• Nuclear Engineering and Technology
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• v.37 no.1
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• pp.79-90
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• 2005

In this paper are reviewed the current status of nuclear fuel management and reactor operational aid tools. In addition, we indicate deficiencies in current capabilities and what future research is judged warranted. For the nuclear fuel management review the focus is on light water reactors and the utilization of stochastic optimization methods applied to the lattice, fuel bundle, core loading pattern, and for BWRs the control rod pattern/core flow design decision making problems. Significant progress in addressing separately each of these design problems on a single cycle basis is noted; however, the outstanding challenge of addressing the integrated design problem over multiple cycles under conditions of uncertainty remains to be addressed. For the reactor operational aid tools review the focus is on core simulators, used to both process core instrumentation signals and as an operator aid to predict future core behaviors under various operational strategies. After briefly reviewing the current status of capabilities, a more in depth review of adaptive core simulation capabilities, where core simulator input data are adjusted within their known uncertainties to improved agreement between prediction and measurement, is presented. This is done in support of the belief that further development of adaptive core simulation capabilities is required to further significantly advance the utility of core simulators in support of reactor operational aid tools.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.18.1-18.9
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• 2020

Background: Bone grafting has been considered the gold standard for hard tissue reconstructive surgery and is widely used for large mandibular defect reconstruction. However, the midface encompasses delicate structures that are surrounded by a complex bone architecture, which makes bone grafting using traditional methods very challenging. Three-dimensional (3D) bioprinting is a developing technology that is derived from the evolution of additive manufacturing. It enables precise development of a scaffold from different available biomaterials that mimic the shape, size, and dimension of a defect without relying only on the surgeon's skills and capabilities, and subsequently, may enhance surgical outcomes and, in turn, patient satisfaction and quality of life. Review: This review summarizes different biomaterial classes that can be used in 3D bioprinters as bioinks to fabricate bone scaffolds, including polymers, bioceramics, and composites. It also describes the advantages and limitations of the three currently used 3D bioprinting technologies: inkjet bioprinting, micro-extrusion, and laserassisted bioprinting. Conclusions: Although 3D bioprinting technology is still in its infancy and requires further development and optimization both in biomaterials and techniques, it offers great promise and potential for facial reconstruction with improved outcome.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.79-85
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• 2018

This paper presents an implementation method of Long Term Evolution-Advanced (LTE-A) Physical Downlink Shared Channel (PDSCH) decoder using a general-purpose multicore Digital Signal Processor (DSP), TMS320C6670. Although the DSP provides some useful coprocessors such as turbo decoder, fast Fourier transformer, Viterbi Coprocessor, Bit Rate Coprocessor etc., it is specific to the base station platform implementation not the mobile terminal platform implementation. This paper shows an implementation method of the LTE-A PDSCH decoder using programmable DSP cores as well as the coprocessors of Fast Fourier Transformer and turbo decoder. First, it uses the coprocessor supported by the TMS320C6670, which can be used for PDSCH implementation. Second, we propose a core programming method using DSP optimization method for block diagram of PDSCH that can not use coprocessor. Through the implementation, we have verified a real-time decoding feasibility for the LTE-A downlink physical channel using test vectors which have been generated from LTE-A Reference Measurement Channel (RMC) Waveform R.6.

• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.67-80
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• 2022

The missile early-warning satellite systems have been developed and upgraded by some space-developed nations, under the inevitable trend that the space is more strongly considered as another battle field than before. As the key function of such a satellite-based early warning system, the prediction algorithm of the missile flight trajectory is studied in the paper. In particular, the evolution computation, receiving broad attention in the artificial intelligence area, is applied to the proposed prediction method so that the global optimum-like solution is found avoiding disadvantage of the previous non-linear optimization search tools. Moreover, using the prediction simulator of the launch vehicle flight trajectory which is newly developed in C# and Python, the paper verifies the performance and the feature of the proposed algorithm.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.175-185
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• 2023

