• Transactions of Materials Processing
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• v.30 no.2
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• pp.61-68
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• 2021

As part of efforts to reduce the weight of automotive body-in-white, a hybrid center pillar with high strength 7075 aluminum alloy (AA7075) sheet and carbon fiber reinforced plastic (CFRP) has been recently studied. In the fabrication of the AA7075-CFRP hybrid center pillar, the AA7075 sheet might go through heating-forming-in-die quenching (HFQ), artificial aging, hybridizing, and then paint baking processes. In this study, we investigate the effects of the heat treatment history associated with each process on the tensile strength of the AA7075 sheet. Typical heat treatment conditions are HFQ for 20 minutes at 480℃ and then cooling down with die, artificial aging of T6 temper for 24 hours at 120℃, hybridizing for 10 minutes at 150℃, and paint baking for 20 minutes at 180℃. The tensile strength of the AA7075 sheet is continuously increased by a series of heat treatments of hybridizing and paint baking and is expected to have yield stress above 500MPa without artificial aging of T6 temper.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.41-42
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• 2018

Various methods that crash the project completion time have been studied. Line-of-Balance (LOB) is well accepted as a useful method that enables steady resource utilization without frequent hire-and-fire of resources for a project having repetitive units. Existing studies involved in LOB-CPM focuses on thesis such as resource leveling and optimization in construction scheduling community. However, crashing methods are not arrived at a full maturity in LOB scheduling, because no one handles steady resource utilization while keeping activity-relationships. This paper proposes a method that crashes project completion time by hybridizing concurrent engineering and LOB scheduling without using additional resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1453-1463
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• 2004

The conventional image segmentation method using level set has been disadvantage since level set function in the gradient-based model evolves depending on the local profile of the edge. In this paper, a new model is introduced by hybridizing level set formulation and complementary smooth function in order to smooth the driving force. We consider an alternative way of getting the complementary function(CF) which is much easier to simulate and makes sense for most cases having no triple junctions. The rule of thumb is that CF must be computed such that the difference between their average and the original CF function should be able to introduce a reliable driving force for the evolution of the level set function. This proposed hybrid method tries to minimize drawbacks the conventional level set method.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.4
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• pp.29-33
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• 2013

Here we report a partially aminated micropattern via direct functionalization and examine eleven different solution-phase probe DNAs hybridizing with the same target DNA on both hydrogen and oxygen terminated diamond. The hybridization intensities determined by epifluorescence microscopy were compared and are influenced strongly by the negatively charged surface and mismatched position of its sequence with immobilized probe DNA.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.1-7
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• 1999

This paper gives a simulation study of a new fuzzy logic control(FLC) approach for the mold level control in continuous casting processes. The proposed FLC is PID type hybridizing the conventional fuzzy PI control and Fuzzy PD control with a simplified design scheme. It is shown that, compared with the conventional control, this new control strategy can achieve superior performance for steady-state response and is more robust against process parameter variations and disturbances.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• The Korean Journal of Zoology
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• v.31 no.4
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• pp.300-308
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• 1988

Human placental alkaline phosphatase (PLAP), one of the oncofetal antigen was purified from placentas through the procedures including butanol extraction, concanavalin A-Sephar-ose, DEAE-cellulose and Sephadex G-200 gel chromatography. Monoclonal antibodies (fibs) against human PMP were produced by hybridizing SP 210-Ag 14 mouse myeloma cells with spleen ceils of Balblc mice immunized with PLAP. Six stable monoclones uvere obtained by cloning tuvice in serial dilutions, and the monoclonal speclfidty of these MAbs was confirmed by biochemical and immunonogical criteria. Tumor marker의 하나인 태반형 alkaline phosphatase(PLAP)에 대한 단일 클론항체의 생산과 분석을 위하여, 태반조직을 재료로 butanol 추출법 및 concanavaline A-Sepharose, DEAE-cellulose, Sephadex G-200 gel 크로마토그라피법에 의하여 PLAP를 순수 분리하였다. 이를 항원으로 하여 하이브리도마 방법에 의해 항-PLAP 단일 클론항체를 생산 분비하는 안정된 6클론세포를 얻었으며 생화학적 및 면역학적 분석방법으로 이들의 단일 클론성을 확인하였다.

• Journal of Microbiology and Biotechnology
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• v.16 no.12
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• pp.2004-2007
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• 2006

An efficient method for the in vitro reassembly of homologous DNA sequences is presented. The proposed method involves obtaining single strands of homologous genes and hybridizing them to obtain partially hybridized heteroduplex DNA; cleaving the single-stranded regions of the heteroduplex DNA using S1 nuclease to generate double-strand DNA fragments; denaturing the double-strand DNA fragments to generate single-strand DNA fragments; conducting a series of polymerase chain reactions (PCR) using the single-strand DNA fragments as internal primers and a mixture of homologous DNA as templates to obtain elongated reassembled DNA; and finally, amplifying the reassembled DNA by a PCR using terminal primers. As a result, DNA reassembly could be achieved between homologous genes with a sequence similarity as low as 78%.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.179-186
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• 2015

This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.