• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.73-79
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• 2021

Recently there are growing interests on the energy conservation and emission reduction. In the fields of architecture and civil engineering, the energy monitoring of structures is required to response the energy issues. In perspective of thermal monitoring, thermal images gains popularity for their rich visual information. With the rapid development of the drone platform, aerial thermal images acquired using drone can be used to monitor not only a part of structure, but wider coverage. In addition, the stereo photogrammetric process is expected to generate 3D point cloud with thermal information. However thermal images show very poor in resolution with narrow field of view that limit the use of drone-based thermal photogrammety. In the study, we aimed to generate 3D thermal point cloud using visible and thermal images. The visible images show high spatial resolution being able to generate precise and dense point clouds. Then we extract thermal information from thermal images to assign them onto the point clouds by precisely establishing photogrammetric collinearity between the point clouds and thermal images. From the experiment, we successfully generate dense 3D thermal point cloud showing 3D thermal distribution over the building structure.

• Journal of IKEEE
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• v.24 no.4
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• pp.1129-1135
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• 2020

The effects of battery aging limit the rechargeable capacity, State of Health(SoH). It is very important to estimate the SoH in the battery monitoring system(BMS) and many algorithms of measuring the internal resistance of the battery were proposed. A method is used by applying a current source of a specific frequency to the battery and measuring the voltage response. When charging harmonic noise is generated in the voltage response, it results in poor resistance measurement accuracy. In this paper, a robust battery internal resistance measurement algorithm is proposed to eliminate the effect of charging noise by integrating the current source and voltage response signals for a certain period. It showed excellent accuracy and stable measurement results. Applying to the BMS for uninterruptible power supply, the usefulness of the proposed method is verified.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.65-74
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• 2009

The risk of health impairment due to poor ventilation, fire and explosion by inflammable materials, and other unintended occurrences is always present in dangerous workplaces such as manholes, underground septic tanks, storage tanks and confined areas. Therefore, it a system which can monitor harmful working environment through sensors in workplace on a realtime basis and keep workers safe from the risk is needed. This paper has attempted to design an inference engine to monitor harmful environments in the workplace. The proposed inference engine has a rule-based system structure using JESS. This system is not confined to a particular computing platform and is easily interlocked with OSGi-based middleware.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.207-218
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• 2024

Food webs have received global attention as next-generation biomonitoring tools; however, it remains challenging because revealing trophic links between species is costly and laborious. Although a link-extrapolation method utilizing published trophic link data can address this difficulty, it has limitations when applied to construct food webs in domestic streams due to the lack of information on endemic species in global literature. Therefore, this study aimed to develop a link extrapolation-based food web model adapted to Korean stream ecosystems. We considered taxonomic similarity of predation and dominance of generalists in aquatic ecosystems, designing taxonomically higher-level matching methods: family matching for all fish (Family), endemic fish (Family-E), endemic fish playing the role of consumers (Family-EC), and resources (Family-ER). By adding the commonly used genus matching method (Genus) to these four matching methods, a total of five matching methods were used to construct 103 domestic food webs. Predictive power of both individual links and food web indices were evaluated by comparing constructed food webs with corresponding empirical food webs. Results showed that, in both evaluations, proposed methods tended to perform better than Genus in a data-poor environment. In particular, Family-E and Family-EC were the most effective matching methods. Our model addressed domestic data scarcity problems when using a link-extrapolation method. It offers opportunities to understand stream ecosystem food webs and may provide novel insights into biomonitoring.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.117-127
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• 2008

Korea has a large number of landslides due to localized torrential downpours and typhoons in summer, causing great human damage and economic losses. In particular, most roads in the Gangwon area are located in mountains, making them expose to a great risk of landslide. Therefore, it is urgent to prepare countermeasures to prevent these landslides. Necessary for that are various slope investigation and high-tech observation techniques for slope maintenance. Recently there have been slope observation techniques using optical fiber sensors, GPS, CCD cameras, Total Station and satellite images; however, these are not used much due to poor economic feasibility, low accuracy and efficiency. This study evaluated accuracy of displacement extraction of model slopes using terrestrial LiDAR to determine its application to landslide monitoring. As a result, it can measure several mm of minute displacement with high accuracy and help to rapidly obtain geographical features of slope.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.2
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• pp.208-218
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• 2014

Objectives: The major objective of this study was to develop and validate a tier 1 exposure model utilizing worker exposure monitoring data and characteristics of worker activities routinely performed at construction sites, in order to estimate worker exposures without sampling. Methods: The Registration, Evaluation, Authorization and Restriction of Chemicals(REACH) system of the European Union(EU) allows the usage of exposure models for anticipating chemical exposure of manufacturing workers and consumers. Several exposure models have been developed such as Advanced REACH Tools(ART). The ART model is based on structured subjective assessment model. Using the same framework, a tier 1 exposure model has been developed. Worker activities at construction sites have been analyzed and modifying factors have been assigned for each activity. Korean Occupational Safety and Health Agency(KOSHA) accrued work exposure monitoring data for the last 10 years, which were retrieved and converted into exposure scores. A separate set of sampling data were collected to validate the developed exposure model. These algorithm have been realized on Excel spreadsheet for convenience and easy access. Results: The correlation coefficient of the developed model between exposure scores and monitoring data was 0.36, which is smaller than those of EU models(0.6~0.7). One of the main reasons explaining the discrepancy is poor description on worker activities in KOSHA database. Conclusions: The developed tier 1 exposure model can help industrial hygienists judge whether or not air sampling is required or not.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.67-77
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• 2017

Demands for housing has diversified recently due to low birth rate and the growth of aging population. Also, a share of idle houses and obsolete houses over 20 years old is gradually rising. Therefore, there is a need for a sustainable, environment-friendly improvement policy that is in line with a new housing paradigm and avoids full-scale new construction, such as a customized housing renovation plan considering local economic circumstances. Therefore, afforestation system applicable to buildings are assessed positively, but lack objective performance evaluation. Through one-year, long-term monitoring of replicated obsolete buildings that have poor insulation performance, this study calculated monthly average power consumption and analyzed power charges by applying pricing plans before and after the revision of progressive tax in order to examine economic effects expected by applying the afforestation system. In the obsolete buildings, the study showed that monthly average power consumption was reduced by 16.6kWh with 5.2% average reduction rate. Highest reduction was made in July at 11.3%. Aggregate monthly power consumption charges were relatively high in winter before and after the revision of progressive tax. Power charges reduction effect was highest in March when monthly power consumption was reduced to 300kWh level by applying the afforestation system.

• Smart Structures and Systems
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• v.6 no.3
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.