• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• Atmosphere
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• v.30 no.4
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.61-79
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• 2022

In the rapidly changing era of knowledge revolution, user-centered IT services are emerging as a very important component of modern business. However, in order to lead IT services into success, traditional capabilities and competences are not good enough. Development of IT services involve service planners and designers as well as traditional systems developers. This detailed segmentation of job and corresponding competences among involved in IT service development brings in new type of conflicts and contradictions that may require special attention for IT services to be properly development and implemented. This study aims to explore and define competences and roles of newly emerging job groups in IT services: planners, designers, and developers. In order to identify underlying competences of these emergeing groups, two stage interviews were conducted. At the first stage, general competence framework is developed across these groups with different skills for similar competence catogories. Using the categories developed at the first stage, members of each groups were asked to rate and assess the competences of other groups. Comparisons of these reciprocal assessment revealed the conceptual differences and biases across these groups. Detail differences are discussed and implications are discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.183-196
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• 2022

Bank stability serves as a prerequisite for the smooth functioning of economic and financial activities in the country. Banks face numerous risks, and liquidity plays an essential role in determining a bank's long-term growth and financial stability. By using the sample of 70 banks of the Gulf Cooperation Council, this study examines the association between funding the liquidity and the creation of liquidity and their impact on bank stability. Firstly, the reciprocal relationship reveals between funding the liquidity and the creation of liquidity by employing the 2SLS regression model. Further, by employing the dynamic GMM model, the research finds that funding liquidity is significant and positively influences bank stability. However, bank stability is significantly negatively influenced by the creation of liquidity, but the combined effect of funding the liquidity and creation of liquidity positively explains the bank stability. Additionally, this study reveals that managerial optimism biases contribute to determining the bank's liquidity and long-term stability. The finding of this study supports the executives, policymakers, and management of banks in understating liquidity risks, efficiency, and bank stability. The findings support regulatory guidelines mainly by the Basel III framework, which places more importance on the joint management of funding the liquidity and creation of liquidity in the economy.

• Journal of Sasang Constitutional Medicine
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• v.34 no.2
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• pp.15-35
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• 2022

Objectives Recent studies have reported that the digestion-related items contribute significantly to the diagnosis of constitution, though these were not described as the indicator for diagnosing Sasang constitution in 『Donguisusebowon(東醫壽世保元)』 「Sasanginbyeonjeunglon(四象人辨證論)」. The purpose of this study was to explore the reasons why such a gap appeared. Methods The digestion-related items and their rationales to be shown in eleven studies on the Questionnaire for Sasang Constitution Diagnosis were reviewed. Thirty primary studies included in two systematic reviews were also reviewed to reconsider the significance of digestion-related items as an indicator for diagnosing Soeumin. Results & Conclusions A few pathways were found in studies on the Questionnaire for Sasang Constitution Diagnosis, which could overestimate the significance of digestion-related items more than actual. Besides, in the primary studies included in two systematic reviews, there was also a possibility of overestimating the importance of digestion-related items due to some biases in the selection of subjects and the conducting of the study. Therefore, there might be room for reconsideration that indigestion was necessarily an indicator for diagnosing Soeumin.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.11-22
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• 2023

In this paper, we propose an efficient satellite selection method for multi-constellation GNSS. The number of visible satellites has increased dramatically recently due to multi-constellation GNSS. By the increased availability, the overall GNSS performance can be improved. Whereas, due to the increase of the number of visible satellites, the computational burden in implementing advanced processing such as integer ambiguity resolution and fault detection can be increased considerably. As widely known, the optimal satellite selection method requires very large computational burden and its real-time implementation is practically impossible. To reduce computational burden, several sub-optimal but efficient satellite selection methods have been proposed recently. However, these methods are prone to the local optimum problem and do not fully utilize the information redundancy between different constellation systems. To solve this problem, the proposed method utilizes the inter-system biases and geometric assignments. As a result, the proposed method can be implemented in real-time, avoids the local optimum problem, and does not exclude any single-satellite constellation. The performance of the proposed method is compared with the optimal method and two popular sub-optimal methods by a simulation and an experiment.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.263-269
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• 2021

The carrier-based differential global navigation satellite system (CD-GNSS) has been garnering significant attention as a promising technology for unmanned vehicles for its high accuracy. The CD-GNSS systems to be used for safety-critical applications should provide a certain level of integrity. The integrity of these systems must be analyzed under various conditions, including fault-free and satellite fault conditions. The systems should be able to detect the faults that can cause large biases on the user position errors and quantify the integrity risk by computing the protection level (PL) to protect the user against the faults that are left undetected. Prior work has derived and investigated the PL for the fault-free condition. In this study, the integrity of the CD-GNSS system under the fault condition is analyzed. The position errors caused by the satellite's fault are compared with the fault-free PL (PL_H0) to verify whether the integrity requirement can be met without computing the PLs for the fault conditions. The simulations are conducted by assuming the ephemeris fault, and the position errors are evaluated by changing the size of the ephemeris faults that missed detection. It was confirmed that the existing fault monitors do not guarantee that the position error under the fault condition does not exceed the PL_H0. Further, the impact of the faults on the position errors is discussed.