• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.247-249
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• 2014

Ubiquitous Sensor Network (USN) environments, sensors and sensor nodes, and coming from heterogeneous sensor networks consist of one another, the characteristics of each component are also very diverse. Thus the sensor and the sensor nodes to interoperability between metadata for a single definition, management is very important. For this, the standard language for modeling sensor SensorML (Sensor Model Language) has. In this paper, sensor devices, sensor nodes and sensor networks for information technology in the application stage XMDR-DAI -based metadata to define the USN. The proposed XMDR-DAI USN based store and retrieve metadata for a method for effectively agent technology. Metadata of the proposed sensor is based SensorML USN environment by maintaining interoperability 50-200 USN middleware or a metadata management system for managing metadata in applications can be utilized directly.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.35-48
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• 2022

In this study, it propose a proof theory method for expressing and reasoning knowledge in a multiagent environment. Since this method determines logical results in a mechanical way, it has developed as a core field from early AI research. However, since the proposition cannot always be proved in any set of closed sentences, in order for the logical result to be determinable, the range of expression is limited to the sentence in the form of a clause. In addition, the resolution principle, a simple and strong reasoning rule applicable only to clause-type sentences, is applied. Also, since the proof theory can be expressed as a meta predicate, it can be extended to the metalogic of the proof theory. Metalogic can be superior in terms of practicality and efficiency based on improved expressive power over epistemic logic of model theory. To prove this, the semantic method of epistemic logic and the metalogic method of proof theory are applied to the Muddy Children problem, respectively. As a result, it prove that the method of expressing and reasoning knowledge and common knowledge using metalogic in a cooperative multiagent environment is more efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.75-77
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• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.

• Biomolecules & Therapeutics
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• v.31 no.4
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• pp.402-410
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• 2023

Long-term administration of levodopa (L-DOPA) to patients with Parkinson's disease (PD) commonly results in involuntary dyskinetic movements, as is known for L-DOPA-induced dyskinesia (LID). 5-Hydroxytryptophan (5-HTP) has recently been shown to alleviate LID; however, no biochemical alterations to aberrant excitatory conditions have been revealed yet. In the present study, we aimed to confirm its anti-dyskinetic effect and to discover the unknown molecular mechanisms of action of 5-HTP in LID. We made an LID-induced mouse model through chronic L-DOPA treatment to 6-hydroxydopamine-induced hemi-parkinsonian mice and then administered 5-HTP 60 mg/kg for 15 days orally to LID-induced mice. In addition, we performed behavioral tests and analyzed the histological alterations in the lesioned part of the striatum (ST). Our results showed that 5-HTP significantly suppressed all types of dyskinetic movements (axial, limb, orolingual and locomotive) and its effects were similar to those of amantadine, the only approved drug by Food and Drug Administration. Moreover, 5-HTP did not affect the efficacy of L-DOPA on PD motor manifestations. From a molecular perspective, 5-HTP treatment significantly decreased phosphorylated CREB and ΔFosB expression, commonly known as downstream factors, increased in LID conditions. Furthermore, we found that the effects of 5-HTP were not mediated by dopamine1 receptor (D1)/DARPP32/ERK signaling, but regulated by AKT/mTOR/S6K signaling, which showed different mechanisms with amantadine in the denervated ST. Taken together, 5-HTP alleviates LID by regulating the hyperactivated striatal AKT/mTOR/S6K and CREB/ΔFosB signaling.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.91-91
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• 2020

IPCC (Intergovernmental Panel on Climate Change) 5차 평가보고서에 의하면 최근 배출 온실가스의 양은 관측 이래 최고 수준이며 온실가스로 인한 기후변화는 인간계와 자연계에 광범위한 영향을 주고 있다고 보고하였다. 기후변화의 영향은 국제적으로 빙하 감소, 사막화, 해수면 상승 등 뚜렷하게 나타나고 있다. 이러한 기후변화에 대응하기 위해 온실가스 완화 정책과 동시에 새로운 기후변화 환경에 적응하는 것이 필요하다. 기후변화 적응이란 현재 나타나고 있거나 미래에 나타날 것으로 예상되는 기후변화의 파급효과와 영향에 대응할 수 있도록 하는 모든 행동이며 이를 위해서는 기후변화 영향분석이 수반되어야 한다. MOTIVE 연구단에서는 기후변화 적응대책 수립의 지원을 목표로 7개 부문(건강, 물관리, 농업, 산림, 생태, 해양, 수산)에서 "한국형 통합평가 모형"을 개발하고 있다. 각 부문에서 개발하는 프로세스 모델은 시스템에 대한 지식을 가진 상황에서 사용하면 신뢰할 수 있는 예측 결과를 얻을 수 있지만, 부문별 통합을 통한 영향 분석 시 타 분야에 대한 지식이 수반되어야 하는 어려움을 가진다. 이를 위해 본 연구에서는 시스템 내의 물리적 프로세스에 대한 요구 없이 입출력 데이터만을 이용하여 결과를 신속하게 추정하는 데이터 모델링(기계학습)을 이용하였다. 데이터 모델링을 위한 데이터는 다양한 자연 현상에 대한 BANPOL(수질 프로세스 모델) 분석을 통한 자료를 이용하여 학습 자료를 구축하였다. 즉, 데이터 모델링은 BANPOL 모델을 대리하는 메타모델이며, 낙동강 표준유역에 대한 유량 및 수질을 높은 상관성으로 추정하였다. 원 모델보다 정확도는 낮을 수 있으나 메타모델의 개발을 통한 웹 시스템을 개발하여 비전문가의 구동 및 신속한 기후 시나리오를 적용할 수 있는 환경을 개발하였다.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.41-49
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• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• Quality Improvement in Health Care
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• v.30 no.1
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• pp.132-152
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• 2024

