• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.819-823
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• 2024

The main criteria used in NEI 18-04 to define SSCs as risk-significant include (1) the SSC is required to keep all LBEs within the F-C target, and (2) the total frequency with the SSC failed exceeds 1% of the limit for at least one of the three cumulative risk metrics used for evaluating the integrated plant risk. The first one is a reasonable criterion in determining the risk significant SSCs. However, the second criterion may not be adequate to serve the purpose of determining the risk significance of SSCs. In the second criterion, the cumulative risk metric values representing the integrated plant risk (less the preventive and mitigative effects of the SSC being evaluated) are compared to a risk limit that represents a very small contribution to the overall integrated plant risk, which corresponds appropriately to the contributions from individual SSCs. The easiest approach to redefine the NEI 18-04 definition of risk-significant SSCs in relation to the integrated plant risk metrics is to compare the difference, between the risk metric value calculated with the SSC failed and the risk metric value calculated with the SSC credited, with 1% of the risk limit established for the integrated plant risk metrics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11B
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• pp.1676-1684
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• 2010

Tactical network environments are characterized by unreliable connectivity, communication links which has limited bandwidth compared to commercial networks and distributed architecture where users have high mobility. The best route should be selected based on the required traffic characteristics which can be a wireless channel environments and a status of nodes which are moving. Those characteristics are self aware and should be a routing decision factor in order to guarantee a reliable data transfer. In this paper we define the requirements of services and traffic characteristics on tactical network environments and suggest the new routing method "AODV-IMTC" based on selective routing metric to enable efficient data transfer in wireless ad hoc networks.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.78-94
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• 2000

The ever increasing demands for enhanced competitiveness of engineered products require a "designing-in-quality" strategy that can effectively and efficiently incorporate concepts of uncertainty, quality, and robustness into design. Engineered design optimization approaches that are typically carried out with respect to a single objective become inadequate to address these multiple set of requirements. This paper presents a design metric for a multi-attribute robust design problem with designer′s preferences on the performance accuracy and the performance precision. The use of this design metric as the robust optimal design criterion in multi-stage experimentation and modeling technique is presented. The effectiveness of the overall design procedure and the performance of the proposed design metric are tested with the aid of IC design and the results are discussed.

• Current Optics and Photonics
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• v.3 no.5
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• pp.429-437
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• 2019

Temperature adaptability is an important metric for evaluating the performance of an optical system. The temperature characteristics of the optical system are closely related to the material and shape of its lens. In this paper, we establish a mathematical model relating the temperature characteristics to the shape and material of the lens. Then a novel assembly structure that can solve the lens constraint and positioning problem is proposed. From those basics, the correctness of the theoretical model and the effectiveness of the assembly structure are verified through simulated analysis of the imaging quality of the optical system, whose operating temperature range is $-60{\sim}100^{\circ}C$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• ETRI Journal
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• v.36 no.1
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• pp.171-174
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• 2014

In weighted bit-flipping-based algorithms for low-density parity-check (LDPC) codes, due to the existence of overconfident incorrectly received bits, the metric values of the corresponding bits will always be wrong in the decoding process. Since these bits cannot be flipped, decoding failure results. To solve this problem, an improved parallel weighted bit flipping algorithm is proposed. Specifically, a reliability-saturation strategy is adopted to increase the flipping probability of the overconfident incorrectly received bits. Simulation results show that the error floor of LDPC codes is greatly lowered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.9-16
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• 2004

An object-oriented design was carried out for the digital photogrammetric processes. Classes were identified and implemented to develop an integrated digital photogrammetry system using a 3 dimensional self-calibration model for CCD cameras. This integrated system is deemed to be a significant progress from the conventional photogrammetric system which is a series of discrete processes. Object oriented methodology was selected for the implementation of the integrated photogrammetric system because it would be a very complex task to get the same result using a procedural programming language. Besides the simplification of development effort, object oriented methodology has further benefits of better management of program in case when updates to parts of the program are necessary. Using the classes designed in this study, a 3 dimensional self-calibration model was developed for a CCD camera. Classes for data input and image handling as well as classes for bundle adjustment were implemented. The bundle adjustment system was further enhanced with member functions to handle additional parameters for principal point coordinates and focal length, thereby, enabling the application to non-metric CCD cameras.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.456-469
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• 2009

We develop a new wireless link quality metric, ECOT(Estimated Channel Occupancy Time) that enables a high throughput route setup in wireless mesh networks. The key feature of ECOT is to be applicable to diverse mesh network environments where IEEE 802.11 MAC (Medium Access Control) variants are used. We take into account the exact operational features of 802.11 MAC protocols, such as 802.11 DCF(Distributed Coordination Function), 802.11e EDCA(Enhanced Distributed Channel Access) with BACK (Block Acknowledgement), and 802.11n A-MPDU(Aggregate MAC Protocol Data Unit), and derive the integrated link metric based on which a high throughput end-to-end path is established. Through extensive simulation in random-topology settings, we evaluate the performance of proposed link metric and present that ECOT shows 8.5 to 354.4% throughput gain over existing link metrics.