• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.290-299
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• 2017

Reconfigurable flight control using various kinds of adaptive control methods has been studied since the 1970s to enhance the survivability of aircraft in case of severe in-flight failure. Early studies were mainly focused on the failure of actuators. Recently, studies of reconfigurable flight controls that can accommodate complex damage (partial wing and tail loss) in conventional aircraft were reported. However, the partial wing loss effects on the aerodynamics of conventional type aircraft are quite different to those of BWB(blended wing body) aircraft. In this paper, a reconfigurable flight control algorithm was designed using a direct model reference adaptive method to overcome the instability caused by a complex damage of a BWB aircraft. A model reference adaptive control was incorporated into the inner loop rate control system enhancing the performance of the baseline control to cope with abrupt loss of stability. Gains of the model reference adaptive control were polled out using the Liapunov's stability theorem. Outer loop attitude autopilot was designed to manage roll and pitch of the BWB UAV as well. A 6-DOF dynamic model was built-up, where the normal flight can be made to switch to the damaged state abruptly reflecting the possible real flight situation. 22% of right wing loss as well as 25% loss for both vertical tail and rudder control surface were considered in this study. Static aerodynamic coefficients were obtained via wind tunnel test. Numerical simulations were conducted to demonstrate the performance of the reconfigurable flight control system.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• Steel and Composite Structures
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• v.29 no.4
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• pp.527-544
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• 2018

The paper presents the study on a change in modal parameters and structural stiffness of cable-stayed Fiberline Bridge made entirely of Glass Fiber Reinforced Polymer (GFRP) composite used for 20 years in the fjord area of Kolding, Denmark. Due to this specific location the bridge structure was subjected to natural aging in harsh environmental conditions. The flexural properties of the pultruded GFRP profiles acquired from the analyzed footbridge in 1997 and 2012 were determined through three-point bending tests. It was found that the Young's modulus increased by approximately 9%. Moreover, the influence of the temperature on the storage and loss modulus of GFRP material acquired from the Fiberline Bridge was studied by the dynamic mechanical analysis. The good thermal stability in potential real temperatures was found. The natural vibration frequencies and mode shapes of the bridge for its original state were evaluated through the application of the Finite Element (FE) method. The initial FE model was created using the real geometrical and material data obtained from both the design data and flexural test results performed in 1997 for the intact composite GFRP material. Full scale experimental investigations of the free-decay response under human jumping for the experimental state were carried out applying accelerometers. Seven natural frequencies, corresponding mode shapes and damping ratios were identified. The numerical and experimental results were compared. Based on the difference in the fundamental natural frequency it was again confirmed that the structural stiffness of the bridge increased by about 9% after 20 years of service life. Data collected from this study were used to validate the assumed FE model. It can be concluded that the updated FE model accurately reproduces the dynamic behavior of the bridge and can be used as a proper baseline model for the long-term monitoring to evaluate the overall structural response under service loads. The obtained results provided a relevant data for the structural health monitoring of all-GFRP bridge.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.1
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• pp.1-8
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• 2019

Reverse engineering involves examining a system or component so as to comprehend its structure, functionality, and operation. Creation of a system model in reverse engineering can serve several purposes: test generation, change impact analysis, and the creation of a new or modified system. When attempting to reverse engineering a system, often the most readily accessible information is the system description, which does not readily lend itself to use in Model Based System Engineering (MBSE). Therefore, it is necessary to be able to transform this description into a diagram, which clearly depicts the behavior of the system as well as the interaction between components. This study demonstrates how sequence diagrams can be extracted from the systems description. Using MBSE software, the sequence diagrams for the Engineered Safety Features Component Control System (ESF-CCS) of the Nuclear Power Plant are created. Sequence diagrams are chosen because they are a means of representing the systems behavior and the interaction between components. In addition, from these diagrams, the system's functional requirements can be elicited. These diagrams then serve as the baseline of the reverse engineering process and multiple system views are subsequently be created from them, thus speeding up the development process. In addition, the use of MBSE ensures that any additional information obtained from auxiliary sources can then be input into the system model, ensuring data consistency.

