• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.157-161
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• 2023

Reliability is one of the computable quality features of the software. To assess the reliability the software reliability growth models(SRGMS) are used at different test times based on statistical learning models. In all situations, Tradational time-based SRGMS may not be enough, and such models cannot recognize errors in small and medium sized applications.Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into consideration in real time in order to decide the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to eradicate the problem of software reliability modeling. In the suggested model, a new distribution model is suggested to improve the reliability estimation method. We compute the model developed and stabilize its efficiency with other popular software reliability growth models from the research publication. Our assessment results show that the proposed Model is worthier to S-shaped Yamada, Generalized Poisson, NHPP.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1512-1523
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• 2022

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

• ETRI Journal
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• 제42권5호
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.476-483
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• 2022

The novel coronavirus pandemic has had a significant impact on society and everyday life. The pandemic imposed a global shutdown leading to many challenges such as the suspension of academic programs at universities. The result of this suspension contributed to the rapid overnight migration of educational activities from traditional face-to-face learning to a virtual environment which until then was unfamiliar to both instructors and students. This study identified the experiences faced by built environment higher education instructors in KwaZulu-Natal, South Africa during this sudden switch to online teaching and learning. This pilot study employed a quantitative research approach to survey instructor experiences on online teaching and learning during a global pandemic. The data was computed and analyzed using IBM Statistical Package for Social Sciences (SPSS) version 27. Descriptive statistics were used to analyze the data collected. The study sample comprised of 20 higher education instructors in the region of the KwaZulu Natal province in South Africa. Findings from the study revealed that instructors faced adaptive challenges with rapidly having to redesign and remodel the mode of academic course delivery and assessments to suit an online platform. Additionally, instructors observed that students faced technological challenges such as connectivity and navigating the online learning management system platforms. The challenges identified by instructors and students can be effectively transformed to opportunities for future learning under the 'new normal'.

• 응용통계연구
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• 제32권1호
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• pp.41-68
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• 2019

멀티 뷰 기법은 데이터를 다양한 관점에서 보려는 접근 방법이며 데이터의 다양한 정보를 통합하여 사용하려는 시도이다. 최근 많은 연구가 진행되고 있는 멀티 뷰 기법에서는 단일 뷰 만을 이용하여 모형을 학습시켰을 때 보다 좋은 성과를 보인 경우가 많았다. 멀티 뷰 기법에서 딥 러닝 기법의 도입으로 이미지, 텍스트, 음성, 영상 등 다양한 분야에서 좋은 성과를 보였다. 본 연구에서는 멀티 뷰 기법이 인간 행동 인식, 의학, 정보 검색, 표정 인식 분야에서 직면한 여러 가지 문제들을 어떻게 해결하고 있는지 소개하였다. 또한 전통적인 멀티 뷰 기법들을 데이터 차원, 분류기 차원, 표현 간의 통합으로 분류하여 멀티 뷰 기법의 데이터 통합 원리를 리뷰 하였다. 마지막으로 딥 러닝 기법 중 가장 범용적으로 사용되고 있는 CNN, RNN, RBM, Autoencoder, GAN 등이 멀티 뷰 기법에 어떻게 응용되고 있는지를 살펴보았다. 이때 CNN, RNN 기반 학습 모형을 지도학습 기법으로, RBM, Autoencoder, GAN 기반 학습 모형을 비지도 학습 기법으로 분류하여 이 방법들이 대한 이해를 돕고자 하였다.

• 융합보안논문지
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• 제18권5_1호
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• pp.45-51
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• 2018

데이터의 활용도와 중요성이 점차 높아짐에 따라 데이터와 관련된 사고와 피해는 점점 증가 하고 있으며, 특히 내부자에 의한 사고는 그 위험성이 더 높다. 이런 내부자의 공격은 전통적인 보안 시스템으로 방어하기 힘들어, 규칙 기반의 이상 행동 탐지 방법이 널리 활용되어오고 있다. 하지만, 새로운 공격 방식 및 새로운 환경과 같이 변화에 유연하게 적응하지 못하는 문제점을 가지고 있다. 본 논문에서는 이에 대한 해결책으로서 통계적 마르코프 모델 기반의 적응형 이상 이동 탐지 프레임워크를 제안하고자 한다. 이 프레임워크는 사람의 이동에 초점을 맞추어 내부자에 의한 위험을 사전에 탐지한다. 이동에 직접적으로 영향을 주는 환경 요소와 지속적인 통계 학습을 통해 변화하는 환경에 적응함으로써 오탐지와 미탐지를 최소화하도록 설계되었다. 프레임워크를 활용한 실험에서는 0.92의 높은 F2-점수를 얻을 수 있었으며, 나아가 정상으로 보여지지만, 의심해볼 이동까지 발견할 수 있었다. 통계 학습과 환경 요소를 바탕으로 행동과 관련된 데이터와 모델링 알고리즘을 다양화 시켜 적용한다면 보다 더 범위 넓은 비정상 행위에 대해 탐지할 수 있는 확장성을 제공한다.

