• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.103-112
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• 2018

In this paper, we propose a method of recognizing emotions using PPG sensor which measures blood flow according to emotion. From the existing PPG signal, we use a method of determining positive emotions and negative emotions in the frequency domain through PSD (Power Spectrum Density). Based on James R. Russell's two-dimensional prototype model, we classify emotions as joy, sadness, irritability, and calmness and examine their association with the magnitude of energy in the frequency domain. It is significant that this study used the same PPG sensor used in wearable devices to measure the top four kinds of emotions in the frequency domain through image experiments. Through the questionnaire, the accuracy, the immersion level according to the individual, the emotional change, and the biofeedback for the image were collected. The proposed method is expected to be various development such as commercial application service using PPG and mobile application prediction service by merging with context information of existing smart phone.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.377-383
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• 2018

Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.127-137
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• 2018

Parturition is an important event for farmers as it provides economic gains for the farms. Thus, the effective management of parturition is essential to farm management. In particular, the unit price of cattle is higher than other livestock and the productivity of cattle is closely associated to farm income. In addition, 42% of calving occurs in the nighttime so accurate parturition predictions are all the more important. In this paper, we propose a method that accurately predicts the calving date by applying core body temperature of cattle to deep learning. The body temperature of cattle can be measured without being influenced by the ambient environment by applying an ingestible bio-sensor in the cattle's rumen. By experiment on cattle, we confirmed this method to be more accurate for predicting calving dates than existing parturition prediction methods, showing an average of 3 hour 40 minute error. This proposed method is expected to reduce the economic damages of farms by accurately predicting calving times and assisting in successful parturitions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.53-60
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• 2020

In this study, smart precast-in-place concrete, such as continuity with Precast any technology that can be the Application of Building Structures and railway stations, civil structures. After the same way in the field installation design based on the criteria railways and derived the right section, through the Static and Dynamic Response Analysis. Dynamic sensor and the triaxial acceleration measured by attaching the sensor acceleration response according to the extent of the damage of Precast Panel Structures and mode of Precast Structures, by comparing the data. Data for the stability and improvement of the uncertainty in along a railroad and Future of Precast Panel Structures of time to replace. This is to use this data as basic data on damage prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.44-44
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• 2022

우리나라의 경우 강수량의 2/3 정도가 하절기에 집중되는 강우특성상 해마다 여름철 홍수기의 탁수 문제가 다양하게 발생하고 있다. 이상강우와 기상이변에 의한 집중강우가 증가 추세이며, '02년 태풍 루사', '03년 태풍 매미', '06년 에위니아'부터 20년 마이삭, 하이선 까지 장마와 태풍에 의한 유입량이 급증하는 시기 탁수의 유입으로 수중 탁도가 급상승하며 댐 저수지 내 탁수 문제가 발생하였다. 특히 연 평균 물사용량의 대부분을 하천 및 댐 저수지를 이용하는 우리나라의 경우 탁수 문제가 장기화될 경우 댐 하류 해당 지역 농업, 공업, 수생태 등 사회적, 환경적으로 많은 문제를 발생시킨다. 이러한 탁수 예측을 통한 대응을 위해 탁수 모델링에 대한 연구가 활발히 진행되고 있다. 탁수를 모델링을 위해서는 유량, 수온, SS 데이터가 필요하다. 이를 위해 국가측정망에서 하천 및 댐 저수지 내 SS를 측정하여 탁수를 측정 하고 있으나 설비가 미흡하여 데이터 해상도가 낮다는 한계점이 있으며 주요 댐 저수지 내에서는 수자원공사에서 관리하는 자동 측정기기를 활용하여 높은 데이터 해상도를 유지 하고 있으나 댐 별, 기상 조건에 따라 미측정 기간이 존재한다. 탁도를 측정을 위한 센서로는 Optical Backscatter Sensor(OBS), YSI 등이 있으며 SS를 측정하기 위한 센서는 레이저부유사측정기(LISST: Laser In-Situ Scattering and Transmissometry) 등의 장비를 이용하고 있다. 하지만 이런 첨단 센서의 경우 또한 수중 고정하여 측정하기에는 장비의 안정성 등의 이유로 한계가 있음에 따라 취득된 유량, 수온, SS, 탁도 데이터를 기반으로 분석을 통해 미측정 기간에 대한 보간이 필요하다. 본 연구에서는 국가 측정망 데이터 및 강우시 유량에 따른 탁수 유입의 증가와 탁수 유입에 따른 항목별 측정 데이터를 기반으로 유량, 수온, SS 미측정 기간을 보간하여 입력자료로 탁수를 모의하여 분석하고자 하였다.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.23-34
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• 2012

Space launch vehicles are designed to fly according to the elaborate pre-determined path. However, if a vehicle went out of the planned trajectory or its thrust terminated abnormally, or if a free-fall atmospheric reentry vehicle tracked by a tracking sensor became impossible to be measured, it is required to attempt to track by a another track equipment or estimate its impact point rapidly. In this paper a new algorithm is proposed, named the ITS-EKF combined with the Integrated Track Splitting (ITS) algorithm and the Extended Kalman Filter (EKF) to obtain the location information of a ballistic projectile without thrust, create its track and maintain it in an environment with clutter. For the reentry vehicle, the track performance is to be verified and the impact point is estimated by applying the simulation through ITS-EKF algorithm. To ensure the proposed algorithm's adequacy, by comparing the track performance and impact point distribution by the ITS-EKF with those of ITS-PF combined with ITS and Particle Filter (PF), it is confirmed that the ITS-EKF algorithm can be used an effective real-time On-line impact point prediction.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.271-277
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• 2020

There are increasing cases of monitoring the structural response of structures using multiple sensors. However, owing to cost and management problems, limited sensors are installed in the structure. Thus, few structural responses are collected, which hinders analyzing the behavior of the structure. Therefore, a technique to predict responses at a location where sensors are not installed to a reliable level using limited sensors is necessary. In this study, a numerical study is conducted to predict the seismic response of low-rise buildings using limited information. It is assumed that the available response information is only the acceleration responses of the first and top floors. Using both information, the first natural frequency of the structure can be obtained. The acceleration information on the first floor is used as the ground motion information. To minimize the error on the acceleration history response of the top floor and the first natural frequency error of the target structure, the method for predicting the mass and stiffness information of a structure using the genetic algorithm is presented. However, the constraints are not considered. To determine the range of design variables that mean the search space, the parameter prediction method based on artificial neural networks is proposed. To verify the proposed method, a five-story structure is used as an example.