• Title/Summary/Keyword: multidimensional data processing

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Structural Alignment: Conceptual Implications and Limitations (구조적 정렬: 개념적 시사점과 한계)

  • Lee Tae-Yeon
    • Korean Journal of Cognitive Science
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    • v.17 no.1
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    • pp.53-74
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    • 2006
  • Similarity has been considered as one of basic concepts of cognitive psychology which is useful for explaining cognitive structure and process. MDS models(Shepard, 1964; Nosofsky, 1991) and Contrast model(Tversky, 1977) were proposed as early models of similarity comparison process. But, there have been a lot of theoretical doubts about the conceptual validity of similarity as a result of empirical findings which could not be explained by early models. Goldstone(1994) assumed that similarity could be defined by alignment processes, and suggested structural alignment as a prospective alternative for solving conceptual controversies so far. In this study, basic assumption and algorithms of MDS models(Shepard, 1944; Nosofsky, 1991) and Contrast model(Tversky, 1977) were described shortly and some theoretical limitations such as arbitrariness of selective attention and correlated structures were discussed as well. The conceptual characteristics and algorithms of SIAM(Goldstone, 1994) were described and how it has been applied to cognitive psychology areas such as categorization, conceptual combination, and analogical reasoning were reviewed. Finally, some theoretical limitations related with data-driven processing and alternative processing and possible directions for structural alignment were discussed.

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Real-time Hand Gesture Recognition System based on Vision for Intelligent Robot Control (지능로봇 제어를 위한 비전기반 실시간 수신호 인식 시스템)

  • Yang, Tae-Kyu;Seo, Yong-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.10
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    • pp.2180-2188
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    • 2009
  • This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

Design of an Inference Control Process in OLAP Data Cubes (OLAP 데이터 큐브에서의 추론통제 프로세스 설계)

  • Lee, Duck-Sung;Choi, In-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.5
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    • pp.183-193
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    • 2009
  • Both On-Line Analytical Processing (OLAF) data cubes and Statistical Databases (SDBs) deal with multidimensional data sets. and both are concerned with statistical summarizations over the dimensions of the data sets. However, there is a distinction between the two that can be made. While SDBs are usually derived from other base data, OLAF data cubes often represent directly the base data. In other word, the base data of SDBs are the macro-data, whereas the core cubiod data in OLAF data cubes are the micro-data. The base table in OLAF is used to populate the data cube with values of the measure attribute, and each record in the base tables is used to populate a cell of the core cuboid. The fact that OLAF data cubes mostly represent the micro-data may make some records be absent in the base table. Some cells of the core cuboid remain empty, if corresponding records are absent in the base table. Wang and others proposed a method for securing OLAF data cubes against privacy breaches. They assert that the proposed method does not depend on specific types of aggregation functions. In this paper, however, it is found that their assertion on aggregate functions is wrong whenever any cell of the core cuboid remains empty. The objective of this study is to design an inference control process in OLAF data cubes which rectifying Wang's error.

Design of a Hierarchical Dimension of the Bill of Materials Type (자재소요명세서 유형 계층차원의 설계)

  • Jang Se-Hyeon;Yu Han-Ju;Choi In-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.4 s.42
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    • pp.243-250
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    • 2006
  • A recursive relationship is a relationship among entities of the same class. N : M recursive relationships can be used to represent bills of materials. A bill of materials is a special data structure that occurs frequently in manufacturing applications. This data structure is a hierarchy. Most business dimensions have a hierarchical structure. In this study, a design of a hierarchical dimension of the bill of materials type is carried out. As with other N : M relationships, an intersection table that shows pairs of related rows is created, and this table is transformed into a dimension in the OLAP(OnLine Analytical Processing) model. This transformation consists of two tasks: (1)replacing the first column of the intersection table with the lowest level of the dimension: and (2)replacing the second column of the table with the only upper level of the dimension. A case multidimensional information system using the hierarchical dimension is also developed.

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Differences in swine gut microbiota in southern region of Republic of Korea (한국 남부 지역별 돼지 장내 미생물생태 비교분석)

  • Kim, Jungman;Guevarra, Robin B.;Nguyen, Son G.;Unno, Tatsuya
    • Korean Journal of Microbiology
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    • v.51 no.1
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    • pp.81-85
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    • 2015
  • Since the banning of antibiotic growth promoters (AGPs), the death of livestock has been increased, thus there is a strong demand for AGP-alternatives. Modulation of gut microbiota has been reported to affect host physiological functions and suggested to be a novel approach for developing AGP-alternatives. However, little has been understood about livestock gut microbiota compared to that of humans. We conducted preliminary study provide fundamental information regarding to regional differences in swine gut microbiota. Swine fecal samples were obtained from farms in Jeju (n=40), Gwangju (n=28), and Haenam (n=30). MiSeq was used to sequence 16S rRNA V4 region, and Mothur pipeline (Schloss et al., 2009) was used for data processing. A total of 5,642,125 reads were obtained and 3,868,143 reads were remained after removing erroneous reads. Analysis of taxonomic composition at the phylum level indicated greater abundance of Firmicutes among Jeju swine, and cluster analysis of distribution of operational taxonomic units also showed regional differences among swine gut microbiota. In addition, correlation analysis between non-metric multidimensional scaling and abundance of phyla suggested that the phyla Actinobacter, Verrucomicrobia, Firmicutes, and Fibrobacteres were driving factors for the regional differences. Livestock gut microbiota may be affected by diet and practices in farms. Our results indicated significant regional differences in swine gut microbiota, suggesting that future livestock gut microbiota studies should be designed with the regional differences in mind.

