• Journal of Korea Foundry Society
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• v.17 no.5
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• pp.465-471
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• 1997

An integrated computer program consisting of a pre-processor, main solver, and post-processor was developed for the design of large steel castings. The pre-processor, based on the AutoCAD, enables the user to produce approval drawings, casting design drawings and mesh diagrams in sequence using a personal computer. In the main solver, two numerical models were employed; one models the fluid flow during mold filling, and the other models the heat transfer and solidification. The post-processor can be used to present simulation results such as flow pattern, mold filling sequences, solidification times, temperature gradients and location of shrinkage defects by color graphics. In order to validate the applicability of the present integrated program, a series of experiments on simple-shaped steel castings were carried out. After the validation of the present model, it was applied to the casting design of the large steel anchor of an SC42 alloy. Various solidification parameters such as a temperature distribution and a solidification time in the casting and the mold were compared with those obtained experimentally. Simulated results predicting shrinkage defects were in good agreement with those obtained experimentally. It was found that the present method can be successfully applied to the quantitative casting design for complex-shaped large steel castings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.561-571
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• 2021

Cofferdams made of teel sheet piles are commonly utilized as support structures for excavation of sea-crossing bridge foundations. As cofferdams are often subject to tide variation, it is imperative to consider potential effects of tide on stability and serviceability of sheet piles, particularly, ultralong steel sheet piles (USSPs). In this study, a real USSP cofferdam constructed using new construction technology in Nanxi River was reported. The design of key parts of USSP cofferdam in the presence of tidal action was first introduced followed by the description of entire construction technology and associated monitoring results. Subsequently, a three-dimensional finite-element model corresponding to all construction steps was established to back-analyze measured deflection of USSPs. Finally, a series of parametric studies was carried out to investigate effects of tide level, soil parameters, support stiffness and construction sequence on lateral deflection of USSPs. Monitoring results indicate that the maximum deflection during construction occurred near the riverbed. In addition, measured stress of USSPs showed that stability of USSP cofferdam strengthened as construction stages proceeded. Moreover, the numerical back-analysis demonstrated that the USSP cofferdam fulfilled the safety requirements for construction under tidal action. The maximum deflection of USSPs subject to high tide was only 13.57 mm at a depth of -4 m. Sensitivity analyses results showed that the design of USSP cofferdam system must be further improved for construction in cohesionless soils. Furthermore, the 5th strut level before concreting played an indispensable role in controlling lateral deflection of USSPs. It was also observed that pumping out water before concreting base slab could greatly simplify and benefit construction program. On the other hand, the simplification in construction procedures could induce seepage inside the cofferdam, which additionally increased the deflection of USSPs by 10 mm on average.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1066-1072
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• 2011

In the method to analyze the relationship in the system call orders, the normal system call orders are divided into a certain size of system call orders to generates gene and use them as the detectors. In the method to consider the system call parameters, the mean and standard deviation of the parameter lengths are used as the detectors. The attack of which system call order is normal but the parameter values are changed, such as the format string attack, cannot be detected by the method that considers only the system call orders, whereas the model that considers only the system call parameters has the drawback of high positive defect rate because of the information obtained from the interval where the attack has not been initiated, since the parameters are considered individually. To solve these problems, it is necessary to develop a more efficient learning and detecting method that groups the continuous system call orders and parameters as the approach that considers various characteristics of system call related to attacking simultaneously. In this article, we detected the anomaly of the system call orders and parameters by applying the temporal concept to the system call orders and parameters in order to improve the rate of positive defect, that is, the misjudgment of anomaly as normality. The result of the experiment where the DARPA data set was employed showed that the proposed method improved the positive defect rate by 13% in the system call order model where time was considered in comparison with that of the model where time was not considered.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.6
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• pp.316-325
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• 2006

In diploid organisms like human, each chromosome consists of two copies. A haplotype is a SNP(single nucleotide polymorphism) sequence information from each copy. Finding the complete map of haplotypes in human population is one of the important issues in human genome. To obtain haplotypes via experimental methods is both time-consuming and expensive. Therefore, inference methods have been used to infer haplotyes from the genotype samples. In this paper, we propose a new approach using genetic algorithm to infer haplotypes, which is based on the model of finding the minimum number of haplotypes that explain the genotype samples. We show that by doing a computational experiment, our algorithm has the correctness similar to HAPAR[1] which is known to produce good results while the execution time of our algorithm is less than that of HAPAR as the input size is increased. The experimental result is also compared with the result by the recent method PTG[2].

