• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Journal of the Korean earth science society
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• v.31 no.6
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• pp.563-572
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• 2010

Both multi-channel temperature monitoring and geophysical electric survey were performed together for an embankment to assess the leakage zone. Temperature variation according to space and time on the inner parts of engineering constructions (e.g.: dam and slope) can be basic information for diagnosing their safety problem. In general, as constructions become superannuated, structural deformation (e.g.: cracks and defects) could be generated by various factors. Seepage or leakage of water through the cracks or defects in old dams will directly cause temperature anomaly. This study shows that the position of seepage or leakage in dam body can be detected by multi-channel temperature monitoring using thermal line sensor. For that matter, diverse temperature monitoring experiments for a leakage physical model were performed in the laboratory. In field application of an old earth fill dam, temperature variations for water depth and for inner parts of boreholes located at downstream slope were measured. Temperature monitoring results for a long time at the bottom of downstream slope of the dam showed the possibility that temperature monitoring can provide the synthetic information about flowing path and quantity of seepage of leakage in dam body. Geophysical data by electrical method are also added to help interpret data.

• Journal of Energy Engineering
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• v.27 no.4
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• pp.103-110
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• 2018

In this paper, we introduce evaluation method of time series prediction model with new approach of Mean Absolute Percentage Error(hereafter MAPE) and Symmetric Mean Absolute Percentage Error(hereafter sMAPE). There are some problems using MAPE and sMAPE. First MAPE can't evaluate Zero observation of dataset. Moreover, when the observed value is very close to zero it evaluate heavier than other methods. Finally it evaluate different measure even same error between observations and predicted values. And sMAPE does different evaluations are made depending on whether the same error value is over-predicted or under-predicted. And it has different measurement according to the each sign, even if error is the same distance. These problems were solved by Maximum Mean Absolute Percentage Error(hereafter mMAPE). we used the absolute maximum of observed value as denominator instead of the observed value in MAPE, when the value is less than 1, removed denominator then solved the problem that the zero value is not defined. and were able to prevent heavier measurement problem. Also, if the absolute maximum of observed value is greater than 1, the evaluation values of mMAPE were compared with those of the other evaluations. With Beijing PM2.5 temperature data and our simulation data, we compared the evaluation values of mMAPE with other evaluations. And we proved that mMAPE can solve the problems that we mentioned.

• Investigative Magnetic Resonance Imaging
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• v.18 no.4
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• pp.290-302
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• 2014

Purpose : Susceptibility-weighted magnetic resonance (MR) sequence is three-dimensional (3D), spoiled gradient-echo pulse sequences that provide a high sensitivity for the detection of blood degradation products, calcifications, and iron deposits. This pictorial review is aimed at illustrating and discussing its main clinical applications. Materials and Methods: SWI is based on high-resolution, 3D, fully velocity-compensated gradient-echo sequences using both magnitude and phase images. To enhance the visibility of the venous structures, the magnitude images are multiplied with a phase mask generated from the filtered phase data, which are displayed at best after post-processing of the 3D dataset with the minimal intensity projection algorithm. A total of 200 patients underwent MR examinations that included SWI on a 3 tesla MR imager were enrolled. Results: SWI is very useful in detecting multiple brain disorders. Among the 200 patients, 80 showed developmental venous anomaly, 22 showed cavernous malformation, 12 showed calcifications in various conditions, 21 showed cerebrovascular accident with susceptibility vessel sign or microbleeds, 52 showed brain tumors, 2 showed diffuse axonal injury, 3 showed arteriovenous malformation, 5 showed dural arteriovenous fistula, 1 showed moyamoya disease, and 2 showed Parkinson's disease. Conclusion: SWI is useful in detecting occult low flow vascular lesions, calcification and microbleed and characterising diverse brain disorders.

