• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.116-124
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• 2023

As the flood damage on small streams increase due to the increase in frequency of extreme climate events, the need to measure hydraulic data of them has increased for disaster risk management. National Disaster Management Institute, Ministry of Interior and Safety develops CADMT, a CCTV-based automatic discharge measurement technology, and operates pilot small streams to verify its performance and develop disaster risk management technology. The research selects two small streams such as the Neungmac and the Jungsunpil streams to develop the Nomograph by using the 4-Parameter Logistic method using only the observed rainfall data from the Automatic Weather System operated by the Korea Meteorological Agency closest to the small streams and discharge data collected by using the CADMT. To evaluate developed Nomograph, the research forecasts floods discharges in each small stream and compares the result with the observed discharges. As a result of the evaluations, the forecasted value is found to represent the observed value well, so if more accurate observed data are collected and the Nomograph based on it is developed in the future, the high-accuracy flood prediction and warning will be possible.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Animal Bioscience
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• v.37 no.8
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• pp.1345-1354
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• 2024

Objective: The objective of this study was to identify candidate genes that play important roles in skeletal muscle development in ducks. Methods: In this study, we investigated the transcriptional sequencing of embryonic pectoral muscles from two specialized lines: Liancheng white ducks (female) and Cherry valley ducks (male) hybrid Line A (LCA) and Line C (LCC) ducks. In addition, prediction of target genes for the differentially expressed mRNAs was conducted and the enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes signaling pathways were further analyzed. Finally, a protein-to-protein interaction network was analyzed by using the target genes to gain insights into their potential functional association. Results: A total of 1,428 differentially expressed genes (DEGs) with 762 being up-regulated genes and 666 being down-regulated genes in pectoral muscle of LCA and LCC ducks identified by RNA-seq (p<0.05). Meanwhile, 23 GO terms in the down-regulated genes and 75 GO terms in up-regulated genes were significantly enriched (p<0.05). Furthermore, the top 5 most enriched pathways were ECM-receptor interaction, fatty acid degradation, pyruvate degradation, PPAR signaling pathway, and glycolysis/gluconeogenesis. Finally, the candidate genes including integrin b3 (Itgb3), pyruvate kinase M1/2 (Pkm), insulin-like growth factor 1 (Igf1), glucose-6-phosphate isomerase (Gpi), GABA type A receptor-associated protein-like 1 (Gabarapl1), and thyroid hormone receptor beta (Thrb) showed the most expression difference, and then were selected to verification by quantitative real-time polymerase chain reaction (qRT-PCR). The result of qRT-PCR was consistent with that of transcriptome sequencing. Conclusion: This study provided information of molecular mechanisms underlying the developmental differences in skeletal muscles between specialized duck lines.

• Animal Bioscience
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• v.37 no.2
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• pp.193-202
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• 2024

Objective: Oxidative stress (OS) is a pathological process arising from the excessive production of free radicals in the body. It has the potential to alter animal gene expression and cause damage to the jejunum. However, there have been few reports of changes in the expression of long noncoding RNAs (lncRNAs) in the jejunum in piglets under OS. The purpose of this research was to examine how lncRNAs in piglet jejunum change under OS. Methods: The abdominal cavities of piglets were injected with diquat (DQ) to produce OS. Raw reads were downloaded from the SRA database. RNA-seq was utilized to study the expression of lncRNAs in piglets under OS. Additionally, six randomly selected lncRNAs were verified using quantitative real-time polymerase chain reaction (qRT-PCR) to examine the mechanism of oxidative damage. Results: A total of 79 lncRNAs were differentially expressed (DE) in the treatment group compared to the negative control group. The target genes of DE lncRNAs were enriched in gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways. Chemical carcinogenesis-reactive oxygen species, the Foxo signaling pathway, colorectal cancer, and the AMPK signaling pathway were all linked to OS. Conclusion: Our results demonstrated that DQ-induced OS causes differential expression of lncRNAs, laying the groundwork for future research into the processes involved in the jejunum's response to OS.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.195-207
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• 2024

