• Journal of Internet Computing and Services
• /
• v.14 no.4
• /
• pp.25-33
• /
• 2013

The procedure of the arbitration which is the tag collision is essential because the multiple tags response simultaneously in the same frequency to the request of the Reader. This procedure is known as Anti-collision and it is a key technology in the RFID system. In this paper, we propose the 4-Bit Pattern Slot Allocation(4-BPSA) algorithm for the high-speed identification of the multiple tags. The proposed algorithm is based on the tree algorithm using the time slot and identify the tag quickly and efficiently through accurate prediction using the a slot as a 4-bit pattern according to the slot allocation scheme. Through mathematical performance analysis, We proved that the 4-BPSA is an O(n) algorithm by analyzing the worst-case time complexity and the performance of the 4-BPSA is improved compared to existing algorithms. In addition, we verified that the 4-BPSA is performed the average 0.7 times the query per the Tag through MATLAB simulation experiments with performance evaluation of the algorithm and the 4-BPSA ensure stable performance regardless of the number of the tags.

• Journal of KIISE:Computer Systems and Theory
• /
• v.37 no.3
• /
• pp.172-183
• /
• 2010

Virtual memory systems (VM) reduce disk I/Os caused by page faults using page prefetching, which reads pages together with a desired page at a page fault in a single disk I/O. Operating systems including 4.4BSD attempt to prefetch as many pages as possible at a page fault regardless of page access patterns of applications. However, such an approach increases a disk access time taken to service a page fault when a high portion of the prefetched pages is not referenced. More seriously, the approach can cause the memory pollution, a problem that prefetched pages not to be accessed evict another pages that will be referenced soon. To solve these problems, we propose an adaptive page prefetching control scheme (APC), which periodically monitors access patterns of prefetched pages in a process unit. Such a pattern is represented as the ratio of referenced pages among prefetched ones before they are evicted from memory. Then APC uses the ratio to adjust the number of pages that 4.4BSD VM intends to prefetch at a page fault. Thus APC allows 4.4BSD VM to prefetch a proper number of pages to have a better effect on reducing disk I/Os, though page access patterns of an application vary in runtime. The experiment of our technique implemented in FreeBSD 6.2 shows that APC improves the execution times of SOR, SMM, and FFT benchmarks over 4.4BSD VM by up to 57%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.239-242
• /
• 1996

본 논문에서는 BP(Back Propagation)에 비해서 빠른 학습시간과 다른 경쟁학습 신경회로망 알고리즘에 비해서 비교적 우수한 성능으로 패턴인식 등에 많이 이용되고 있는 LVQ(Learning Vector Quantization) 알고리즘의 성능을 향상시키기 위한 방법을 논의하고자 한다. 일반적으로 LVQ는 음(negative)의 학습을 하기 때문에 초기 가중치가 제대로 설정되지 않으면 발산할 수 있다는 단점이 있으며, 경쟁학습 계열의 신경망이기 때문에 출력 층의 뉴런 수에 따라 성능에 큰 영향을 받는다고 알려져 있다.[1]. 지도학습 형태를 지닌 LVQ의 경우에 학습패턴이 n개의 클래스를 가지고, 각 클래스 별로 학습패턴의 수가 같은 경우에 일반적으로 전체 출력뉴런에 대해서 (출력뉴런수/n)개의 뉴런을 각 클래스의 목표(desired) 클러스터로 할당하여 학습을 수행하는데, 본 논문에서는 각 클래스에 동일한 수의 출력뉴런을 할당하지 않고, 학습데이터에서 각 클래스의 분산을 추정하여 각 클래스의 분산을 추정분산에 비례하게 목표 출력뉴런을 할당하고, 초기 가중치도 추정분산에 비례하게 각 클래스의 초기 임의 위치 입력백터를 사용하여 학습을 수행하는 방법을 제안한다. 본 논문에서 제안하는 방법은 분류하고자 하는 데이터에 대해서 필요한 최적의 출력뉴런 수를 찾는 것이 아니라 이미 결정되어 있는 출력뉴런 수에 대해서 각 클래스에 할당할 출력 뉴런 수를 데이터의 추정분산에 의해서 결정하는 것으로, 추정분산이 크면 상대적으로 많은 출력 뉴런을 할당하고 작으면 상대적으로 적은 출력뉴런을 할당하고 초기 가중치도 마찬가지 방법으로 결정하며, 이렇게 하면 정해진 출력뉴런 개수 안에서 각 클래스 별로 분류의 어려움에 따라서 출력뉴런을 할당하기 때문에 미학습 뉴런이 줄어들게 되어 성능의 향상을 기대할 수 있으며, 실험적으로 제안된 방법이 더 나은 성능을 보임을 확인했다.initially they expected a more practical program about planting than programs that teach community design. Many people are active in their own towns to create better environments and communities. The network system "Alpha Green-Net" is functional to support graduates of the course. In the future these educational programs for citizens will becomes very important. Other cities are starting to have their own progrms, but they are still very short term. "Alpha Green-Net" is in the process of growing. Many members are very keen to develop their own abilities. In the future these NPOs should become independent. To help these NPOs become independent and active the educational programs should consider and teach about how to do this more in the future.단하였는데 그 결과, 좌측 촉각엽에서 제4형의 신경연접이 퇴행성 변화를 나타내었다. 그러므로 촉각의 지각신경세포는 뇌의 같은 족 촉각엽에 뻗어와 제4형 신경연접을 형성한다고 결론되었다.$/ 값이 210 $\mu\textrm{g}$/$m\ell$로서 효과적인 저해 활성을 나타내었다 따라서, 본 연구에서 빈

