• Asian Journal of Innovation and Policy
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• v.2 no.2
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• pp.240-264
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• 2013

The study compares network structures that emerged in three inter-organisational projects set up under the MSC Malaysia initiative by the Government of Malaysia. These consortia are seen as policy-driven inter-organisational networks and, with data collected through interviews; the links among the organisations are mapped to gain an understanding of the structures that emerged in these networks. The findings provide lessons for other emerging countries that are embarking on similar projects i.e. cluster-oriented developments with policy-driven inter-organisational networks. These findings are seen as particularly useful when emerging countries invest in technology-related projects and invite multinational companies to work together with local firms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.224-242
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• 2010

Data routing in wireless sensor networks must be energy-efficient because tiny sensor nodes have limited power. A cluster-based hierarchical routing is known to be more efficient than a flat routing because only cluster-heads communicate with a sink node. Existing hierarchical routings, however, assume unrealistically large radio transmission ranges for sensor nodes so they cannot be employed in real environments. In this paper, by considering the practical transmission ranges of the sensor nodes, we propose a clustering and routing method for hierarchical sensor networks: First, we provide the optimal ratio of cluster-heads for the clustering. Second, we propose a d-hop clustering scheme. It expands the range of clusters to d-hops calculated by the ratio of cluster-heads. Third, we present an intra-cluster routing in which sensor nodes reach their cluster-heads within d-hops. Finally, an inter-clustering routing is presented to route data from cluster-heads to a sink node using multiple hops because cluster-heads cannot communicate with a sink node directly. The efficiency of the proposed clustering and routing method is validated through extensive simulations.

• Journal of Preventive Medicine and Public Health
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• v.33 no.1
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• pp.51-55
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• 2000

Objectives . In large-scale field trials, randomization by cluster is frequently used because of the administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might of otherwise arise. Cluster randomization trials are experiments in which intact social unit, e.g., families, schools, cities, rather than independent individuals are randomly allocated to intervention groups. The positive correlation among responses of subjects from the same cluster is in matter in cluster randomization. This thesis is to compare the results of three randomization methods by standard error of estimator of treatment effect. Methods : We simulated cholesterol data varing the size of the cluster and the level of the correlation in clusters and analyzed the effect of cholesterol-lowering agent. Results : In intra-cluster randomization the standard error of the estimator of treatment effect is smallest relative to that in inter-cluster randomization and that in individual randomization. Conclusions : Infra-cluster randomization is the most efficient in its standard error of estimator of treatment effect but other factor should be considered when selecting a specific randomization method.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.126-136
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• 1996

In multi-item production system, GT(Group Technology) is used effectively in order to cluster various parts into groups. GT is based on clustering parts which have similar features, and these features are classified into two properties, namely crisp(quantitative) feature and fuzzy(qualitative) feature. Especially, many difficult problems are often faced that have to evaluate the properties of parts with the crisp and fuzzy feature together. As the basis of determining the similarity of inter-parts, in this method, one aggregate value is calculated on each part. However, because the above aggregate value is only gained from simple additive weighted sum, there is one problem in this method that has been handled the combination effect of inter-parts. For these reasons, in this paper, a proposed method is suggested for representing combination effect in order to cluster parts that have crisp and fuzzy properties into groups using ${\lambda}$-fuzzy measure and fuzzy integral.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1068-1073
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• 1987

Traditional circuit partitioning algorithm using the cluster development method, which is suitable for such applications as single chip floor planning or multiple layer PCB system placement, where the clusters are formed so that inter-cluster nets are localized within the I/O connector pins, may not be appropriate for the functiona block placement in truly 3-D electronic modules. 3-D hybrid IC is one such example where the inter-layer routing as well as the intra-layer routing can be maximally incorporated to reduce the overall circuit size, cooling requirements and to improve the speed performance. In this paper, we propose a new algorithm called MBE(Minimum Box Embedding) for the layer assignment of each functional block in 3-D hybrid IC design. The sequence of MBE is as follows` i) force-directed relaxation in 3-D space, ii) exhaustive search for the optimal orientation of the slicing plane and iii) layer assignment. The algorithm is first explaines for a 2-D reduced problem, and then extended for 3-D applications. An example result for a circuit consisting of 80 blocks has been shown.

• Transactions of the Society of Information Storage Systems
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• v.6 no.1
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• pp.31-36
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• 2010

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to correct errors of binary data stored in holographic data storage system, a new method for reduction errors is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers. By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise in the holographic data storage system is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• Journal of Technology Innovation
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• v.13 no.3
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• pp.197-224
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• 2005

Recently, the notion of National Innovation System (NIS) has attracted considerable attention as a key driver of the economic success. Amongst others, the Northeast Asian countries deserve highlight as central cases of NIS. This research attempts to examine inter-industrial knowledge flows and structure among Northeast Asian countries. To this end, Korea, Japan and Taiwan are selected and the patent citation data, a proxy of disembodied knowledge flows, from United Stated Patents and Trademark Office (USPTO) are employed for cluster analysis and network analysis. Some meaningful findings are presented and distinctive characteristics of respective countries are contrasted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• International Journal of Industrial Entomology and Biomaterials
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• v.41 no.2
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• pp.56-62
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• 2020

Mulberry (Morus spp. family: Moraceae) has prime importance in the sericulture industry, and its foliage is the only natural feed of the silkworm Bombyx mori L. Traditional classification methods using morphological traits were largely unsuccessful in assessing the diversity and relationships among different mulberry species because of environmental influences on the traits of interest. For these reasons, it is difficult to differentiate between the varieties and cultivars of Morus spp. In the present study, inter-simple sequence repeat (ISSR) markers were used to investigate the genetic diversity of 48 mulberry samples genotyped using nine ISSR primers. The ISSR markers exhibited polymorphisms (53.2%) among mulberry genotypes. Furthermore, similarity coefficient estimated for these ISSR markers was found to vary between 0.67 and 0.99 for the combined pooled data. The phenogram drawn using the UPGMA cluster method based on combined pooled data of the ISSR markers divided the 48 mulberry genotypes into seven major groups. No genetic association was found in the collection area, and there was a mixed pattern between the mulberry lines. The hybridization between different mulberry species is highly likely to be homogenized due to natural hybridization.

• Proceedings of the IEEK Conference
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• summer
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.