• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.565-586
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• 2022

Cloud storage's elastic expansibility not only provides flexible services for data owners to store their data remotely, but also reduces storage operation and management costs of their data sharing. The data outsourced remotely in the storage space of cloud service provider also brings data security concerns about data integrity. Data integrity verification has become an important technology for detecting the integrity of remote shared data. However, users without data access rights to verify the data integrity will cause unnecessary overhead to data owner and cloud service provider. Especially malicious users who constantly launch data integrity verification will greatly waste service resources. Since data owner is a consumer purchasing cloud services, he needs to bear both the cost of data storage and that of data verification. This paper proposes a verification control algorithm in data integrity verification for remotely outsourced data. It designs an attribute-based encryption verification control algorithm for multiple verifiers. Moreover, data owner and cloud service provider construct a common access structure together and generate a verification sentinel to verify the authority of verifiers according to the access structure. Finally, since cloud service provider cannot know the access structure and the sentry generation operation, it can only authenticate verifiers with satisfying access policy to verify the data integrity for the corresponding outsourced data. Theoretical analysis and experimental results show that the proposed algorithm achieves fine-grained access control to multiple verifiers for the data integrity verification.

• Journal of Ginseng Research
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• v.46 no.1
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• pp.11-22
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• 2022

Spinal cord injury (SCI) is defined as damage to the spinal cord that temporarily or permanently changes its function. There is no definite treatment established for neurological complete injury patients. This study investigated the effect of ginseng extract and ginsenosides on neurological recovery and antioxidant efficacies in rat models following SCI and explore the appropriate dosage. Searches were done on PubMed, Embase, and Chinese databases, and animal studies matches the inclusion criteria were selected. Pair-wise meta-analysis and subgroup analysis were performed. Ten studies were included, and the overall methodological qualities were low quality. The result showed ginseng extract and ginsenosides significantly improve neurological function, through the Basso, Beattie, and Bresnahan (BBB) locomotor rating scale (pooled MD = 4.40; 95% CI = 3.92 to 4.88; p < 0.00001), significantly decrease malondialdehyde (MDA) (n = 290; pooled MD = -2.19; 95% CI = -3.16 to 1.22; p < 0.0001) and increase superoxide dismutase (SOD) levels (n = 290; pooled MD = 2.14; 95% CI = 1.45 to 2.83; p < 0.00001). Both low (<25 mg/kg) and high dosage (25 mg/kg) showed significant improvement in the motor function recovery in SCI rats. Collectively, this review suggests ginseng extract and ginsenosides has a protective effect on SCI, with good safety and a clear mechanism of action and may be suitable for future clinical trials and applications.

• Trans-
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• v.13
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• pp.117-140
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• 2022

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.

• Journal of Environmental Health Sciences
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• v.48 no.5
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• pp.272-281
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• 2022

Background: The constant consumption of chemical products owing to expanding industrialization has led to an increase in public interest in chemical substances. As the production and disposal processes for these chemical products cause environmental problems, regional information on the hazard level of chemical substances is required considering their effects on humans and in order to ensure environmental safety. Objectives: This study aimed to identify hazard contribution and spatiotemporal characteristics by region and chemical by calculating a hazard-based result score using pollutant release and transfer register (PRTR) data. Methods: This study calculated the chemical discharge and hazard-based result score from the Risk-Screening Environmental Indicators (RSEI) model, analyzed their spatiotemporal patterns, and identified hotspot areas where chemical discharges and high hazard-based scores were concentrated. The amount of chemical discharge and hazard-based risk scores for 250 cities and counties across South Korea were calculated using PRTR data from 2011 to 2018. Results: The chemical discharge (high densities in Incheon, Daegu, and Busan) and hazard-based result scores (high densities in Incheon, Chungcheongnam-do, and some areas of Gyeongsangnam-do Province) showed varying spatial patterns. The chemical discharge (A, B) and hazard-based result score (C, D) hotspots were identified. Additionally, identification of the hazard-based result scores revealed differences in the type of chemicals contributing to the discharge. Ethylbenzene accounted for ≥80% of the discharged chemicals in the discharge hotspots, while chromium accounted for >90% of the discharged chemicals in the hazard-based result score hotspots. Conclusions: The RSEI hazard-based result score is a quantitative indicator that considers the degree of impact on human health as a toxicity-weighted value. It can be used for the management of industries discharging chemical substances as well as local environmental health management.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Genomics & Informatics
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• v.20 no.1
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• pp.12.1-12.7
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• 2022

The two complete mitochondrial genomes (mitogenomes) of Paedocypris progenetica, the smallest fish in the world which belonged to the Cyprinidae family, were sequenced and assembled. The circular DNA molecules of mitogenomes P1-P. progenetica and S3-P. progenetica were 16,827 and 16,616 bp in length, respectively, and encoded 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and one control region. The gene arrangements of P. progenetica were identical to those of other Paedocypris species. BLAST and phylogenetic analyses revealed variations in the mitogenome sequences of two Paedocypris species from Perak and Selangor. The circular DNA molecule of P. progenetica yield a standard vertebrate gene arrangement and an overall nucleotide composition of A 33.0%, T 27.2%, C 23.5%, and G 15.5%. The overall AT content of this species was consistent with that of other species in other genera. The negative GC-skew and positive AT-skew of the control region in P. progenetica indicated rich genetic variability and AT nucleotide bias, respectively. The results of this study provide genomic variation information and enhance the understanding of the mitogenome of P. progenetica. They could later deliver highly valuable new insight into data for phylogenetic analysis and population genetics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.380-386
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• 2022

This paper proposes a method for structuring the preprocessing process of a training image when color is applied using Pix2Pix, one of the adversarial generative neural network techniques. This paper concentrate on the prediction result can be damaged according to the degree of light reflection of the training image. Therefore, image preprocesisng and parameters for model optimization were configured before model application. In order to increase the image resolution of training and prediction results, it is necessary to modify the of the model so this part is designed to be tuned with parameters. In addition, in this paper, the logic that processes only the part where the prediction result is damaged by light reflection is configured together, and the pre-processing logic that does not distort the prediction result is also configured.Therefore, in order to improve the usability, the accuracy was improved through experiments on the part that applies the light reflection tuning filter to the training image of the Pix2Pix model and the parameter configuration.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• The Journal of Korean Medicine
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• v.42 no.2
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• pp.107-119
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• 2021

Objectives: This study was aimed to report changes in clinical symptoms and signs after treatment with Korean medicine in patients who suffered from calf edema due to deep vein thrombosis (DVT). Methods: A 46-year-old male patient, who discharged home after receiving standardized treatment for acute DVT in the right leg, suffered pain and heat sense due to edema of the affected calf. Oral administration of herbal medicine (Gyejibongnyeong-hwan 4 g, twice daily) and acupuncture therapy were given to him for 6 weeks. The change in chief complaints, Villalta score, the right and left side difference of the circumference and the skin surface temperature of both calves, and blood level of D-dimer were observed before and after treatment. Results: The patient's chief complaints began to significantly improve from 2 weeks after treatment, and disappeared completely within 4 weeks. At the end of the treatment, a substantial decrease in the difference between the right and left calves in the circumference and skin surface temperature was observed. This effect was maintained even at the time of follow-up 3 months after the end of treatment, and the patient completely recovered indoor and outdoor life to the level before onset. Conclusions: This case suggests that Korean medicine treatment, including Gyejibongnyeong-hwan administration and acupuncture therapy, can be a viable option to improve edema and related clinical problems in the affected limbs due to DVT.