• Korean Journal of Transplantation
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• v.31 no.4
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• pp.157-169
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• 2017

Regulatory T cells (Treg) naturally rein in immune attacks, and they can inhibit rejection of transplanted organs and even reverse the progression of autoimmune diseases in mice. The initial safety trials of Treg against graft-versus-host disease (GVHD) provided evidence that the adoptive transfer of Treg is safe and capable of limiting disease progression. Supported by such evidence, numerous clinical trials have been actively investigating the efficacy of Treg targeting autoimmune diseases, type I diabetes, and organ transplant rejection, including kidney and liver. The limited quantity of Treg cells harvested from peripheral blood and subsequent in vitro culture have posed a great challenge to large-scale clinical application of Treg; nevertheless, the concept of CAR (chimeric antigen receptor)-Treg has emerged as a potential resolution to the problem. Recently, two CAR-T therapies, tisagenlecleucel and axicabtagene ciloleucel, were approved by the US FDA for the treatment of refractory or recurrent acute lymhoblastic leukemia. This approval could serve as a guideline for the production protocols for other genetically engineered T cells for clinical use as well. The phase I and II clinical trials of these agents has demonstrated that genetically engineered and antigen-targeting T cells are safe and efficacious in humans. In conclusion, both the promising results of Treg cell therapy from the clinical studies and the recent FDA approval of CAR-T therapies are paving the way for CAR-Treg therapy in clinical use.

• Journal of Veterinary Clinics
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• v.39 no.6
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• pp.400-404
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• 2022

A 3-year-old Thoroughbred gelding presented with left laryngeal hemiplegia with a history of laryngoplasty (tie-back surgery) failure. Postoperative endoscopy revealed no abduction or no inflammatory changes in the left arytenoid cartilage. The owner opted for the horse to undergo partial arytenoidectomy due to failed laryngoplasty. A tracheostomy tube was intubated through a mid-cervical tracheotomy to secure the airway under general anesthesia, and; laryngotomy was performed to access the arytenoid cartilage in dorsal recumbency. A partial arytenoidectomy was performed with endoscopic assistance through the left nostril, and the left arytenoid cartilage was removed, excluding the muscular process. Antibiotic and anti-inflammatory agents were administered postoperatively, and the incision site was cleaned using normal saline and antibiotic ointment twice daily. On the 12th postoperative day, endoscopy revealed redundant corniculate process mucosa at the surgical site, which was removed using rongeur forceps directly through the previous laryngotomy incision. The horse showed no significant complications during the hospitalization. Two months after surgery, the surgical site reportedly recovered with no evidence of granulation tissue. The horse returned to training and racing 3 and 7 months postoperatively, respectively. This is the first case report of a partial arytenoidectomy in a horse in South Korea. In this case, the horse returned to training after partial arytenoidectomy without significant complications, indicating that partial arytenoidectomy could be beneficial for failed laryngoplasty.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.167-173
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• 2023

As research has been published to predict future data using regression analysis or artificial intelligence as a method of analyzing economic indicators. In this study, we designed a system that predicts prospective futures prices using artificial intelligence that utilizes topic probability data obtained from past news articles using topic modeling. Topic probability distribution data for each news article were obtained using the Latent Dirichlet Allocation (LDA) method that can extract the topic of a document from past news articles via unsupervised learning. Further, the topic probability distribution data were used as the input for a Long Short-Term Memory (LSTM) network, a derivative of Recurrent Neural Networks (RNN) in artificial intelligence, in order to predict prospective futures prices. The method proposed in this study was able to predict the trend of futures prices. Later, this method will also be able to predict the trend of prices for derivative products like options. However, because statistical errors occurred for certain data; further research is required to improve accuracy.