• The Korean Journal of Franchise Management
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• v.10 no.4
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• pp.31-42
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• 2019

Purpose : This study can provide various implications for the franchisors to expand activities related to franchise support or to develop andoperate an education program for foodservice franchise owners. Research design, data, and methodology : For those purpose, first, the literatureand literature related to the competency of domestic franchise owner were collected and reviewed through the Korea Education and Research Information Service (RISS). Second, the questionnaire was prepared based on the theoretical basis prepared through previous studies. Based onthe foodservice franchise owner's competency model presented by Kim & Lee (2019b), 13 franchise owner's competencies were marked with both 'What is' levels and 'What should be' levels. Therefore, the total questionnaire consists of 26 questions. Third, questionnaires were distributed and collected. This study used data from 55 surveys which were gathered from foodservice franchise owners in Seongnam-si. SPSS 25.0 was used to analyze the collected survey data. Descriptive and frequency analysis were conducted to analyze the demographic characteristics of the study subjects. Next, we conduct a t-test to analyze the difference between the level of 'What is' competencies by the franchise owners and the level of 'What should be' competencies. Descriptive statistics were used to derive the priorities of the 'What should be' competencies. The Locus for Focus model was used to derive the priorities of the required competencies. Result : Four competencies of team leadership, teamwork and cooperation, customer service, technical·professional·managerial expertise were found to be the first to be developed. Conclusions : The conclusions of this study are as follows. First, teamwork and cooperation competnecy, and team leadership competency, which are derived from the core competencies of foodservice franchise owners, are among the leadership competencies required as managers of organizations. Second, customer service competency and ttechnical·professional·managerial expertise competency derived from the core competencies of restaurant franchise owners belong to the job competencies. Third, the results of this study suggest that the foodservice franchisors will be able that will serve as a baseline to support the foodservice franchisors and franchise owners for sustainable mutual growth by encouraging their will and encouraging them to create results.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.170-177
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• 2021

Background: The International Commission on Radiological Protection is preparing to provide reference dose coefficients for environmental radioiodine intake based on newly developed age-specific biokinetic models. However, the biokinetics of iodine has been reported to be strongly dependent on the dietary intake of stable iodine; for example, the thyroidal uptake of iodine may be substantially lower in iodine-rich regions than in iodine-deficient regions. Therefore, this study attempted to establish a system of age-specific thyroid dose estimation for South Koreans, whose daily iodine intakes are significantly higher than that of the world population. Materials and Methods: Korean age-specific biokinetic parameters and thyroid masses were derived based on the previously developed Korean adult model and the Korean anatomical reference data for adults, respectively. This study complied with the principles used in the development of age-specific biokinetic models for world population and used the ratios of baseline values for each age group relative to the value for adults to derive age-specific values. Results and Discussion: Biokinetic model predictions based on the Korean age-specific parameters showed significant differences in iodine behaviors in the body compared to those predicted using the model for the world population. In particular, the Korean age-specific thyroid dose coefficients for ¹²⁹I and ¹³¹I were considerably lower than those calculated for the world population (25%-76% of the values for the world population). Conclusion: These differences stress the need for Korean-specific internal dose assessments for infants and children, which can be achieved by using the data calculated in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.42-55
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• 2021

[Purpose] This study investigated the effects of marine phytoplankton supplementation (Oceanix®, Tetraselmis chuii) on 1) maximal isometric strength and immune function in healthy humans following a oneweek high-intensity resistance-training program and 2) the proinflammatory cytokine response to exercise in a rat model. [Methods] In the human trial, 22 healthy male and female participants were randomly divided into marine phytoplankton and placebo groups. Following baseline testing, participants underwent a 14-day supplement loading phase before completing five consecutive days of intense resistance training. In the rat model, rats were randomly divided into four groups (n=7 per condition): (i) control, (ii) exercise, (iii) exercise + marine phytoplankton (2.55 mg/kg/day), or (iv) exercise + marine phytoplankton (5.1 mg/kg/day). Rats in the exercising groups performed treadmill exercise 5 days per week for 6 weeks. [Results] In the human model, marine phytoplankton prevented significant declines in the isometric peak rate of force development compared to placebo. Additionally, salivary immunoglobulin A concentration was significantly lower following the resistance training protocol in the placebo group but not in the marine phytoplankton group. Marine phytoplankton in exercising rats decreased intramuscular levels and serum concentrations of tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) and intramuscular concentrations of malondialdehyde. [Conclusion] Marine phytoplankton prevented decrements in indices of functional exercise recovery and immune function. Mechanistically, these outcomes could be prompted by modulating the oxidative stress and proinflammatory cytokine response to exercise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.