• The KIPS Transactions:PartC
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• v.14C no.3 s.113
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• pp.277-284
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• 2007

The routing algorithm many used in the wireless sensor network features the clustering method to reduce the amount of data transmission from the energy efficiency perspective. However, the clustering method results in high energy consumption at the cluster head node. Dynamic clustering is a method used to resolve such a problem by distributing energy consumption through the re-selection of the cluster head node. Still, dynamic clustering modifies the cluster structure every time the cluster head node is re-selected, which causes energy consumption. In other words, the dynamic clustering approaches examined in previous studies involve the repetitive processes of cluster head node selection. This consumes a high amount of energy during the set-up process of cluster generation. In order to resolve the energy consumption problem associated with the repetitive set-up, this paper proposes the Round-Robin Cluster Header (RRCH) method that fixes the cluster and selects the head node in a round-robin method The RRCH approach is an energy-efficient method that realizes consistent and balanced energy consumption in each node of a generated cluster to prevent repetitious set-up processes as in the LEACH method. The propriety of the proposed method is substantiated with a simulation experiment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.18-25
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• 2007

In sensor networks, sensor nodes have limited computational capacity, power and memory. Thus energy efficiency is one of the most important requirements. How to extend the lifetime of wireless sensor networks has been widely discussed in recent years. However, one of the most effective approaches to cope with power conservation, network scalability, and load balancing is clustering technique. The function of a cluster head is to collect and route messages of all the nodes within its cluster. Cluster heads must be changed periodically for low energy consumption and load distribution. In this paper, we propose an energy-aware cluster head selection algorithm and Distance Estimation-based distributed Clustering Algorithm (DECA) in wireless sensor networks, which exchanges cluster heads for less energy consumption by distance estimation. Our simulation result shows that DECA can improve the system lifetime of sensor networks up to three times compared to the conventional scheme.

• Archives of Plastic Surgery
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• v.44 no.5
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• pp.413-419
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• 2017

Background In breast reconstruction using implants after unilateral mastectomy, it is challenging to create a natural, ptotic contour, and asymmetry is a potential drawback. To achieve breast symmetry and an ideal shape for both breasts, we performed contralateral augmentation in patients undergoing breast reconstruction with implants. Methods Patients underwent unilateral mastectomy and 2-stage reconstruction. During the second stage of the procedure, contralateral augmentation mammoplasty was performed. Preoperatively, we obtained the patients' demographic information, and we then assessed breast volume, the volume and dimensions of the inserted implants, and complications. Breast symmetry was observed by the surgeon and was assessed by measuring the disparity between the final volume of each breast. Results Contralateral augmentation was performed in 52 cases. When compared to patients who did not undergo a contralateral balancing procedure, patients who received contralateral augmentation were younger, thinner, and had smaller breasts. During implant selection for contralateral augmentation, we chose implants that were approximately 1 cm shorter in width, 1 level lower in height, and 1 or 2 levels lower in projection than the implants used for reconstruction. The postoperative breast contours were symmetric and the final volume discrepancy between each breast, which was measured by 3-dimensional scanning, was acceptable. Conclusions We demonstrate that contralateral augmentation can be recommended for patients who perceive their breasts to be small and not beautiful in order to achieve an ideal and beautiful shape for both breasts. Furthermore, this study offers guidelines for selecting the implant that will lead to the optimal aesthetic outcome.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.214-214
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• 2022

Soluble starch synthases (SSs) elongate α-glucans from ADP-Glc to the glucan nonreducing ends and play a critical role in synthesizing resistant starch in the rice. A total of 10 SSs isoforms were reported in rice, including granules-bound starch synthase I (GBSSI), GBSSII, starch synthase I (SSI), SSIIa (SSII-3), SSIIb (SSII-2), SSIIc (SSII-1), SSIIIa (SSIII-2), SSIIIb (SSIII-1), SSIVa (SSIV-1), and SSIVb (SSIV-2). SSIII proteins are involved in forming the B chain and elongating cluster filling chains in amylopectin metabolism. The functions of SSIIIb (SSIII-1) are less clear as compared to SSs. Here, we sought to shed light on the genetic diversity profiling of the SSIII-1 gene in 374 rice accessions composed of 54 wild-type accessions and 320 bred cultivars (temperate japonica, indica, tropical japonica, aus, aromatic, and admixture). In total, 17 haplotypes were identified in the SSIII-1 coding region of 320 bred cultivars, while 44 haplotypes were detected from 54 wild-type accessions. The genetic diversity indices revealed the most negative Tajima's D value in the temperate-japonica, followed by the wild type, while Tajima's D values in other ecotypes were positive, indicating balancing selection. Nucleotide diversity in the SSIII-1 region was highest in the wild group (0.0047) while lowest in temperate-japonica. Lower nucleotide diversity in the temperate-japonica is evidenced by the negative Tajima's D and suggested purifying selection. The fixation index (F_ST) revealed a very high level of gene flow (low F_ST) between the tropical-japonica and admixture groups (F_ST=-0.21) followed by admixture and wild groups (-0.04), indica and admixture groups (0.02), while low gene flow with higher F_ST estimates between the temperate-japonica and aus groups (0.72), tropical-japonica and aromatic groups (0.71), and temperate-japonica and admixture groups (0.52). Taken together, our study offers insights into haplotype diversity and evolutionary fingerprints of SSIII-1. It provides genomic information to increase the resistant starch content of cooked rice.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.212-212
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• 2022

