• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.295-305
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• 2013

Purpose: Recent research showed that total mixed ration (TMR) feeding for pigs improved the productivity and reduced feed cost and manure odor. An automatic TMR feeding system was developed for this study because the conventional feeder cannot deliver the TMR containing roughage. Methods: Conventional feeding systems and physical properties of TMR were surveyed, and performance tests of the conventional feeder were conducted to develop a TMR feeder. Based on the TMR feeder was developed and installed, driving, measuring weight, radio frequency identification (RFID) reading, and discharging test for feeding were conducted to ensure the performance. Results: Moisture content, density, and angle of repose of the TMR 1 (mixture of 30% cut IRG silage and 70% concentrates) were 31.6%, 387 $kg/m^3$, and $51^{\circ}$, respectively. Moisture content, density, and angle of repose of the TMR 2 (mixture of 45% concentrates, 30% cut IRG silage and by-products, 10% bean curd refuse, 10% others, and 5% fermenter) were 22.2%, 544 $kg/m^3$, and $50^{\circ}$, respectively. The coefficient of variation (C.V.) of conventional concentrate feeding were 1.9~4.1%, and C.V. of TMR containing 1~3% cut IRG roughage feeding by conventional feeder were 9~42%. The conventional disc type feeder was not suitable for TMR feeding because the supply unit was clogged. The C.V. of TMR 1 was 0.6~7.9% when 0.5~10 kg of the TMR supplied, and it was suitable for feeding grow-finish pigs and sows. On the contrary, the C.V. with TMR 2 was 28% when 0.5 kg of the TMR supplied, and it was not suitable for feeding sows. Conclusions: The TMR feeder developed in this study was suitable for feeding grow-finish pigs because the feeder performed stably with over 5.0 kg feed. However, the feeder showed a lack of accuracy for feeding sows because the amount of each feed was more than 0.5 kg per a feeding. Therefore, the improvement of outlet structure for accurate feeding is needed for sow feeding.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.747-761
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• 2024

Recently, modern vehicles have been controlled by Electronic Control Units (ECUs), by which the safety and convenience of drivers are highly improved. It is known that a luxury vehicle has more than 100 ECUs to electronically control its function. However, the modern vehicles are getting targeted by cyber attacks because of this computer-based automotive system. To address the cyber attacks, automotive manufacturers have been developing some methods for securing their vehicles, such as automotive Intrusion Detection System (IDS). This development is only allowed to the automotive manufacturers because they have databases for their in-vehicle network (i.e., DBC Format File) which are highly confidential. This confidentiality poses a significant challenge to external researchers who attempt to conduct automotive security researches. To handle this restricted information, in this paper, we propose a method to partially understand the DBC Format File by analyzing in-vehicle network traffics. Our method is designed to analyze Controller Area Network (CAN) traffics so that checksum signals are identified in CAN Frame Data Field. Also, our method creates a Lookup Set by which a checksum signal is correctly estimated for a given message. We validate our method with the publicly accessible dataset as well as one from a real vehicle.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Korean Journal of Child Education & Care
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• v.18 no.3
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• pp.91-104
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• 2018

Objective: The purpose of the study was to explore early childhood teachers' content knowledge and knowledge level on unification education through an analysis of a concept map (Novak & Gowin, 1984). Methods: The subjects, including 65 early childhood teachers in a metropolitan area, were asked to draw concept maps illustrating their understanding of unification education for young children. The collected concept maps were analyzed using the methods utilized by Novak and Gowin (1984) as well as You and Kim (2018). Results: In terms of early childhood teachers' content knowledge, 282 superordinate concepts and 1,766 subordinate concepts were shown. The 282 superordinate concepts were categorized into 7 representative superordinate concepts: understanding of North Korean, understanding of separation/unification, identification as Korean citizen, unification policy, relationship between South Korea & North Korea, educational activities, connection with home and society. In terms of early childhood teachers' knowledge level, the numbers of subordinate concepts and hierarchical level were shown to be varied according to the 7 representative superordinate concepts. Conclusion/Implications: Discussions were included to support and enhance early childhood teachers' content knowledge on unification education for young children through the development of comprehensive programs and teacher education.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.19-28
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• 2006

To develop a crisis management for aviation safety, this study has defined crisis management includes risk management which is eliminates or lowers risks prior to accidents and emergency response after the accidents. This study takes a look at different kinds of crisis managements, risk managements and statistical methods of other nations and fields in order to develop a risk management model. Through surveys which have 102 risk factors that include air traffic control, maintenance and airport sectors, the weight of each risk factor was calculated and the probability was divided to develop a model for risk management. The risk management model of this study is conducted using four steps (risk management plan, risk factor identification, weight and probability analysis, decision making) and 4 standards of weight along with 5 standards of probability This study takes a look at Predictions through a quantitative method using a risk index for the risk management model An effective risk management model should have a wide and continuous collection of data and adopt various methods using this model. The crisis management could not be very effective only using a pre-active risk management. So it should also be conducted by using a pro-active response system to protect additional damage and to prevent accidents of the same nature. From the results, the most important points were the establishment of command and control accountabilities, and cooperation of related organizations.

