• Journal of Ginseng Research
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• v.47 no.1
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• pp.54-64
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• 2023

Background: Panax ginseng Meyer (P. ginseng) is a traditional natural/herbal medicine. The amelioration on inflammatory bowel disease (IBD) activity rely mainly on its main active ingredients that are referred to as ginsenosides. However, the current literature on gut microbiota, gut microbiota-host co-metabolites, and systems pharmacology has no studies investigating the effects of ginsenoside on IBD. Methods: The present study was aimed to investigate the role of ginsenosides and the possible underlying mechanisms in the treatment of IBD in an acetic acid-induced rat model by integrating metagenomics, metabolomics, and complex biological networks analysis. In the study ten ginsenosides in the ginsenoside fraction (GS) were identified using Q-Orbitrap LC-MS. Results: The results demonstrated the improvement effect of GS on IBD and the regulation effect of ginsenosides on gut microbiota and its co-metabolites. It was revealed that 7 endogenous metabolites, including acetic acid, butyric acid, citric acid, tryptophan, histidine, alanine, and glutathione, could be utilized as significant biomarkers of GS in the treatment of IBD. Furthermore, the biological network studies revealed EGFR, STAT3, and AKT1, which belong mainly to the glycolysis and pentose phosphate pathways, as the potential targets for GS for intervening in IBD. Conclusion: These findings indicated that the combination of genomics, metabolomics, and biological network analysis could assist in elucidating the possible mechanism underlying the role of ginsenosides in alleviating inflammatory bowel disease and thereby reveal the pathological process of ginsenosides in IBD treatment through the regulation of the disordered host-flora co-metabolism pathway.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.212-219
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• 2023

Vessel Traffic Service (VTS) area presents a complex traffic pattern due to ships entering or leaving the port to utilize port facilities, as well as ships passing through the coastal area. To ensure safe and efficient management of maritime traffic, VTS operators continuously monitor and control vessels in real time. However, during periods of high traffic congestion, the workload of VTS operators increases, which can result in delayed or inadequate VTS services. Therefore, it would be beneficial to predict traffic congestion and congested areas to enable more efficient traffic control. Currently, such prediction relies on the experience of VTS operators. In this paper, we defined vessel traffic congestion from the perspective of a VTS operator. We proposed a method to generate traffic networks using historical navigational data and predict traffic congestion and congested areas. Experiments were performed to compare prediction results with real maritime data (Daesan port VTS) and examine whether the proposed method could support VTS operators.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.265-275
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• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.2-5
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• 2022

Human organs in the body have a complex structure, and in particular, the small intestine is about 7m long, so endoscopy is not easy and the risk of endoscopy is high. Currently, the test is performed with a capsule endoscope, and the test time is very long. The doctor connects the removed storage device to the computer to store the patient's capsule endoscope image and reads it using a program, but the capsule endoscope test results in a long image length, which takes a lot of time to read. In addition, in the case of the small intestine, there are many curves due to villi, so the occlusion area or light and shade of the image are clearly visible during the examination, and there may be cases where lesions and abnormal signs are missed during the examination. In this paper, we provide a method of assisting small intestine capsule endoscopic lesion examination using artificial neural networks to shorten the doctor's image reading time and improve diagnostic reliability.

• Journal of Microbiology and Biotechnology
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• v.33 no.8
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• pp.1013-1022
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• 2023

Arbuscular mycorrhizal fungi (AMF) are widespread soil endophytic fungi, forming mutualistic relationships with the vast majority of land plants. Biochar (BC) has been reported to improve soil fertility and promote plant growth. However, limited studies are available concerning the combined effects of AMF and BC on soil community structure and plant growth. In this work, a pot experiment was designed to investigate the effects of AMF and BC on the rhizosphere microbial community of Allium fistulosum L. Using Illumina high-throughput sequencing, we showed that inoculation of AMF and BC had a significant impact on soil microbial community composition, diversity, and versatility. Increases were observed in both plant growth (the plant height by 8.6%, shoot fresh weight by 12.1%) and root morphological traits (average diameter by 20.5%). The phylogenetic tree also showed differences in the fungal community composition in A. fistulosum. In addition, Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed that 16 biomarkers were detected in the control (CK) and AMF treatment, while only 3 were detected in the AMF + BC treatment. Molecular ecological network analysis showed that the AMF + BC treatment group had a more complex network of fungal communities, as evidenced by higher average connectivity. The functional composition spectrum showed significant differences in the functional distribution of soil microbial communities among different fungal genera. The structural equation model (SEM) confirmed that AMF could improve the microbial multifunctionality by regulating the rhizosphere fungal diversity and soil properties. Our findings provide new information on the effects of AMF and biochar on plants and soil microbial communities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.