• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.343-350
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• 2015

This paper presents a context recognition method using environmental sound signals, which is applied to a mobile-based client monitoring system. Seven acoustic contexts are defined and the corresponding environmental sound signals are obtained for the experiments. To evaluate the performance of the context recognition, MFCC and LPCC method are employed as feature extraction, and statistical pattern recognition method are used employing GMM and HMM as acoustic models, The experimental results show that LPCC and HMM are more effective at improving context recognition accuracy compared to MFCC and GMM respectively. The recognition system using LPCC and HMM obtains 96.03% in recognition accuracy. These results demonstrate that LPCC is effective to represent environmental sounds which contain more various frequency components compared to human speech. They also prove that HMM is more effective to model the time-varying environmental sounds compared to GMM.

• Journal of Korean Society on Water Environment
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• v.34 no.5
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• pp.462-469
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• 2018

There are differences in method and cycle of flow rate survey depending on purpose of the operating department. To verify and use results of flow data according to the purpose, flow data of the directly measured and tele monitoring system were compared to verify validity. Flow measurement in the Ministry of Environment is aimed at setting up a standard flow of target water quality for water quality management and securing flow data of low and normal water level seasons for water quality evaluation. In this study, correlation analysis result ($R^2$) of same time zone data by direct measurement and tele monitoring system (TMS) at Seombon D point, a unit watershed of Seomjin river, for six years ('10 ~ '15) according to implementation of Total Daily Maximum Load (TDML) was wading 0.716, boating 0.962 and on bridge 0.943, and effectiveness of measurement method was verified by characteristics of flow duration as a season of dry and low-water; normal and high water are appropriate for wading, boating, and on bridge respectively. Results revealed it is reasonable to use directly measured results using the wading and boating method for low water level and dry seasons, and TMS data for rainy seasons. It can be used important data for future policy decisions.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.165-173
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• 2014

Open-pit mining method, is safe relatively work as compared with underground mining. And high yield, low production cost, has the advantage that it can provide a lot of production. But deforestation, tailings and slag deposition, mineral debris, dust, water, noise, land subsidence, sediment runoff discharge, I have internalized environmental disaster predisposing factors, such as landslides. Thus, it may be noted, also by typical environmental regulations. We try to deal with the changes in open pit terrain and environment related issues. Then, use the recovery period and the open pit mine and ecology off the gun, environment-friendly development of accurate monitoring methods, systems of this development is required. The use of open platforms and open source GIS tools have been developed during this period, it needs to develop spatial information environment monitoring system open pit mine construction.

• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.787-800
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• 2006

This study is to develop the drought monitoring system of Korea using drought indices such as PDSI, SPI and MSWSI. To analyze the applicability of three different drought indices, monthly based drought indices have been calculated using various kinds of meteorological and hydrologic data. Also past drought events have been investigated to compare the simulation results, which are the severity, duration and locations using monthly drought indices. The drought map which is made by using PDSI shows the best accordance with past drought events in its severity and duration as well. Also SPI(3) shows good accordance with past drought events. As a results of this study, we concluded that three different drought indices can be used as an effective tool for quantitative drought monitoring.

• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• Smart Structures and Systems
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• v.29 no.1
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Journal of Sensor Science and Technology
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• v.9 no.2
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• pp.127-133
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• 2000

We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.459-464
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• 2023

The mobile house of this article is provided with stand-alone power system that uses photovoltaic energy and enables sensing and environmental monitoring. Excess power generated is stored in lithium batteries, which enable smooth operation of the mobile house even in environment in which solar energy cannot be used. The house has been designed that its systems can be operated continuously by diesel power generation even when photovoltaic energy cannot be generated due to long rainy season or heavy snow. BMS (batter management system) has been constructed for photovoltaic and power management, and monitors the charge/discharge and usage amount of photovoltaic energy. Various sensing data are recorded and transmitted automatically, and the design allows for wireless monitoring by means of computer and smartphone app. The container house proposed in this study enables efficient energy management by performing optimal energy operation in remote areas, parks, event venues, and construction sites where there is no system power source.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.11
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• pp.1587-1594
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• 2003

Increased energy costs have placed demands for improved combustion efficiency, high equipment availability, low maintenance and safe operation. Furthermore low NO$_x\$ modification, installed due to strict environmental legislation, requires very careful combustion management. The flame monitoring system has been developed specially to satisfy these requirements. We aimed at gaining the relationship between the burner flame image and emissions such as NO$_x$ and unburned carbon in furnace by utilizing the image processing method. For the first step of development, its possibility test was undertaken with bench furnace. The test proceeded to the second step with pilot furnace and the system was observed to be effective for evaluating the combustion conditions. By using this technology, it is possible to perform continuous monitoring of the combustion conditions and instant detection of individual changes for each burner to prevent future loss of ignition. This may contribute to the saving of burner adjusting times for the changes of loads and fuels and to the reduction of the slagging as well.