• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.248-256
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• 2021

Now that we're in the big data era, data has taken on a new significance as the storage capacity has exploded from trillion bytes to petabytes at breakneck pace. As the use of cloud computing expands and becomes more commonly accepted, several businesses and institutions are opting to store their requests and data there. Cloud storage's concept of a nearly infinite storage resource pool makes data storage and access scalable and readily available. The majority of them, on the other hand, favour a single cloud because of the simplicity and inexpensive storage costs it offers in the near run. Cloud-based data storage, on the other hand, has concerns such as vendor lock-in, privacy leakage and unavailability. With geographically dispersed cloud storage providers, multicloud storage can alleviate these dangers. One of the key challenges in this storage system is to arrange user data in a cost-effective and high-availability manner. A multicloud storage architecture is given in this study. Next, a multi-objective optimization problem is defined to minimise total costs and maximise data availability at the same time, which can be solved using a technique based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions known as the Pareto-optimal set.. When consumers can't pick from the Pareto-optimal set directly, a method based on Principal Component Analysis (PCA) is presented to find the best answer. To sum it all up, thorough tests based on a variety of real-world cloud storage scenarios have proven that the proposed method performs as expected.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.42-51
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• 1994

Visual features of a mushromm(Lentinus Edodes L.) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading look simple, it decision making underneath the simple action comes from the result of the complex neural processing of visual image. Recently, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, the neuro -net based computer visual information processing is the promising approach toward the automation in the agricultural field. In this paper, first, the neuro - net based classification of simple geometric primitives were done and the generalization property of the network was tested for degraded primitives. And then the neuro-net based grading system was developed for a mushroom. A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features of sampled mushrooms and their corresponding grades were used as input/output pairs for training the neural network. The grading performance of the trained network for the mushrooms graded previously by the expert were also presented.

• Korean System Dynamics Review
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• v.12 no.1
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• pp.89-114
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• 2011

With steady increase in the demand for door-to-door delivery services, courier service providers are facing a constant pressure to reduce delivery price in order to secure quantity order and to improve the efficiency and reliability for customer service. The objective of this research is to explain the structure of the door-to-door delivery including door-to-door delivery service demand, sorting capacity in the hub-terminal, and the system performance measures such as service reliability networks using causal loop diagram(CLD). Based on causal relation, this research describes a comprehensive approach to examine how capacity reliability contributes to the renewal of the firm's competences through its reciprocal relationship with travel time reliability and connectivity reliability. Also, this research suggests the policy alternatives to improve effectiveness and efficiency of door-to-door delivery network in response to increase customer satisfaction and profit maximization of the courier service providers: operational, tactical, and strategic levels.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.145-145
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• 2021

Water distribution networks (WDNs) are designed to satisfy water requirement of an urban community. One of the central issues in human history is providing sufficient quality and quantity of water through WDNs. A WDN consists of a great number of pipelines with different ages, lengths, materials, and sizes in varying degrees of deterioration. The available annual budget for rehabilitation of these infrastructures only covers part of the network; thus it is important to manage the limited budget in the most cost-effective manner. In this study, a novel pipe replacement scheduling approach is proposed in order to smooth the annual investment time series based on a life cycle cost assessment. The proposed approach is applied to a real WDN currently operating in South Korea. The proposed scheduling plan considers both the annual budget limitation and the optimum investment on pipes' useful life. A non-dominated sorting genetic algorithm is used to solve a multi-objective optimization problem. Three decision-making objectives, including the minimum imposed LCC of the network, the minimum standard deviation of annual cost, and the minimum average age of the network, are considered to find optimal pipe replacement planning over long-term time period. The results indicate that the proposed scheduling structure provides efficient and cost-effective rehabilitation management of water network with consistent annual budget.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1260-1283
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• 2019