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.2
• /
• pp.1-9
• /
• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of Korea Water Resources Association
• /
• v.44 no.4
• /
• pp.295-304
• /
• 2011

For effective water quality management, it is necessary to secure reliable water quality information. There are many variables that need to be included in a comprehensive practical monitoring network : representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples, especially sampling location is considered to be the most important issues. Until now, monitoring network design for water quality management was set according to the qualitative judgments, which is a problem of representativeness. In this paper, we propose network design system for optimal water quality monitoring using the scientific statistical techniques. Network design system is made based on the SAS program of version 9.2 and configured with simple input system and user friendly outputs considering the convenience of users. It applies to Excel data format for ease to use and all data of sampling location is distinguished to sheet base. In this system, time plots, dendrogram, and scatter plots are shown as follows: Time plots of water quality variables are graphed for identifying variables to classify sampling locations significantly. Similarities of sampling locations are calculated using euclidean distances of principal component variables and dimension coordinate of multidimensional scaling method are calculated and dendrogram by clustering analysis is represented and used for users to choose an appropriate number of clusters. Scatter plots of principle component variables are shown for clustering information with sampling locations and representative location.

• Journal of KIISE:Information Networking
• /
• v.34 no.3
• /
• pp.203-217
• /
• 2007

In this paper, a performance evaluation model of Networks with the multiple-buffered crossbar switches is proposed and examined. Buffered switch technique is well known to solve the data collision problem of the switch networks. The characteristic of a network with crossbar switches is determined by both the connection pattern of the switches and the limitation of data flow in a each switch. In this thesis, the evaluation models of three different networks : Multistage interconnection network, Fat-tree network, and other ordinary communication network are developed. The proposed evaluation model is developed by investigating the transfer patterns of data packets in a switch with output-buffers. Two important parameters of the network performance, throughput and delay, are evaluated. The proposed model takes simple and primitive switch networks, i.e., no flow control and drop packet, to demonstrate analysis procedures clearly. It, however, can not only be applied to any other complicate modern switch networks that have intelligent flow control but also estimate the performance of any size networks with multiple-buffered switches. To validate the proposed analysis model, the simulation is carried out on the various sizes of networks that uses the multiple buffered crossbar switches. It is shown that both the analysis and the simulation results match closely. It is also observed that the increasing rate of Normalized Throughput is reduced and the Network Delay is getting bigger as the buffer size increased.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.80-91
• /
• 2010

