• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3501-3523
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• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• Reproductive and Developmental Biology
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• v.32 no.1
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• pp.65-70
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• 2008

Members of the Vps (Vacuolar protein sorting) protein family involved in the formation of the retromer complex have been discovered in a variety of species such as yeast, mouse, and human. A mammalian retromer complex is composed of Vps26, Vps29, and Vps35 proteins and plays and important role in cation-independent mannose-6-phosphate receptor retrieval from the endosome to the trans-Golgi network. In this study, we have identified the full-length sequences of the retromer components of Vps26, Vps29, and Vps35 in micro pigs. The cDNA sequences of these retromer components have been determined and the result showed there is 99% homology among the component counterparts from mouse, micro pigs, and humans. In addition, the retromer complexes formed with hetero-components were found in the brain of micro pigs. Based on above results, we suggest mammalian Vps components are well conserved in micro pigs.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.455-464
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• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• Journal of Microbiology and Biotechnology
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• v.30 no.9
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• pp.1355-1366
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• 2020

Sediment bacterial communities are critical to the biogeochemical cycle in river ecosystems, but our understanding of the relationship between sediment bacterial communities and their specific input streams in rivers remains insufficient. In this study, we analyzed the sediment bacterial community structure in a local river receiving discharge of urban domestic sewage by applying Illumina MiSeq high-throughput sequencing. The results showed that the bacterial communities of sediments samples of different pollution types had similar dominant phyla, mainly Proteobacteria, Actinobacteria, Chloroflexi and Firmicutes, but their relative abundances were different. Moreover, there were great differences at the genus level. For example, the genus Bacillus showed statistically significant differences in the hotel site. The clustering of bacterial communities at various sites and the dominant families (i.e., Nocardioidaceae, and Sphingomonadaceae) observed in the residential quarter differed from other sites. This result suggested that environmentally induced species sorting greatly influenced the sediment bacterial community composition. The bacterial co-occurrence patterns showed that the river bacteria had a nonrandom modular structure. Microbial taxonomy from the same module had strong ecological links (such as the nitrogenium cycle and degradation of organic pollutants). Additionally, PICRUSt metabolic inference analysis showed the most important function of river bacterial communities under the influence of different types of domestic sewage was metabolism (e.g., genes related to xenobiotic degradation predominated in residential quarter samples). In general, our results emphasize that the adaptive changes and interactions in the bacterial community structure of river sediment represent responses to different exogenous pollution sources.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.177-184
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• 2022

The automatic defect sorting function of machinery parts is being introduced to the automation of the manufacturing process. In the final stage of automation of the manufacturing process, it is necessary to apply computer vision rather than human visual judgment to determine whether there is a defect. In this paper, we introduce a deep learning method to improve the classification performance of typical mechanical parts, such as welding parts, galvanized round plugs, and electro galvanized nuts, based on the results of experiments. In the case of poor welding, the method to further increase the depth of layer of the basic deep learning model was effective, and in the case of a circular plug, the surrounding data outside the defective target area affected it, so it could be solved through an appropriate pre-processing technique. Finally, in the case of a nut plated with zinc, since it receives data from multiple cameras due to its three-dimensional structure, it is greatly affected by lighting and has a problem in that it also affects the background image. To solve this problem, methods such as two-dimensional connectivity were applied in the object segmentation preprocessing process. Although the experiments suggested that the proposed methods are effective, most of the provided good/defective images data sets are relatively small, which may cause a learning balance problem of the deep learning model, so we plan to secure more data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.17-34
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• 2022

This paper develops a model for dynamic station assignment to optimize the Demand Responsive Transit (DRT) operation. In the process of optimization, we use the bus travel time as a variable for DRT management. In addition, walking time, waiting time, and delay due to detour to take other passengers (detour time) are added as optimization variables and entered for each DRT passenger. Based on a network around Anaheim, California, reserved origins and destinations of passengers are assigned to each demand responsive bus, using K-means clustering. We create a model for selecting the dynamic station and bus route and use Non-dominated Sorting Genetic Algorithm-III to analyze seven scenarios composed combination of the variables. The result of the study concluded that if the DRT operation is optimized for the DRT management, then the bus travel time and waiting time should be considered in the optimization. Moreover, it was concluded that the bus travel time, walking time, and detour time are required for the passenger.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.119-134
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• 2018

A knowledge map as the taxonomy used in a knowledge repository should be structured to support and supplement knowledge activities of users who sequentially inquire and select knowledge for problem solving. The conventional knowledge map with a hierarchical structure has the advantage of systematically sorting out types and status of the knowledge to be managed, however it is not only irrelevant to knowledge user's process of cognition and utilization, but also incapable of supporting user's activity of querying and extracting knowledge. This study suggests a methodology for constructing a networked knowledge map that can support and reinforce the referential navigation, searching and selecting related and chained knowledge in term of contents, between knowledge. Regarding a keyword as the semantic information between knowledge, this research's networked knowledge map can be constructed by aggregating each set of knowledge links in an automated manner. Since a keyword has the meaning of representing contents of a document, documents with common keywords have a similarity in content, and therefore the keyword-based document networks plays the role of a map expressing interactions between related knowledge. In order to examine the feasibility of the proposed methodology, 50 research papers were randomly selected, and an exemplified networked knowledge map between them with content relevance was implemented using common keywords.

• Animal cells and systems
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• v.4 no.2
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• pp.157-163
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• 2000

Yeast pheromone a-factor is a 13-amino acid peptide hormone that is synthesized as a part of a larger precursor, prepro-$\alpha$-factor, consisting of a signal peptide and a proregion of 64 amino acids. The carboxy-terminal half of the precursor contains four tandem copies of mature $\alpha$-factor. To investigate the molecular basis of intracellular sorting, proteolytic processing, and storage of the peptide hormone, yeast prepro-$\alpha$-factor precursors were heterologously expressed in rat pituitary $GH_3 cells. When cells harboring the precursor were metabolically labeled, a species of approximately 27 kD appeared inside the cells. Digestion with peptide: N-glycosidase F (PNG-F) shifted the molecular mass to a 19 kD, suggesting that the 27 kD protein was the glycosylated form as in yeast cells. The nascent polypeptide is efficiently targeted to the ER in the $GH_3 cells, where it undergoes cleavage of its signal peptide and core glycosylation to generate glycosylated pro-a-factor. To look at the post ER intracellular processing, the pulse-labelled cells were chased up to 2 hrs. The nascent propeptides disappeared from the cells at a half life of 30 min and only 10-25% of the newly synthesized, unprocessed precursors were stored intracellularly after the 2 h chase. However, about 20% of the pulse-labeled pro-$\alpha$-factor precursors were secreted into the medium in the pro-hormone form. With increasing chase time, the intracellular level of propeptide decreased, but the amount of secreted propeptide could not account for the disappearance of intracellular propeptide completely. This disappearance was insensitive to lysosomotropic agents, but was inhibited at $16^{circ}C or 20^{\circ}C$, suggesting that the turnover of the precursors was not occurring in the secretory pathway to trans Golgi network (TGN) or dependent on acidic compartments. From these results, it is concluded that a pan of these heterologous precursors may be processed at its paired dibasic sites by prohormone processing enzymes located in TGN/secretpry vesicles producing small peptides, and that the residual unprocessed precursors may be secreted into the medium rather than degraded intracellularly.