• Asian-Australasian Journal of Animal Sciences
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• v.20 no.5
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• pp.682-692
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• 2007

This study presents an automatic system to predict parturition time in the crated sows. The system relies on ultrasonic transducers mounted from above along the length of the crate. Using a 40 kHz time of flight (TOF) single envelope wave, the momentary distances between the sensors are measured. Therefore, the local momentary height of the sow and the momentary posture, i.e. standing posture (SDP), kneeling posture (KP), sitting posture (STP) and lateral lying posture (LLP) are determined. Crated sows change their postures from standing to lying and vice versa which follows a characteristic pattern. As parturition approaches, sows exhibit uneasiness, restlessness and the stand up sequence (SUS, the posture transition from LLP to SDP) rate increases because of labor pains. In time series, the SUS rate demonstrates a peak and it happens approximately 0-12 h before parturition. In this paper, the basic parturition threshold value method (BPTVM) and the same hour method (SHM) are proposed for predicting parturition, both of which are based on the SUS rate. The BPTVM mainly detects the peak of the SUS rate. As the SUS rate exceeds the threshold value, the parturition becomes predictable. Moreover, the SHM calculates the difference in the SUS rates between a particular time of day and the corresponding time of the preceding day. Compared to the BPTVM, the SHM can eliminate the circadian rhythm of the SUS rate influenced by feeding behavior. Using the SHM the parturition can be approximately predicted within hours. In an attempt to define the threshold parameters of predicting parturition, a data set with 32 sows of the SUS rate are used to estimate assumable predicting probability. The results show the assumable probability of the parturition prediction within 9 h is 96.9% for the SHM and 84.4% for the BPTVM. Moreover, the SHM can even reach a 75% probability of prediction within three hours of parturition. We conclude that the SHM is more accurate and is more useful for parturition time prediction. When parturition is detected, the proposed algorithm generates a warning signal which can inform human personnel to protect the mother and newborn piglets.

• Journal of Wetlands Research
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• v.23 no.1
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• pp.27-34
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• 2021

In this study, the main influencing factors of the occurrence of cyanobacteria at each of the eight Multifunctional weirs were derived using a random forest, and a categorical prediction model based on a Algal bloom warning system was developed. As a result of examining the importance of variables in the random forest, it was found that the upstream points were directly affected by weir operation during the occurrence of cyanobacteria. This means that cyanobacteria can be managed through efficient security management. DO and E.C were indicated as major influencers in midstream. The midstream section is a section where large-scale industrial complexes such as Gumi and Gimcheon are concentrated as well as the emissions of basic environmental facilities have a great influence. During the period of heatwave and drought, E.C increases along with the discharge of environmental facilities discharged from the basin, which promotes the outbreak of cyanobacteria. Those monitoring sites located in the middle and lower streams are areas that are most affected by heat waves and droughts, and therefore require preemptive management in preparation for the outbreak of cyanobacteria caused by drought in summer. Through this study, the characteristics of cyanobacteria at each point were analyzed. It can provide basic data for policy decision-making for customized cyanobacteria management.

• Journal of the Korean Geosynthetics Society
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• v.21 no.3
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• pp.71-78
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• 2022

In this study, the applicability of underground displacement sensors was considered through rock blasting tests to develop a relatively inexpensive and efficient slope failure prediction system that can quickly detect the risk of slope failure in advance and issue predictions and warnings with accurate judgment. In the blasting experiment, the sensor located close to the blasting source showed a large displacement due to crushing inside the rock and the sensor located away from the blasting source showed a relatively small strain. This study confirmed that the wired and wireless type underground displacement sensor system can be applied to measure the behavior of the rock slope, and it can be used as a basic data for establishing an early warning system to predict slope failure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.163-174
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• 2024

