• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.147-154
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• 2007

Network topology in ad hoc networks keeps changing because of node mobility and no limitation in number of nodes. Therefore, the scalability of routing protocol is of great importance, However, table driven protocols such as DSDV have been known to be suitable for relatively small number of nodes and low node mobility, Various protocols like FSR, OLSR, and PCDV have been proposed to resolve scalability problem but vet remain to be proven for their comparative superiority for scalability, In this paper, we compare and amine them by employing various network deployment scenarios as follows: network dimension increase's while keeping node density constant node density increases while keeping network dimension fixed, and the number of sessions increase with the network dimension and the number of nodes fixed. the DSDV protocol showed a low scalability despite that it imposes a low overhead because its convergence speed against topology change is slow, The FSR's performance decreased according to the increase of overhead corresponding to increasing number of nodes, The OLSR with the shortest convergence time among them shows a good scalability, but turned out to be less scalable than the PCDV that uses a clustering because of its relatively high overhead.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.121-130
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• 2011

This paper highlights the reconstruction of the rectilinear type of 3D rooftop model from satellite image data using centroid neural network. The main idea of the proposed 3D reconstruction method is based on the grouping of 3D line segments. 3D lines are extracted by 2D lines and DEM (Digital Elevation Map) data evaluated from a pair of stereo images. Our grouping process consists of two steps. We carry out the first grouping process to group fragmented or duplicated 3D lines into the principal 3D lines, which can be used to construct the rooftop model, and construct the groups of lines that are parallel each other in the second step. From the grouping result, 3D rooftop models are reconstructed by the final clustering process. High-resolution IKONOS images are utilized for the experiments. The experimental result's indicate that the reconstructed building models almost reflect the actual position and shape of buildings in a precise manner, and that the proposed approach can be efficiently applied to building reconstruction problem from high-resolution satellite images of an urban area.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.598-603
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• 2014

In wireless sensor networks, two methods have been generally used to track continuously moving object: a user query-based method and a periodic report-based method. Although the former method generates more overhead as a result of the user queries, the former one is also an energy-efficient method that does not transfer unnecessary information. For the user query-based method, a virtual tree that consist of sensor nodes is used to perform the user query and the sensor reporting. The tree stores the information of the mobile objects, and the stored information triggers a report b the user query. However, in case of a fast-moving object, the tracking accuracy decreases as a result of the time delay of the end-to-end repeated query. In order to solve this problem, we propose a query-relay method that reduces the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to adjacent nodes according to the movement of mobile object that is tracked. When the query messages are relayed. The end-to-end querying time delay is reduced. and a simulation shows that our method is superior to existing ones in terms of tracking accuracy.

• Korean Journal of Weed Science
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• v.5 no.2
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• pp.96-102
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• 1985

A study on the competition-ecological classification of the 10 prominent paddy weed species around bulrush (Scirpus juneoides) to simplify the weed problem concept for the rice production. A serial assessments on the competition ability in space and dry matter production(nutrient depletion) of respective weed species and paddy rice, and the data were used to compute the phenotypic similarity by Single Link Clustering method. Both growth response of weed species in mono- and under the paddy rice standing was very similar (r = 0.969), but the reduction rate as affected by paddy rice standing was negatively correlated with the ability in space-competition(r=-0.513). Dendrogram of 10 weed species based on the phenotypic similarity computed in 4 characters in mono- and under the paddy rice standing was also similar, as Echinochloa c., Ludwigia p., Cyperus s., and Scirpus m. in I-group, Eleocharis k., Scirpus j, in II-group, and Juncus e., Potamogeton d. in III-group, respectively. Also, that of paddy rice to 10 weed species showed Fimbristylis m., Scirpus j., Eleocharis k., Scirpus m., Juncus e. in I-group, and Ludwigia p., Potamogeton d., Monochoria v. in II-group, respectively. The integrated dendrogram by the above two data indicate the I-group with Fimbristylis m., Scirpus j., Eleocharis k. and Juncus e., as higher growth response with relatively lower competition ability to paddy rice, II-group with Cyperus s., Echinochloa c., Potamogeton d., and Ludwigia p., as higher both in growth and competition, and the last, III-group with Monochoria v., and Scirpus m., as lower growth but higher competition, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.935-946
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• 2016

This study aims to explore cognitive biases relating the core competences of science and instructional strategy in reducing the level of cognitive biases. The literature review method was used to explore cognitive biases and science education experts discussed the relevance of cognitive biases to science education. Twenty nine cognitive biases were categorized into five groups (limiting rational causal inference, limiting diverse information search, limiting self-regulated learning, limiting self-directed decision making, and category-limited thinking). The cognitive biases in limiting rational causal inference group are teleological thinking, availability heuristic, illusory correlation, and clustering illusion. The cognitive biases in limiting diverse information search group are selective perception, experimenter bias, confirmation bias, mere thought effect, attentional bias, belief bias, pragmatic fallacy, functional fixedness, and framing effect. The cognitive biases in limiting self-regulated learning group are overconfidence bias, better-than-average bias, planning fallacy, fundamental attribution error, Dunning-Kruger effect, hindsight bias, and blind-spot bias. The cognitive biases in limiting self-directed decision-making group are acquiescence effect, bandwagon effect, group-think, appeal to authority bias, and information bias. Lastly, the cognitive biases in category-limited thinking group are psychological essentialism, stereotyping, anthropomorphism, and outgroup homogeneity bias. The instructional strategy to reduce the level of cognitive biases is disused based on the psychological characters of cognitive biases reviewed in this study and related science education methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.87-93
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• 2009

