• Smart Structures and Systems
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• 제25권3호
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• pp.285-299
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• 2020

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

• 한국컴퓨터정보학회논문지
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• 제26권10호
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• pp.157-165
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• 2021

최근 대량의 텍스트 분석을 위해 딥 러닝(Deep Learning)을 활용하는 연구들이 활발히 수행되고 있으며, 특히 대량의 텍스트에 대한 학습 결과를 특정 도메인 텍스트의 분석에 적용하는 사전 학습 언어 모델(Pre-trained Language Model)이 주목받고 있다. 다양한 사전 학습 언어 모델 중 BERT(Bidirectional Encoder Representations from Transformers) 기반 모델이 가장 널리 활용되고 있으며, 최근에는 BERT의 MLM(Masked Language Model)을 활용한 추가 사전 학습(Further Pre-training)을 통해 분석 성능을 향상시키기 위한 방안이 모색되고 있다. 하지만 전통적인 MLM 방식은 신조어와 같이 새로운 단어가 포함된 문장의 의미를 충분히 명확하게 파악하기 어렵다는 한계를 갖는다. 이에 본 연구에서는 기존의 MLM을 보완하여 신조어에 대해서만 집중적으로 마스킹을 수행하는 신조어 표적 마스킹(NTM: Newly Coined Words Target Masking)을 새롭게 제안한다. 제안 방법론을 적용하여 포털 'N'사의 영화 리뷰 약 70만 건을 분석한 결과, 제안하는 신조어 표적 마스킹이 기존의 무작위 마스킹에 비해 감성 분석의 정확도 측면에서 우수한 성능을 보였다.

• Clinical Psychopharmacology and Neuroscience
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• 제16권4호
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• pp.449-460
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• 2018

Objective: Prior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated. Methods: Twenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy. Results: Children/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward. Conclusion: These data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision-making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.267-275
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• 2021

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• 한국방재학회 논문집
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• 제8권5호
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• pp.77-83
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• 2008

본 연구에서는 정부차원에서 충남 연기군 일대에 추진되고 있는 행정중심복합도시 대상지역($132\;km^2$)에 대하여 재해대책을 위한 토지피복 분류 및 식생활력도(NDVI) 평가를 시도하였다. 활용한 영상은 아리랑 2호, LANDSAT, Aster 영상이며 해상도에 따른 분류의 한계를 비교, 평가하였다. 대상지역은 주로 산지와 논과 밭 등의 경작지이므로 특히 논과 밭의 분류에 주의를 기울였다. 아리랑2호 영상의 분류에 있어서는 고해상영상 분류를 위한 세그먼테이션 기법을 적용하였다. 분류의 정확도를 평가하기 위해 표본적으로 현장조사를 실시하여 검사하였으며 국가 토지이용도 및 토지대장의 지목과 비교하였다. 얻어진 결과는 shape file의 형태로 주제도를 완성하였으며 이는 행정중심복합도시의 미래지향적 개발계획을 위한 정책결정에 많은 도움이 될 것이다.

• 한국자동차공학회논문집
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• 제23권4호
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• pp.388-395
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• 2015

The waste gate valve (WGV) for gasoline vehicles operate in a harsh high-temperature environment. Hence, WGVs are typically made of Inconel 713C, which is a type of Ni-based superalloy. Recently, the metal injection molding (MIM) process has attracted considerable attention for parts used under high-temperature conditions. In this study, an MIM analysis for the head and other parts of the WGV is conducted using a commercial CAE program Moldflow. Further, optimal manufacturing conditions are determined by analyzing flow characteristics at various injection times and locations. Moreover, to improve the accuracy of the analysis results, we compare the actual temperature of the mold during injection processing with that observed through the analysis. As the results, metal injection patterns of analysis are well in accord with these of short shot test. And the temperature variations of analysis is also very similar with those of feedstock when metal injection molding.

• Smart Structures and Systems
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• 제6권9호
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제26권1호
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• pp.1-11
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• 2015

Phenomenology has been developed by philosophers like Kant and Husserl since the late 18th century. Jaspers, a German psychiatrist, adopted it into psychopathology studies and accumulated data by closely observing and recording the patients' symptoms and signs. Among descriptions done even before the psychopathology or diagnostic criteria of disorders in the field of child psychiatry was established, we can find exact and valuable descriptions matching the autism spectrum disorder or attention deficit/hyperactivity disorder. The diagnostic criteria of modern childhood psychiatric disorders were established based on these grounds. Phenomenological/descriptive methods in various psychiatric fields lead to medical study methods for social phenomenon such as oiettolie, hikikomori, and internet game addiction. Since Romanian orphans were adopted to the western world, descriptive studies along with neurobiological studies on the influence of stimulus deprivation on emotional and physical development are being conducted. While phenomenology, which was adopted by Jaspers to verify psychopathology, was developed mainly by observation and description, recent studies are explaining such descriptive phenomena even at the synapse level due to advances in neurobiology. Although phenomenological/descriptive psychiatry, describing precise and detailed experiences of patients, is less applied nowadays among modern study methods, we must remember that such descriptions may lead to biological studies and provide evidence to improve the accuracy of choosing and applying treatment methods.