Currently, the urban railway operating agency is improving the emergency telephone in operation into an IP-based "trackside integrated interface communication facility" that can support a variety of additional services in order to quickly respond to emergency situations within the tunnel. This study is based on this Analyze the needs of various stakeholders regarding the design of a system architecture that establishes an IoT sensor network environment to detect abnormal situations in the tunnel and transmits the collected information to the control center to predict disaster situations in advance, and defines the system requirements. In addition, a scenario model for disaster response was provided through the presentation of a service model. Through this, the perspective of responding to urban railway disasters changes from reactive response to proactive prevention, thereby ensuring safe operation of urban railways and preventing major industrial accidents.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.841-849
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• 2008

Natural disaster such as inundation due to the typhoon induced storm-surge has inflicted severe losses on the coastal area. The problem of global warming and sea surface rising has issued and thus influences the increase of frequency and potential power of storm-surge. What we can do is to make intelligent effort to predict and prevent the losses through the early warning and prevention activity from the accurate prediction and forecasting about the time-varying storm-surge height and its arriving time resulted from the numerical simulation with sea observations. In this paper, we developed the web service based GIS-Enabled storm-surge visualization system to predict and prevent the storm-surge disasters. Moreover. for more accurate topography around coastal area and fine-grid storm-surge numerical model, we have accomplished GIS-based coastal mapping through LiDAR measurement.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.412-421
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• 2013

Nowadays, small and medium-sized earthquakes occur frequently in the west coast of Korea. The earthquake induced damages on the harbor structure such as quay wall possibly make a severe impact on national economy. Therefore, not only a seismic design for the structures but warning system for seismic damage right after the occurrence of earthquake should be developed. In this study, seismic fragility analysis was performed to be given to earthquake damage prediction system for quay wall structures in Busan and Incheon harbor. Four types of structures such as pier-type, caisson type, counterfort type, block-type were analyzed and fragility curves of functional performance level and collapse prevention level based on displacement criteria were found. Regression analyses by using the results of the two ports were done for possible use in other port structures.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.161-171
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• 2018

This study set out to identify crime vulnerable areas with the GIS spatial analysis technique for the prediction of crimes. Crime vulnerable areas were extracted from the statistics of crimes with the GIS hotspot analysis technique and the inverse distance weighted(IDW) method applied to different crimes according to places and use districts. The scope of surveillance and weight were calculated for each of CPTED surveillance elements including CCTV, streetlamp, patrol division, and police substation. Maps of crime vulnerable areas were overlapped one after another to make a CPTED-based one expressed in four grades(safety, attention, warning, and risk).

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.76-92
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• 2018

This study aimed to establish database of rainfall inundation area by rainfall scenarios and conduct a real time prediction for urban flood mitigation. the data leaded model was developed for the mapping of inundated area with rainfall forecast data provided by korea meteorological agency. for the construction of data leaded model, 1d-2d modeling was applied to Gangnam area, where suffered from severe flooding event including september, 2010. 1d-2d analysis result agree with observed in term of flood depth. flood area and flood occurring report which maintained by NDMS(national disaster management system). The fitness ratio of the NDMS reporting point and 2D flood analysis results was revealed to be 69.5%. Flood forecast chart was created using pre-flooding database. It was analyzed to have 70.3% of fitness in case of flood forecast chart of 70mm, and 72.0% in case of 80mm flood forecast chart. Using the constructed pre-flood area database, it is possible to present flood forecast chart information with rainfall forecast, and it can be used to secure the leading time during flood predictions and warning.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.129-138
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• 2019

In this study, the rebar rock bolt sensor and GFRP rock bolt sensor, which can be monitored, were embedded in a large model slope, and the behavior of slopes occurred in the early stage of slope collapse was analyzed after performing the field failure test, numerical analysis of the individual element method and finite element method. By comparing and analyzing the field test and numerical analysis results, field applicability of rock slope collapse monitoring on the rebar rock bolt sensor and GFRP rock bolt sensor was investigated. Through this study, smart slope collapse prediction and warning system was developed, which can be used to induce effective evacuation of residents living in the collapsible area by detecting landslide and ground decay precursor information in advance.