• Journal of KIISE:Computer Systems and Theory
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• v.27 no.1
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• pp.34-43
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• 2000

Replicated data that is used for fault tolerant distributed system requires replica control protocol to maintain data consistency. The one of replica control protocols is quorum consensus method which accesses replicated data by getting majority approval. If site failure or communication link failure occurs and any one can't get quorum consensus, it degrades the availability of data managed by quorum consensus protocol. So it needs for ghost to replace the failed site. Because ghost is not full replica but process which has state information using meta data, it is important to simplify meta data. In order to maintain availability and simplify meta data, we propose a method to use cohort set as ghost's meta data. The proposed method makes it possible to organize meta data in 2N+logN bits and to have higher availability than quorum consensus only with cohort set and dynamic linear voting protocol. Using Markov model we calculate proposed method's availability to analyze availability and compare it with existing protocols.

• Korean Journal of Social Welfare
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• v.36
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• pp.259-283
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• 1998

This research is based on the assumption that Korea's social consensus among two sides of industry and government on Feb. 2 '98 was not just temporary response to the economic crisis caused by International Monetary Fund financial fund. Some arguments on the applicability of social democracy and corporatism and D. Donnison's perspective on Democratic Socialism are employed in search of an appropriate social consensus model for Korean situation. In the process of analyzing Korean situation in historical context it becomes apparent that there is embryo of stable social consensus beyond the level of tentative social cooperation to overcome the economic crisis. The next step applying the social consensus model to the employment policy for people with disability levels up empirical and theoretical validity of this research. It casts some implications for disability labour market which demands another kind of social consensus among people with disability, employers and the government. These implications include a desirable solution for hard lives of unemployed people with disability in economic difficulties and current policy issues facing Korean government.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.226-234
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• 2022

With advances in autonomous vehicles and networked control, there is a growing interest in the consensus control of a multi-agents system to control multi-agents with distributed control beyond the control of a single agent. Since consensus control is a distributed control, it is bound to have delay in a practical system. In addition, it is often difficult to have a very accurate mathematical model for a system. Even though a reinforcement learning (RL) method was developed to deal with these issues, it often experiences slow convergence in the presence of large uncertainties. Thus, we propose a slide RL which combines the sliding mode control with RL to be robust to the uncertainties. The structure of a sliding mode control is introduced to the action in RL while an auxiliary sliding variable is included in the state information. Numerical simulation results show that the slide RL provides comparable performance to the model-based consensus control in the presence of unknown time-varying delay and disturbance while outperforming existing state-of-the-art RL-based consensus algorithms.

• Atmosphere
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• v.25 no.2
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• pp.375-387
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• 2015

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON's except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON's except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON's at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

• Journal of Contemporary Eastern Asia
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• v.12 no.2
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• pp.61-87
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• 2013

This study applies grounded theory to develop an emic substantive theory of regional integration in Greater East Asia. The role of norms and policies is explored through discursive content analysis of a wide theoretical sample of official elite policy statements dealing with regionalism and related policy areas. A resulting model of regional integration titled "Harmony through Holistic Engagement" is then discussed in relation to the etic consensus on the phenomenon. The study concludes that the etic consensus on the nature of regional integration in Greater East Asia greatly differs from the results of an emic approach and thus reflects a normative consensus rather than a descriptive reality.

• ETRI Journal
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• v.43 no.2
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• pp.357-370
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• 2021

The evolution of blockchain-based systems has enabled researchers to develop nextgeneration e-voting systems. However, the classical consensus method of blockchain, that is, Proof-of-Work, as implemented in Bitcoin, has a significant impact on energy consumption and compromises the scalability, efficiency, and latency of the system. In this paper, we propose a hybrid consensus model (PSC-Bchain) composed of Proof of Credibility and Proof of Stake that work mutually to address the aforementioned problems to secure e-voting systems. Smart contracts are used to provide a trustworthy public bulletin board and a secure computing environment to ensure the accuracy of the ballot outcome. We combine a sharding mechanism with the PSC-Bchain hybrid approach to emphasize security, thus enhancing the scalability and performance of the blockchain-based e-voting system. Furthermore, we compare and discuss the execution of attacks on the classical blockchain and our proposed hybrid blockchain, and analyze the security. Our experiments yielded new observations on the overall security, performance, and scalability of blockchain-based e-voting systems.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.41-58
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• 2009

This research investigated how people are influenced to adopt online review. We applied the Elaboration Likelihood Model (ELM) and the Technology Acceptance Model (TAM) to this study. Our research model highlights the assessment of online review usefulness as a mediator from online review quality to online review adoption. This research predicted online review consensus has a role to bulid up online reviw usefulness. This study also includes vividness and perceived similarity as determinants of online review quality. Survey data reflect user's perceptions of actual online review they read. Results support most of research hypotheses except hypothesis related to moderating effect of user involvement. This research offers a model for understanding online review user's acceptance. Additional theoretical and practical implications are also discussed in the paper.

• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Atmosphere
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• v.21 no.2
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• pp.209-220
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• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.