• Agricultural and Biosystems Engineering
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• 제2권1호
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• pp.15-23
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• 2001

On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows’voices. The order of this filter was determined by examining the voice characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The characteristics of the amplitude envelope of the voice signal was investigated by analyzing the sequence of the short time variance both in time and frequency domains, and the new parameters were defined. One of the coefficients o the linear prediction filter generating the voice signal, the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the short time variance and the coefficients of the linear prediction filter generating the sequence of the short time variance of the voice signal can differentiate the two cows.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• 한국대기환경학회지
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• 제15권5호
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• pp.545-554
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• 1999

We would like to develop a short-term model to predict the time-related concentration of ozone whose reaction mechanism is complex. The paper targets Seoul where an ozone alert system has recently been employed. In order to develop a short-term prediction model for ozone, we suggested the Ozone Peak Indicator(OPI), an equivalent of the potential daily maximum ozone concentration, with precursors being the only limiting factor, and we calculated the Ozone Peak Indicarot as OPI={$rac{(O_3)_{max}cdot(H_{eH})_{max}(Rad)_{max}$ to preclude the influence of mixing height and solar radiation on the daily maximum ozone concentration. The OPI on the day of the prediction is to be calcultated by using the relation between OPI and the initial value of precursors. The basic prediction formula for time-related ozone concentration was established as $O_3(1)={(OPI)cdot Rad(t-2)H_{eH}}$, using the OPI, solar radiation two hours before prediction and mixing height. We developed, along with the basic formula for predicting photochemical oxidants, "SEOM"(Seoul Empirical Oxidants Model), a Fortran program that helps predict solar radiation and mixing height needed in the prediction of ozone pollution. When this model was applied to Seoul and an analysis of the correlation between the observed and the predicted ozone concentrations was made through SEOM, there appeared a very high correlation, with a coefficient of 0.815. SEOM can be described as a short-term prediction model for ozone concentration in large cities that takes into account the initial values of precursors, and changes in solar radiation and mixing height. SEOM can reflect the local characteristics of a particular and region can yield relatively good prediction results by a simple data input process.t process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• 산업경영시스템학회지
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• 제43권4호
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• 대기
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• 제17권4호
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• 정보처리학회논문지A
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• 제16A권5호
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• pp.347-356
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• 2009

정교한 분기 예측기의 설계는 오늘날의 프로세서 성능 향상에 중요한 역할을 하게 되었다. 분기 예측의 정확도가 더욱 더 중요해 지면서 정확도의 향상을 위한 다수의 기법들이 제안되었지만, 기존의 연구들은 예측 지연 시간을 간과하는 경향이 있었다. 본 논문에서는 예측 지연 시간 문제를 해결하고자 조기 예측 기법 (ESP, Early Start Prediction)을 제안한다. 조기 예측 기법은 분기 예측에 있어서 활용되는 분기 명령어의 주소 대신 그것과 일대일 대응이 되는 기본 블록의 시작 주소 (BB_SA, Basic Block Start Address)를 이용한다. 즉, 분기 명령어의 주소가 사용되는 기존의 환경에서, BB_SA를 활용하여 조기 예측을 시작함으로써, 예측 지연 시간을 숨긴다. 또한 제안된 기법은 짧은 간격 숨김 기법(short interval hiding technique)을 통해 보다 더 나은 성능 향상을 기대할 수 있다. 실험 결과 본 논문에서 제안된 기법은 예측 지연 시간을 줄임으로써, 예측 지연 시간이 1 사이클인 이상적인 분기 예측기의 성능에 0.25% 이내로 근접한 IPC 결과를 얻었다. 또한 기본 블록의 시작주소와 분기 명령어 사이에 짧은 간격을 가질 경우에 대한 개선 방법을 추가적으로 적용시킬 경우, 기존의 방식과 비교하여 평균 4.2%, 최대 10.1%의 IPC 향상을 가져왔다.

• 콘크리트학회논문집
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• 제12권4호
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• pp.69-78
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• 2000

Creep of concrete is the most dominating factor affecting time-dependent deformations of concrete structures. Especially, creep deformation for design and construction in prestressed concrete structures should be predicted accurately because of its close relation with the loss in prestree of prestressed concrete structures. Existing creep-prediction models for special applications contain several impractical factors such as the lack ok accuracy, the requirement of long-term test and the lack of versatility for change in material properties, ets., which should be improved. In order to improve those drawbacks, a methodology to modify the creep-prediction equation specified in current Korean concrete structures design standard (KCI-99), which underestimates creep of concrete and does not consider change of condition in mixture design, is proposed. In this study, short-term creep tests were carried out for early-age concrete within 28 days after loading and their test results on influencing factors in the equation are analysed. Then, the prediction equation was modified by using the early-age creep test results. The modified prediction equation was verified by comparing their results with results obtained from long-term creep test.

• Smart Structures and Systems
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• 제33권5호
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• 품질경영학회지
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• 제47권3호
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.