• Korean Journal of Remote Sensing
• /
• v.33 no.5_1
• /
• pp.547-557
• /
• 2017

In this paper, a new spectral unmixing based target detection algorithm is proposed which adopted Iterative Error Analysis as a tool for extraction of background endmembers by using the target spectrum to be detected as initial endmember. In the presented method, the number of background endmembers is automatically decided during the IEA by stopping the iteration when the maximum change in abundance of the target is less than a given threshold value. The proposed algorithm does not have the dependence on the selection of image endmembers in the model-based approaches such as Orthogonal Subspace Projection and the target influence on the background statistics in the stochastic approaches such as Matched Filter. The experimental result with hyperspectral image data where various real and simulated targets are implanted shows that the proposed method is very effective for the detection of both rare and non-rare targets. It is expected that the proposed method can be effectively used for mineral detection and mapping as well as target object detection.

• Journal of the Korean earth science society
• /
• v.42 no.6
• /
• pp.623-631
• /
• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2004.10a
• /
• pp.252-255
• /
• 2004

Design modification of the stamping die for the upper member of a front end module carrier is carried out in order to improve the feeling qualify of the final product. The small inferiority induced by wrinkling near the wall of the FEM upper member has been inspected after the draw-forming process. The finite element simulation shows that the excess metal is developed by the irregular contact of the blank the tool and it remains after the final stroke. This paper proposes two guidelines for the modification: one is to add the draw-bead; and the other is to modify the tool shape such as the forming shape at the wall. Simulation results show that the proposed guidelines both guarantee the improved feeling quality.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.3
• /
• pp.104-111
• /
• 2014

In this paper, we deal with robotic automation for assembling car brake modules. A car brake module is comprises of a torque member, two brake pads, and two pad liners. In the assembly process, brake pads and pad liners are needed to be inserted in a torque member. If we use a typical robotic hand for the assembly, task time takes too long. So, we propose two methods. The first method is to use an end effector that has five grippers capable of gripping five assembly parts. In the first method we attached the implemented end effector to a conventional 6 degrees of freedom industrial manipulator and performed the bake module assembly task. Experimental results show that the task time is remarkably reduced. The brake module assembly task needs the robot to change its orientation frequently, so, in the second method, we added one degree of freedom to the end effector that is used in the first method. By attaching it to a conventional 6 degrees of freedom industrial manipulator, we composed a 7 degrees of freedom redundant manipulator. A redundant manipulator has the advantage of flexible manipulation so the robot can change its orientation easily and can perform assembly task very fast. Experimental results show that the second method dramatically reduce whole task time for brake module assembly.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.253-258
• /
• 2014

In smartphone sensing which monitors various social phenomena of the individuals by using embedded sensors, managing metadata is one of the important issue to process large-scale data, improve the data quality, and share collected data. In this paper, we proposed a document clustering scheme for the large-scale metadata management architecture which is designed as a hybrid back-end consisting of a cluster head and member nodes to reduce the server-side overhead. we also verified that the proposed scheme is more efficient than the distance based clustering scheme in terms of the server-side overhead through simulation results.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2006.11a
• /
• pp.409-412
• /
• 2006

빠르게 변화하는 시장 및 기업 간의 경쟁 환경에서 기업의 의사결정권자들은 보다 신속한 의사결정을 내려야 하고, 의사결정의 위험을 최소화해야 하는 무거운 중책이 새롭게 추가 되었다. 이에 비즈니스 인텔리전스는 주로 고차원의 분석을 필요로 하는 시장분석가나, IT조직의 소수 멤버들을 위한 여러가지 BI툴을 제공 하였다. 과거의 비즈니스 인텔리전스 제품 가격이나 솔루션 구축에 따른 비용은 사용자가 적음에도 불구하고 만만치 않았다. 최근 들어, 환경 변화와 사용자의 요구의 다양성에 따라 기업 내의 많은 사용자들은 데이터를 분석하길 원한다. 또한 기업의 업무를 보다 원할히 진행시키기 위해 많은 의사결정이 하부조직에서 이루어지고 있으며, 그에 따라 현장 직원들에게 의사결정에 대한 책임이 부과되고 있다. 또한 BI 제품의 데이터 저장소의 기술차이에 따라 호환성이 떨어지는 플랫폼을 기반으로 보고서를 작성하였다. 이에 본 논문에서는 XMLA 웹서비스를 이용하여 다중 플랫폼을 지원하는 자바 기반의 리포팅 툴과 연동 가능한 OLAP/데이터마이닝 비즈니스 인텔리전스 툴을 제안한다. 구현 시스템은 다양한 형태로 표현 가능한 프론트엔드 툴을 제공함으로써 최종 사용자의 편의성을 제공하며 BI의 기능을 지원한다.

• Korean Journal of Remote Sensing
• /
• v.21 no.6
• /
• pp.457-468
• /
• 2005

Increasing of impervious surface resulting from urban development has negative impacts on urban environment. Therefore, it is absolutely necessary to estimate and quantify the temporal and spatial aspects of impervious area for study of urban environment. In many cases, conventional image classification methods have been used for analysis of impervious surface fraction. However, the conventional classification methods have shortcoming in estimating impervious surface. The DN value of the each pixel in imagery is mixed result of spectral character of various objects which exist in surface. But conventional image classification methods force each pixel to be allocated only one class. And also after land cover classification, it is requisite to additional work of calculating impervious percentage value in each class item. This study used the spectral mixture analysis to overcome this weakness of the conventional classification methods. Four endmembers, vegetation, soil, low albedo and high albedo were selected to compose pure land cover objects. Impervious surface fraction was estimated by adding low albedo and high albedo. The study area is the Tanchon watershed which has been rapidly changed by the intensive development of housing. Landsat imagery from 1988, 1994 to 2001 was used to estimate impervious surface fraction. The results of this study show that impervious surface fraction increased from $15.6\%$ in 1988, $20.1\%$ in 1994 to $24\%$ in 2001. Results indicate that impervious surface fraction can be estimated by spectral mixture analysis with promising accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.12
• /
• pp.2885-2890
• /
• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. Raspberrypi is a linux system which can be used as a sensor node. Pi can be used to construct IP based Hadoop clusters. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time. The experimentation showed that with more execution power and memory volume, Pi could be appropriate for a member node of the cluster, accomplishing precise classification for sensor localization using machine learning.