• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.168-168
• /
• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권8호
• /
• pp.2188-2208
• /
• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• 한국컴퓨터정보학회논문지
• /
• 제28권8호
• /
• pp.67-75
• /
• 2023

주가 예측은 금융시장에서 중요하게 다뤄지고 있는 주제이지만 영향을 미칠 수 있는 다수의 요소들로 인해 어려운 주제로 고려되고 있다. 본 논문에서는 시계열 예측 모델 (LSTM, GRU)과 데이터의 시간적 의존성을 고려하지 않는 비 시계열 예측 모델 (RF, SVR, KNN, LGBM)을 주가 예측에 적용하여 성능을 비교하고 분석하였다. 또한 주가 데이터와 기술적 분석 보조지표, 재무제표 지표, 매수매도 지표, 공매도, 외국인 지표 등 다양한 데이터를 조합 및 활용하여 최적의 예측 요소를 찾아내고 업종별로 주가 예측에 영향을 미치는 주요 요소들을 분석했다. 하이퍼파라미터 최적화 과정을 통해 알고리즘별 예측 성능을 향상 시키는 과정도 진행하여 성능에 영향을 주는 요인을 분석하였다. 변수 선택과 하이퍼 파라미터 최적화 과정을 거친 결과, 시계열 예측 알고리즘인 GRU, 그리고 LSTM+GRU의 예측 정확도가 가장 높은 것으로 나타났다.

• 전기전자학회논문지
• /
• 제26권1호
• /
• pp.62-66
• /
• 2022

최근 데이터 기반의 딥러닝 기술을 적용하여 비면허 대역의 다양한 통신 신호를 분류하는 연구가 활발히 수행되고 있다. 하지만, 복잡한 신경망 모델 사용을 기반으로 이뤄진 이러한 접근법은 높은 연산 능력을 필요로 하게 되어, 자원 제약적인 무선 인터페이스 및 사물인터넷(Internet of Things) 장비에서는 사용이 제약된다. 본 연구에서는 비면허 대역의 무선 이기종 기술을 인지하기 위한 데이터 기반의 접근 방법을 살펴보고, 신호의 특징 추출 및 데이터화의 효율화 문제를 다룬다. 구체적으로, 비면허 대역의 다른 종류의 무선 통신 기술을 구분하기 위해 수신 신호 강도 측정을 기반으로 한 시계열 데이터를 이용해 합성곱 신경망(Convolutional Neural Network, CNN) 모델을 학습시켜 신호를 분류하는 방법을 살펴본다. 이 과정에서 동일한 구조의 신경망 모델의 경량화를 위한 효율적 신호의 시계열 데이터 정보 수집시 주파수 대역의 특징을 함께 특징화하는 방법을 제안하고, 그 효과를 평가한다. Bluetooth 호환의 Ubertooth 장비를 이용한 실측 기반의 실험 결과는 제안된 샘플링 기법이 동일한 신경망에 대해서 10% 수준의 샘플링 데이터 이용만으로도 동일한 정확도를 유지함을 보여준다.

• 천문학회보
• /
• 제44권2호
• /
• pp.77.3-77.3
• /
• 2019

We present an analysis of the stellar population of compact elliptical galaxies (cEs) in various environments. Following conventional selection criteria of cEs, we created a list of cE candidates in the redshift range of z < 0.05 using SDSS DR12 catalog. We finally selected cEs with low-luminosity (Mg > 18.7 mag), small effective radius (Re < 600 pc), and high velocity dispersion (> 60 kms-1). We divide our cE sample into those inside and outside of the one virial radius of the bright (Mr < -21 mag) nearby host galaxy which is then defined as cEs with (cEw) and without (cEw/o) host galaxy, respectively. We investigated the stellar population properties of cEs based on the Hb, Mgb, Fe 5270, and Fe 5335 line strengths from the OSSY catalog. We found that cEw has a systematically higher metallicity than cEw/o. In the velocity dispersion-Mgb distribution, while cEw/o follows the relation of early-type galaxies, cEw are found to have a systematically higher metallicity than cEw/o at a given velocity dispersion. The different feature in the metallicity between cEw and cEw/o can suggest that two different scenarios can be provided in the formation of cEs. cEw would be the remnant cores of the massive progenitor galaxies that their outer parts have been tidally stripped by massive neighbor galaxies (i.e., nurture origin). On the other hand, cEw/o are likely to be faint-end of early-type galaxies maintaining in-situ evolution (i.e., nurture origin).

