candidate list of HAMs. Third, we derived the final list for HAMs in acute care settings through expert panel surveys. Results: From pre-existing HAM list, preliminary list consisting of 42 medication class/ingredients was derived. Eight assistant staff to support patient safety tasks and 39 pharmacists in charge of medication safety responded to the survey. Additional 44 medication were listed from national patient safety incident report data, KAERS data and common medications involved in prescribing errors and dispensing errors from survey data. A list of mandatory and optional HAMs consisting of 10 and 6 medication classes, respectively, was developed by consensus of the expert group. Conclusion: We developed national level HAM list for Korean acute care setting from pre-existing lists, analyzing medication error data, survey and expert panel consensus.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.869-875
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• 2009

Safety is a big issue in the construction sites. For safe and secure management, tracking locations of construction resources such as labors, materials, machineries, vehicles and so on is important. The materials, machineries and vehicles could be controlled by computer, whereas the movement of labors does not have fixed pattern. So, the location and movement of labors need to be monitored continuously for safety. In general, Global Positioning System(GPS) is an opt solution to obtain the location information in outside environments. But it cannot be used for indoor locations as it requires a clear Line-Of-Sight(LOS) to satellites Therefore, indoor location monitoring system could be a convenient alternative for environments such as tunnel and indoor building construction sites. This paper presents a case study to investigate feasibility of Wi-Fi based indoor location monitoring system in construction site. The system is developed by using fingerprint map of gathering Received Signal Strength Indication(RSSI) from each Access Point(AP). The signal information is gathered by Radio Frequency Identification (RFID) tags, which are attached on a helmet of labors to track their locations, and is sent to server computer. Experiments were conducted in a shield tunnel construction site at Guangzhou, China. This study consists of three phases as follows: First, we have a tracking test in entrance area of tunnel construction site. This experiment was performed to find the effective geometry of APs installation. The geometry of APs installation was changed for finding effective locations, and the experiment was performed using one and more tags. Second, APs were separated into two groups, and they were connected with LAN cable in tunnel construction site. The purpose of this experiment was to check the validity of group separating strategy. One group was installed around the entrance and the other one was installed inside the tunnel. Finally, we installed the system inner area of tunnel, boring machine area, and checked the performance with varying conditions (the presence of obstacles such as train, worker, and so on). Accuracy of this study was calculated from the data, which was collected at some known points. Experimental results showed that WiFi-based indoor location system has a level of accuracy of a few meters in tunnel construction site. From the results, it is inferred that the location tracking system can track the approximate location of labors in the construction site. It is able to alert the labors when they are closer to dangerous zones like poisonous region or cave-in..

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.882-885
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• 2014

The duration and frequency of flooding and not last long, by the time climate change drought. The increased accordingly by reducing stream flow and year variation. This trend is expected to continue, and change towards a comprehensive analysis of such quantity, quality and management of water resources are managed. Flood warning system is called to perform them electronically to the management of water resources such as these to be in the organic water-related basic data acquisition, storage, processing and utilization. Can be divided into hydrological observations and flood warning systems alert system broadcast system. Hydrological observation system is the measurement from the hydrological stations (water level, rainfall, water) that can be observed hydrological status of the dam basin hydrological observation data transmitted to the central office, located at the dam monitoring and control system through a variety of networks including satellite, and the collected defined as the system that sent the K-water head office in 1 minute increments hydrological observation data. Headquartered in support of this decision. Dimensions of the dam are provided in addition to inward. Channeled through various hydrologic analysis and leveraging the data transfer. This paper looks at ways to build out hydrological observation system.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.173-183
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• 2009

We present a real-time monitoring system for detecting anomalous network events using the entropy. The entropy accounts for the effects of disorder in the system. When an abnormal factor arises to agitate the current system the entropy must show an abrupt change. In this paper we deliberately model the Internet to measure the entropy. Packets flowing between these two networks may incur to sustain the current value. In the proposed system we keep track of the value of entropy in time to pinpoint the sudden changes in the value. The time-series data of entropy are transformed into the two-dimensional domains to help visually inspect the activities on the network. We examine the system using network traffic traces containing notorious worms and DoS attacks on the testbed. Furthermore, we compare our proposed system of time series forecasting method, such as EWMA, holt-winters, and PCA in terms of sensitive. The result suggests that our approach be able to detect anomalies with the fairly high accuracy. Our contributions are two folds: (1) highly sensitive detection of anomalies and (2) visualization of network activities to alert anomalies.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.258-270
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• 2016

