• Journal of Arbitration Studies
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• v.17 no.2
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• pp.23-41
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• 2007

The role of ICC Court of Arbitration in ICC Arbitration is critical in maintaining the good reputation and worldwide recognition. While most arbitration institutions are the products of regional on national private associations, which play a relatively limited role in appointing or confirming the arbitrators, the Court of Arbitration is not only international in the appointment of arbitrators through the each National Committee, but also intervene in the confirmation of the prospective arbitrators proposed by the parties. Thus the ICC Arbitration is undoubtedly the most highly-supervised form of institutional arbitration available. The purpose of this paper is to examine the appointment and confirmation system of ICC Arbitration, to find the distinctive features of the ICC Rules of Arbitration and to check how to apply the features in the Rules of International Arbitration for the Korean Commercial Arbitration Board(KCAB Rules). Although the KCAB Rules have inherent limitations in the appointment of the arbitrators comparing with the ICC Court. They do not have any confirmation system of the arbitrator proposed by the parties. Although no arbitral institutions is in a position to guarantee completely the ultimate quality and efficacy of the process, the ICC, more than any other institution has historically endeavored to do so through a combination of the efforts of its International Court of Arbitration and National Committees. Composed of legal professionals of more than 75 nationalities, the Court, with the support of its permanent Secretariat in Paris, brings to bear on the decisions that it is responsibility to make the collective and disparate knowledge and experience of a multinational body. Therefore, if the KCAB wants to attract many international disputes, it should try to benchmark the ICC Rules of Arbitration, expecially the Article 9, to secure the prominent arbitrators throughout the world, even though a lot of limitations are exist. The positive role of the ICC Court of Arbitration gives us very important signal.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.911-917
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• 2023

In today's world, we find ourselves facing energy crises due to factors such as war and climate crises. To prepare for these energy crises, many researchers continue to study systems related to energy monitoring and conservation, such as energy management systems, energy monitoring, and energy conservation. In line with these efforts, nations are making it mandatory for energy-consuming facilities to implement these systems. However, these facilities, limited by space and energy constraints, are exploring ways to improve. This research explores the operation of a data collection system using low-performance embedded devices. In this context, it proves that an optimized version of RocksDB, a Key-Value store, outperforms traditional databases when it comes to time-series data. Furthermore, a comprehensive database evaluation tool was employed to assess various databases, including optimized RocksDB and regular RocksDB. In addition, heterogeneous databases and evaluations are conducted using a UD Benchmark tool to evaluate them. As a result, we were able to see that on devices with low performance, the time required was up to 11 times shorter than that of other databases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.7-11
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• 2004

This paper proposes a new immunotronic approach for the fault detection in hardware. The suggested method is, inspired by biology and its implementation is based on genetic algorithm. Tolerance conditions in the immunotronic system for fault detection correspond to the antibodies in the biological immune system. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity and GA optimization is employed to select mature tolerance conditions in immunotronic fault detection system. The suggested method is applied to the fault detection for MCNC benchmark FSMs (finite state machines) and its effectiveness is demonstrated by the computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.125-130
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• 2016

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• East Asian Economic Review
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• v.21 no.2
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• pp.147-165
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• 2017

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.459-470
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• 2012

This paper summarizes a study for the modal analysis and model updating conducted using the monitoring data obtained from the Canton Tower of 610 m tall, which was established as an international benchmark problem by the Hong Kong Polytechnic University. Modal properties of the tower were successfully identified using frequency domain decomposition and stochastic subspace identification methods. Finite element model updating using the measurement data was further performed to reduce the modal property differences between the measurements and those of the finite element model. Over-fitting during the model updating was avoided by using an optimization scheme with a regularization term.

• International Journal of Safety
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• v.11 no.2
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• pp.22-25
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• 2012

This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.

• International Journal of High-Rise Buildings
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• v.3 no.3
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• pp.191-198
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• 2014

The tall building and super tall building has been a common building type in China, with multiple functions and complex geometry. Composite construction is broadly used in tall building structures and constitutes the mixed structure together with concrete and steel constructions. The mixture of the constructions is purposely designed for specific area based on the analysis results to achieve the best cost-effectiveness. New types of composite construction are conceived of by engineers for columns and walls. Material distribution is more flexible and innovative in the structural level and member level. However the reliability of computer model analysis should be verified carefully. Further researches in the design and build of composite construction are necessary to ensure the success of its application. Composite or Mixture Index is suggested to be used as a performance benchmark.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.88-99
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• 2007

The application of a process view, as complement to the traditional functional division, is often a way to highlight organisational improvement potential. This paper examines the process of providing university level education in quality management, using Sweden as an example. The purpose is to assess the performance of university education as part of the supply chain of providing quality management to a society. This has been done by studying the actual offering compared to a notional benchmark of best performance. Preliminary results indicate that there could be a significant improvement potential in both providing more education of the right type and in the right way. A lot of similar basic courses are given but with varying names, possibly reflecting difficulties in defining the area of quality management and its constituents. An important reason for the detected improvement potential seems to be the lack of ownership of the studied supply chain of providing university level quality education to the Swedish society.