• 농업과학연구
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• 제49권3호
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• pp.483-493
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• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

• Smart Structures and Systems
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• 제29권2호
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• pp.301-309
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• 2022

Protective coatings are most widely used anticorrosive structures for steel structures. The corrosion under the coating damages the host material, but this damage is completely hidden. Therefore, a field-applicable under-coating-corrosion visualization method has been desired for a long time. Laser ultrasonic technology has been studied in various fields as an in situ nondestructive inspection method. In this study, a comparative analysis was carried out between a guided-wave ultrasonic propagation imager (UPI) and pulse-echo UPI, which have the potential to be used in the field of under-coating-corrosion management. Both guided-wave UPI and pulse-echo UPI were able to successfully visualize the corrosion. Regarding the field application, the guided-wave UPI performing Q-switch laser scanning and piezoelectric sensing by magnetic attachment exhibited advantages owing to the larger distance and incident angle in the laser measurement than those of the pulse-echo UPI. Regarding the corrosion visualization methods, the combination of adjacent wave subtraction and variable time window amplitude mapping (VTWAM) provided acceptable results for the guided-wave UPI, while VTWAM was sufficient for the pule-echo UPI. In addition, the capability of multiple sensing in a single channel of the guided-wave UPI could improve the field applicability as well as the relatively smaller size of the system. Thus, we propose a guided-wave UPI as a tool for under-coating-corrosion management.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• 센서학회지
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• 제32권2호
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Journal of Electrochemical Science and Technology
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• 제14권1호
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• pp.51-58
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• 2023

The effective detection of human chorionic gonadotropin (HCG) is considerably important for the clinical diagnosis of both of early pregnancy and nonpregnancy-related diseases. In this work, a simple and sensitive electrochemical sandwich-type immunosensing platform was designed by synthesizing b-cyclodextrin (CD) functionalized graphene (CD/GN) hybrid as simultaneously sensing platform and signal transducer coupled with rhodamine b (RhB) as probe. In brief, GN offers large surface area and high conductivity, while CD exhibits superior host-guest recognition capability, thus the primary antibody (Ab1) of HCG can be bound into the cavities of CD/GN to form stable Ab1/CD/GN inclusion complex; meanwhile, the secondary antibody (Ab2) and RhB can also enter into the cavities, producing RhB/Ab2/CD/GN complex. Then, by using Ab1/CD/GN as sensing platform and RhB/Ab2/CD/GN as signal transducer (in which RhB was signal probe), a simple sandwich-type immunosensor was constructed. Under the optimum parameters, the designed immunosensor exhibited a considerable low analytical detection of 1.0 pg mL^-1 and a wide linearity of 0.002 to 10.0 ng mL^-1 for HCG, revealing the developed sandwich-type electrochemical immunosensing platform offered potential real applications for the determination of HCG.

• Earthquakes and Structures
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• 제22권5호
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• 천문학회보
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• 제46권2호
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• pp.62.1-62.1
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• 2021

Interstellar objects originate from other stellar systems. Thus, they contain information about the stellar systems that cannot be directly explored; the information includes the formation and evolution of the stellar systems and the possibility of life. The examples observed so far are 1l/Oumuamua in 2017 and 2l/Borisov in 2019. In this talk, we present the possibility of detecting interstellar objects using the Heliospheric Imagers designed for space weather research and forecasting by observing solar wind in interplanetary space between the Sun and Earth. Because interstellar objects are unpredictable events, the detection requires observations with wide coverage in spatial and long duration in temporal. The near-real time data availability is essential for follow-up observations to study their detailed properties and future rendezvous missions. Heliospheric Imagers provide day-side observations, inaccessible by traditional astronomical observations. This will dramatically increase the temporal and spatial coverage of observations and also the probability of detecting interstellar objects visiting our solar system, together with traditional astronomical observations. We demonstrate that this is the case. We have used data taken from Solar TErrestrial RElation Observatory (STEREO)/Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) HI-1. HI-1 is off-pointed from the Sun direction by 14 degrees with 20 degrees of the field of view. Using images observed from 2007 to 2019, we have found a total of 223 small objects other than stars, galaxies, or planets, indicative of the potential capability to detect interstellar objects. The same method can be applied to the currently operating missions such as the Parker Solar Probe and Solar Orbiter and also future L5 and L4 missions. Since the data can be analyzed in near-real time due to the space weather purposes, more detailed properties can be analyzed by follow-up observations in ground and space, and also future rendezvous missions. We discuss future possible rendezvous missions at the end of this talk.