As the volume of image data increases dramatically, its good organization of image data is crucial for efficient image retrieval. Clustering is a typical way of organizing image data. However, traditional clustering methods have a difficulty of requiring a user to provide the number of clusters as a parameter before clustering. In this paper, we discuss an approach for clustering image data that does not require the parameter. Basically, the proposed approach is based on Cross-Association that finds a structure or patterns hidden in data using the relationship between individual objects. In order to apply Cross-Association to clustering of image data, we convert the image data into a graph first. Then, we perform Cross-Association on the graph thus obtained and interpret the results in the clustering perspective. We also propose the method of hierarchical clustering and the method of outlier detection based on Cross-Association. By performing a series of experiments, we verify the effectiveness of the proposed approach. Finally, we discuss the finding of a good value of k used in k-nearest neighbor search and also compare the clustering results with symmetric and asymmetric ways used in building a graph.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.27-36
• /
• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• Journal of KIISE:Software and Applications
• /
• v.29 no.1_2
• /
• pp.80-90
• /
• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Proceedings of the Materials Research Society of Korea Conference
• /
• 2009.11a
• /
• pp.34.1-34.1
• /
• 2009

1970년대 WernerKern에 의해서 개발된 RCA 습식 세정 공정은 이후 메가소닉 기술 개발과 더불어 현재까지반도체 세정 공정에서 필수 공정으로 알려져 있다. 하지만, 반도체패턴의 고집적화 미세화에 따라 메가소닉을 기반으로 하는 세정기술은 패턴 붕괴 및 나노 입자 제거의 한계를 드러내면서 난관에 봉착하고 있으며, 특히, 기존의 Batch식에서 매엽식으로 세정 방식이 전환은 새로운 개념의 메가소닉 기술 개발을 요구하게 되었다. 메가소닉을 사용한습식 세정공정은 메가소닉에 의한 캐비테이션 효과 (Cavitation Effect)에 따른 충격파 및음압 (Acoustic Streaming)에 의한 입자제거를 주요 메커니즘으로 한다. 메가소닉 주파수와 Boundary Layer 두께는, $\delta=\surd(2v/\omega)$($\delta$=두께, v=유체속도), $\omega=2{\pi}f$ (f=주파수), 으로 표현할 수 있다. 위의 식에 따르면, 메가소닉을 이용한 세정공정에서 주파수가 높아질수록 Boundary Layer의 두께가 감소하며, 이는제거 가능한 입자의 크기가 작아짐을 의미하며, 다시말해, 1 MHz 보다 2 MHz 메가소닉 세정장비에서 미세 입자 세정에 유리함을 예상할 수 있다. 본연구에서는 매엽식 세정장비를 사용하여, 1MHz 및 2MHz 콘-타입 (Cone-Type) 메가소닉 장치를 100nm이하 세정 입자에 대한 입자 제거효율을 평가하였다. 입자 제거 효율을 평가하기 위하여, 표준 형광입자(63nm/104nm 형광입자, Duke Scientifics, USA)를각각 IPA에 분산시킨 후, 실리콘 쿠폰 웨이퍼 ($20mm{\times}20mm$)를 일정시간 동안 Dipping 한 후, 고순도 질소로 건조시켜 오염하였다. 매엽식 세정장비(Aaron, Korea)에 1MHz와 2MHz의 콘-타입메가소닉 발진기 (Durasonic, Korea)를 각각 장착하였다.입자 오염 및 세정 후 입자 개수 측정 및 오염입자의 Mapping은 형광현미경 (LV100D, Nikon, Japan)과 소프트웨어(Image-proPlus, MediaCybernetics, USA)를 사용하여 평가하였으며, Hydrophone을 사용하여 메가소닉에서 발생되는 음압의 균일도를 각 조건에서 측정하였다. 각각의 세정공정은 1MHz와 2MHz 메가소닉 발진기 각각에서 1W, 3W, 5W 파워로 1분간 처리하였으며, 매질을 초순수를 사용하였다. 104nm 형광 입자는 1MHz 와 2 MHz 메가소닉 세정기와 모든 세정 공정조건에서 약 99%의 세정효율인 반면, 63nm 형광입자의 경우는 전체적인세정 결과가 80% 대로 감소하였다. 본 연구를 통하여, 입자크기의 미세화에 따른 입자제거효율이 크게 감소 하는 것을 확인할 수 있으며, 기존 Batch식 메가소닉 대비 단시간 및 낮은 전압에서 동일 혹은높은 세정 효율을 얻었다. 다만, 1MHz와 2MHz 메가소닉에서의 세정력은 큰 차이를 관찰 할 수 없었는데, 주파수변화에 따른 세정효율 측정을 위하여 미세 입자를 사용한 추가 실험이 필요 할 것이다.