• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.20-29
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• 2023

This study analyzed the relationship between harmful cyanobacterial abundance and environmental factors in order to figure out the causes of the recent increase of cyanobacteria in Lake Dukdong from 2019 to 2021. Lake Dukdong, which is used as a drinking water source for Gyeongju City, has an algae alert system in place. Lake Dukdong has maintained good water quality, but algae alert level 1 (over 1,000 cells/mL) has been issued in recent years. As a result of Pearson correlation analysis (from May to Oct.), the cell density of Microcystis and Aphanizomenon, which form part of the most harmful cyanobacteria genus, were significantly positively correlated with the water temperature and water storage volume. T-test was performed to compare the data from 2016-2018 and 2019-2021 (from May to Oct.). The average density of harmful cyanobacteria cells increased about six-fold from 54 to 344 cells/mL. There were significant differences in water temperature, pH, total nitrogen (TN), total phosphorus (TP), TN/TP ratio, water storage volume, and cyanobacterial cell density. Water temperature increased from 19.2 to 22.8 ℃. TP concentration increased from 0.017 to 0.028 mg/L. The main cause of the recent increase of harmful cyanobacteria in Lake Dukdong is thought to be the increase in water temperature, TP concentration, and water storage volume from 2019 and 2021, resulting in more favorable conditions for cyanobacterial growth.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.2-78.2
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• 2019

The temperature control of a scientific device is essential because extreme temperature conditions can cause hazard issues for the operation. We developed a software which can interact with the temperature sensor using the GMT SDK(Giant Magellan Telescope Software Development Kit) version 1.6.0. The temperature sensor interacts with the EtherCAT(Ethernet for Control Automation Technology) slave via the hardware adapter, sending and receiving data by a packet. The PDO(Process Data Object) and SDO(Service Data Object), which are the packet interacts with each EtherCAT slave, are defined on the TwinCAT program that enables the real-time control of the devices. The user can receive data from the device via grs(GMT Runtime System) tools and log service. Besides, we programmed the software to print an alert message on the log when the temperature condition changes to certain conditions.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.3
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.180-199
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• 2008

Technology has had a tremendous impact on our daily lives. Recently, technology and its impact on aging has become an expanding field of inquiry. A major reason for this interest is that the use of technology can help older people who experience deteriorating health to live independently. In this paper we give a brief review of the in-home monitoring technologies for the elderly. In the pilot study, we analyze the possibility of employing the data generated by a continuous, unobtrusive nursing home monitoring system for predicting elevated(abnormal)pulse pressure(PP) in elderly(PP=systolic blood pressure-diastolic blood pressure). Our sensor data capture external information(behavioral) about the resident that is subsequently reflected in the predicted PP. By continuously predicting the possibility of elevated pulse pressure we may alert the nursing staff when some predefined threshold is exceeded. This approach may provide additional blood pressure monitoring for the elderly persons susceptible to blood pressure variations during the time between two nursing visits. We conducted a retrospective pilot study on two residents of the TigerPlace aging in place facility with age over 70, that had blood pressure measured between 100 and 300 times during a period of two years. The pilot study suggested that abnormal pulse pressure can be reasonably well estimated (an area under ROC curve of about 0.75) using apartment bed and motion sensors.

• Journal of muscle and joint health
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• v.22 no.3
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• pp.149-159
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• 2015

Purpose: This study aimed to identify falls and related risks of hospitalized patients in order to provide an baseline data to develop effective nursing intervention programs for fall prevention. Methods: The data on 120 patients who experienced falls from 2010 to 2013 during their hospitalization were collected from the patient' electronic medical records of an university hospital. Data were analyzed with descriptive statistics using SPSS/WIN 20.0. Results: Over 60% of the patients who experienced falls during their hospitalization was 65 years or older, and most of them had hypertension. Majority of the subjects needed help to perform daily activities (64%) and complained of general weakness (49.2%). Prior to the falls, the patients were taking average 2.52 medications to treat hypertension. The Fall accident was mostly frequently occurred in their hospital room (59.2%), or in bed (44.2%). The patients aged 70 years and older were significantly less alert than younger group, and taking more cardiovascular medications. Most fall risk factors were not significantly different for age, gender, and department category. Conclusion: The study findings suggest the need to emphasize the nurses to be more actively aware of fall risk factors and to provide aggressive interventions for preventing falls in hospitalized patients.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.42-48
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• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.240-246
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• 1990

The security control can be defined as all control actions and counter-measures to return the operating state of the system to a normal state. In an emergency state, fault clearing and/or overload suppression is enabled as a security control in order to prevent the extension of the fault. In the alert state, counter-measures should be set up in advance for the dangerous points of the system operation in drder to protect the system from expected accidents. In the normal state, the routine scenario is conducted to analyze system state. In the decision-making of the classification of system states, the heuristic and experienced knowledge can be well applied and thus application of expert system to this area attains considerable achievements. In this study, it is attempted to extract empirical rules through heuristic analysis and establish the knowledge base. Finally, the incorporation method with the conventional program packages in proposed. The expert system is designed to select an appropriate method and to perform the corresponding package. The input data can be automatically set up by using the data base. The computation results can be automatically added to the data base.

• IE interfaces
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• v.21 no.1
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• pp.33-42
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• 2008

To analyze tens of thousands of alarms triggered by the intrusion detections systems (IDS) a day has been very time-consuming, requiring human administrators to stay alert for all time. But most of the alarms triggered by the IDS prove to be the false positives. If alarms could be correctly classified into the false positive and the false negative, then we could alleviate most of the burden of human administrators and manage the IDS far more efficiently. Therefore, we present a new approach based on attribute-oriented induction (AOI) to classify alarms into the false positive and the false negative. The experimental results show the proposed approach performs very well.