• 디자인융복합연구
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• 제16권5호
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• pp.125-140
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• 2017

본 연구는 머신러닝의 기법이 도시 형태를 분석 및 추론하는 복잡한 과정에 적용 되었을 때, 도시 공간의 변화를 감지하고 분석하며 예측 할 수 있는 가능성을 사례 연구의 근거를 통해 제시하고자 한다. 사례 연구는 미국 보스톤의 메인 스트리트를 대상으로 도시 형태를 분석하는 과정에 머신러닝의 기법을 적용 실험하여 그 효용성을 예증했던 2006년의 선행 연구의 결과를 2016년 도시 형태와 현상을 비교 재해석하여, 10년간의 변화를 도시적 관점, 정보 환경의 관점, 기술적 관점에서 분석하고 이에 유효한 도시 모니터링의 시사점을 도출했다. 먼저, 다중 참여형 정보 수집의 플랫폼이 열리면서 대용량 데이터를 실시간으로 수집할 수 있는 기술적으로 가능해 졌다. 로봇이나 드론 등 인공지능이 탑재된 기계들을 사용하여 도시 정보를 취득하고 개입할 수 있는 가능성과 신산업의 요구에 맞추어 도시 정보 체계를 바꿀 수 있는 가능성이 열려있다. 결론적으로, 현 도시의 당면 문제에 집중하고 각 지역의 특성에 맞는 모니터링 전략을 세우는 것이 중요하며, 국내에서는 최근 도시 재생의 관점이 강조되고 있으므로 그 실천적인 연구가 필요하다.

• 영어영문학
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• 제57권6호
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• pp.1111-1130
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• 2011

Reading postmodern texts certainly situates readers in roles different from the ones we have been used to. Recently, postmodern metafiction forms a significant body of children's literature that is intended to challenge and transform the conventions of books in the digital age. While many studies have been done as to how child readers have capabilities to appreciate and interpret postmodern metafiction picture books, few studies on teachers and preservice teachers' reactions are not readily available. The role of teachers and preservice teachers are crucial for child readers to have access to affluent reading resources. This study discusses how preservice teachers read and respond to postmodern metafiction picture books using a deconstructive approach by means of binary opposites. Data was collected with 14 preservice teachers as to their likes/dislikes, reading levels, and reading paths about postmodern metafiction picture books. Expected pedagogical implications for literacy and language education were requested to address in their reading diaries and response papers. With their likes/ dislikes, since binary opposites always imply the hierarchy of power and value, the likes is apparently more valued and appreciated over their dislikes. This differentiated values are discussed in more detail with three recurring themes-Education, Morals and Behavior, and Tradition. With reading levels, there seems to be a gap existing between the authors' implied reader and literary critics' and the preservice teachers' ideal readers for the postmodern metafiction picture books. Although many studies have already revealed young readers' capability of appreciating postmodern metafiction, it depends a lot more on the teachers and preservice teachers whether children's right to have access to affluent literacy resources is respected or not. Preservice teachers' awareness of the potential of postmodern metafiction will work as an initial step to bring and realize the new reading path and new literacies in classrooms. By challenging metanarratives of children's literature, preservice teachers' readings of postmodern picture books reveals potentials to raise different reading paths and develop new literacies and other educational implications.