• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.13 no.9
• /
• pp.836-844
• /
• 2001

For the radiant floor heating system, the possibility of suboptimal prediction control was investigated by computer simulation and experiment. For this study, TRANSYS program was used and an experimental facility consisting of two rooms (3$\times$4.4$\times$2.8m) was built. The facility enabled simultaneous comparison of two different control strategies which implemented in a separate room. Results showed that outdoor air temperature prediction control was superior to the conventional outdoor air temperature compensation control for radiant floor heating system. However, more research for fine prediction of outside air temperature was required in the future.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.40 no.1
• /
• pp.43-48
• /
• 1998

In order to provide basic information for the estimation of evapotranspiration for grass (Joycia Japonica), both field lysimeter experiment and model prediction were performed to estimate daily ET Various methods were used to predict daily reference crop ET and crop coefficients. Measured mean daily ET during the 1997 growing season was 4.5mm Model predicted mean daily ET during the 1997 growing season varied from 3.6 to 4.7mm depending on the prediction model Crop coefficients varied from 0.96 to 1.27 depending on the prediction model Comparison of the seven reference crop ET prediction methods used in this study shows that the Penman-Monteith method gave the smallest ET while the Hargreaves method gave the largest ET. The crop coefficient by the corrected Penman method was 1.03, which is closest to 1.0, suggesting that this method may he the best prediction method.

• Journal of Information Technology Services
• /
• v.14 no.4
• /
• pp.121-135
• /
• 2015

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.9
• /
• pp.81-86
• /
• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.104-109
• /
• 2021

In the aftermath of the global pandemic that started in 2019, there have been many changes in the import/export and supply/demand process of agricultural products in each country. Amid these changes, the necessity and importance of each country's food self-sufficiency rate is increasing. There are several conditions that must accompany efficient agricultural activities, but among them, temperature is by far one of the most important conditions. For this reason, the need for high-accuracy climate data for stable agricultural activities is increasing, and various studies on climate prediction are being conducted in Korea, but data that can visually confirm climate prediction data for farmers are insufficient. Therefore, in this paper, we propose an artificial intelligence-based temperature prediction algorithm that can predict future temperature information by collecting and analyzing temperature data of farms in Gyeonggi-do in Korea for the last 10 years. If this algorithm is used, it is expected that it can be used as an auxiliary data for agricultural activities.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.247-275
• /
• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

• Journal of information and communication convergence engineering
• /
• v.19 no.4
• /
• pp.241-247
• /
• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.88-97
• /
• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1176-1179
• /
• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Journal of Advanced Navigation Technology
• /
• v.21 no.4
• /
• pp.365-370
• /
• 2017

This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.