• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.16-26
• /
• 2024

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.5
• /
• pp.147-156
• /
• 2013

Each field of modern society, industrialization and the development of science and technology are rapidly changing. However, as a side effect of rapid social change has caused various problems. Crime of the side effects of rapid social change is a big problem. In this paper, a model for predicting crime and Markov chains applied to the crime, predictive modeling is proposed. Markov chain modeling of the existing one with the overall status of the case determined the probability of predicting the future, but this paper predict the events to increase the probability of occurrence probability of the prediction and the recent state of the entire state was divided by the probability of the prediction. And the whole state and the probability of the prediction and the recent state by applying the average of the prediction probability and the probability of the prediction model were implemented. Data was applied to the incidence of crime. As a result, the entire state applies only when the probability of the prediction than the entire state and the last state is calculated by dividing the probability value. And that means when applied to predict the probability, close to the crime was concluded that prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.6
• /
• pp.1562-1582
• /
• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.

• Asian-Australasian Journal of Animal Sciences
• /
• v.11 no.5
• /
• pp.580-585
• /
• 1998

The study was conducted to determine if variation in protein yield can be explained by expressions of early lactation somatic cell score (SCS) and if prediction can be improved by including SCS among the predictors. A data set was prepared (n = 663,438) from Wisconsin Dairy Improvement Association (USA) records for protein yield with sample days near 20. Stepwise regression was used requiring F statistic (p < .01) for any variable to stay in the model. Separate analyses were run for 12 combinations of four seasons and first three parities. Selection of SCS variables was not consistent across seasons or lactations. Coefficients of detennination ($R^2$) ranged from 51 to 61% with higher values for earlier lactations. Including any expression of SCS in the prediction equations improved $R^2$ by < 1 %. SCS was associated with milk yield on the sample day, but the association was not strong enough to improve the prediction of future yield when other expressions of milk yield were in the model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.105-108
• /
• 2007

Recently, due to the high interests for well being life, claims about noise ad vibration are increased very rapidly. Therefore more reliable and exact evaluation of noise impact has been required. Until now in Korea, only one prediction equation for the assessment of railway environmental noise was used. But that equation has many limits and weak points because of the update was not performed from 1993. So in this study, the performance of another equation proposed by authors was compared to the former. As a result, proposed prediction equation may be used to evaluate the impact of railway noise in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.295-298
• /
• 2004

Automobile door-handle is assembled with three parts that are base, skin and cover. Over-molding processing makes assembly of the base and skin. The skin part that was made by PVC polymer has various thickness. Plastic injection molding simulation of part including significant changed thickness as skin is an inaccuracy comparing with real injection molding. To solve this problem, two commercial flow prediction software that are Moldflow MPI and MAPS 3D were used in this study. Simulations were conducted for three types mesh. Taguchi method was applied for simulation experiments. It will be need to compare with simulation results and real over-molding behavior in the near future.

• International Journal of Concrete Structures and Materials
• /
• v.10 no.sup3
• /
• pp.75-85
• /
• 2016

Large scale constructions needs to estimate a possibility for pumping concrete. In this paper, the state of the art on prediction of concrete pumping including analytical and experimental works is presented. The existing methods to measure the rheological properties of slip layer (or called lubricating layer) are first introduced. Second, based on the rheological properties of slip layer and parent concrete, models to predict concrete pumping (flow rate, pumping pressure, and pumpable distance) are explained. Third, influencing factors on concrete pumping are discussed with the test results of various concrete mixes. Finally, future need for research on concrete pumping is suggested.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.26 no.1
• /
• pp.109-116
• /
• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.19-22
• /
• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

• Health Policy and Management
• /
• v.12 no.3
• /
• pp.54-76
• /
• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.