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.73-75
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• 2017

Debris disks are important observational clues to understanding on-going planetary system formation. They are usually identified by significant mid-infrared excess on top of the photospheric emission of a central star on the basis of prediction from J-, H-, and Ks-band fluxes and the stellar model spectra. For bright stars, 2MASS near-infrared fluxes suffer large uncertainties due to the near-infrared camera saturation. Therefore we have performed follow-up observations with the IRSF 1.4 m near-infrared telescope located in South Africa to obtain accurate J-, H-, and Ks-band fluxes of the central stars. Among 754 main-sequence stars which are detected in the AKARI $18{\mu}m$ band, we have performed photometry for 325 stars with IRSF. As a result, we have successfully improved the flux accuracy of the central stars from 9.2 % to 0.5 % on average. Using this dataset, we have detected $18{\mu}m$ excess emission from 57 stars in our samples with a $3{\sigma}$ level. We find that some of them have high ratios of the excess to the photospheric emission even around very old stars, which cannot be explained by the current planet-formation theories.

• BMB Reports
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• v.40 no.4
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• pp.486-493
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• 2007

Atopic dermatitis (AD) is a chronic inflammatory skin disease and the pathogenesis of AD is associated with the release of various cytokines/chemokines due to activated $Th_2$ immune responses. Synthetic oligodeoxynucleotides (ODNs) containing unmethylated CpG dinucleotide in the context of particular base sequence (CpG motifs) are known to have the immunostimulatory activities in mice and to convert from Th2 to Th1 immune responses in AD. We aimed to investigate that CpG ODN, especially phosphodiester form, can stimulate the protective immunity in NC/Nga mice with AD. We isolated BMDCs from NC/Nga mice and then, cultured with GM-CSF and IL-4 for 6 days, and treated for 2 days by either phosphorothioate ODN or phosphodiester ODN. CpG ODN-treated DCs resulted in more production of IL-12. When CpG ODN-treated DCs were intravenously injected into the NC/Nga mice, the NC/Nga mice with CpG ODN-treated DCs showed significant improvement of AD symptoms and decrease of IgE level. Histopathologically, the NC/Nga mice skin with CpG ODN-treated DCs showed the decreased IL-4 and TARC expression comparing with non-injected mice. These results may suggest that phosphodiester CpG ODN-treated DCs might function as a potent adjuvant for AD in a mouse model.

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.17-25
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• 2005

There are some critical drawbacks in the use of biomarkers for a global assessment of the toxicological impacts many chemicals and environmental pollutants have, primarily due to an individual biomarker's specificity for an explicit chemical or toxicant. In other words, the biomarker-based assessment methodology used to analyze toxicological effects lacks a high-throughput capability. Therefore, eco-toxicogenomics, or the study of toxicogenomics with organisms present within a given environmental locale, has recently been introduced with the advent of the so-called "-omics" era, which began with the creation of microarray technologies. Fish are comparable with humans in their toxicological responses and thus data from toxicogenomic studies performed with fish could be applied, with appropriate tools and implementation protocols, to the evaluation of environments where human or animal health is of concern. At present, there have been very active research streams for developing expression sequence tag (EST) databases (DBs) for zebra fish and rainbow trout. Even though few reports involve toxicogenomic studies with fish, a few groups have successfully fabricated and used cDNA microarrays or oligo DNA chips when studying the toxicological impacts of hypoxia or some toxicants with fish. Furthermore, it is strongly believed that this technology can also be implemented with non-model fish. With the standardization of DNA microarray technologies and ample progress in bioinformatics and proteomic technologies, data obtained from DNA microarray technologies offer not only multiple biomarker assays or an analysis of gene expression profiles, but also a means of elucidating gene networking, gene-gene relations, chemical-gene interactions, and chemical-chemical relationships. Accordingly, the ultimate target of eco-toxicogenomics should be to predict and map the pathways of stress propagation within an organism and to analyze stress networking.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.793-799
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• 2009

Esterel is an imperative synchronous language that is well-adopted to specify reactive systems. Programmers sometimes want simple validations that can be applied while the system is under development. Since a reactive system reacts to environment changes, a test data is a sequence of input events. Generating proper test data by hand is complex and error-prone. Although several test data generators exist, they are hard to learn and use. Mostly, system designers need test data to reach a specific status of a target program. In this paper, we develop a test data generator to generate test input sequences for debugging Esterel programs. Our tool is focused on easy usage; users can describe test data properties with simple specifications. We show a case study in which the test data generator is used for a practical development process.