• Journal of Navigation and Port Research
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• v.32 no.3
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• pp.245-250
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• 2008

Recently the numerical prediction technique is applied to many fields, because numerical models are developed so much for last decades. The real-time ocean prediction system is one of them and is capable of providing the real-time marine information for users to promote the safety af maritime traffic and preservation of marine resources. The system is composed of observing system, data distribution system and modelling system. In this study authors develop the modelling system and show the results as preliminary study for establishing the real-time ocean prediction system in Busan port. The system test is performed only for M2 tidal modelling due to the lack qf observation data, thus a full-scale test is required in future if enough data are provided Also observing system and data distribution system will be constructed continuously in future, then service for real-time data for users will be initiated.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.387-402
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• 2005

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.768-773
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• 2011

The prediction method of future events using the time histories of velocity or pressure, etc., is a useful way for controlling various air vehicles. For example, the sensors of velocity or pressure can be used to extract the time mode coefficients of eigenmode of flow field, and then the result is applied to suppress wake or drag. The velocity information is mapped to the entire flow field, so this mapping function can be used to predict the future events based on the current information. The mapping function is composed of the huge amount of weight parameters, so the efficient way of finding these parameters is needed. Here, the neural network algorithm is studied to draw a mapping function using the number and location of velocity sensors.

• Journal of Hospice and Palliative Care
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• v.26 no.1
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• pp.1-6
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• 2023

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• Journal of Ecology and Environment
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• v.41 no.11
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• pp.310-317
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• 2017

Background: Many studies about climate-related range shift of plants have focused on understanding the relationship between climatic factors and plant distributions. However, consideration of adaptation factors, such as dispersal and plant physiological processes, is necessary for a more accurate prediction. This study predicted the future distribution of marlberry (Ardisia japonica), a warm-adapted evergreen broadleaved shrub, under climate change in relation to the dispersal ability that is determined by elapsed time for the first seed production. Results: We introduced climate change data under four representative concentration pathway (RCP 2.6, 4.5, 6.0, and 8.5) scenarios from five different global circulation models (GCMs) to simulate the future distributions (2041~2060) of marlberry. Using these 20 different climate data, ensemble forecasts were produced by averaging the future distributions of marlberry in order to minimize the model uncertainties. Then, a dispersal-limited function was applied to the ensemble forecast in order to exam the impact of dispersal capacity on future marlberry distributions. In the dispersal-limited function, elapsed time for the first seed production and possible dispersal distances define the dispersal capacity. The results showed that the current suitable habitats of marlberry expanded toward central coast and southern inland area from the current southern and mid-eastern coast area in Korea. However, given the dispersal-limited function, this experiment showed lower expansions to the central coast area and southern inland area. Conclusions: This study well explains the importance of dispersal capacity in the prediction of future marlberry distribution and can be used as basic information in understanding the climate change effects on the future distributions of Ardisia japonica.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.798-800
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• 1995

The neural network predictive controller(NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output(Neural Network Predictor) and the other one is for control the plant(Neural Network Controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and prediction error. The NNP forecasts the future output based upon the current control input and the estimated control output. The method is applied to the control of temperature in boiler systems. The proposed NNPC is compared with the other conventional control methods such as PID controller, neural network controller with specialized learning architecture, and one-step-ahead controller. The computer simulation and experimental results show that the proposed method has better performances than the other methods.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.53-61
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• 1985

The purpose of the study is to establish models of land use prediction system for development and management of land resources using remotely sensed data as well as ancillary data in the context of multi-disciplinary approach in the application to CheJoo Island. The model adopts multi-date processing techniques and is a spatial/temporal land-use projection strategy emerged as a synthesis of the probability transition model and the discriminant-annlysis model. A discriminant model is applied to all pixels in CheJoo landscape plane to predict the most likely change in land use. The probability transition model provides the number of these pixels that will convert to different land use in a gives future time increment. The synthetic model predicts the future change in land use and its volume of pixels in the landscape plane.