• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.64-71
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• 2017

A damage estimation method is presented for jacket-type support structure of offshore wind turbine using a change of modal properties due to damage and committee of neural networks for effective structural health monitoring. For more practical monitoring, it is necessary to monitor the critical and prospective damaged members with a limited number of measurement locations. That is, many data channels and sensors are needed to identify all the members appropriately because the jacket-type support structure has many members. This is inappropriate considering economical and practical health monitoring. Therefore, intensive damage estimation for the critical members using a limited number of the measurement locations is carried out in this study. An analytical model for a jacket-type support structure which can be applied for a 5 MW offshore wind turbine is established, and a training pattern is generated using the numerical simulations. Twenty damage cases are estimated using the proposed method. The identified damage locations and severities agree reasonably well with the exact values and the accuracy of the estimation can be improved by applying the committee of neural networks. A verification experiment is carried out, and the damage arising in 3 damage cases is reasonably identified.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.846-855
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• 2021

Eco-friendly and renewable energy sources are actively being researched in recent times, and of shore wind power generation requires advanced design technologies in terms of increasing the capacities of wind turbines and enlarging wind turbine installation vessels (WTIVs). The WTIV ensures that the hull is situated at a height that is not affected by waves. The most important part of the WTIV is the leg structure, which must respond dynamically according to the wave, current, and wind loads. In particular, the wave load is composed of irregular waves, and it is important to know the exact dynamic response. The dynamic response analysis uses a single degree of freedom (SDOF) method, which is a simplified approach, but it is limited owing to the consideration of random waves. Therefore, in industrial practice, the time-domain analysis of random waves is based on the multi degree of freedom (MDOF) method. Although the MDOF method provides high-precision results, its data convergence is sensitive and difficult to apply owing to design complexity. Therefore, a dynamic amplification factor (DAF) estimation formula is developed in this study to express the dynamic response characteristics of random waves through time-domain analysis based on different variables. It is confirmed that the calculation time can be shortened and accuracy enhanced compared to existing MDOF methods. The developed formula will be used in the initial design of WTIVs and similar structures.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.29-36
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• 2007

According to the using of electric power source such as solar cell, fuel cell and wind energy, consumer is supplied from distributed generator and electric power company. Therefore, it is required that the real-time measurement and control instrument for those distributed generator. In this paper, describes the development of measuring equipment for the power system with distributed generator. The equipment has real-time measure function and communication with PC via USB port, which is used for various purposes such as investigation of transient state, and is compared with measurement instrument to verify the reliability.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.39-44
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• 2011

Recently, due to people's incontinent use all over the world, fossil fuels such as coal, oil, and natural gas were nearly to be exhausted and also causes serious environment pollutions. Therefore, there is a strong need to develop solar, wind, hydro, biomass, geothermal to replace fossil fuels to prevent suffering from above problems. Wish advances in sensor technology, such data is collected as a kind of stream data which arrives in an online manner so that it is characterized as high- speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. Therefore, the traditional data processing techniques are not fit to deal with stream data. In this paper, we propose a kalman filter-based algorithm to process renewable stream data.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.593-602
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• 2020

Recently, as research on smart farms has been actively conducted, systems for efficiently cultivating crops have been introduced and various energy systems using renewable energy such as solar, geothermal and wind power generation have been proposed to save the energy. In this paper, we propose a new and renewable energy convergence system for crops that provides energy independence and improved crop cultivation environment. First, we present LPWA-based communication node and gateway for ICT-based data collection. Then we propose an integrated monitoring server that collects energy data, crop growth data, and environmental data through a communication node and builds it as big data to perform optimal energy management that reflects the characteristics of the environment for cultivating crops. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2140-2146
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• 2014

Ocean buoys are operated for safe navigation and collecting ocean data. Recently, to reducing marine buoy's damage by ocean weather's bad condition and collision with vessels has been conducted in several field research. This paper's experiment is buoy condition monitoring about predefined data form by users. As a result using Wireless remote control board applying a radio signal processing algorithms, it can observe buoy's state at an interval of three minutes on the land. Acquired data type is changeable according to ocean weather condition or buoy's purpose of using in advance. Also, this paper conducted an experiment such as data-transmission's stability and wireless communication's availability. As results of the analysis of the transmitted data, the solar, wind and wave power indicates the maximum amount of power, 50 W, 20 W and 40 W respectively. The communication system proven through this research can apply to buoy or other ocean facility.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Journal of the Korean Geosynthetics Society
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• v.15 no.1
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• pp.1-10
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• 2016

Recently, offshore wind farms are increasingly expected, because there are huge resource and large site in offshore. Jeju island has optimum condition for constructing a wind energy farm. Unlike the mainland, Jeju island has stratified structure distribution between rock layers sediments due to volcanic activation. In these case, it can be occur engineering problems in whole structures as well as the safety of foundation as the thickness and distribution of sediment under top rock layer can not support sufficiently the structure. In this study, field lateral load test of the pile for analyzing lateral behavior of the offshore wind turbine which is embedded in basalt. After calculating the subgrade resistance and the horizontal deflection from the measured strain to derive p-y curve from the lateral load test results, the subgrade resistance amplifies the error in the process of differentiation and the error of piecewise polynomial curve fitting is the smallest. In order to calculate the horizontal deflection from the measured strain, the six-order polynomial was used.