• Journal of Advanced Navigation Technology
• /
• v.11 no.3
• /
• pp.252-258
• /
• 2007

There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

• Journal of the Korea Society for Simulation
• /
• v.30 no.1
• /
• pp.75-85
• /
• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.113-115
• /
• 2015

In this paper, we proposed an algorithm based on movement prediction using rate of change of the attribute information of nodes what is called WRC(Weighted Rate Control) in delay tolerant networks(DTNs). Existing DTN routing algorithms based on movement prediction communicate by selecting relay nodes increasing connectivity with destination node. Thus, because the mobile nodes are in flux, the prediction algorithms that do not reflect the newest attribute information of node decrease reliability. In this paper, proposed algorithm approximate speed and direction of attribute information of node and analysis rate of change of attribute information of node. Then, it predict movement path of node using proposed weight. As the result, proposed algorithm show that network overhead and transmission delay time decreased by predicting movement path of node.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.9 no.2
• /
• pp.81-86
• /
• 2014

In the scalable video coding standard, inter-layer prediction based on the coding information of the base layer was adopted to increase the coding performance. This prediction tool results in new syntax elements called motion_prediction_flag (mPF) and residul_prediction_flag(rPF), which are carried to notify the motion vector predictor (MVP) and reference block required in the motion compensation of the decoder. In this paper, an efficient coding method for mPF is proposed to enhance coding efficiency of the salable video coding standard. Through an analysis on the transmission of mPF based on the relationship between the MVPs, we discover the conditions where mPF is unnecessary at the decoder and suggest a modified rate-distortion (RD) cost function to make RD optimization more effective. Simulation results show that the proposed method offers BD rate savings of approximately 1.4%, compared with the conventional SVC standard.

• Architectural research
• /
• v.24 no.1
• /
• pp.1-11
• /
• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

• Journal of Communications and Networks
• /
• v.11 no.2
• /
• pp.104-114
• /
• 2009

In this paper, a technique is proposed that enables secondary users to evaluate channel availability in cognitive radio networks. Here, secondary users estimate the utilization of channels via predicting the traffic pattern of primary user, and select a proper channel for radio transmission. The proposed technique reduces the channel switching rate of secondary users (the rate of switching from one channel to another) and the interference on primary users, while maintaining a reasonable call blocking rate of secondary users.

• Corrosion Science and Technology
• /
• v.23 no.4
• /
• pp.324-333
• /
• 2024

Atmospheric corrosion poses a significant threat to durability of metallic materials and safety of structures, making precise prediction of corrosion rates crucial in industrial and engineering settings. Understanding the exact rate of corrosion is essential. However, accurate inclusion of various environmental factors that can influence atmospheric corrosion in the calculation of corrosion rate is a complex challenge. This study introduces a physics-based model that incorporates electrochemical methods and considers active surface area affected by surface contaminants to estimate atmospheric corrosion rate of carbon steel. The model can evaluate corrosion levels using key factors such as chloride deposition rate, relative humidity, and the presence of surface particles. By integrating these considerations, this model moves beyond empirical estimations, providing a more stable prediction of corrosion rate that is less susceptible to environmental variations. This model provides a robust tool for defense applications, offering precise insights into the dynamics of atmospheric corrosion that could enhance the maintenance and safety of weapon systems.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2056-2059
• /
• 2003

The rate control is very important to solve the difficulties arising from bit-rate on transmission through channel and to improve video quality. It is very important to point out that the amount of output bit obtained the encoding process using rate controller brings many problems on the transmission of channels and furthermore output bitstream decoded affects directly on the visual quality of displayed subject. In this paper, the effective rate control algorithm by rate-distortion modeling using MPEG-4 encoder is proposed. The proposed rate control has applied different weighting by VOP prediction type and even in the same VOP prediction type, the predicted reference allocates more bit. Through these bit allocation the minimization of distortion can be achieved preventing propagation of quantization error The amount of saved bitstream obtained by the proposed algorithm in this thesis is allocated to I-VOP using region of interest(ROI) selective enhancement on the next GOV encoding process and this process brought the improvement of visual quality.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.11C
• /
• pp.1077-1084
• /
• 2006

This paper proposed a fast algorithm to reduce the amount of calculation for inter prediction which takes a great deal of the operational time in H.264/AVC. This algorithm decides a search range according to the direction of predicted motion vector, and then performs an adaptive spiral search for the candidates with JM(Joint Model) FME(Fast Motion Estimation) which employs the rate-distortion optimization(RDO) method. Simultaneously, it decides a threshold cost value for each of the variable block sizes and performs the motion estimation for the variable search ranges with the threshold. These activities reduce the great amount of the complexity in inter prediction encoding. Experimental results by applying the proposed method .to various video sequences showed that the process time was decreased up to 80% comparing to the previous prediction methods. The degradation of video quality was only from 0.05dB to 0.19dB and the compression ratio decreased as small as 0.58% in average. Therefore, we are sure that the proposed method is an efficient method for the fast inter prediction.

• Journal of KIISE:Information Networking
• /
• v.34 no.3
• /
• pp.156-167
• /
• 2007

In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.