• The Journal of Internal Korean Medicine
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• v.44 no.2
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• pp.107-116
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• 2023

Objective: This case study reports on the results of the Korean medical treatment of a major aphthous oral ulcer patient. Methods: A 19-year-old male Korean patient with a major aphthous oral ulcer received acupuncture, herbal medicine, and moxibustion for three weeks in a hospital. Results: After treatment, changes were observed in the numeric rating scale (NRS) from 8 to 5, World Health Organization oral toxicity scale (WHO OTS) from grades 3 to 2, oral perception guide from 11 to 15, and Oral Health Impact Profile-14 (OHIP-14) from 19 to 34. Conclusion: It is worth examining the progress of Korean medical treatment for a major aphthous oral ulcer patient.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.174-179
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• 2023

Bulk trailers, used for the transportation of powdered materials, such as cement and fly ash, are crucial in the construction industry. The speedy exhaustion of powdered materials stored in the tank of bulk trailers is relevant to improving transportation efficiency and reducing transportation costs. The exhaust time can be reduced by developing an automatic control system to replace the manual exhaust operation. The instantaneous or accumulated exhausts of powdered materials must be measured for automatic control of the bulk trailer exhaust system. Accordingly, we previously proposed a recurrent neural network (RNN) model that estimated the instantaneous exhaust based on low-cost pressure sensor signals without an expensive flowmeter for powders. Although our previous study utilized only an RNN model, models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are also widely utilized for time-series estimation. This study compares the performance of three neural network models (MLP, CNN, and RNN) in estimating instantaneous and accumulated exhausts. In terms of the instantaneous exhaust estimation, the difference in the performance of neural network models was insignificant (that is, 8.64, 8.62, and 8.56% for the MLP, CNN, and RNN, respectively, in terms of the normalized root mean squared error). However, in the case of the accumulated exhaust, the performance was excellent in the order of CNN (1.67%), MLP (2.03%), and RNN (2.20%).

• The Journal of Korean Obstetrics and Gynecology
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• v.22 no.1
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• pp.203-230
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• 2009

Purpose: In order to know therapies used in the field of Obsterics & Gynecology of Oriental medicine. Methods: This survey investigated papers about clinical study, literature investigation for therapeutic methods and treatment reports published from 1999. 2${\sim}$2008. 11. Results: In the clinical studies, acupuncture is more frequently used than herbal medicine. But in the treatment reports, herbal medicine is most frequently used except some cases. Besides acupuncture and moxibustion was used frequently. In the treatment reports various acupuncture methods were used except vaginal bleeding or abdominal pain cases. Whole body acupuncture was used mainly. Pharmacopuncture, Sa-am Acupuncture, auricular acupuncture was also used frequently. And Dong's acupuncture, Hwa acupuncture was used sometimes. Moxibustion was tend to be used for dysfunctional uterine bleeding, menopausal disorder, urinary incontinence, vaginal bleeding or abdominal pain during pregnancy period, hyperemesis, recurrent miscarriage, postpartum disease, lochiorrhea. Also other treatment methods was used such as external therapy, aromatherapy, herbal retention enema, fumigation, electric lipolysis acupuncture, Chuna manual medicine, obesity management, acupuncture at uterus cervix, magnetic innervation therapy, exercise, cupping and physical therapy. Conclusion: These results suggests that acupuncture, moxibustion and herbal medicine are most frequent therapy in the field of Obsterics & Gynecology of Oriental medicine. And other therapy are used such as external therapy, aromatherapy, herbal retention enema, fumigation and so on.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.175-192
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• 2022

Artificial intelligence-based natural language processing technology is playing an important role in helping users write English-language documents. For academic documents in particular, the English proofreading services should reflect the academic characteristics using formal style and technical terms. But the services usually does not because they are based on general English sentences. In addition, since existing studies are mainly for improving the grammatical completeness, there is a limit of fluency improvement. This study proposes an automatic academic English editing methodology to deliver the clear meaning of sentences based on the use of technical terms. The proposed methodology consists of two phases: misspell correction and fluency improvement. In the first phase, appropriate corrective words are provided according to the input typo and contexts. In the second phase, the fluency of the sentence is improved based on the automatic post-editing model of the bidirectional recurrent neural network that can learn from the pair of the original sentence and the edited sentence. Experiments were performed with actual English editing data, and the superiority of the proposed methodology was verified.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.