• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1013-1018
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• 2021

Due to the recent accident caused by the automated vehicle, discussions on the ethical aspects of AI have been actively underway. This paper confirms that AI is inevitably connected to ethical components through the concepts and techniques related to robots-AI, and argues that ethical aspects are built-in, not post facto. Furthermore, this devises a solution to the trolley dilemma that can serve as a clue to ethical problems associated with automated vehicles. Preferentially, that process contains writing Bayesian networks. Next, only important and influential data are left after the pre-processing stage, and crowd-sourcing & extrapolation is used to calculate the exact figures of the networks. Through this process, this argues that humans' subjects are certainly included in implementing algorithms and models and discusses the necessity and direction of engineering liberal arts, especially education of ethics that distinguished from major education to prevent distortions and biases abouts AI systems.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.101-125
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• 2021

This paper aims from various angles to examine the context of the problem situations that Jang Woo-Jae describes from contemporary korean society, to examine which chance of thinking is in this case given to the audience, and to examine whether through the plays in the theater this chance of thinking can be referred to public opinion. This study considers not only the contribution of Jang's thought experiment that is connected to the dramaturgical function including the aspects of dramatic form and content to dramatic effects for the audience, but also the possibility of an arrangement for a new way of thinking by the audience. Thus, Jang's thought experiment should be understood in connection with the whole message of his plays. And this study researches, how an new attention to the public nature of common world, which should be demanded for the sake of a mature civil society comes into existence within the point of contention in every play based on Jang's thought experiment. Finally this paper inspects through Jang's plays the possibility of contribution of performing art to contemporary korean society, and takes account of self-examination in meta-dimension of the theater regarding the role and meaning for the sake of human being and gemeinschaft.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Journal of Pharmacopuncture
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• v.25 no.4
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• pp.326-343
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• 2022

Neurological disorders represent a substantial healthcare burden worldwide due to population aging. Acorus gramineus Solander (AG) and Acorus tatarinowii Schott (AT), whose major component is asarone, have been shown to be effective in neurological disorders. This review summarized current information from preclinical and clinical studies regarding the effects of extracts and active components of AG and AT (e.g., α-asarone and β-asarone) on neurological disorders and biomedical targets, as well as the mechanisms involved. Databases, including PubMed, Embase, and RISS, were searched using the following keywords: asarone, AG, AT, and neurological disorders, including Alzheimer's disease, Parkinson's disease, depression and anxiety, epilepsy, and stroke. Meta-analyses and reviews were excluded. A total of 873 studies were collected. A total of 89 studies were selected after eliminating studies that did not meet the inclusion criteria. Research on neurological disorders widely reported that extracts or active components of AG and AT showed therapeutic efficacy in treating neurological disorders. These components also possessed a wide array of neuroprotective effects, including reduction of pathogenic protein aggregates, antiapoptotic activity, modulation of autophagy, anti-inflammatory and antioxidant activities, regulation of neurotransmitters, activation of neurogenesis, and stimulation of neurotrophic factors. Most of the included studies were preclinical studies that used in vitro and in vivo models, and only a few clinical studies have been performed. Therefore, this review summarizes the current knowledge on AG and AT therapeutic effects as a basis for further clinical studies, and clinical trials are required before these findings can be applied to human neurological disorders.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• The Journal of the Society of Stroke on Korean Medicine
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• v.12 no.1
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• pp.61-67
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• 2011

Background : Prevalence of spasticity because of stroke are 40% patients after 12 month. Spasticity caused decrease of range of motor, motor function, and active daily living. Electroacupuncture widely used stroke. But it is been studied by systematic review between spasticity and electroacupuncture. This study is aimed to efficacy of electroacupuncture for spasticity because of stroke. Methods : We had used pubmed(www.pubmed.com) and cochrane library(www.thecochranelibrary.com) database. Limits are'human','randomized controlled trial'and'all adult 19+ years'in pubmed. The period was until 15, september, 2011. We used MeSH(Medical Subject Headings terms. The search words were'stroke'[mesh],'muscle spasticity'[mesh and 'electroacupuncture'[mesh]. In cochrane library, we used spasticity and electroacupuncture in cochrane library. We found 19 studies. But only 3 studies were included for inclusion criteria. Results : The appropriate 3 studies were different from subject, acupoint, duration of treatment, endpoint and etc. But these studies were effective for spasticity because of stroke. Conclusion : These studies were not meta analysis because of heterogeneity. But the above results might explain the electroacupuncture were effective for spasticity and further study needed to verify and standard electroacupuncture study for spasticity.

• Restorative Dentistry and Endodontics
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• v.46 no.2
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• pp.26.1-26.15
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• 2021

Objectives: The aim of the present systematic review was to investigate the cryopreservation process of dental pulp mesenchymal stromal cells and whether cryopreservation is effective in promoting cell viability and recovery. Materials and Methods: This systematic review was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the research question was determined using the population, exposure, comparison, and outcomes strategy. Electronic searches were conducted in the PubMed, Cochrane Library, Science Direct, LILACS, and SciELO databases and in the gray literature (dissertations and thesis databases and Google Scholar) for relevant articles published up to March 2019. Clinical trial studies performed with dental pulp of human permanent or primary teeth, containing concrete information regarding the cryopreservation stages, and with cryopreservation performed for a period of at least 1 week were included in this study. Results: The search strategy resulted in the retrieval of 185 publications. After the application of the eligibility criteria, 21 articles were selected for a qualitative analysis. Conclusions: The cryopreservation process must be carried out in 6 stages: tooth disinfection, pulp extraction, cell isolation, cell proliferation, cryopreservation, and thawing. In addition, it can be inferred that the use of dimethyl sulfoxide, programmable freezing, and storage in liquid nitrogen are associated with a high rate of cell viability after thawing and a high rate of cell proliferation in both primary and permanent teeth.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• Korean Journal of Cognitive Science
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• v.34 no.3
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• pp.197-226
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• 2023

Generative Text Summarization is one of the Natural Language Processing tasks. It generates a short abbreviated summary while preserving the content of the long text. ROUGE is a widely used lexical-overlap based metric for text summarization models in generative summarization benchmarks. Although it shows very high performance, the studies report that 30% of the generated summary and the text are still inconsistent. This paper proposes a methodology for evaluating the performance of the summary model without using the correct summary. AggreFACT is a human-annotated dataset that classifies the types of errors in neural text summarization models. Among all the test candidates, the two cases, generation summary, and when errors occurred throughout the summary showed the highest correlation results. We observed that the proposed evaluation score showed a high correlation with models finetuned with BART and PEGASUS, which is pretrained with a large-scale Transformer structure.