• Title/Summary/Keyword: advanced benchmark

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Identifying Top K Persuaders Using Singular Value Decomposition

  • Min, Yun-Hong;Chung, Ye-Rim
    • Journal of Distribution Science
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    • v.14 no.9
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    • pp.25-29
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    • 2016
  • Purpose - Finding top K persuaders in consumer network is an important problem in marketing. Recently, a new method of computing persuasion scores, interpreted as fixed point or stable distribution for given persuasion probabilities, was proposed. Top K persuaders are chosen according to the computed scores. This research proposed a new definition of persuasion scores relaxing some conditions on the matrix of probabilities, and a method to identify top K persuaders based on the defined scores. Research design, data, and methodology - A new method of computing top K persuaders is computed by singular value decomposition (SVD) of the matrix which represents persuasion probabilities between entities. Results - By testing a randomly generated instance, it turns out that the proposed method is essentially different from the previous study sharing a similar idea. Conclusions - The proposed method is shown to be valid with respect to both theoretical analysis and empirical test. However, this method is limited to the category of persuasion scores relying on the matrix-form of persuasion probabilities. In addition, the strength of the method should be evaluated via additional experiments, e.g., using real instances, different benchmark methods, efficient numerical methods for SVD, and other decomposition methods such as NMF.

Dynamic Load Balancing and Network Adaptive Virtual Storage Service for Mobile Appliances

  • Ong, Ivy;Lim, Hyo-Taek
    • Journal of Information Processing Systems
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    • v.7 no.1
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    • pp.53-62
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    • 2011
  • With the steady growth of mobile technology and applications, demand for more storage in mobile devices has also increased. A lightweight block-level protocol, Internet Advanced Technology Attachment (iATA), has been developed to deliver a cost-effective storage network solution for mobile devices to obtain more storage. This paper seeks to contribute to designing and implementing Load Balancing (LB), Network Monitoring (NM) and Write Replication (WR) modules to improve the protocol's scalability and data availability. LB and NM modules are invoked to collect system resources states and current network status at each associate node (server machine). A dynamic weight factor is calculated based on the collected information and sent to a referral server. The referral server is responsible to analyze and allocate the most ideal node with the least weight to serve the client. With this approach, the client can avoid connecting to a heavily loaded node that may cause delays in subsequent in-band I/O operations. Write replication is applied to the remaining nodes through a WR module by utilizing the Unison file synchronization program. A client initially connected to node IP A for write operations will have no hindrances in executing the relevant read operations at node IP B in new connections. In the worst case scenario of a node crashing, data remain recoverable from other functioning nodes. We have conducted several benchmark tests and our results are evaluated and verified in a later section.

Two-dimensional nonconforming finite elements: A state-of-the-art

  • Choi, Chang-Koon;Kim, Sun-Hoon;Park, Young-Myung;Chung, Keun-Young
    • Structural Engineering and Mechanics
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    • v.6 no.1
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    • pp.41-61
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    • 1998
  • A state-of-the-art report on the new finite elements formulated by the addition of nonconforming displacement modes has been presented. The development of a series improved nonconforming finite elements for the analysis of plate and shell structures is described in the first part of this paper. These new plate and shell finite elements are established by the combined use of different improvement schemes such as; the addition of nonconforming modes, the reduced (or selective) integration, and the construction of the substitute shear strain fields. The improvement achieved may be attributable to the fact that the merits of these improvement techniques are merged into the formation of the new elements in a complementary manner. It is shown that the results obtained by the new elements give significantly improved solutions without any serious defects such as; the shear locking, spurious zero energy mode for the linear as well as nonlinear benchmark problems. Recent developments in the transition elements that have a variable number of mid-side nodes and can be effectively used in the adaptive mesh refinement are presented in the second part. Finally, the nonconforming transition flat shell elements with drilling degrees of freedom are also presented.

The nuclear fuel cycle code ANICCA: Verification and a case study for the phase out of Belgian nuclear power with minor actinide transmutation

  • Rodriguez, I. Merino;Hernandez-Solis, A.;Messaoudi, N.;Eynde, G. Van den
    • Nuclear Engineering and Technology
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    • v.52 no.10
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    • pp.2274-2284
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    • 2020
  • The Nuclear Fuel Cycle Code "ANICCA" has been developed by SCK•CEN to answer particular questions about the Belgian nuclear fleet. However, the wide range of capabilities of the code make it also useful for international or regional studies that include advanced technologies and strategies of cycle. This paper shows the main features of the code and the facilities that can be simulated. Additionally, a comparison between several codes and ANICCA has also been made to verify the performance of the code by means of a simulation proposed in the last NEA (OECD) Benchmark Study. Finally, a case study of the Belgian nuclear fuel cycle phase out has been carried out to show the possible impact of the transmutation of the minor actinides on the nuclear waste by the use of an Accelerator Driven System also known as ADS. Results show that ANICCA accomplishes its main purpose of simulating the scenarios giving similar outcomes to other codes. Regarding the case study, results show a reduction of more than 60% of minor actinides in the Belgian nuclear cycle when using an ADS, reducing significantly the radiotoxicity and decay heat of the high-level waste and facilitating its management.

