• Smart Structures and Systems
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• v.29 no.4
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• pp.617-624
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• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Smart Structures and Systems
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• v.29 no.2
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• pp.337-350
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• 2022

This paper focuses on predicting the post-heating mechanical properties of cementitious composites reinforced with multi-scale additives using the Artificial Neural Network (ANN) approach. A total of four different feed-forward ANN models are developed using 261 data sets collected from 18 published sources. The models are optimized using 12 input parameters selected based on a comprehensive literature review to predict the residual compressive strength, the residual flexural strengths, elastic modulus, and fracture energy of heat-damaged cementitious specimens. Furthermore, the ANN is employed to predict the impact of several variables including; the content of polypropylene (PP) microfibers and carbon nanotubes (CNTs) used in the concrete, mortar, or paste mix design, length of PP fibers, the average diameter of CNTs, and the average length of CNTs. The influence of the studied parameters is investigated at different heating levels ranged from 25℃ to 800℃. The results demonstrate that the developed ANN models have a strong potential for predicting the mechanical properties of the heated cementitious composites based on the mixing ingredients in addition to the heating conditions.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.475-486
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• 2022

The purpose of this paper is to examine the use of material flow cost accounting (MFCA) in Vietnam's coal-fired thermal power plants. This study is based on the contingency and system theories to explain the application of management tools and analyze steps of input, output, and process in manufacturing. Costs in producing process-based MFCA include material cost, energy cost, system cost, and waste management cost. The exploratory case study methodology is used to describe and answer two questions, namely "How coal flow cost is recognized?" and "Why waste in material consumption can be harmful to the environment?". By analyzing the Quang Ninh and Pha Lai coal-fired thermal power plants that are the typical plants, this paper identifies the flow of primary material in these plants as a basis for determining losses for the business. The material flow of coal-fired thermal power plants provides the basis for the use of the MFCA. The manufacturing of electrical items in these plants is divided into four stages, each with its own set of losses. As a result, some phases in the application of MFCA are suggested, as well as some other elements required for MFCA application in coal-fired thermal power plants.

• Clean Technology
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• v.29 no.1
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• pp.59-70
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• 2023

This study aims to introduce a biowaste processing system that uses spent coffee grounds and implement a real options method to evaluate the proposed process. Energy systems based on eco-friendly fuels lack sufficient data, and thus along with conventional approaches, they lack the techno-economic assessment required for great input qualities. On the other hand, real options analysis can estimate the different costs of options, such as continuing or abandoning a project, by considering uncertainties, which can lead to better decision-making. This study investigated the feasibility of a biowaste processing method using spent coffee grounds to produce biofuel and considered three different valuation models, which were the net present value using discounted cash flow, the Black-Scholes and binomial models. The suggested biowaste processing system consumes 200 kg/h of spent coffee grounds. The system utilizes a tilted-slide pyrolysis reactor integrated with a heat exchanger to warm the air, a combustor to generate a primary heat source, and a series of condensers to harness the biofuel. The result of the net present value is South Korean Won (KRW) -225 million, the result of the binomial model is KRW 172 million, and the result of the Black-Scholes model is KRW 1,301 million. These results reveal that a spent coffee ground-related biowaste processing system is worthy of investment from a real options valuation perspective.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4534-4550
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• 2022

Integrated severe accident codes should be capable of simulating not only specific physical phenomena but also entire plant behaviors, and in a sufficiently fast time. However, significant uncertainty may exist owing to the numerous parametric models and interactions among the various phenomena. The primary objectives of this study are to present best-practice uncertainty and sensitivity analysis results regarding the evolutions of severe accidents (SAs) and fission product source terms and to determine the effects of mitigation measures on them, as expected during a short-term station blackout (STSBO) of a reference pressurized water reactor (optimized power reactor (OPR)1000). Three reference scenarios related to the STSBO accident are considered: one base and two mitigation scenarios, and the impacts of dedicated severe accident mitigation (SAM) actions on the results of interest are analyzed (such as flammable gas generation). The uncertainties are quantified based on a random set of Monte Carlo samples per case scenario. The relative importance values of the uncertain input parameters to the results of interest are quantitatively evaluated through a relevant sensitivity/importance analysis.

• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.18-24
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• 2023

Severe natural disasters and man-made attacks such as terrorism are causing unprecedented disruptions in power systems. Due to rapid climate change and the aging of energy infrastructure, both the frequency of failure and the level of damage are expected to increase. Resilience is a concept proposed to respond to extreme disaster events that have a low probability of occurrence but cause enormous damage and is defined as the ability of a system to recover to its original function after a disaster. Resilience is a comprehensive indicator that can include system performance before and after a disaster and focuses on preparing for all possible disaster scenarios and having quick and efficient recovery actions after an incident. Various studies have been conducted to evaluate resilience, but studies on economic damage considering the duration of a power outage are scarce. In this study, we propose an optimal algorithm that can identify failures after an extreme disaster and restore the load on the distribution system through emergency distributed power generation input and system reconfiguration. After that, the cost of power outage damage is analyzed by applying VoLL and CDF according to each restoration strategy.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.730-731
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• 2015

This study aims at giving the framework to estimate the environmental load at planning and schematic phase. With increasing awareness of environmental issues, the effort to reduce the environmental impacts caused by human activity has been increasingly enlarged. So far most of researches estimating CO₂ emissions have analyzed energy consumption based on BOQ (Bills of Quantity) acquired after detailed design. There is also lack of reliability in the estimated environmental impact using the basic unit of a facility at the planning stage, because it uses a limited specific section of historical data. Thus, this study is targeted at developing framework to assess reliable environmental loads based on information available at project early phases by making case-bases from historical design information on PSC Beam Bridge. Historical database is built on the basis of the LCA (Life Cycle Assessment) and in order to set input information for estimating model, the literature about information in an early project phase are reviewed. Using the information available in the planning and schematic design stage, the Framework is presented to estimate the environmental load in an early stage in the project. Developing an environmental load estimation model in accordance with the Framework presented in this study, it is expected that the environmental load in the initial project phase can be estimated more quickly and accurately.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Journal of Powder Materials
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• v.31 no.2
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• pp.137-145
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• 2024

In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.