• Journal of Information Processing Systems
• /
• v.18 no.6
• /
• pp.719-728
• /
• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.321-328
• /
• 2024

This paper designs a disease prediction algorithm to diagnose migraine among the types of diseases in advance by learning algorithms using machine learning-based time series analysis. This study utilizes patient data statistics, such as electroencephalogram activity, to design a prediction algorithm to determine the onset signals of migraine symptoms, so that patients can efficiently predict and manage their disease. The results of the study evaluate how accurate the proposed prediction algorithm is in predicting migraine and how quickly it can predict the onset of migraine for disease prevention purposes. In this paper, a machine learning algorithm is used to analyze time series of data indicators used for migraine identification. We designed an algorithm that can efficiently predict and manage patients' diseases by quickly determining the onset signaling symptoms of disease development using existing patient data as input. The experimental results show that the proposed prediction algorithm can accurately predict the occurrence of migraine using machine learning algorithms.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.622-624
• /
• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.185-194
• /
• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.647-652
• /
• 2000

This paper proposes a collision warning/avoidance algorithm using a trajectory prediction method. This algorithm is based on 2-dimensional kinematics and the Kalman filter has been used to obtain the information of the object vehicle. This algorithm has been investigated via computer simulation and showed a good trajectory prediction performance. The proposed collision warning/avoidance algorithm would enhanced driver acceptance for a collision warning/avoidance system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.16 no.11
• /
• pp.1035-1043
• /
• 2004

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The feedforward prediction algorithm and the cascade control algorithm were developed to regulate the CO level in a tunnel. The feedforward prediction algorithm composed of the traffic estimation algorithm and the CO density prediction algorithm, and the cascade control algorithm composed of the jet fan control algorithm and the air velocity setpoint algorithm. The verification of control algorithms was carried out by dynamic models developed from the actual tunnel data. The simulation results showed that control algorithms developed for this study were effective for the control of the tunnel ventilation system.

• Proceedings of the Korean Information Science Society Conference
• /
• 1998.10c
• /
• pp.360-362
• /
• 1998

In the original mixtures of experts framework, the parameters of the network are determined by gradient descent, which is naturally slow. In [2], the Expectation-Maximization(EM) algorithm is used instead, to obtain the network parameters, resulting in substantially reduced training times. This paper presents the new EM algorithm for prediction. We show that an Efficient training algorithm may be derived for the HME network. To verify the utility of the algorithm we look at specific examples in time series prediction. The application of the new EM algorithm to time series prediction has been quiet successful.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.397-400
• /
• 2016

In this paper, we propose an efficient algorithm for computing architecture for high-performance Inter Prediction SAD HEVC encoder. HEVC Motion Estimation (ME) of the Inter Prediction is a process for searching for the currently high prediction block PU and the correlation in the interpolated reference picture in order to remove temporal redundancy. ME algorithm uses full search(FS) or fast search algorithm. Full search technique has the guaranteed optimal results but has many disadvantages which include high calculation and operational time due to the motion prediction with respect to all candidate blocks in a given search area. Therefore, this paper proposes a new algorithm which reduces the computational complexity by reusing the SAD operation in full search to reduce the amount of calculation and computational time of the Inter Prediction. The proposed algorithm is applied to an HEVC standard software HM16.12. There was an improved operational time of 61% compared to the traditional full search algorithm, BDBitrate was decreased by 11.81% and BDPSNR increased by about 0.5%.

• Journal of Information Processing Systems
• /
• v.17 no.2
• /
• pp.213-226
• /
• 2021

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.6
• /
• pp.730-735
• /
• 2006

This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.