The Overview of a Digital Power System Simulator for Large Power System Analysis

  • Kim, Tae-Kyun;Kim, Yong-Hak;Shin, Jeong-Hoon;Choo, Jin-Boo
    • KIEE International Transactions on Power Engineering
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    • v.3A no.2
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    • pp.93-99
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    • 2003
  • This paper deals with the development and testing of a large-scale, realtime digital power system simulator for the Korean Electric Power Corporation. The KEPS Simulation Center is located at KEPCO's research center (KEPRI) in Taejon, South Korea and has been operated since September 2001. The KEPS Simulation Center includes a wide range of off line power system simulation and analysis tools, as well as an advanced realtime digital simulator for the study of large scale AC and DC system performance. Because the application scope of the KEPS realtime simulator is broad and because the network models being considered are significantly larger and more complex than in traditional realtime simulator applications, many developments and tests have been required during the course of the project. In this paper, the authors describe some of these developments and present results from various benchmark tests that have been performed.

FAST-ADAM in Semi-Supervised Generative Adversarial Networks

  • Kun, Li;Kang, Dae-Ki
    • International Journal of Internet, Broadcasting and Communication
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    • v.11 no.4
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    • pp.31-36
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    • 2019
  • Unsupervised neural networks have not caught enough attention until Generative Adversarial Network (GAN) was proposed. By using both the generator and discriminator networks, GAN can extract the main characteristic of the original dataset and produce new data with similarlatent statistics. However, researchers understand fully that training GAN is not easy because of its unstable condition. The discriminator usually performs too good when helping the generator to learn statistics of the training datasets. Thus, the generated data is not compelling. Various research have focused on how to improve the stability and classification accuracy of GAN. However, few studies delve into how to improve the training efficiency and to save training time. In this paper, we propose a novel optimizer, named FAST-ADAM, which integrates the Lookahead to ADAM optimizer to train the generator of a semi-supervised generative adversarial network (SSGAN). We experiment to assess the feasibility and performance of our optimizer using Canadian Institute For Advanced Research - 10 (CIFAR-10) benchmark dataset. From the experiment results, we show that FAST-ADAM can help the generator to reach convergence faster than the original ADAM while maintaining comparable training accuracy results.

Validation of UNIST Monte Carlo code MCS using VERA progression problems

  • Nguyen, Tung Dong Cao;Lee, Hyunsuk;Choi, Sooyoung;Lee, Deokjung
    • Nuclear Engineering and Technology
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    • v.52 no.5
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    • pp.878-888
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    • 2020
  • This paper presents the validation of UNIST in-house Monte Carlo code MCS used for the high-fidelity simulation of commercial pressurized water reactors (PWRs). Its focus is on the accurate, spatially detailed neutronic analyses of startup physics tests for the initial core of the Watts Bar Nuclear 1 reactor, which is a vital step in evaluating core phenomena in an operating nuclear power reactor. The MCS solutions for the Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) core physics benchmark progression problems 1 to 5 were verified with KENO-VI and Serpent 2 solutions for geometries ranging from a single-pin cell to a full core. MCS was also validated by comparing with results of reactor zero-power physics tests in a full-core simulation. MCS exhibits an excellent consistency against the measured data with a bias of ±3 pcm at the initial criticality whole-core problem. Furthermore, MCS solutions for rod worth are consistent with measured data, and reasonable agreement is obtained for the isothermal temperature coefficient and soluble boron worth. This favorable comparison with measured parameters exhibited by MCS continues to broaden its validation basis. These results provide confidence in MCS's capability in high-fidelity calculations for practical PWR cores.

Capital Structure and Its Determinants: Evidence from Vietnam

  • NGUYEN, Tan Gia;NGUYEN, Lan;NGUYEN, Tuan Duc
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.10
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    • pp.1-10
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    • 2021
  • This paper attempts to investigate the determinants of capital structure of Vietnamese firms and also shed light on some of the factors of the modern theory of capital structure which is relevant for explaining the capital structure in advanced countries which are also relevant in the context of Vietnam. Using panel data from more than 1000 Vietnamese listed enterprises census 2017-2020, the paper finds that leverage ratio of Vietnamese firms is significantly related to probability. The firms have high level of fixed assets which they use as collateral, resulting in higher debt ratio, which is in line with the pecking order theory. The result also confirm that highly targeted debt ratio is positively correlated with the industry characteristics (using real estate firms as a benchmark), in which firm operates. Furthermore, consistent with the trade-off hypothesis, the leverage ratio is positively affected by non - debt tax shield. The result confirms that a large number of companies are state - owned, will have an insignificant impact of firm's size (as reverse proxy for bankruptcy cost) on leverage ratio. We also find that there is no distinction between state-owned enterprises and private enterprises due to strict adherence to the rules set by the Vietnamese government. Distinct from other countries, corporate income tax has slight impact on capital structure in Vietnamese firms.

Adaptive Weight Collaborative Complementary Learning for Robust Visual Tracking

  • Wang, Benxuan;Kong, Jun;Jiang, Min;Shen, Jianyu;Liu, Tianshan;Gu, Xiaofeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.1
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    • pp.305-326
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    • 2019
  • Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

Weighted sum multi-objective optimization of skew composite laminates

  • Kalita, Kanak;Ragavendran, Uvaraja;Ramachandran, Manickam;Bhoi, Akash Kumar
    • Structural Engineering and Mechanics
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    • v.69 no.1
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    • pp.21-31
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    • 2019
  • Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.