• Journal of Ginseng Research
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• v.40 no.2
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• pp.97-104
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• 2016

Background: Rhizobacteria play an important role in plant defense and could be promising sources of biocontrol agents. This study aimed to screen antagonistic bacteria and develop a biocontrol system for root rot complex of Panax notoginseng. Methods: Pure-culture methods were used to isolate bacteria from the rhizosphere soil of notoginseng plants. The identification of isolates was based on the analysis of 16S ribosomal RNA (rRNA) sequences. Results: A total of 279 bacteria were obtained from rhizosphere soils of healthy and root-rot notoginseng plants, and uncultivated soil. Among all the isolates, 88 showed antagonistic activity to at least one of three phytopathogenic fungi, Fusarium oxysporum, Fusarium solani, and Phoma herbarum mainly causing root rot disease of P. notoginseng. Based on the 16S rRNA sequencing, the antagonistic bacteria were characterized into four clusters, Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetesi. The genus Bacillus was the most frequently isolated, and Bacillus siamensis (Hs02), Bacillus atrophaeus (Hs09) showed strong antagonistic activity to the three pathogens. The distribution pattern differed in soil types, genera Achromobacter, Acidovorax, Brevibacterium, Brevundimonas, Flavimonas, and Streptomyces were only found in rhizosphere of healthy plants, while Delftia, Leclercia, Brevibacillus, Microbacterium, Pantoea, Rhizobium, and Stenotrophomonas only exist in soil of diseased plant, and Acinetobacter only exist in uncultivated soil. Conclusion: The results suggest that diverse bacteria exist in the P. notoginseng rhizosphere soil, with differences in community in the same field, and antagonistic isolates may be good potential biological control agent for the notoginseng root-rot diseases caused by F. oxysporum, Fusarium solani, and Panax herbarum.

• The Journal of the Korean Society for Microbiology
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• v.21 no.2
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• pp.261-270
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• 1986

A total of 339 urethral, vaginal swab and eye discharge materials from the out-patients in the hospitals of Seoul area was microbiologically collected for the detection of Chlamydia trachomatis infection during May through August, 1985. McCoy cell culture system (MCC) and enzyme-likned immunosorbent assay (EIA) methods were employed in this study as the tools for the detection of C. trachomatis, and the detectabilities of two methods were compared. The results obtained in this study are summarized as follows: 1. The positive rate of C. trachomatis in 339 swab specimens was 18.6%, and the rate in females (20.1%) was much higher than that in males (7.1%). 2. The positive rate of C. trachomatis infection the prostitutes was the highest (24.2%), and the rate in the eye discharge specimens obtained from the new barns was 12.8%. 3. The positive rates of C. trachmoatis infection detected in the specimens from the patients with vaginitis and leucorrhea, with infertility, with cystitis and with nongonococcal urethritis were 17.2%, 21.9%. 18.0% and 7.1%, respectively. 4. The positive rate of C. trachomatis infection in 20-25 age group was 30.5%. This rate was the highest among the other age groups. 5. The positive rate of C. trachomatis infection in the randomly screened 89 swab specimens by EIA (30.3%) was much higher than the rate detected by MCC (18.6%). 6. The positive rate of C. trachomatis infection in females detected by EIA was also much higher than in males, and the 20-25 age group showed the highest positive rate as compared to the other age groups. 7. Sensitivity and specificity of EIA for the detection on C. trachomatis were 100% and 88.6%, respectively, in case that MCC was regarded as perfect method. In summarizing the above results, it is known that considerable cases with genital diseases and with eye discharges were associated with C. trachomatis, and that EIA method is recommendable for the detection of C. trachomatis especially in the specimens swabed from the genital tracts.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.413-426
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• 2023

Species distribution model (SDM) is used to preserve biodiversity and climate change impact. To evaluate biodiversity, various studies are being conducted to utilize and apply SDM. However, there is insufficient research to provide useful information by identifying the current status and recent trends of SDM research and discussing implications for future research. This study analyzed the trends and flow of academic papers, in the use of SDM, published in academic journals in South Korea and provides basic information that can be used for related research in the future. The current state and trends of SDM research were presented using philological methods and text-mining. The papers on SDM have been published 148 times between 1998 and 2023 with 115 (77.7%) papers published since 2015. MaxEnt model was the most widely used, and plant was the main target species. Most of the publications were related to species distribution and evaluation, and climate change. In text mining, the term 'Climate change' emerged as the most frequent keyword and most studies seem to consider biodiversity changes caused by climate change as a topic. In the future, the use of SDM requires several considerations such as selecting the models that are most suitable for various conditions, ensemble models, development of quantitative input variables, and improving the collection system of field survey data. Promoting these methods could help SDM serve as valuable scientific tools for addressing national policy issues like biodiversity conservation and climate change.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• The Journal of the Korean bone and joint tumor society
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• v.11 no.2
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• pp.160-167
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• 2005

Purpose : Distal femur and proximal tibia are the common sites affected by giant cell tumor of bone. There are a variety of treatment modality including wide excision and intralesional curettage. We evaluated the local recurrence rates and the post-operative functional scores of giant cell tumors around knee joint and investigated the identification of possible prognostic factors for recurrence. Materials and Methods: We reviewed 41 patients pathologically confirmed as giant cell tumors around knee joint that have undergone intralesional curettage using high-speed burr and methylmethacrylate. We evaluated the recurrence rate and post-surgical functional score and possible prognostic factors for recurrence, such as, gender, age, tumor location, size, subchondral invasion, intra-articular invasion and the Campanacci Grades. Mean follow up period was 50 (12-122) months. Results: The recurrence rate was 17% and mean recurrence onset was 10 months postoperatively. According to Musculoskeletal Tumor Society (MSTS) functional evaluation system, the average score was 27.8(93%) and 78% had excellent function. According to our study, suspected prognostic factors revealed not significant for recurrence. Conclusion: We found no significant recurrence related factors. Intralesional excision with high-sped burring and PMMA provides a low recurrence rate, similar to others in the literature, and good functional scores.