Low earth orbit (LEO) satellite broadband network is a crucial part of the space information network. LEO satellite constellation design is a top-level design, which plays a decisive role in the overall performance of the LEO satellite network. However, the existing works on constellation design mainly focus on the coverage criterion and rarely take network performance into the design process. In this article, we develop a unified framework for constellation optimization design in LEO satellite broadband networks. Several design criteria including network performance and coverage capability are combined into the design process. Firstly, the quality of service (QoS) metrics is presented to evaluate the performance of the LEO satellite broadband network. Also, we propose a network stability model for the rapid change of the satellite network topology. Besides, a mathematical model of constellation optimization design is formulated by considering the network cost-efficiency and stability. Then, an optimization algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is provided for the problem of constellation design. Finally, the proposed method is further evaluated through numerical simulations. Simulation results validate the proposed method and show that it is an efficient and effective approach for solving the problem of constellation design in LEO satellite broadband networks.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.15-24
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• 2007

The recognition of postal address images is indispensable for the automatic sorting of postal envelopes. The process of the address image recognition is composed of three steps-address image preprocessing, character recognition, address interpretation. The extracted character images from the preprocessing step are forwarded to the character recognition step, in which multiple candidate characters with reliability scores are obtained for each character image extracted. aracters with reliability scores are obtained for each character image extracted. Utilizing those character candidates with scores, we obtain the final valid address for the input envelope image through the address interpretation step. The envelope sorting rate depends on the performance of all three steps, among which character recognition step could be said to be very important. The good character recognizer would be the one which could produce valid candidates with very reliable scores to help the address interpretation step go easy. In this paper, we propose the method of generating character candidates with reliable recognition scores. We utilize the existing MLP(multilayered perceptrons) neural network of the address recognition system in the current automatic postal envelope sorters, as the classifier for the each image from the preprocessing step. The MLP is well known to be one of the best classifiers in terms of processing speed and recognition rate. The false alarm problem, however, might be occurred in recognition results, which made the address interpretation hard. To make address interpretation easy and improve the envelope sorting rate, we propose promising methods to reestimate the recognition score (confidence) of the existing MLP classifier: the generation method of the statistical recognition properties of the classifier and the method of the combination of the MLP and the subspace classifier which roles as a reestimator of the confidence. To confirm the superiority of the proposed method, we have used the character images of the real postal envelopes from the sorters in the post office. The experimental results show that the proposed method produces high reliability in terms of error and rejection for individual characters and non-characters.

• Interdisciplinary Bio Central
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• v.5 no.1
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• pp.1.1-1.8
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• 2013

High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1327-1334
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• 2017

The Bubble sort graph is node symmetric, and can be used in the data sorting algorithm. In this research we propose and analyze that Half Bubble sort graph that improved the network cost of Bubble sort graph. The Half Bubble sort graph's number of node is n!, and its degree is ${\lfloor}n/2{\rfloor}+1$. The Half Bubble sort graph's degree is $${\sim_=}0.5$$ times of the Bubble sort, and diameter is $${\sim_=}0.9$$ times of the Bubble sort. The network cost of the Bubble sort graph is $${\sim_=}0.5n^3$$, and the network cost of the half Bubble sort graph is $${\sim_=}0.2n^3$$. We have proved that half bubble sort graph is a sub graph of the bubble sort graph. In addition, we proposed a routing algorithm and analyzed the diameter. Finally, network cost is compared with the bubble sort graph.

• Journal of Gifted/Talented Education
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• v.23 no.6
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• pp.881-898
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• 2013

The purpose of this study was to compare the difference of semantic network about sedimentary environment between science gifted students and non-gifted students through field trip. The fifteen high school science gifted students and non-gifted students were participated in the field trip for this study. The geological field trip was performed in Chaseokgang, which suitable learning place for sedimentary environments. Data included field trip observation and students' inquiry activity reports. The inquiry activity reports were analyzed using the semantic network analysis method. The results of this study were as follows: First, the semantic network of science gifted students was larger and complex structures than that of the non-gifted students. Second, science gifted students were interpreted that changes of sedimentary environment was connected with the sorting and the roundness. On the other hand, semantic network of non-gifted students were become fragmented because they were not interpreted sedimentary environment related to scientific concepts.