• Journal of Korean Neurosurgical Society
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• 제67권1호
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• pp.103-114
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• 2024

Objective : Decompressive craniectomy (DC) with duroplasty is one of the common surgical treatments for life-threatening increased intracranial pressure (ICP). Once ICP is controlled, cranioplasty (CP) with reinsertion of the cryopreserved autologous bone flap or a synthetic implant is considered for protection and esthetics. Although with the risk of autologous bone flap resorption (BFR), cryopreserved autologous bone flap for CP is one of the important material due to its cost effectiveness. In this article, we performed conventional statistical analysis and the machine learning technique understand the risk factors for BFR. Methods : Patients aged >18 years who underwent autologous bone CP between January 2015 and December 2021 were reviewed. Demographic data, medical records, and volumetric measurements of the autologous bone flap volume from 94 patients were collected. BFR was defined with absolute quantitative method (BFR-A) and relative quantitative method (BFR%). Conventional statistical analysis and random forest with hyper-ensemble approach (RF with HEA) was performed. And overlapped partial dependence plots (PDP) were generated. Results : Conventional statistical analysis showed that only the initial autologous bone flap volume was statistically significant on BFR-A. RF with HEA showed that the initial autologous bone flap volume, interval between DC and CP, and bone quality were the factors with most contribution to BFR-A, while, trauma, bone quality, and initial autologous bone flap volume were the factors with most contribution to BFR%. Overlapped PDPs of the initial autologous bone flap volume on the BRF-A crossed at approximately 60 mL, and a relatively clear separation was found between the non-BFR and BFR groups. Therefore, the initial autologous bone flap of over 60 mL could be a possible risk factor for BFR. Conclusion : From the present study, BFR in patients who underwent CP with autologous bone flap might be inevitable. However, the degree of BFR may differ from one to another. Therefore, considering artificial bone flaps as implants for patients with large DC could be reasonable. Still, the risk factors for BFR are not clearly understood. Therefore, chronological analysis and pathophysiologic studies are needed.

• 산업융합연구
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• 제21권7호
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• pp.29-39
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• 2023

최근 국내‧외적으로 많은 지진이 발생하고 있는 상황에서, 우리나라의 건물은 내진설계 및 지진피해에 매우 취약한 상황이다. 따라서 현 연구의 목적은 건물에 대한 지진취약도 등급화 및 위험건물 밀도분석을 수행하는 효과적인 방법을 발굴하고 이를 모델화하여, 시범지역(서울시)자료를 활용해 검증해 보는데 있다. 이를 위해 활용된 두 가지 모델링 기법 중, 통계 분석 기법의 예측정확도는 87%였고, 머신러닝 기법은 Random Forest모델의 예측정확도가 가장 높았으며, 해당 모델의 Test Set 정확도는 97.1%로 도출되었다. 분석결과, 구별 등급화 결과는 광진구와 송파구가 상대적으로 위험하다고 예측되었으며, 위험건물 밀도분석은 서초구, 관악구, 강서구가 상대적으로 위험하다고 예측되었다. 최종적으로, 통계분석 기법을 활용한 분석결과가 머신러닝 기법을 활용한 분석결과보다 위험하게 도출되었으나, 우리나라에서는 지진 강도 6.5(MMI)가 내진설계의 기준인데, 서울시 건물의 약 18.9%가 내진설계 되어있는 것으로 확인된 것을 고려하면, 머신러닝 기법의 결과가 더 정확할 것으로 예측되었다. 현 연구는 인구 및 인프라와 경찰서, 소방서 등을 고려 않은 오직 건물만을 고려한 한계점이 있으며, 해당 한계를 포함해 수행하면 더욱 포괄적인 연구가 될 것이다.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.63-69
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• 2021

Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권11호
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• pp.837-844
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• 2010

운율구 경계 예측은 대화체 음성합성을 실현하기 위한 주요한 자연언어처리 기술 중 하나이다. 본 논문은 자연스러운 한국어 운율구 경계 예측을 실현하고자 기존의 학습 자질을 대신할 새로운 학습 자질을 제안한다. 이 새로운 자질들은 기존의 학습 자질보다 실제 언어생활에서 운율구 경계 발생에 영향을 미치는 여러 요인을 더 잘 반영한다. 특히, 수작업으로 구축한 운율구 경계 예측 규칙을 이용하여 추출한 학습 자질은 높은 정확도 향상에 이바지한다. 본 논문에서 제안한 새로운 학습 자질을 바탕으로 CRFs(Conditional Random Fields)를 이용하여 운율구 경계 예측 모델을 만들었다. 그 결과 3단계 운율구 경계(강한 경계, 약한 경계, 운율구 내부 비경계) 예측에서 86.63%의 정확도를, 6단계 운율구 경계(상승조/하강조 강한 경계, 상승조/하강조/평탄조 약한 경계, 운율구 내부 비경계) 예측에서는 81.14%의 정확도를 보였다.