• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.1-6
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• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.23-30
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• 2014

Coastal aquifer in Muan, Jeonnam, has experienced heavy seawater intrusion caused by the extraction of a substantial amount of groundwater for the agricultural purpose throughout the year. It was observed that groundwater level dropped below sea level due to heavy pumping during a dry season, which could accelerate seawater intrusion. Therefore, water level needs to be monitored and managed to prevent further seawater intrusion. The purpose of this study is to develop the autoregressive-cross-regressive (ARCR) models that can predict the present or future groundwater level using its own previous values and pumping events. The ARCR model with pumping and water level data of the proceeding five hours (i.e., the model order of five) predicted groundwater level better than that of the model orders of ten and twenty. This was contrary to expectation that higher orders do increase the coefficient of determination ($R^2$) as a measure of the model's goodness. It was found that the ARCR model with order five was found to make a good prediction of next 48 hour groundwater levels after the start of pumping with $R^2$ higher than 0.9.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.31 no.3
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• pp.185-193
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• 2021

Objectives: Inhalation toxicity testing of chemical substances to identify carcinogenicity requires a long time and considerable cost, so the selection of test candidates is a very important aspect. This study was performed to determine optimal procedures for selecting carcinogenic inhalation toxicity test substances as conducted by the Occupational Safety and Health Research Institute (OSHRI). Methods: At the beginning, a database was constructed containing complex information such as usage amount, hazard, carcinogenicity prediction, and testability in order to select chemicals requiring carcinogenicity testing. Selection of test substances was carried out with priority given to usage, carcinogenicity, and testability. Results: Chemicals used in large quantities in industrial fields and strongly suspected of carcinogenicity were winnowed down to 12 substances, and these substances were scheduled for future testing by OSHRI. Conclusions: For the stable and reliable operation of carcinogenicity tests as conducted by OSHRI, this study standardized the procedures for selecting carcinogenicity test substances and suggested the introduction of various carcinogenicity prediction techniques.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.596-601
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• 2013

In the current pavement construction practice, the state agencies traditionally determine the quality of the as-constructed pavement mix based on individual mixture material parameters (e.g., air voids, cement or asphalt content, aggregate gradation, etc.) and consider these parameters as key variables to influence payment schedule to the contractors and the present and future quality of the as-constructed mixture. A set of empirically pre-determined pay adjustment schedule for each parameter that was differently developed and being used by the individual agencies is then applied to a given project, in order to judge whether each parameter conforms to the designated specifications and consequently the contractor may either be rewarded or penalized in accordance with the payment schedule. With an improved quality assurance system, the Performance Related Specification, the individual parameters are not utilized as a direct judgment factor; rather, they become independent variables within a performance prediction function which is directly used to predict the performance. The quantified performance based on the prediction model is then applied to evaluate the pavement quality. This paper presents the brief history of the quality assurance in asphalt pavement construction including the Performance Related Specifications, statistical performance models in terms of fatigue and rutting distresses, as an example of the performance prediction models, and envisions the possibilities as to how this Performance Related Specification could be utilized in other infrastructures construction quality assurance.

• Korea Trade Review
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• v.47 no.6
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• pp.291-305
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• 2022

In this study, the volume of Pohang Port was predicted. All cargo of Pohang port, iron ore, steel, and bituminous coals were selected as prediction targets. SARIMA, Prophet, and Neural Prophet were used as analysis methods. The predictive power of each model was verified, and a predictive model with high performance was used to predict the volume of goods in Pohang port. As a result of the analysis, it was found that Neural Prophet showed the highest performance in all predictive power. As a result of predicting the future volume of goods until August 2027 using Neural Prophet, it was found that the volume of all items in Pohang port was decreasing. In particular, it was analyzed that the decline in steel cargo was steep. In order to increase the volume of cargo at Pohang port, it is necessary to diversify the cargo handled at Pohang port and check the policy of increasing the volume of cargo.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.46-51
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• 2011

High-rise buildings are widely being constructed in the Middle-East, South-East, and East Asia. These buildings are usually willing to stand for the landmark of the region and, therefore, exhibit some extraordinary features such as super-tall height, elevation set-backs, overhangs, or free-form exterior surface, all of which makes the construction difficult, complex, and even unsafe at some construction stages. In addition to the elaborately planned construction sequence, prediction and monitoring of building's movement during construction and after completion are required for precise and safe construction. This is often called the Building Movement Control during construction. This study describes Building Movement Control of the KLCC Tower, a 58-story office building currently being built right next to the famous PETRONAS Twin Towers. The main items of the Building Movement Control for the KLCC Tower are axial shortening and verticality. Preliminary prediction of these items are already carried out by the structural design team but more accurate prediction based on construction stage analysis and combined with time-dependent material testing, field monitoring, and site survey is done by the main contractor. As of September 2010, the Tower is under construction at level 30, where the plan abruptly changes from rectangle to triangle. Findings and troubleshooting until the current construction stage are explained in detail and implementations are suggested for future applications.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.42-49
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• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.