• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.157-160
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• 2022

본 논문에서는 다중 인물 포함 단일 영상으로부터 파라미터 기반 3차원 휴먼 모델 생성 기법 중 최근 발표된 SOTA 기법 4가지에 대해 대표적인 데이터 셋들에 대해 사전 학습 모델을 사용한 복원 성능 비교 실험을 수행하였다. 실험결과, CLIFF 기법과 PyMAF-x 기법이 PARE 기법이나 ROMP 기법에 비해 우수한 결과를 보였다.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.95-99
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• 2023

Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.459-466
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• 2023

In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.284-291
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• 2022

Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

• Clinical and Experimental Reproductive Medicine
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• v.49 no.4
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• pp.225-238
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• 2022

The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.

• Smart Structures and Systems
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• v.29 no.5
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• pp.715-728
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• 2022

Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.387-390
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• 2021

최근 전자 상거래 시장이 급격한 성장을 이루면서 고객들의 급변하는 니즈를 파악하는 것이 기업들의 수익에 직결되는 요소로 인식되고 있다. 이에 기업들은 고객들의 니즈를 신속하고 정확하게 파악하기 위해, 기축적된 고객 관련 각종 데이터를 활용하려는 시도를 강화하고 있다. 기존 시도들은 주로 구매 행동 예측에 중점을 두었으나 고객 행동의 전후 과정을 해석하는데 있어 어려움이 존재했다. 본 연구에서는 고객이 구매한 상품을 확정 또는 환불하는 행동을 취할 때 해당 행동이 발생하는데 있어 어떤 요소들이 작용하였는지를 파악하고, 어떤 고객이 환불할 지를 예측하는 예측 모형을 새롭게 제시한다. 예측 모형 구현에는 트리 기반 앙상블 방법을 사용해 예측력을 높인 XGBoost 기법을 적용하였으며, 고객 의도에 영향을 미치는 요소들을 파악하기 위하여 대표적인 설명가능한 인공지능(XAI) 기법 중 하나인 SHAP 기법을 적용하였다. 이를 통해 특정 고객 행동에 대한 각 요인들의 전반적인 영향 뿐만 아니라, 각 개별 고객에 대해서도 어떤 요소가 환불결정에 영향을 미쳤는지 파악할 수 있었다. 이를 통해 기업은 고객 개개인의 의사 결정에 영향을 미치는 요소를 파악하여 개인화 마케팅에 사용할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.294-294
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• 2021

강우에 따른 수위예측은 수자원 관리 및 재해 예방에 있어 중요하다. 기존의 수문분석은 해당지역의 지형 데이터, 매개변수 최적화 등 수위예측 분석에 있어 어려움을 동반한다. 최근 AI(Artificial Intelligence) 기술의 발전에 따라, 수자원 분야에 AI 기술을 활용하는 연구가 수행되고 있다. 본 연구에서는 데이터 간의 관계를 포착할 수 있는 AI 기반의 기법을 이용하여 강우에 따른 수위예측을 실시하였다. 연구대상 유역으로는 과거 수문데이터가 풍부한 설마천 유역으로 선정하였다. AI 기법으로는 머신러닝 중 SVM (Support Vector Machine)과 Gradient boosting 기법을 이용하였으며, 딥러닝으로는 시계열 분석에 사용되는 RNN (Recurrent Neural Network) 중 LSTM (Long Short-Term Memory) 네트워크을 이용하여 수위 예측 분석을 수행하였다. 성능지표로는 수문분석에 주로 사용되는 상관계수와 NSE (Nash-Sutcliffe Efficiency)를 이용하였다. 분석결과 세 기법 모두 강우에 따른 수위예측을 우수하게 수행하였다. 이 중, LSTM 네트워크는 과거데이터를 이용한 보정기간이 늘어날수록 더욱 높은 성능을 보여주었다. 우리나라의 집중호우와 같은 긴급 재난이 우려되는 상황 시 수위예측은 빠른 판단을 요구한다. 비교적 간편한 데이터를 이용하여 수위예측이 가능한 AI 기반 기법을 적용할 시 위의 요구사항을 충족할 것이라 사료된다.