• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.91-97
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• 2017

This study contains research on a bio-monitoring system (BMS) capable of detecting abnormal high water temperatures, the shell valve movements (SVMs) of Pacific oysters (Crassostrea gigas), which were measured at four different temperature (5, 10, 20 and $30^{\circ}C$) under laboratory conditions. All the Pacific oysters were kept under fasting conditions for 3 days to prevent the influence of food and excretions before the onset of the experiments. SVMs did not detect at $5^{\circ}C$. However, SVMs increased with an increase in temperature (at $10^{\circ}C$ : $6.31{\pm}2.18times/hr$ and at $20^{\circ}C$: $22.0{\pm}10.0times/hr$). At $30^{\circ}C$, SVMs were divided into two groups: those with no SVMs as at $5^{\circ}C$ and those with SVMs similar to conditions at $20^{\circ}C$($23.9{\pm}9.35times/hr$). This indicates oyster shells maintain a closed condition due to a decrease in metabolism at $30^{\circ}C$, although some Pacific oysters had active SVMs due to an increase in metabolism. If a BMS using the SVM status of Pacific oysters was installed to monitor abnormal high water around oyster farms, early warning levels and serious alerts might be made available more rapidly for SVMs of more than ca. 30 times/hr and closing conditions in a matter of hours, respectively. Therefore, a BMS using the SVMs of Pacific oysters might be an effective early warning system for abnormal high water temperatures.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.23-32
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• 2024

This study utilized minimum number of demographic variables and first-semester GPA of students to predict the final academic status of students at a vocational college in Seoul. The results from XGBoost and LightGBM models revealed that these variables significantly impacted the prediction of students' dismissal. This suggests that early academic performance could be an important indicator of potential academic dropout. Additionally, the possibility that academic years required to award an associate degree at the vocational college could influence the final academic status was confirmed, indicating that the duration of study is a crucial factor in students' decisions to discontinue their studies. The study attempted to model without relying on psychological, social, or economic factors, focusing solely on academic achievement. This is expected to aid in the development of an early warning system for preventing academic dropout in the future.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.689-692
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• 1996

The environment surrounded by industries is represented by the 3Cs : Customers, Competition, and Changes. The 3Cs drive industries to pursue external business targets such as customer's needs and marketplaces with BPR (Business Process Reengineering). BPR addresses core business process. One of these core business processes is product development. This product development process has been reengineered by the concept of CE (Concurrent Engineering). The aim of the paper is to build frameworks of CE to clarify the CE concept. This paper begins with investigating the product development process from the perspectives of three drivers: cost, quality and speed. CE frameworks are then followed. The first frmework is concerned with the CE definition and thus three keyphrases are extracted : from the outset, concurrent design and systematic approach. Concerned with the CE implementation, the second framework is composed of five components: generalist & specialist, cross-function team, enabling tools & techniques, success metrics, and total visibility. This paper concludes that the CE practice is hard to achieve because of the 'dont't-tell-them-early' attitude of upstream people, and the 'wait-and-see' attitude of downstream people. As resolution, a change management program is recommended that changes an employees mind-set. This paper also supposes computer systems which facilitate and keep automatic track of the CE process as engineered. Finally it gives a warning that computer systems alone do not guarantee success without being preceded by process re-engineering.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.53
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• pp.45-54
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• 1999

Manufacturing today is becoming increasingly competitive. If a company is to exist and successfully compete, it must pay very careful attention to production management, total quality assurance and total proactive maintenance issues. Overall machine performance, repair efficiency, system level utilization, productivity and quality of output need to be optimized as possible. To accomplish that objective, the behavior of manufacturing equipment and systems need to be monitored and measured continuously if it is possible. Then early warning of possible failure should be generated and proacted on that type of the situation to improve overall operation performance of manufacturing environment. In this paper, Proactive maintenance framework using performance-based reliability structure as enabler technology is proposed. Its paradigm enables one to maximize system through-put and product quality as well as resources in the performance domain. In the case of inadequate knowledge of the failure mechanics, this empirical modeling concept along with performance degradation knowledge can serve as an important product and process improvement tool. The real-time framework extension to proposed framework uses on-line performance information and is capable of projecting the remaining useful period.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1589-1597
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• 2021

Submarine groundwater discharge (SGD) in coastal areas is gaining importance as a major transport route that bring nutrients and trace metals into the ocean. This paper describes the analysis of the seasonal changes and spatiotemporal characteristicsthrough the modeling monthly SGD for 35 years from 1986 to 2020 for the Nakdong river basin. In this study, we extracted 210 watersheds and SGD estimation points using the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model). The average annual SGD of the Nakdong River basin was estimated to be 466.7 m²/yr from the FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) recharge data of 10 km which is the highest resolution global model applicable to Korea. There was no significant time-series variation of SGD in the Nakdong river basin, but the concentrated period of SGD was expanded from summer to autumn. In addition, it was confirmed that there is a large amount of SGD regardless of the season in coastal area nearby large rivers, and the trend has slightly increased since the 1980s. The characteristics are considered to be related to the change in the major precipitation period in the study area, and spatially it is due to the high baseflow-groundwater in the vicinity of large rivers. This study is a precedentstudy that presents a modeling technique to explore the characteristics of SGD in Korea, and is expected to be useful as foundational information for coastal management and evaluating the impact of SGD to the ocean.