Wireless mesh networks (WMNs) are emerging technologies that provide ubiquitous environments and wireless broadband access. The aggregate capacity of WMNs can be improved by minimizing the effect of channel interference. The IEEE 802.11b/g standard which is mainly used for the network interface technology in WMNs provides 3 multiple channels. We must consider the channel scanning delay and the channel dependency problem to effectively assign channels in like these multi-channel WMNs. This paper proposes a cluster-based channel assignment (CB-CA) algorithm for multi-channel WMNs to solve such problems. The CB-CA does not perform the channel scanning and the channel switching through assigning co-channel to the inter-cluster head (CH) links. In the CB-CA, the communication between the CH and cluster member (CM) nodes uses a channel has no effect on channels being used by the inter-CH links. Therefore, the CB-CA can minimize the interference within multi-channel environments. Our simulation results show that CB-CA can improve the performance of WMNs.

• Spatial Information Research
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• v.7 no.1
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• pp.1-12
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• 1999

The DTM(Digital Terrain Model) in GIS(Geographical Information System) shows the elevation from interpolation using data points surveyed. In panoramic flat landform, pixel size, resolution of source data may not be the problem in using DTM However, in mountainous landform like Korea, appropriate resolution accuracy of source data are important factors to represent the topography concerned. In this study, the difference in contour interval of source data, the resolution after interpolation, and different data structures were compared to figure out the accuracy of slope calculation using DTM from the topographic maps of Togyusan National Park Two types of GIS softwares, Idrisi(grid) ver. 2.0 using the altitude matrices and ArcView(TIN) ver. 3.0a using TIN were used for this purpose. After the analysis the conclusions are as follows: 1) The coarser resolution, the more smoothing effect inrepresenting the topography. 2) The coarser resolution the more difference between the grid-based Idrisi and the TIN-based ArcView. 3) Based on the comparison analysis of error for 30 points from clustering, there is not much difference among 10, 20, 30 m resolution in TIM-based Airview ranging from 4.9 to 6.2n However, the coarser resolution the more error for elevation and slope in the grid-based Idrisi. ranging from 6.3 to 10.9m. 4) Both Idrisi and ArcView could net consider breaklines of lanform like hilltops, valley bottoms.

• ALGAE
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• v.32 no.4
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• pp.359-377
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• 2017

Pyropia haitanensis (T. J. Chang et B. F. Zheng) N. Kikuchi et M. Miyata is one of the most commercially useful macroalgae cultivated in southeastern China. In red algae, the biosynthesis of terpenoids through 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway can produce a direct influence on the synthesis of many biologically important metabolites. In this study, two genes of cDNAs, 1-deoxy-D-xylulose-5-phosphate synthase (DXS) and 1-deoxy-D-xylulose-5-phosphate reductase (DXR), which encoding the first two rate-limiting enzymes among MEP pathway were cloned from P. haitanensis. The cDNAs of P. haitanensis DXS (PhDXS) and DXR (PhDXR) both contained complete open reading frames encoding polypeptides of 764 and 426 amino acids residues, separately. The expression analysis showed that PhDXS was significant differently expressed between leafy thallus and conchocelis as PhDXR been non-significant. Additionally, expression of PhDXR and its downstream gene geranylgeranyl diphosphate synthase were both inhibited by fosmidomycin significantly. Meanwhile, we constructed types of phylogenetic trees through different algae and higher plants DXS and DXR encoding amino acid sequences, as a result we found tree clustering consequences basically in line with the "Cavalier-Smith endosymbiotic theory." Whereupon, we speculated that in red algae, there existed only complete MEP pathway to meet needs of terpenoids synthesis for themselves; Terpenoids synthesis of red algae derivatives through mevalonate pathway came from two or more times endosymbiosis of heterotrophic eukaryotic parasitifer. This study demonstrated that PhDXS and PhDXR could play significant roles in terpenoids biosynthesis at molecular levels. Meanwhile, as nuclear genes among MEP pathway, PhDXS and PhDXR could provide a new way of thinking to research the problem of chromalveolata biological evolution.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.81-90
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• 2020

The purpose of this study is to study how to apply block-chain technology to prevent data forgery and alteration in the defense sector of AI(Artificial intelligence). AI is a technology for predicting big data by clustering or classifying it by applying various machine learning methodologies, and military powers including the U.S. have reached the completion stage of technology. If data-based AI's data forgery and modulation occurs, the processing process of the data, even if it is perfect, could be the biggest enemy risk factor, and the falsification and modification of the data can be too easy in the form of hacking. Unexpected attacks could occur if data used by weaponized AI is hacked and manipulated by North Korea. Therefore, a technology that prevents data from being falsified and altered is essential for the use of AI. It is expected that data forgery prevention will solve the problem by applying block-chain, a technology that does not damage data, unless more than half of the connected computers agree, even if a single computer is hacked by a distributed storage of encrypted data as a function of seawater.