• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.115-122
• /
• 2023

This study utilized a discrete element method (DEM) simulation, as one of the virtual field trials, to predict the impact of tillage depth on the rotary blade shaft during rotavator tilling. The virtual field for the simulation was generated according to soil properties observed in an actual field. Following the generation of particles for the virtual field, a sequence of calibration steps followed to align the mechanical properties more closely with those of real soil. Calibration was conducted with a focus on bulk density and shear torque, resulting in calibration errors of just 0.02% for bulk density and 0.52% for shear torque. The prediction of the load on a rotary tiller's blade shaft involved a three-pronged approach, considering shaft torque, draft force, and vertical force. In terms of shaft torque, the values exhibited significant increases of 42.34% and 36.91% for every 5-centimeter increment in tillage depth. Similarly, the vertical force saw substantial growth by 40.41% and 36.08% for every 5-centimeter increment. In contrast, the variation in draft force based on tillage depth was comparatively lower at 18.49% and 0.96%, indicating that the effect of tillage depth on draft force was less pronounced than its impact on shaft torque and vertical force. From a perspective of agricultural machinery research, this study provides valuable insights into the DEM soil modeling process, accounting for changes in soil properties with varying tillage depths. These findings are expected to be instrumental in future agricultural machinery design studies.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.129-143
• /
• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Korean journal of applied entomology
• /
• v.54 no.2
• /
• pp.91-97
• /
• 2015

The sweetpotato whitefly, Bemisia tabaci, is the major insect pest that transmitted over 100 plant viruses including tomato yellow leaf curl virus (TYLCV) of tomato plant as virus vector in the world. In this study, cDNA library of whitefly was constructed using Gateway system for selecting target gene in order to control of B. tabaci using virus-induced gene silencing (VIGS) vector with RNAi. First of all, when using oligo d(T) rimer, the calculated titer of cDNA library was confirmed with $1.4{\times}10^4$ clones and average insert sizes was confirmed with 1 kb. However, insert size was very big for construction of cDNA. Otherwise, when using attB-N25 random primer and sonication for 6 sec, the calculated titer of cDNA library was confirmed with $1.04{\times}10^5$ clones. But mostly insert band wasn't identified on the electrophoresis, because it seemed that insert size is too small (${\leq}100bp$), also the size of identified insert was somewhat big. Finally, when using oligo d(T) primer and sonication for 1 sec, cDNA insert of whitefly was appropriated for VIGS with 300-600 bp. However, cDNA sequence included a poly A and titer was very low to $5.2{\times}10^2$ clones. It was supposed that heat shock transformation was used instead of electro-transformation. It is considered that when constructing cDNA library for using VIGS vector, (1) random primer should be used for First strand cDNA synthesis in order to remove poly A and (2) sonication for 1 sec should be performed in order to get appropriated insert size and (3) electro-transformation should be performed in order to improve transformation efficiency.

• Journal of Plant Biotechnology
• /
• v.43 no.3
• /
• pp.261-271
• /
• 2016

Citrus is an economically important fruit crop widely growing worldwide. However, citrus production largely depends on natural hybrid selection and bud sport mutation. Unique botanical features including long juvenility, polyembryony, and QTL that controls major agronomic traits can hinder the development of superior variety by conventional breeding. Diverse factors including drastic changes of citrus production environment due to global warming and changes in market trends require systematic molecular breeding program for early selection of elite candidates with target traits, sustainable production of high quality fruits, cultivar diversification, and cost-effective breeding. Since the construction of the first genetic linkage map using isozymes, citrus scientists have constructed linkage maps using various DNA-based markers and developed molecular markers related to biotic and abiotic stresses, polyembryony, fruit coloration, seedlessness, male sterility, acidless, morphology, fruit quality, seed number, yield, early fruit setting traits, and QTL mapping on genetic maps. Genes closely related to CTV resistance and flesh color have been cloned. SSR markers for identifying zygotic and nucellar individuals will contribute to cost-effective breeding. The two high quality citrus reference genomes recently released are being efficiently used for genomics-based molecular breeding such as construction of reference linkage/physical maps and comparative genome mapping. In the near future, the development of DNA molecular markers tightly linked to various agronomic traits and the cloning of useful and/or variant genes will be accelerated through comparative genome analysis using citrus core collection and genome-wide approaches such as genotyping-by-sequencing and genome wide association study.

