• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.57-65
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• 2019

The purpose of this study was to develop an environment field monitoring system to measure crop growth. The environment field monitoring system consisted of sensors, a data acquisition system, and GPS. The sensors used in the environment field monitoring system consisted of an ambient sensor, a soil sensor, and an intensity sensor. The temperature and humidity of the atmosphere were measured with the ambient sensor. The temperature, humidity, and EC of the soil were measured with the soil sensor. The data acquisition system was developed using the Arduino controller. The field monitoring data were collected before a rainy day, on a rainy day, and after the rainy day. The measured data using the environment field monitoring system were compared with the Daejeon regional meteorological office data. The correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office was analyzed for performance evaluation. The correlation of the temperature and humidity of the atmosphere was analyzed because the Daejeon regional meteorological office only provided data for the temperature and humidity of the atmosphere. The correlation coefficients were 0.86 and 0.90, respectively. The result showed a good correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office. Therefore, the developed system could be applied to monitoring the field environment of agricultural crops.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.436-452
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• 2008

In this study, the settlement prediction method based on field monitoring data under preloading improvement with ramp loading is developed. Settlement behavior can be predicted with field monitored settlement throughout the entire preloading process including ramp loading followed by constant loading. The developed method is verified by comparing its predicted results with results from physical model tests and field monitoring data.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1105-1112
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• 2022

Field data monitoring systems such as renewable energy generation and smart farm integrated control are developing from PC and server to mobile first, and various wireless communication and application services have emerged with the development of IoT technology. Low-power wide-area networks are services optimized for low-power, low-capacity, and low-speed data transmission, and data collected in the field is transmitted to designated storage servers or cloud-based data platforms, enabling data monitoring. In this paper, we implement an IoT repeater that collects field data with a single device and transmits it to a wireless carrier cloud data flat using a low-power wide-area network, and a monitoring app using it. Using this, the system configuration is simpler, the cost of deployment and operation is lower, and effective data accumulation is possible.

• Smart Structures and Systems
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• v.31 no.6
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• pp.585-599
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• 2023

Field monitoring techniques offer an attractive approach for understanding bridge behavior under in-service loads. However, the investigations on bridge behavior under high-speed train load using field monitoring data are limited. The focus of this study is to explore the structural behavior of an in-service long-span steel truss arch bridge based on field monitoring data. First, the natural frequencies of the structure, as well as the train driving frequencies, are extracted. Then, the train-induced bearing displacement and structural strain are explored to identify the effects of train loads and bearings. Subsequently, a sensitivity analysis is performed for the impact factor of strain responses with respect to the train speed, train weight, and temperature to identify the fundamental issues affecting these responses. Additionally, a similar sensitivity analysis is conducted for the peak acceleration. The results indicate that the friction force in bearings provides residual deformations when two consecutive trains are in opposite directions. In addition, the impact factor and peak acceleration are primarily affected by train speed, particularly near train speeds that result in the resonance of the bridge response. The results can provide additional insight into the behavior of the long-span steel truss bridges under in-service high-speed train loads.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1043-1060
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• 2016

The engineering background of this article is an ancient wooden building with extremely high historic and cultural values in Tibet. A full understanding of the dynamic behaviour of this historic building under in-service environments is the basis to assess the condition of the structure, especially its responses to earthquake, environmental and operational loading. A dynamic monitoring system has been installed in the building for over one year and the large amounts of high quality data have been obtained. The paper aims at studying the dynamic behaviour of the wooden building in seismic and operational conditions using the field monitoring data. Specifically the effects of earthquake and crowd loading on the structure's dynamic response are investigated. The monitoring data are decomposed into principal components using the Singular Spectrum Analysis (SSA) technique. The relationship between the average acceleration amplitude and frequencies of the principle components and operational conditions has been discussed. One main contribution is to understand the health condition of complex ancient building based on large databases collected on the field.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.109-121
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• 2020

Converting the agricultural land-use of rice field to upland has been increasingly conducted as farmers encourages themselves to grow higher value-added crops on rice fields under the policy support. Comparing to rice field, Upland shows different characteristic of discharge due to the slope, scale, and shape of field and characteristics of rainfall event. In this study, we designed the experiment fields reflecting flat-upland characteristics with different land scale, and tried to collect the discharge and load data. Soybeans and corn were selected as target crops considering the possibility of large-scale cultivation and crop demand. The cultivation was conducted during the growth period in 2019 with 3 different field scales. Hence, we have collected the discharge data from 17 rainfall events and the load data for 8 rainfall events. As a result, the magnitude of rainfall events and the discharge duration were found to have a strong positive correlation and field discharge occurred during the period by 55% to 83% of rainfall duration. Besides we found other relationships and characteristics of rainfall event, discharge, and pollutant load and also pointed out that continuous monitoring and more data are required to derive statistically significant results. Compared with slope-field monitoring data obtained from the precedent research, the runoff ratio of the flat-fields was significantly lower than slope-fields. Overall the discharge in the slop and flat-fields shows appreciably different characteristics so that the related researches need to be further conducted to reasonably assess environmental impact of agricultural activities at flat-field.

• Smart Structures and Systems
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• v.6 no.3
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• pp.263-275
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• 2010

In the last decade, wireless sensor networks have emerged as a promising technology that could accelerate progress in the field of structural monitoring. The main advantages of wireless sensor networks compared to conventional monitoring technologies are fast deployment, small interference with the surroundings, self-organization, flexibility and scalability. These features could enable mass application of monitoring systems, even on smaller structures. However, since wireless sensor network nodes are battery powered and data communication is the most energy consuming task, transferring all the acquired raw data through the network would dramatically limit system lifetime. Hence, data reduction has to be achieved at the node level in order to meet the system lifetime requirements of real life applications. The objective of this paper is to discuss some general aspects of data processing and management in monitoring systems based on wireless sensor networks, to present a prototype monitoring system for civil engineering structures, and to illustrate long-term field test results.