As the era of ChatGPT and generative AI technologies unfolds, the marketing industry stands on the precipice of a paradigm shift. Innovations such as GPT-4, DALL-E 2, and Mid-journey Stable Diffusion possess the capacity to dramatically transform the methods by which advertisers reach and engage with customers. The potential applications of these advanced tools herald a new age for the marketing and advertising sectors, offering unprecedented opportunities for growth and optimization. Nevertheless, the rapid adoption of generative AI within these industries presents a unique set of challenges, particularly for organizations that lack the necessary technological infrastructure and human capital to effectively leverage these innovations. As a result, a competitive crisis may emerge, exacerbating existing disparities between well-equipped enterprises and their less technologically adept counterparts. In this article, we undertake a comprehensive exploration of the implications of generative AI for the future of marketing, examining both its potential benefits and drawbacks. We consider the possible impact of these developments on the advertising and marketing industries at large, as well as the ways in which professionals operating within these fields may need to adapt to remain competitive in an increasingly AI-driven landscape. By providing a holistic overview of the challenges and opportunities associated with generative AI, this study aims to elucidate the complex dynamics at play in the ongoing evolution of the marketing and advertising sectors.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.6
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• pp.608-614
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• 2023

The fast filling process of high-pressure hydrogen has an important impact on the filling efficiency and safety. In this paper, a specific study is carried out on the thermophysical phenomena during the fast filling process. Starting from the gas state equation of hydrogen, the change law of the hydrogen storage temperature is obtained, and then the temperature rise prediction is constructed. The model can clarify the relationship between the filling parameters and the temperature rise during the fast filling process, thereby revealing the flow and heat transfer laws of the fast charging process. To improve the theoretical research basis for the evaluation of vehicle-mounted hydrogen fast charging capacity, temperature prediction and optimization of hydrogenation methods.

• Journal of Korea Water Resources Association
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• v.45 no.1
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• pp.15-27
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• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• Cartoon and Animation Studies
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• s.14
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• pp.151-164
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• 2008

Cross platform game has brought about the expansion of game market, which results in technology innovation overcoming the limit of game consumption. The new model integrates both off and online game services. Gamers can now enjoy game service regardless of age, time, and space. If the technology evolution model of digital contents like cross-platform game engine can provide contents for several platform at the same time, the interactive service can be utilized into maximum level. It is also necessary to allocate, switch data as well as to innovate the transmission technology of data according to each platform. Providing the same contents for several platform as many as possible can be the most suitable strategy to enhance the efficiency and profits. However if the interactive service can be accomplished completely, the development of data switching technology and distribution should be made. To be a leader in the next digital contents market, one should develop the network engine technology which can embody the optimization of consumption in the interactive network service.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.70-77
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• 2009

In an RFID network consisting of a single reader and many tags, a framed and slotted ALOHA, which provides a number of slots for the tags to respond, was introduced for arbitrating a collision among tags' responses. In a framed and slotted ALOHA, the number of slots in each frame should be optimized to attain the maximal efficiency in tag cognizance. While such an optimization necessitates the knowledge about the number of tags, the reader hardly knows it. In this paper, we propose a tag cognizance scheme based on framed and slotted ALOHA, which is characterized by directly taking a Bayes action on the number of slots without estimating the number of tags separately. Specifically, a Bayes action is yielded by solving a decision problem which incorporates the prior distribution the number of tags, the observation on the number of slots in which no tag responds and the loss function reflecting the cognizance rate. Also, a Bayes action in each frame is supported by an evolution of prior distribution for the number of tags. From the simulation results, we observe that the pair of evolving prior distribution and Bayes action forms a robust scheme which attains a certain level of cognizance rate in spite of a high discrepancy between the Due and initially believed numbers of tags. Also, the proposed scheme is confirmed to be able to achieve higher cognizance completion probability than a scheme using classical estimate of the number of tags separately.