AKARI performed about 10,000 spectroscopic observations with the Infrared Camera (IRC) during its mission phase. These IRC observations provide unique spectroscopic data at near- and mid-infrared wavelengths for studies of the next few decades because of its high sensitivity and unique wavelength coverage. In this paper, we present the current status of the activity for improving the IRC spectroscopic data reduction process, including the toolkit and related data packages, and also discuss the goal of this project.

A data simulator and reduction package for the Devasthal Optical Telescope Integral Field Spectro- graph (DOTIFS) has been developed. Since data reduction for the Integral Field Spectrograph (IFS) requires complicated procedures due to the complex nature of the integral spectrograph, common reduc- tion procedures are usually not directly applicable for such an instrument. Therefore, the development of an optimized package for the DOTIFS is required. The data simulator observes artificial object and simulates CCD images for the instrument considering various effects; e.g. atmosphere, sky background, transmission, spectrograph optics aberration, and detector noise. The data reduction package has been developed based on the outcomes from the DOTIFS data simulator. The reduction package includes the entire processes for the reduction; pre-processing, at-fielding, and sky subtraction. It generates 3D data cubes as a final product, which users can use for science directly.

Real-time data reduction pipeline for the Korea Microlensing Telescope Network (KMTNet) was developed by Korea Astronomy and Space Science Institute (KASI). The main goal of the data reduction pipeline is to find variable objects and to record their light variation from the large amount of observation data of about 200 GB per night per site. To achieve the goal we adopt three strategic implementations: precision pointing of telescope using the cross correlation correction for target fields, realtime data transferring using kernel-level file handling and high speed network, and segment data processing architecture using the Sun-Grid engine. We tested performance of the pipeline using simulated data which represent the similar circumstance to CTIO (Cerro Tololo Inter-American Observatory), and we have found that it takes about eight hours for whole processing of one-night data. Therefore we conclude that the pipeline works without problem in real-time if the network speed is high enough, e.g., as high as in CTIO.

We have designed data processing server system to include data archiving, photometric processing and light curve analysis for KMTNet (Korea Microlensing Telescope Network). Outputs of each process are reported to the main photometric database, which manages the whole processing steps and archives the photometric results. The database is developed using ORACLE 11g Release 2 engine. It allows to select objects applying any set of criteria such as RA/DEC coordinate and Star ID, etc. We tested the performance of the database using the OGLE photometric data. The searching time for querying 70,000,000 records was under 1 second. The database is fully accessed using query forms via web page.

  이 논문은 자동차 차체 조립과정에서, 품질관리의 일환으로써, 비접촉 자동측정시스템을 이용하여 검사해야 하는 수많은 비독립적인 검사점을 다변량분산분석과 주성분분석을 이용하여 효율적으로 검사점을 감소시키는 방법을 설명하고 있다. 이 연구의 목적은 다변량분산분석, 주성분 분석의 개념과 이러한 기법들을 산업체 제조분야에서 응용하는 방법을 설명하여 독자의 사례 응용 이해를 돕는데 있으며, 또한 특히 주성분분석을 이용하여 수 많은 비독립적인 검사점을 어떻게 유

  The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it mus

Instance-based learning methods like the nearest neighbour classifier have been proven to perform well in pattern classification on many fields. Despite their high classification accuracy, they suffer from high storage requirement, computational cost and sensitivity to noise. In this paper, we present a data reduction method for classification techniques based on entropy-based partitioning and center instances. Experimental results show that the new algorithms achieve a high data reduction rate as well as classification accuracy.