This study was conducted to test the green-tide mitigation technique in the lower part of the Sook Stream (Chusori) of Daecheong Reservoir from June 27 to August 24, 2014. And the effects were compared with weekly monitoring result of the watching station of the algae alert system (AAS) as well as test beds reach. The green-tide in a test bed was begun from the upstream, and it was gradually transferred and spread toward the downstream by the hydrological factors. The total amount of algae removed by algae removal device during the test period was 33,920 kg, and solids dewatered by natural gravity was 8,480 kg. Also chlorophyll-a content was 2.83 kg, the number of blue-green algae cells was equivalent to $78.6{\times}10^{14}$ cells. Compared with the results of the watching station of AAS, the pre-concentrate removal work in the outbreak waters was able to suggest the possibility of green-tide mitigation. In addition, an effective management of the green-tide was required spatial and temporal occurrence information and practical device technology. Particularly, the optimal timing of algae removal in the river-reservoir hybrid system was recommended at times before the monsoon rainy season and reached the lowest water level.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.450-453
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• 2005

A forest fire occurs mainly as natural factor such as wind, temperature or human factor such as light. Recently, the most of forest fire prevention is prediction or prevision against forest fire by using remote sensing technology. However in order to forest fire prevention, the remote sensing has many limitations such as high cost and advanced technologies and so on. Therefore, we need to multisensor integration system that utilize not only remote sensing but also in-situ sensing in order to reduce large damage of forest fire though analysis of happen cause and prediction routing of occurred forest fire. In this paper we propose a multisensor integration system that offers prediction information of factors and route of forest fire by integrates collected data from remote sensor and in-situ sensor for forest fire prevention. The proposed system is based on wireless sensor network for collect observed data from various sensors. The proposed system not only offers great quality information because firstly, raw data level fuse different format of collected data from remote and in-situ sensor but also accomplish information level fusion based on result of first stage. Offered information from our system can help early prevention of factor and early prevision against occurred forest fire which transfer to SMS service or alert service into monitoring interface of administrator.

• Mass Spectrometry Letters
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• v.13 no.3
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• pp.58-83
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• 2022

We developed analytical methods using high performance chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS) for the simultaneous determination of 80 unapproved compounds in dietary supplements. The target compounds for analysis were unapproved ingredients (e.g., pharmaceuticals) that have potential adverse effects on consumers owing to accidental misuse, overuse, and interaction with other medication in dietary supplement. Two analytical methods were tested to identify the optimal validation results according to AOAC guideline. As a result, limit of quantification (LOQ) was 0.14-0.5 ㎍ mL^-1; linearity (r²) was ≥ 0.99; accuracy (expressed as recovery) was 78.9-114%; precision (relative standard deviation) was ≤ 4.28% in the HPLC method. In the LC-MS/MS method, LOQ was 0.01-2 ng mL^-1, linearity (r²) was ≥0.98, accuracy was 71.7-119%; precision was ≤ 12.5%. The developed methods were applied to 51 dietary supplements collected from 2019 to 2021 through MFDS alert system. Based on our previous monitoring study, major compounds were icariin, sibutramine, yohimbine, sildenafil, tadalafil, sennosides (A, B), cascarosides (A, B, C, D), and phenolphthalein. In this study, we re-analyzed samples of detected compounds, and evaluated the statistical difference using Bland-Altman analysis to compare two analytical approaches between HPLC and LC-MS/MS. These results showed a good agreement between two methods that can be used to monitor the unapproved ingredients in dietary supplements. The developed two methods are complementarily suitable for monitoring the adulteration of 80 unapproved compounds in dietary supplements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.139-143
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• 2019

Measurement of flow rate is required in various fields. Water meters are often used at home, and flow meters are used in water and sewage plants, petrochemical industries and so on.. A system is needed to monitor the flow rate in real time and notify immediately when flow rate is abnormal. Recently, with the development of the IoT it is possible to construct such devices at low cost. WeMos can be programmed with Arduino IDE as a mini wifii IoT module. The flow sensor can output a digital pulse proportional to the flow rate. In this paper, we developed the flow monitoring and warning system using WeMos and IoT technology. When the system operates, it calculates the flow rate, sends the value as JSON format to the server, monitors the flow rate as graph from the remote with the smartphone. We also implement the system to promptly send alert message to the smart phone using Pushbullet when the flow rate is abnormal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.369-372
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• 2021

These days, people spend more time sitting at a desk for studies or work. Also, because people continue to use computers, smartphones, and tablet PCs often during break times, their posture is getting worse. Maintaining a position of bad posture for an extended period of time causes problems with the musculoskeletal system related to the neck, shoulders, and spine. Additionally, problems such as physical fatigue and posture deformation are predicted to expand to a wide range of age groups. Therefore, the core function of the system we are developing is to ensure correct sitting posture and to receive alert notifications via the created mobile application. To create the system, a flex sensor, pressure sensor, and tilt sensor are attached to a chair and utilized. The flex sensor detects and compares the amount of bending in the chair's posture and transmits this value to an Arduino Uno R3 board. Additionally, information such as body balance and incline angle are collected to determine whether or not the current sitting posture is correct. When the posture is incorrect, a notification is sent through the mobile application to indicate to the user and the monitoring app that their posture is not correct. The system proposed in this study is expected to be of great help in future posture-related research.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.