• Journal of Trauma and Injury
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• v.34 no.1
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• pp.3-12
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• 2021

Purpose: To evaluate the severity of trauma, many scoring systems and predictive models have been presented. The quick Sequential Organ Failure Assessment (qSOFA) is a simple scoring system based on vital signs, and we expect it to be easier to apply to trauma patients than other trauma assessment tools. Methods: This study was a cross-sectional study of trauma patients who visited the emergency department of Jeju National University Hospital. We excluded patients under the age of 18 years and unknown outcomes. We calculated the qSOFA, the Modified Early Warning Score (mEWS), Revised Trauma Score (RTS), and Injury Severity Score (ISS) based on patients' initial vital signs and assessments performed in the emergency department (ED). The primary outcome was mortality within 14 days of trauma. We analyzed qSOFA scores using multivariate logistic regression analysis and compared the predictive accuracy of these scoring systems using the area under the receiver operating characteristic curve (AUROC). Results: In total, 27,764 patients were analyzed. In the multivariate logistic regression analysis of the qSOFA, the adjusted odds ratios with 95% confidence interval (CI) for mortality relative to a qSOFA score of 0 were 27.82 (13.63-56.79) for a qSOFA score of 1, 373.31 (183.47-759.57) for a qSOFA score of 2, and 494.07 (143.75-1698.15) for a qSOFA score of 3. In the receiver operating characteristic (ROC) curve analysis for the qSOFA, mEWS, ISS, and RTS in predicting the outcomes, for mortality, the AUROC for the qSOFA (AUROC [95% CI]; 0.912 [0.871-0.952]) was significantly greater than those for the ISS (0.700 [0.608-0.793]) and RTS (0.160 [0.108-0.211]). Conclusions: The qSOFA was useful for predicting the prognosis of trauma patients evaluated in the ED.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.117-129
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• 2009

This paper studied the safety management network system of infrastructure which constructed smart sensors, closed-circuit television(CCTV) and monitoring system. This safety management of infrastructure applied to bridge, cut slop and tunnel, embankment etc. The system applied to technologies of standardization guidelines, data acquirement technologies, data analysis and judgment technologies, system integration setup technology, and IT technologies. It was constructed safety management network system of various infrastructure to improve efficient management and operation for many infrastructure. Integrated safety management network system of infrastructure consisted of the real-time structural health monitoring system of each infrastructure, integrated control center, measured data transmission using i of tet web-based, collecting data using sf ver, early alarm system which the dangerous event of infrastructure occurred. Integrated control center consisted of conference room, control room to manage and analysis the data, server room to present the measured data and to collect the raw data. Early alarm system proposed realization of warning and response within 5 minute or less through development of sensor-based progress report and propagation automation system using the media such as MMS, VMS, EMS, FMS, SMS and web services of report and propagation. Based on this, the most effective u-Infrastructure Safety Management System is expected to be stably established at a less cost, thus making people's life more comfortable. Information obtained from such systems could be useful for maintenance or structural safety evaluation of existing structures, rapid evaluation of conditions of damaged structures after an earthquake, estimation of residual life of structures, repair and retrofitting of structures, maintenance, management or rehabilitation of historical structures.

• The Journal of Bigdata
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• v.1 no.1
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• pp.85-94
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• 2016

Traditional Anti-Money Laundering (AML) software applications monitor bank customer transactions on a daily basis using customer historical information and account profile data to provide a "whole picture" to bank management. With the advent of Big Data, these applications could be benefited from size, variety, and speed of unstructured data, which have not been used in AML applications before. This study analyses the weaknesses of a bank's current AML systems and proposes an AML systems taking advantage of Big Data. For example, early warning of AML risk can be improved by exposing identities and uncovering hidden relationships through predictive and entity analytics on real-time and outside data such as SNS data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.304-311
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• 2013

There have been increasing cases for farmers to install automated weather stations (AWS) at their farms and orchards in order to take countermeasures to more frequent weather disasters caused by climate variability and weather extremes. Although raw data are the same, the additive values as agrometeorological information may vary depending on data processing methods. User demands on appropriate information could also be different among crop species, cropping systems and even cultivars. We designed an internet based AWS data processing and display system to help diverse users (e.g., farmers), extension workers to access their weather data on specific demands. The system was implemented at a rural catchment with 52 $km^2$ land area where 14 automated weather stations are in operation. This note introduces the system and describes the major modules in detail. By linking regional AWS networks, a feasibility for this system as an early warning system is also discussed.