• Journal of the Korea Concrete Institute
• /
• v.24 no.3
• /
• pp.275-283
• /
• 2012

Steel Plate for Rebar Connection was recently developed to splice rebars in delayed slab-wall joints in high-rise building, slurry wall-slab joints, temporary openings, etc. It consists of several couplers and a thin steel plate with shear key. Cyclic loading tests on slab-wall joints were conducted to verify structural behavior of the joints having Steel Plate for Rebar Connection. For comparison, joints with Rebend Connection and without splices were also tested. The joints with Steel Plate for Rebar Connection showed typical flexural behavior in the sequence of tension re-bar yielding, sufficient flexural deformation, crushing of compression concrete, and compression rebar buckling. However, the joints with Rebend Connection had more bond cracks in slabs faces and spalling in side cover-concrete, even though elastic behavior of the joints was similar to that of the joints with Steel Plate for Re-bar Connection. Consequently, the joints with Rebend Connection had less strengths and deformation capacities than the joints with Steel Plate for Re-bar Connection. In addition, stiffness of the joints with Rebend Connection degraded more rapidly than the other joints as cyclic loads were applied. This may be caused by low elastic modulus of re-straightened rebars and restraightening of kinked bar. For two types of diameters (13mm and 16mm) and two types of grades (SD300 and SD400) of rebars, the joints with Steel Plate for Rebar Connection had higher strength than nominal strength calculated from actual material properties. On the contrary, strengths of the joints with Rebend Connection decreased as bar diameter increased and as grade becames higher. Therefore, Rebend Connection should be used with caution in design and construction.

• Journal of Plant Biotechnology
• /
• v.39 no.1
• /
• pp.33-48
• /
• 2012

As a scientific curiosity to understand the structure and the function of crops and experimental efforts to apply it to plant breeding, genetic maps have been constructed in various crops. Especially, in the case of Brassica crop, genetic mapping has been accelerated since genetic information of model plant $Arabidopsis$ was available. As a result, the whole $B.$ $rapa$ genome (A genome) sequencing has recently been done. The genome sequences offer opportunities to develop molecular markers for genetic analysis in $Brassica$ crops. RFLP markers are widely used as the basis for genetic map construction, but detection system is inefficiency. The technical efficiency and analysis speed of the PCR-based markers become more preferable for many form of $Brassica$ genome study. The massive sequence informative markers such as SSR, SNP and InDels are also available to increase the density of markers for high-resolution genetic analysis. The high density maps are invaluable resources for QTLs analysis, marker assisted selection (MAS), map-based cloning and comparative analysis within $Brassica$ as well as related crop species. Additionally, the advents of new technology, next-generation technique, have served as a momentum for molecular breeding. Here we summarize genetic and genomic resources and suggest their applications for the molecular breeding in $Brassica$ crop.

• Korean Journal of Microbiology
• /
• v.46 no.1
• /
• pp.21-26
• /
• 2010

As the causative agent of Anthrax, Bacillus anthracis causes an acute fatal disease in herbivores such as cattle, sheep, and horses as well as humans. The therapeutics and prevention of anthrax currently available are based on antibiotics and the live attenuated vaccine strains, which may be problematic due to the emergency of antibiotic resistant strains or residual virulence in those vaccine strains. Therefore, it has been required to develop novel therapeutics and vaccines which are safer and applicable to humans. Recently, the development of the multivalent vaccine targeting both spores and vegetative cells of B. anthracis along with anthrax toxin has been reported. In our attempts to screen potential candidates for those multivalent vaccines, the whole genomic expression library of B. anthracis was constructed in this study. To the end, the partial digests of the genomic DNA from B. anthracis (ATCC 14578) with Sau3AI were ligated with the inducible pET30abc expression vectors, resulting in approximately $1{\times}10^5$ clones in E. coli BL21(DE3). The redundancy test by DNA nucleotide sequencing was performed for the randomly selected 111 clones and found 56 (50.5%) B. anthracis genes, 17 (15.3%) vector sequences, and 38 (34.2%) unknown genes with no sequence homology by BLAST. An inducible expression of the recombinant proteins was confirmed by Western blot. Interestingly, some clones could react with the antiserum against B. anthracis. These results imply that the whole genomic library constructed in this study can be applied for analyzing the immunomes of B. anthracis.