Purpose: This study aimed to identify and evaluate interprofessional education (IPE) interventions for healthcare professional students in East Asian countries. Methods: The reporting of this study followed the Preferred Reporting Items of Systematic Reviews and Meta-Analysis guidelines. A literature search was conducted using seven electronic databases: PubMed, EMBASE, CINAHL, Scopus, Web of Science, ERIC, and ProQuest Dissertations & Theses Global. Joanna Briggs Institute Critical Appraisal Checklists were also used to appraise the quality of the included studies. The outcomes of IPE interventions were classified based on a modified Kirkpatrick model. Results: This review included 30 studies predominantly conducted in Singapore, South Korea, and Taiwan. The prevalent research design was a one-group pre-posttest design, and most IPE interventions occurred as single events. Approximately 70% of the studies involved students from two healthcare professions, mainly nursing and medicine. Simulations, group discussions, and lectures have emerged as the most common teaching methodologies, with almost half of the studies leveraging a combination of these techniques. The IPE content primarily focused on interprofessional teamwork, communication, and clinical patient care situations; these included the management of septic shock. The effectiveness of the IPE interventions was mainly evaluated through self-reported measures, indicating improvements in attitudes, perceptions, knowledge, and skills, aligning with Level 2 of the modified Kirkpatrick model. Nonetheless, the reviewed studies did not assess changes in the participants' behavior and patient results. Conclusion: IPE interventions promise to enhance interprofessional collaboration and communication skills among health professional students. Future studies should implement rigorous designs to assess the effectiveness of IPE interventions. Moreover, when designing IPE interventions, researchers and educators should consider the role of cultural characteristics in East Asian countries.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1881-1895
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• 2015

Background: The vascular endothelial growth factor family has been implicated in tumorigenesis and metastasis. The prognostic value of each vascular endothelial growth factor family member, particular VEGF/VEGFR co-expression, in patients with non-small lung cancer remains controversial. Materials and Methods: Relevant literature was identified by searching PubMed, EMBASE and Web of Science. Studies evaluating expression of VEGFs and/or VEGFRs by immunohistochemistry or ELISA in lung cancer tissue were eligible for inclusion. Hazard ratios (HRs) and 95% confidence intervals (CIs) from individual study were pooled by using a fixed- or random-effect model, heterogeneity and publication bias analyses were also performed. Results: 74 studies covering 7,631 patients were included in the meta-analysis. Regarding pro-angiogenesis factors, the expression of VEGFA (HR=1.633, 95%CI: 1.490-1.791) and VEGFR1 (HR=1.924, 95%CI: 1.220-3.034) was associated separately with poor survival. Especially, VEGFA over-expression was an independent prognostic factor in adenocarcinoma (ADC) (HR=1.775, 95%CI: 1.384-2.275) and SCC (HR=2.919, 95%CI: 2.060-4.137). Co-expression of VEGFA/VEGFR2 (HR=2.011, 95%CI: 1.405-2.876) was also significantly associated with worse survival. For lymphangiogenesis factors, the expression of VEGFC (HR=1.611, 95%CI: 1.407-1.844) predicted a poor prognosis. Co-expression of VEGFC/VEGFR3 (HR=2.436, 95%CI: 1.468-4.043) emerged as a preferable prognostic marker. Conclusions: The expression of VEGFA (particularly in SCC and early stage NSCLC), VEGFC, VEGFR1 indicates separately an unfavorable prognosis in patients with NSCLC. Co-expression VEGFA/VEGFR2 is comparable with VEGFC/VEGFR3, both featuring sufficient discrimination value as preferable as prognostic biologic markers.