• Genomics & Informatics
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• 제9권2호
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• pp.45-51
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• 2011

How personality forms and whether personality genes exist are long-studied questions. Various concepts and theories have been presented for centuries. Personality is a complex trait and is developed through the interaction of genes and the environment. Twin and family studies have found that there are critical genetic and environmental components in the inheritance of personality traits, and modern advances in genetics are making it possible to identify specific variants for personality traits. Although genes that were found in studies on personality have not provided replicable association between genetic and personality variability, more and more genetic variants associated with personality traits are being discovered. Here, we present the current state of the art on genetic research in the personality field and finally list several of the recently published research highlights. First, we briefly describe the commonly used self-reported measures that define personality traits. Then, we summarize the characteristics of the candidate genes for personality traits and investigate gene variants that have been suggested to be associated with personality traits.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 추계학술대회 논문집
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• pp.1468-1476
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• 2006

The goal of this thesis is to support safety and reliability characteristics of software intensive critical systems. The verification method developed is innovative from current state of the art in what concerns the verification viewpoint adopted: focusing on software faults, and not, like many other approaches purely on fulfilling functional requirements. As a first step and based on a number of well defined criteria a comparison was made of available literature in the area of static non formal non probabilistic software fault removal techniques. But, None of the techniques evaluated fulfilled all criteria set in isolation. Therefore a new technique was developed based on a combination of two existing techniques: the FMEA and FTA. These two techniques complement each other very well. It is possible to integrate both techniques with commonly used techniques at system level. The resulting new technique can be shown to combine nearly all aspects of existing fault removal techniques.

• Journal of Communications and Networks
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• 제18권2호
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• pp.135-149
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• 2016

Cloud radio access network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• 항공우주의학회지
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• 제31권2호
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• pp.33-37
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• 2021

In the era of the 4th industrial revolution, the aviation industry is also growing remarkably with the development of artificial intelligence and networks, so it is necessary to study a new concept of crew resource management (CRM), which is required in the process of operating state-of-the-art equipment. The automation system, which has been treated only as a tool, is changing its role as a decision-making agent with the development of artificial intelligence, and it is necessary to set clear standards for the role and responsibility in the safety-critical field. We present a new perspective on the automation system in the CRM program through the understanding of the autonomous system. In the future, autonomous system will develop as an agent for human pilots to cooperate, and accordingly, changes in role division and reorganization of regulations are required.

• Advances in Computational Design
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• 제8권1호
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• 공업화학
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• 제34권4호
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• pp.365-382
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• 2023

A lithium metal anode with high energy density has the potential to revolutionize the field of energy storage systems (ESS) and electric vehicles (EVs) that utilize rechargeable lithium-based batteries. However, the formation of lithium dendrites during cycling reduces the performance of the battery while posing a significant safety risk. In this review, we discuss various strategies for achieving dendrite-free lithium metal anodes, including electrode surface modification, the use of electrolyte additives, and the implementation of protective layers. We analyze the advantages and limitations of each strategy, and provide a critical evaluation of the current state of the art. We also highlight the challenges and opportunities for further research and development in this field. This review aims to provide a comprehensive overview of the different approaches to achieving dendrite-free lithium metal anodes, and to guide future research toward the development of safer and more efficient lithium metal anodes.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.587-591
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• 2005

The case of laboratory-acquired SARS Corona virus infection in Taiwan has revealed a number of weaknesses in management, construction, and oversight of laboratories. Also, with the increased demands for bio-safety laboratory, there is an urgent need to develop a uniform and comprehensive guidance for architects and construction engineers in the preparation of design and construction. This research investigates the key elements for designers, engineers, and potential owners in biosafety laboratory design and construction. It defines key elements and determines major relationships and standards that should be adhered to when developing site layout. In addition to layout planning and design guidance of biosafety laboratory, this research also interviews the perspective of architects and survey the state-of-the-art technology in Taiwan. It represents the portraits by site investigation. The purpose of the research is to provide guideline of design and avoid potential future conflict to ensure the critical continuity of functions.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.402-404
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• 2022

Recent research has shown that deep learning models are vulnerable to adversarial attacks not only in the digital but also in the physical domain. This becomes very critical for applications that have a very high safety concern, such as self-driving cars. In this study, we propose a physical adversarial attack technique for one of the common tasks in self-driving cars, namely segmentation of the urban scene. Our method can create a texture on a wall so that it can be misclassified as a road. The demonstration of the technique on a state-of-the-art cityscape pretrained model shows a fairly high success rate, which should raise awareness of more potential attacks in self-driving cars.