• Journal of the Korean Society for Library and Information Science
• /
• v.15
• /
• pp.225-266
• /
• 1988

Various theories and techniques for the subject catalog have been developed since Charles Ammi Cutter first tried to formulate rules for the construction of subject headings in 1876. However, they do not seem to be appropriate to Korean language because the syntax and semantics of Korean language are different from those of English and other European languages. This study therefore attempts to develop a new Korean subject indexing system, namely Korean String Indexing System(KOSIS), in order to increase the use of subject catalogs. For this purpose, advantages and disadvantages between the classed subject catalog nd the alphabetical subject catalog, which are typical subject ca-alogs in libraries, are investigated, and most of remarkable subject indexing systems, in particular the PRECIS developed by the British National Bibliography, are reviewed and analysed. KOSIS is a string indexing based on purely the syntax and semantics of Korean language, even though considerable principles of PRECIS are applied to it. The outlines of KOSIS are as follows: 1) KOSIS is based on the fundamentals of natural language and an ingenious conjunction of human indexing skills and computer capabilities. 2) KOSIS is. 3 string indexing based on the 'principle of context-dependency.' A string of terms organized accoding to his principle shows remarkable affinity with certain patterns of words in ordinary discourse. From that point onward, natural language rather than classificatory terms become the basic model for indexing schemes. 3) KOSIS uses 24 role operators. One or more operators should be allocated to the index string, which is organized manually by the indexer's intellectual work, in order to establish the most explicit syntactic relationship of index terms. 4) Traditionally, a single -line entry format is used in which a subject heading or index entry is presented as a single sequence of words, consisting of the entry terms, plus, in some cases, an extra qualifying term or phrase. But KOSIS employs a two-line entry format which contains three basic positions for the production of index entries. The 'lead' serves as the user's access point, the 'display' contains those terms which are themselves context dependent on the lead, 'qualifier' sets the lead term into its wider context. 5) Each of the KOSIS entries is co-extensive with the initial subject statement prepared by the indexer, since it displays all the subject specificities. Compound terms are always presented in their natural language order. Inverted headings are not produced in KOSIS. Consequently, the precision ratio of information retrieval can be increased. 6) KOSIS uses 5 relational codes for the system of references among semantically related terms. Semantically related terms are handled by a different set of routines, leading to the production of 'See' and 'See also' references. 7) KOSIS was riginally developed for a classified catalog system which requires a subject index, that is an index -which 'trans-lates' subject index, that is, an index which 'translates' subjects expressed in natural language into the appropriate classification numbers. However, KOSIS can also be us d for a dictionary catalog system. Accordingly, KOSIS strings can be manipulated to produce either appropriate subject indexes for a classified catalog system, or acceptable subject headings for a dictionary catalog system. 8) KOSIS is able to maintain a constistency of index entries and cross references by means of a routine identification of the established index strings and reference system. For this purpose, an individual Subject Indicator Number and Reference Indicator Number is allocated to each new index strings and new index terms, respectively. can produce all the index entries, cross references, and authority cards by means of either manual or mechanical methods. Thus, detailed algorithms for the machine-production of various outputs are provided for the institutions which can use computer facilities.

• Korean Journal of Agricultural Science
• /
• v.29 no.2
• /
• pp.43-52
• /
• 2002

This study was performed to provide the basic data for preservation and improvement of genetic resources according to finding genetic construction obtained from analysis of genetic characteristics of $\beta$-casein gene in Korean Native goat and Saanen using the PCR-RFLP. This study confirmed the amplified products of 481bp fragments obtained from the amplification of $\beta$-casein loci by PCR. The $\beta$-casein AB genotype showed 481, 284 and 197bp, and $\beta$-casein BB genotype showed 284 and 197bp fragments in Korean Native goat and Saanen. The frequencies of $\beta$-casein genotype in Korean Native goat were 6.25 and 93.75% for AA and AB and the frequencies of $\beta$-casein genotype in Saanen were 57.14 and 42.86% for AA and AB types. The frequencies of $\beta$-casein A and B alleles were 0.031 and 0.969 in Korean Native goat and the frequencies of $\beta$-casein A and B alleles are 0.286 and 0.714 in Saanen, respectively. The nucleotide sequence of $\beta$-casein gene of Korean Native goat was 97.71% higher homology with 11 nucleotide sequences difference of that of goat reported in GeneBank (M90556). Therefore, this study of molecular genetic characteristics by the analysis of genetic polymorphism and sequencing for $\beta$-casein gene should be used as basic and applying data for preservation and improvement of genetic resources in Korean Native goat breeding.