This study selected major drought events that occurred in the Jeonnam region from 1991 to 2023, examining both meteorological and hydrological drought occurrence mechanisms. The daily drought index was calculated using rainfall and dam storage as input data, and the drought propagation characteristics from meteorological drought to hydrological drought were analyzed. The characteristics of the 2022-23 drought, which recently occurred in the Jeonnam region and caused serious damage, were evaluated. Compared to historical droughts, the duration of the hydrological drought for 2022-2023 lasted 334 days, the second longest after 2017-2018, the drought severity was evaluated as the most severe at -1.76. As a result of a linked analysis of SPI (StandQardized Precipitation Index), and SRSI (Standardized Reservoir Storage Index), it is possible to suggest a proactive utilization for SPI(6) to respond to hydrological drought. Furthermore, by confirming the similarity between SRSI and SPI(12) in long-term drought monitoring, the applicability of SPI(12) to hydrological drought monitoring in ungauged basins was also confirmed. Through this study, it was confirmed that the long-term dryness that occurs during the summer rainy season can transition into a serious level of hydrological drought. Therefore, for preemptive drought response, it is necessary to use real-time monitoring results of various drought indices and understand the propagation phenomenon from meteorological-agricultural-hydrological drought to secure a sufficient drought response period.

• Journal of Food Hygiene and Safety
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• v.39 no.3
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• pp.260-265
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• 2024

During the coronavirus 2019 (COVID-19) pandemic, wastewater-based epidemiology was used for surveying infectious diseases. In this study, wastewater surveillance was employed to monitor COVID-19 outbreaks. Wastewater influent samples were collected from four sewage treatment plants in Ulsan (Gulhwa, Yongyeon, Nongso, and Bangeojin) between August 2022 and August 2023. The samples were concentrated using the polyethylene glycol-sodium chloride pretreatment method. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was extracted and detected using real-time polymerase chain reaction. Next generation sequences was used to perform correlation analysis between SARS-CoV-2 concentrations and COVID-19 cases and for COVID-19 variant analysis. A strong correlation was observed between SARS-CoV-2 concentrations and COVID-19 cases (correlation coefficient, r = 0.914). The COVID-19 variant analysis results were similar to the clinical variant genomes of three epidemics during the study period. In conclusion, monitoring COVID-19 via analyzing wastewater facilitates early recognition and prediction of epidemics.

• The Journal of Bigdata
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• v.8 no.2
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• pp.125-131
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• 2023

Globally, drug side effects rank among the top causes of death. To effectively respond to these adverse drug reactions, a shift towards an active real-time monitoring system, along with the standardization and quality improvement of data, is necessary. Integrating individual institutional data and utilizing large-scale data to enhance the accuracy of drug side effect predictions is critical. However, data sharing between institutions poses privacy concerns and involves varying data standards. To address this issue, our research adopts a federated learning approach, where data is not shared directly in compliance with privacy regulations, but rather the results of the model's learning are shared. We employ the Common Data Model (CDM) to standardize different data formats, ensuring accuracy and consistency of data. Additionally, we propose a drug monitoring system that enhances security and scalability management through a cloud-based federated learning environment. This system allows for effective monitoring and prediction of drug side effects while protecting the privacy of data shared between hospitals. The goal is to reduce mortality due to drug side effects and cut medical costs, exploring various technical approaches and methodologies to achieve this.

• Journal of the Daesoon Academy of Sciences
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• v.49
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• pp.265-294
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• 2024

This study begins with a question about research methods in humanities. It is grounded in the humanities, focusing on the changes that have brought light and darkness to the humanities, and focusing on discourse regarding research methods that explore those changes. If the role of the humanities is to prevent the proverbial "gray rhino," unlike the sciences, and if the humanities have a role to play in moderating the uncontrollable development of the sciences, what kind of research methods should humanities pursue. Furthermore, what kind of research methods should be pursued in the humanities, in line with the development of the sciences and the changing environment? This study discusses research methods in the humanities as follows: first, in Section 2, I advocate for the collaboration between humanities and scientific methods, utilizing accumulated assets produced by humanities and continuously introducing scientific methods. Prediction of change is highly precise and far-reaching in engineering and the natural sciences. However, it is difficult to approach change in these fields in a macro or integrated manner. Because they are not precise, they are not welcome in disciplines that deal with the real world. This is primarily the responsibility of humanities. Where science focuses on precision, humanities focuses on questions of essence. This is because while the ends of change have varied throughout history, the nature of change has not varied that much. Section 3 then discusses the changing environment, proposals for changes to humanistic research methods, reviews and proposals inductive change research methods, and makes some suggestions for humanistic change research. The data produced by the field of humanities accumulated by humankind in the past is abundant and has a wide range of applications. In the future, we should not only actively accept the results of scientific advances but also actively seek systematic humanistic approaches and utilize them across disciplinary boundaries to find solutions at the intersection of scientific methods and humanistic assets.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.