Secret Key-Dimensional Distribution Mechanism Using Deep Learning to Minimize IoT Communication Noise Based on MIMO (MIMO 기반의 IoT 통신 잡음을 최소화하기 위해서 딥러닝을 활용한 비밀키 차원 분배 메커니즘)

  • Cho, Sung-Nam;Jeong, Yoon-Su
    • Journal of Convergence for Information Technology
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    • v.10 no.11
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    • pp.23-29
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    • 2020
  • As IoT devices increase exponentially, minimizing MIMO interference and increasing transmission capacity for sending and receiving IoT information through multiple antennas remain the biggest issues. In this paper, secret key-level distribution mechanism using deep learning is proposed to minimize MIMO-based IoT communication noise. The proposed mechanism minimizes resource loss during transmission and reception process by dispersing IoT information sent and received through multiple antennas in batches using deep learning. In addition, the proposed mechanism applied a multidimensional key distribution processing process to maximize capacity through multiple antenna multiple stream transmission at base stations without direct interference between the APs. In addition, the proposed mechanism synchronizes IoT information by deep learning the frequency of use of secret keys according to the number of IoT information by applying the method of distributing secret keys in dimension according to the number of frequency channels of IoT information in order to make the most of the multiple antenna technology.

Remote Sensing Information Models for Sediment and Soil

  • Ma, Ainai
    • Proceedings of the KSRS Conference
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    • 2002.10a
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    • pp.739-744
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    • 2002
  • Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

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Disproportional Insertion Policy for Improving Query Performance in RFID Tag Data Indices (RFID 태그 데이타 색인의 질의 성능 향상을 위한 불균형 삽입 정책)

  • Kim, Gi-Hong;Hong, Bong-Hee;Ahn, Sung-Woo
    • Journal of KIISE:Databases
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    • v.35 no.5
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    • pp.432-446
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    • 2008
  • Queries for tracing tag locations are among the most challenging requirements in RFID based applications, including automated manufacturing, inventory tracking and supply chain management. For efficient query processing, a previous study proposed the index scheme for storing tag objects, based on the moving object index, in 3-dimensional domain with the axes being the tag identifier, the reader identifier, and the time. In a different way of a moving object index, the ranges of coordinates for each domain are quite different so that the distribution of query regions is skewed to the reader identifier domain. Previous indexes for tags, however, do not consider the skewed distribution for query regions. This results in producing many overlaps between index nodes and query regions and then causes the problem of traversing many index nodes. To solve this problem, we propose a new disproportional insertion and split policy of the index for RFID tags which is based on the R*-tree. For efficient insertion of tag data, our method derives the weighted margin for each node by using weights of each axis and margin of nodes. Based the weighted margin, we can choose the subtree and the split method in order to insert tag data with the minimum cost. Proposed insertion method also reduces the cost of region query by reducing overlapped area of query region and MBRs. Our experiments show that the index based on the proposed insertion and split method considerably improves the performance of queries than the index based on the previous methods.

SOM-Based $R^{*}-Tree$ for Similarity Retrieval (자기 조직화 맵 기반 유사 검색 시스템)

  • O, Chang-Yun;Im, Dong-Ju;O, Gun-Seok;Bae, Sang-Hyeon
    • The KIPS Transactions:PartD
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    • v.8D no.5
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    • pp.507-512
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    • 2001
  • Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

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Facial Expression Control of 3D Avatar using Motion Data (모션 데이터를 이용한 3차원 아바타 얼굴 표정 제어)

  • Kim Sung-Ho;Jung Moon-Ryul
    • The KIPS Transactions:PartA
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    • v.11A no.5
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    • pp.383-390
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    • 2004
  • This paper propose a method that controls facial expression of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. And we setup its system. The space of expression is created from about 2400 frames consist of motion captured data of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. But this space is not such a space where one state can go to another state via the straight trajectory between them. We derive trajectories between two states from the captured set of expressions in an approximate manner. First, two states are regarded adjacent if the distance between their distance matrices is below a given threshold. Any two states are considered to have a trajectory between them If there is a sequence of adjacent states between them. It is assumed . that one states goes to another state via the shortest trajectory between them. The shortest trajectories are found by dynamic programming. The space of facial expressions, as the set of distance matrices, is multidimensional. Facial expression of 3D avatar Is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the multidimensional scaling(MDS). To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. As a result of that, users estimate that system is very useful to control facial expression of 3D avatar in real-time.