• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.49-60
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• 1999

This paper addresses a welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in the welding process planning, it has not been considered through a systematic approach, but it depends rather on empirical knowledge. Thus, an effective task sequencing for robot arc welding is required. Welding perations can be classified by the number of welding robots. Genetic algorithms are applied to tackle those welding task sequencing problems. A genetic algorithm for traveling salesman problem (TSP) is utilized to determine welding task sequencing for a MultiWeldline-SingleLayer problem. Further, welding task sequencing for multiWeldline-MultiLayer welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is also proposed to solve multi-robot welding sequencing : MultiWeldline with multi robots. Finally, the genetic algorithm are implemented for the welding task sequencing of three dimensional weld plate assemblies. Robot welding operations conforming to the algorithms are simulated in graphic detail using a robot simulation software IGRIP.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.1-6
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• 2021

Next-generation sequencing (NGS) technologies have changed the process of genetic diagnosis from a gene-by-gene approach to syndrome-based diagnostic gene panel sequencing (DPS), diagnostic exome sequencing (DES), and diagnostic genome sequencing (DGS). A priori information on the causative genes that might underlie a genetic condition is a prerequisite for genetic diagnosis before conducting clinical NGS tests. Theoretically, DPS, DES, and DGS do not require any information on specific candidate genes. Therefore, clinical NGS tests sometimes detect disease-related pathogenic variants in genes underlying different conditions from the initial diagnosis. These clinical NGS tests are expensive, but they can be a cost-effective approach for the rapid diagnosis of rare disorders with genetic heterogeneity, such as the glycogen storage disease, familial intrahepatic cholestasis, lysosomal storage disease, and primary immunodeficiency. In addition, DES or DGS may find novel genes that that were previously not linked to human diseases.

• Journal of Genetic Medicine
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• v.20 no.2
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• pp.31-38
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• 2023

Rare diseases, even though defined as fewer than 20,000 in South Korea, with over 8,000 rare Mendelian disorders having been identified, they collectively impact 6-8% of the global population. Many of the rare diseases pose significant challenges to patients, patients' families, and the healthcare system. The diagnostic journey for rare disease patients is often lengthy and arduous, hampered by the genetic diversity and phenotypic complexity of these conditions. With the advent of next-generation sequencing technology and clinical implementation of exome sequencing (ES) and genome sequencing (GS), the diagnostic rate for rare diseases is 25-50% depending on the disease category. It is also allowing more rapid new gene-disease association discovery and equipping us to practice precision medicine by offering tailored medical management plans, early intervention, family planning options. However, a substantial number of patients remain undiagnosed, and it could be due to several factors. Some may not have genetic disorders. Some may have disease-causing variants that are not detectable or interpretable by ES and GS. It's also possible that some patient might have a disease-causing variant in a gene that hasn't yet been linked to a disease. For patients who remain undiagnosed, reanalysis of existing data has shown promises in providing new molecular diagnoses achieved by new gene-disease associations, new variant discovery, and variant reclassification, leading to a 5-10% increase in the diagnostic rate. More advanced approach such as long-read sequencing, transcriptome sequencing and integration of multi-omics data may provide potential values in uncovering elusive genetic causes.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.69-74
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• 2004

A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.23 no.2
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• pp.1-7
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• 2023

Inherited metabolic disorders (IMD) are a group of disorders involving various metabolic pathways. Genetic diagnosis of IMD has been challenging because of extremely heterogeneous nature and extensive laboratory and/or phenotype overlap. Conventional genetic diagnosis was a gene-by-gene approach that needs a priori information on the causative genes that might underlie the IMD. Recent implementation of next-generation sequencing (NGS) technologies has changed the process of genetic diagnosis from a gene-by-gene approach to simultaneous analysis of targeted genes possibly associated with the IMD using gene panels or using whole exome/genome sequencing (WES/WGS) covering entire human genes. Clinical NGS tests can be a cost-effective approach for the rapid diagnosis of IMD with genetic heterogeneity and are becoming standard diagnostic procedures.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.15-34
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• 1994

This paper considers the genetic algorithms(GAs) for the mixed model assembly line sequencing(MMALS) in which the objective is to minimize the overall line length. To apply the GAs to the MMALS, the representation, selection, genetic sequencing operators, and genetic parameters are studied. Especially, the existing sequencing binary operators such as partially map crossover(PMX), cycle crossover(CX), and order crossover (OX) are modified to be suitable for the MMALS, and a new sequencing binary operator called immediate successor relationship crossover (ISR) is introduced. These binary operators mentioned above and/or unary operators such as swap, insertion, inversion, displacement, and splice are compared to find operators which work well in the MMALS. Experimental results indicate that 1) among the binary operators ISR operator is the best, followed by the modified OX, and the modified PMX, with the modified CX being the worst, 2) among the unary operators inversion operator is the best, followed by displacement, swap, and insertion, with splice being the worst, and 3) in general, the unary operators perform better than the binary operators for the MMALS.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.115-125
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• 2004

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed-model U-lines (MMULs). Balancing and sequencing problems are important for an efficient use of MMULs and are tightly related with each other. However, in almost all the existing researches on mixed-model production lines, the two problems have been considered separately. A genetic algorithm for balancing and sequencing in mixed-model U line is proposed. A presentation method and genetic operators are proposed. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that the proposed algorithm is promising in solution quality.

• Journal of the Korea Safety Management & Science
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• v.9 no.4
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• pp.129-136
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• 2007

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of Ginseng Research
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• v.33 no.1
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• pp.55-58
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• 2009

Generally, the direct sequencing through PCR is faster, easier, cheaper, and more practical than clone sequencing. Frequently, standard PCR amplification is usually interpreted by mispriming internal or external regions of the target template. Normally, DNA fragments were eluted from the gel using Gel extraction kit and subjected to direct sequencing or cloning sequencing. Cloning sequencing has often troublesome and needs more time to analyze for many samples. Since touchdown (TD) PCR can generate sufficient and highly specific amplification, it reduces unwanted amplicon generation. Accordingly, TD PCR is a good method for direct sequencing due to amplifying wanted fragment. In plants the 5S-rRNA gene is separated by simple spacers. The 5S-rRNA gene sequence is very well-conserved between plant species while the spacer is species-specific. Therefore, the sequence has been used for phylogenetic studies and species identification. But frequent occurrences of spurious bands caused by complex genomes are encountered in the product spectrum of standard PCR amplification. In conclusion, the TD PCR method can be applied easily to amplify main 5S-rRNA and direct sequencing of panax ginseng cultivars.

• Journal of Genetic Medicine
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• v.12 no.1
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• pp.12-18
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• 2015

Malformations of cortical development (MCD) cover a broad spectrum of developmental disorders which cause the various clinical manifestations including epilepsy, developmental delay, and intellectual disability. MCD have been clinically classified based on the disruption of developmental processes such as proliferation, migration, and organization. Molecular genetic studies of MCD have improved our understanding of these disorders at a molecular level beyond the clinical classification. These recent advances are resulted from the development of massive parallel sequencing technology, also known as next-generation sequencing (NGS), which has allowed researchers to uncover novel molecular genetic pathways associated with inherited or de novo mutations. Although an increasing number of disease-related genes or genetic variations have been identified, genotype-phenotype correlation is hampered when the biological or pathological functions of identified genetic variations are not fully understood. To elucidate the causality of genetic variations, in vivo disease models that reflect these variations are required. In the current review, we review the use of NGS technology to identify genes involved in MCD, and discuss how the functions of these identified genes can be validated through in vivo disease modeling.