• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.4 s.97
• /
• pp.220-228
• /
• 1999

The machining accuracy has been improved with the development of NC machine tools and cutting tools. However, it is difficult to obtain a high degree of accuracy when machining deep pocket with long end mill, since machining accuracy is mainly dependant on the stiffness of the cutting tool. To improve surface accuracy in machining deep pocket using end mill, the performance by down cut and up cut is compared theoretically and experimentally. To verify usefulness of up milling, various experiments were carried out. As a result, it is found that up milling produce more accurate surface than down milling in machining deep pocket. For effective application of up milling, various values in helix angle, number of teeth, radial depth of cut and axial depth of cut are applied in experiment.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.81-81
• /
• 2002

Classification is considered as one of the processes of extracting attributes from satellite imagery and is one of the usual functions in the commercial satellite image processing software. Accuracy of classification plays a key role in deciding the usage of its results. Many tremendous efforts far the higher accuracy have been done in such fields; training area selection, classification algorithm. Our research is one of these effort in different manners. In this research, we conduct classification using multiple satellite image data and evidential approach. We statistically consider the posterior probabilities and certainty in maximum likelihood classification and methodologically Dempster's orthogonal sums. Unfortunately, accuracy for the whole data sets has not assessed yet, but accuracy assessments in training fields and check fields shows accuracy improvement over 10% in overall accuracy and over 0.1 in kappa index.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.6 no.4
• /
• pp.173-194
• /
• 2011

This paper analyzes quantitative economic effect of accuracy improvement in location based services. This analysis is motivated from the situation that Korea Communications Commission is preparing the notification for 'location accuracy standard.' The analysis includes three level of impacts. Firm level analyzes the impact on revenue of firms. Social level additionally includes consumers' surplus in LBS industry. Lastly, economywide level includes ripple effects to another industries, which uses input-output analysis. The result of analysis shows that the economic value reaches to considerable amount and cost-benefit ratio is considerably high. The consumers' surplus is much larger than revenue increase. This result supports the intuition that many of location based services are for public interest and that the services have much externalities among firms and consumers. This means that the role of government is very important in the execution of accuracy improvement.

• KIISE Transactions on Computing Practices
• /
• v.20 no.9
• /
• pp.498-506
• /
• 2014

With the development of smart transportation, people are likely to find their paths by using navigation and map application. However, the existing retrieval system cannot output the correct retrieval result due to the inaccurate query. In order to remedy this problem, set-based POI search algorithm was proposed. Subsequently, additionally a method for measuring POI name similarity and POI search algorithm supporting classifying duplicate characters were proposed. These algorithms tried to compensate the insufficient part of the compensate set-based POI search algorithm. In this paper, accuracy improvement methods for measuring string similarity in POI data retrieval system are proposed. By formulization, similarity measurement scheme is systematized and generalized with the development of transportation. As a result, it improves the accuracy of the retrieval result. From the experimental results, we can observe that our accuracy improvement methods show better performance than the previous algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.27-35
• /
• 2021

This paper proposes a method to mitigate the Catastrophic Forgetting phenomenon in which artificial neural networks forget information on previous data. This method adjusts the Regularization strength by measuring the relationship between previous data and present data. MNIST and EMNIST data were used for performance evaluation and experimented in three scenarios. The experiment results showed a 0.1~3% improvement in the accuracy of the previous task for the same domain data and a 10~13% improvement in the accuracy of the previous task for different domain data. When continuously learning data with various domains, the accuracy of all previous tasks achieved more than 50% and the average accuracy improved by about 7%. This result shows that neural network learning can be properly performed in a CL environment in which data of different domains are successively entered by the method of this paper.

• Transportation Technology and Policy
• /
• v.8 no.6
• /
• pp.99-105
• /
• 2011

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2009.10a
• /
• pp.85-88
• /
• 2009

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.587-607
• /
• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Korean Journal of Agricultural Science
• /
• v.49 no.3
• /
• pp.431-440
• /
• 2022

Hanwoo cattle are a unique and historical breed in Korea that have been genetically improved and maintained by the national evaluation and selection system. The aim of this study was to provide information that can help improve the accuracy of the estimated breeding values in Hanwoo cattle by showing the difference between the imputation reference chip platforms of genomic data and the scaling factor of the genetic relationship matrix (GRM). In this study, nine sets of data were compared that consisted of 3 reference platforms each with 3 different scaling factors (-0.5, 0 and 0.5). The evaluation was performed using MTG2.0 with nine different GRMs for the same number of genotyped animals, pedigree, and phenotype data. A five multi-trait model was used for the evaluation in this study which is the same model used in the national evaluation system. Our results show that the Hanwoo custom v1 platform is the best option for all traits, providing a mean accuracy improvement by 0.1 - 0.3%. In the case of the scaling factor, regardless of the imputation chip platform, a setting of -1 resulted in a better accuracy increased by 0.5 to 1.6% compared to the other scaling factors. In conclusion, this study revealed that Hanwoo custom v1 used as the imputation reference chip platform and a scaling factor of -0.5 can improve the accuracy of the estimated breeding value in the Hanwoo population. This information could help to improve the current evaluation system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.11
• /
• pp.2653-2660
• /
• 2013

Representative sleep disorders represent sleep apnea and snoring. Although researches to diagnose sleep disorders as solutions for these problems are going on, the original researches only diagnose and measure sleep disorders but ones to find out the reasons are not activated much. Therefore, to reinforce this, this paper suggests sleep disorder improvement system based on multi-sensor. To design the system proposed in this paper, the entire system's structure was found and data's flow was planned. To ensure that the system works, mobile application and user interface was built based on Android. To test the results on accuracy of sleep disorders improvement system based on ontology using multi sensor built and planned in this paper, a scenario was written. As a result of testing inference results' accuracy changing factor values of sleep disorders following test scenario, proposed sleep disorders improvement system's accuracy was checked.