• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.178-180
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• 2021

In this paper, we design a system to monitor environmental data in fish farms in real-time and provide machine learning-based prediction services to prevent damage on fish farms caused by changes in the sea environment. The proposed system will install an IoT device module consisting of sensors that can measure hydrogen concentration, salinity, dissolved oxygen, and water temperature, which can be transferred to Cloud DB using LTE or LoRa communication technology and then monitor the real-time condition through a web or mobile application. In addition, it has a function to prepare for changes within the environment of fish farms by applying machine learning-based prediction technology using collected data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.374-380
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• 2022

This paper shows the results of a study that predicts changes in seawater conditions in sea farms using machine learning-based long short term memory (LSTM) algorithms. Hardware was implemented using dissolved oxygen, salinity, nitrogen ion concentration, and water temperature measurement sensors to collect seawater condition information from sea farms, and transferred to a cloud-based Firebase database using LoRa communication. Using the developed hardware, seawater condition information around fish farms in Tongyeong and Geoje was collected, and LSTM algorithms were applied to learning results using these actual datasets to obtain predictive results showing 87% accuracy. Flask and REST APIs were used to provide users with predictive results for each of the four parameters, including dissolved oxygen. These predictive results are expected to help fishermen reduce significant damage caused by fish group death by providing changes in sea conditions in advance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.1
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• pp.71-83
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• 2004

The purpose of this study is to present an analysis method for the prediction of the changes m ground conditions. To this end, three dimensional convergence displacements are analyzed in several ways to estimate the trend of displacement changes. Three-dimensional arching effect is occurred around the unsupported excavation surface including tunnel face when a tunnel is excavated in a stable rock mass. If the ground condition ahead of tunnel face changes or a weak zone exists, a diagnostic trend of displacement change is observed by the 3 dimensional measurement and numerical analysis. Therefore, the change of ground condition and the existence of a weak zone ahead of tunnel face can be predicted by monitoring 3-dimensional absolute displacements during excavation, and applying the methodology (the ratio of L/C, $C/C_o$, etc.) presented in this study.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.132-135
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• 1990

This paper presents prediction of insulation lifetime in stress. Essentially, Epoxy resin, when it was subjected to different types of aging condition, produced to varieties of electrical properties and lifetime using spectroscopy and breakdown test. The relationships between the structural and electrical changes of aged epoxy were Investigated.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.365-370
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• 2000

Sea dike construction for the tidal flat reclamation works in estuary and coast may change the characteristics of tidal motion and wave conditions in the region. In turn, a new hydraulic condition provides the impacts on sediment transport pattern and forms a new morphological environment. Also, morphological changes during the closure works of sea dike are closely related with a safy of sea dike. Therefore, the prediction of morphological changes is required secure the safe closure work and the economic design of sea dikes. To investigate morphological changes due to sea dike construction, hydrodynamic changes of tides and waves have to be evaluated, then sediment transport and sea bottom changes are computed. Mathematical modelling is required for representation of interrelation of tidal motion, wave and sediment transport. In this study, numerical model MORSYS is applied to compute the hydrodynamics and morphological changes around the closure gap for Saemankuem dike. This model allows a flexible integration of the module for waves, currents, sediment transport and bottom changes.

• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.30-34
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• 2001

In the present study, we studied the method of predicting the on-design and on-design point performance of axial flow fan with adjustable pitch blades. With the change of stagger angle of axial flow fan with adjustable pitch blade, flow rate and pressure can be changed. Because of this merit adjustable pitch fans are used in many industrial facility. When changing stagger angle or estimating the performance at a wide range of off-design condition, incidence angle changes greatly as the flow rate changes. Therefore, the deviation angle at the blade exit is estimated by the correlation considering the effects of blade design, incidence angle variation. In the loss model, we used known pressure loss model for blade boundary layer and wake, secondary flow, endwall boundary layer and tip leakage flow. The results of modified deviation angle model and experiment were compared for the usefulness of the modified model.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.463-467
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• 2015

Mega-shock means a sporadic event such as the earning shock, which occurred by sudden market changes, and it can cause serious problems of profit loss of international construction projects. Therefore, the early response and prevention by analyzing and predicting the Mega-shock is critical for successful project delivery. This research is preliminary study to develop a prediction model that supports market condition analysis and Mega-shock forecasting. To avoid disadvantages of classic statistical approaches that assume the market factors are linear and independent and thus have limitations to explain complex interrelationship among a range of international market factors, the research team explored the Fractal Theory that can explain self-similarity and recursiveness of construction market changes. The research first found out correlation of the major market factors by statistically analyzing time-series data. The research then conducted a base of the Fractal analysis to distinguish features of fractal from data. The outcome will have potential to contribute to building up a foundation of the early shock warning system for the strategic international project management.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.105-108
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• 2005

The radiated noise from the automotive intake system should be predicted at the design stage. To this end, the precise measurement of in-duct acoustic source parameters of the intake system, i.e., the source strength and source impedance, is essential. Most of previous works on the measurement of acoustic source parameters were performed under a fixed engine speed condition. However, the requirement of vehicle manufacturer is the noise radiation pattern as a function of engine speed. In this study, the direct method was employed to measure the source parameters of engine intake system under a fixed engine speed and engine run-up condition. It was noted that the frequency spectra of source impedance hardly changes with varying the engine speed. Thus, it is reasonable to calculate the source strength under the engine run-up condition by assuming that source impedance is invariant with engine speed. Measured and conventional source models, i.e., constant pressure source, constant velocity source, and non-reflective source, were utilized to predict insertion loss and radiated sound pressure level. A reasonable prediction accuracy of radiated sound pressure level spectra from the intake system was given in the test vehicle when using the measured source characteristics which were acquired under the operating condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.