• Economic and Environmental Geology
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• v.46 no.2
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• pp.105-121
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• 2013

This study introduces integrated approach on detection of a leakage in a sea wall based on remote sensing, electric resistivity survey, electromagnetic survey, and borehole survey for the Seosan A-Region sea wall. The satellite temperature distribution from Landsat ETM+ data identifies water leakage distribution and period by analyzing temperature mixing patterns between sea water and fresh water. Electric resistivity survey provides both horizontal and vertical anomaly distributions over the sea wall showing below average electric resistivity. Electromagnetic survey(electrical conductivity survey) reveals the potential possible leakage areas with minimal background impact by comparing electrical conductivity values between high and low tides. Borehole image processing system confirmed the locations of anomalies identified from the other survey methods and distributions of vertical fracture zones. The integrated approach identified 41.7% of the sea wall being the most probable area vulnerable to water leakage and effectively approximated both horizontal and vertical distribution of water leakage. The integrated analysis of remote sensing, electric resistivity survey, electromagnetic survey and borehole survey is considered to be an optimal method in identifying water leakage distribution, period, and extent of fractures knowledged from the boreholes.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.49-56
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• 2018

As the energy generated by earthquake, tsunami, etc. propagates through the air and disturbs the electron density in the ionosphere, the perturbation can be detected by analyzing the ionospheric delay in satellite signal. The electron density in the ionosphere is affected by various factors such as solar activity, latitude, season, and local time. To distinguish from the anomaly, therefore, it is required to inspect the normal trend of the ionosphere. Also, as the perturbation magnitude diminishes by distance it is necessary to develop an appropriate algorithm to detect long-distance disturbances. In this paper, normal condition ionosphere trend is analyzed via IONEX data. We selected monitoring value that has no tendency and developed an algorithm to effectively detect the long-distance ionospheric disturbances by using the lasting characteristics of the disturbances. In the end, we concluded the $2^{nd}$ derivative of ionospheric delay would be proper monitoring value, and the false alarm with the developed algorithm turned out to be 1.4e-6 level. It was applied to 2011 Tohoku earthquake case and the ionospheric disturbance was successfully detected.

• Ocean and Polar Research
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• v.44 no.4
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• pp.355-364
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• 2022

When the 60-ton-class patrol boat '72' of the Korea Coast Guard (KCG) was on duty and she accidentally collided with another patrol boat ('207', 200-ton-class) and sank. A month-long search found a small amount of lost items, but neither the crew nor the ship was found. For the first time in 39 years since the accident, the Korea Institute of Ocean Science and Technology (KIOST) searched the boat 72 using the latest integrated geophysical techniques. A number of sonar images presumed to be of a sunken ship was acquired using a combined system of side scan sonar and marine magnetometer, operated at an altitude of approximately 30 m from the seabed. At the same time, a strong magnetic anomaly (100 nT) was detected in one place, indicating the presence of an iron ship. A video survey using a remotely operated underwater vehicle (ROV) confirmed the presence of a shielding part of a personal firearm at the stern of the sunken vessel. Based on these comprehensive data, the sunken vessel discovered in this exploration was assumed to be '72'. This result is meaningful in terms of future ocean exploration and underwater archaeology, as the integrated system of various geophysical methods is an efficient means of identifying objects present in the water.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.53-66
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• 2018

The information of surface reflectance ($R_{sfc}$) is important for the heat balance and the environmental/climate monitoring. The $R_{sfc}$ sensitivity to error-induced variables for the Geostationary Environment Monitoring Spectrometer (GEMS) retrieval from geostationary-orbit satellite observations at 300-500 nm was investigated, utilizing polar-orbit satellite data of the MODerate resolution Imaging Spectroradiometer (MODIS) and Ozone Mapping Instrument (OMI), and the radiative transfer model (RTM) experiment. The variables in this study can be cloud, Rayleigh-scattering, aerosol, ozone and surface type. The cloud detection in high-resolution MODIS pixels ($1km{\times}1km$) was compared with that in GEMS-scale pixels ($8km{\times}7km$). The GEMS detection was consistent (~79%) with the MODIS result. However, the detection probability in partially-cloudy (${\leq}40%$) GEMS pixels decreased due to other effects (i.e., aerosol and surface type). The Rayleigh-scattering effect in RGB images was noticeable over ocean, based on the RTM calculation. The reflectance at top of atmosphere ($R_{toa}$) increased with aerosol amounts in case of $R_{sfc}$<0.2, but decreased in $R_{sfc}{\geq}0.2$. The $R_{sfc}$ errors due to the aerosol increased with wavelength in the UV, but were constant or slightly decreased in the visible. The ozone absorption was most sensitive at 328 nm in the UV region (328-354 nm). The $R_{sfc}$ error was +0.1 because of negative total ozone anomaly (-100 DU) under the condition of $R_{sfc}=0.15$. This study can be useful to estimate $R_{sfc}$ uncertainties in the GEMS retrieval.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.