• 한국엔터테인먼트산업학회논문지
• /
• 제13권6호
• /
• pp.95-102
• /
• 2019

본 글은 주로 호젠기 감독님은 창작한 영화 작품 <훈>과 <그 산, 저 사람> 두 편을 연구한다. 세 부분으로 호젠기 감독님 향토를 소재로 한 영화의 미학적 스타일을 전면적으로 연구하다: 제1부는 향토 장르 영화의 특징을 논술한다. 각각 영화의 제재 선택, 주제 표현, 인물 만들기 및 감정 표현에서 논술을 전개한다. 제2부는 작품의 화면, 소리, 색채의 세 가지 측면에서 각각 논술을 펼쳐 향토 장르 영화의 미학적인 풍격을 한층 더 반영하였다. 제3부는 향토 장르 영화 발전 과정에서의 당혹스러움과 그것이 어떻게 발전했는지를 분석한다. 본 눈문은 호젠기의 향토영화 미학적 스타일을 분석해 중국 향토영화의 발전에 대한 가치와 시사점을 제시한다.

• Asia pacific journal of information systems
• /
• 제30권3호
• /
• pp.568-589
• /
• 2020

Recently, Social media has become a concern for businesses, including Small and Medium Enterprises (SMEs). SMEs began to adopt social media to support their performance. To benefit from the application of social media, SMEs must implement the right strategy. This study aims to analyze the factors that influence the use of social media in SMEs. Furthermore, alignment between social media functionalities and strategies and their effect on SME's performance are investigated. A case study is conducted in Gymi, a garment SMEs in East Java, Indonesia. The data collection includes interviews with the owner of SMEs, observations, and document analysis. Data analysis is performed by pattern matching, which matches the patterns from the literature with data from the case study. The results of this study show that cost-effectiveness, interactivity, and compatibility are factors that influence the use of social media in Gymi. The social media used by Gymi are Instagram, Facebook, YouTube, WhatsApp, and LINE. However, the main social media used to support Gymi's functions is Instagram. Gymi has a relatively good social media strategy as it has defined a specific goal, target audience, and channel selection for social media (Instagram). It also has specific resources and policies to handle social media. Gymi monitors and evaluates their social media content activities. These strategies are aligned with the Instagram feature used to support Gymi's function, particularly marketing, sales, customer service, and to some extent, internal operation. The alignment contributes to Gymi's performance measured by the increase in reputation (number of Instagram followers) and sales.

• Korean Journal of Radiology
• /
• 제23권9호
• /
• pp.921-930
• /
• 2022

Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on ¹⁸F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

• 한국축산식품학회지
• /
• 제44권2호
• /
• pp.284-298
• /
• 2024

Restructured meat (RM) products are gaining importance as an essential component of the meat industry due to consumers' interest in health benefits. RM products imply the binding or holding of meat, meat by-products, and vegetable proteins together to form a meat product with meat's sensory and textural properties. RM products provide consumers with diversified preferences like the intake of low salt, low fat, antioxidants, and high dietary fiber in meat products. From the point of environmental sustainability, RM may aid in combining underutilized products and low-valued meat by adequately utilizing them instead of dumping them as waste material. RM processing technique might also help develop diversified and new hybrid meat products. It is crucial to have more knowledge on the quality issues, selection of binding agents, their optimum proportion, and finally, the ideal processing techniques. It is observed in this study that the most crucial feature of RM could be its healthy products with reduced fat content, which aligns with the preferences of health-conscious consumers who seek low-fat, low-salt, high-fiber options with minimal synthetic additives. This review briefly overviews RM and the factors affecting the quality and shelf life. Moreover, it discusses the recent studies on binding agents in processing RM products. Nonetheless, the recent advancements in processing and market scenarios have been summarized to better understand future research needs. The purpose of this review was to bring light to the ways of sustainable and economical food production.

• International Journal of Computer Science & Network Security
• /
• 제24권5호
• /
• pp.111-118
• /
• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.