Soluble starch synthase (SS) IV-2 is one of the starch synthase gene family members and responsible for starch chain elongation interacting with other rice eating and cooking quality controlling genes (e.g., AGPlar and PUL). SSIV-2 is mainly expressed in leaves, especially at grain-filling stage and its alleles can significantly affect rice quality. Here, we investigated the genetic diversity and population structure analyses of SSIV-2 gene by using 374 rice accessions. This rice set was grouped into 320 cultivated bred (subsequently classified into temperate japonica, indica, tropical japonica, aus, aromatic and admixture) and 54 wild rice. Haplotyping of cultivated rice accessions provided a total of 7 haplotypes, and only three haplotypes are functional indicating four substituted SNPs in two exons of chromosome 5: T/A and G/T in exon 4, and C/G and G/A in exon 13. Including the wild, a highest diverse group (0.0041), nucleotide diversity analysis showed temperate japonica (0.0001) had a lowest diversity value indicating the origin information of this gene evolution. Higher and positive Tajima5s D value of indica (1.9755) indicate a selective signature under balancing selection while temperate japonica (-0.9018) was in lowest Tajima's D value due to a recent selective sweep by positive selection. We found the most diverse genetic components of the wild in PCA but shared in some portion with other cultivated groups. Fixation index (F_ST-values) and phylogenetic analysis indicate a closer relationship of the wild with indica (F_ST=0.256) than to its association to both of temperate japonica (F_ST=0.589). Structure analysis shows a clear separation of cultivated subpopulations at every K value, but genetic components were admixed within the wild illustrating the same genetic background with japonica and indica in some proportion.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.81-90
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• 2013

Material flow control (MFC) is a kind of operational policy to control of the movement of raw materials, components, and products through the manufacturing lines. It is very important because it varies throughput, line cycle time, and work-in-process (WIP) under the same manufacturing environments. MFC can be largely categorized into three types such as Push, Pull, and Hybrid. In this paper, we set various manufacturing environments to compare five existing MFC mechanisms: Push, Pull, and Hybrid (CONWIP, Gated MaxWIP, Critical WIP Loops, etc). Three manufacturing environments, manufacturing policies (make to stock and make to order), demand (low, medium, high), and line balancing (balanced, unbalanced, and highly unbalanced) are considered. The MFCs are compared in the point of the five functional efficiencies and the proposed compounded efficiency. The simulation results shows that the Push is superior in the functional efficiency and GMWIP is superior in the compounded efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.577-594
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• 2020

Service Function Chaining (SFC) supports services through linking an ordered list of functions. There may be multiple instances of the same function, which provides a challenge to select available instances for all the functions in an SFC and generate a specific Service Function Path (SFP). Aiming to solve the problem of SFP selection, we propose an architecture consisting of distributed SFP algorithm and central control mechanism. Nodes generate distributed routings based on the first function and destination node in each service request. Controller supervises all of the distributed routing tables and modifies paths as required. The architecture is scalable, robust and quickly reacts to failures because of distributed routings. Besides, it enables centralized and direct control of the forwarding behavior with the help of central control mechanism. Simulation results show that distributed routing tables can generate efficient SFP and the average cost is acceptable. Compared with other algorithms, our design has a good performance on average cost of paths and load balancing, and the response delay to service requests is much lower.

• Journal of Life Science
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• v.18 no.7
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• pp.1011-1014
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• 2008

The ability or inability to taste phenylthiocarbamide (PTC) is a classic inherited trait that has been best-studied in human populations. Also, variation in PTC perception has been correlated with dietary preferences and thus may have important consequence for diet-related diseases in modem populations. The recent identification of the TAS2R38 gene (PTC gene) which is a member of TAS2R family of bitter taste receptor genes and three common polymorphisms in the gene is highly correlated with taste sensitivity to PTC. Balancing natural selection has acted to maintain high frequency of both alleles of the gene in human population. Future detailed studies of the relationships between molecular mechanisms and taste function may have therapeutic implications, such as helping patients to consume beneficial bitter-tasting compounds.