• Steel and Composite Structures
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• v.49 no.3
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• Korean Journal of Materials Research
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• v.34 no.2
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• pp.85-94
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• 2024

The optimization of deacetylation process parameters for producing chitosan from isolated chitin shrimp shell waste was investigated using response surface methodology with central composite design (RSM-CCD). Three independent variables viz, NaOH concentration (X₁), radiation power (X₂), and reaction time (X₃) were examined to determine their respective effects on the degree of deacetylation (DD). The DD of chitosan was also calculated using the baseline approach of the Fourier Transform Infrared (FTIR) spectra of the yields. RSM-CCD analysis showed that the optimal chitosan DD value of 96.45 % was obtained at an optimized condition of 63.41 % (w/v) NaOH concentration, 227.28 W radiation power, and 3.34 min deacetylation reaction. The DD was strongly controlled by NaOH concentration, irradiation power, and reaction duration. The coefficients of correlation were 0.257, 0.680, and 0.390, respectively. Because the procedure used microwave radiation absorption, radiation power had a substantial correlation of 0.600~0.800 compared to the two low variables, which were 0.200~0.400. This independently predicted robust quadratic model interaction has been validated for predicting the DD of chitin.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.200-208
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• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.

• Earthquakes and Structures
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• v.26 no.3
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• pp.203-218
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• 2024

Beam-column joints in the frame structure are at high risk of brittle shear failure which would lead to significant residual deformation and even the collapse of the structure during an earthquake. In order to improve the damage issue and enhance the recoverability of the beam-column joints, a sector lead rubber damper (SLRD) has been developed. The SLRD can increase the bearing capacity and energy dissipation capacity, and also demonstrating recoverability of seismic performance following cyclic loading. In this paper, the hysteretic behavior of SLRD was experimentally investigated in terms of the regular hysteretic behavior, large deformation behavior and fatigue behavior. Furthermore, a parametric analysis was performed to study the influence of the primary design parameters on the hysteretic behavior of SLRD. The results show that SLRD resist the exerted loading through the shear capacity of both rubber parts coupled with the lead cores in the pre-yielding stage of lead cores. In the post-yielding phase, it is only the rubber parts of the SLRD that provide the shear capacity while the lead cores primarily dissipate the energy through shear deformation. The SLRD possesses a robust capacity for large deformation and can sustain hysteretic behavior when subjected to a loading rotation angle of 1/7 (equivalent to 200% shear strain of the rubber component). Furthermore, it demonstrates excellent fatigue resistance, with a degradation of critical behavior indices by no more than 15% in comparison to initial values even after 30 cycles. As for the designing practice of SLRD, it is recommended to adopt the double lead core scheme, along with a rubber material having the lowest possible shear modulus while meeting the desired bearing capacity and a thickness ratio of 0.4 to 0.5 for the thin steel plate.

• Smart Structures and Systems
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• v.33 no.2
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• pp.119-131
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• 2024

Drift angle is a significant index for diagnosing post-event structures. A common way to estimate this drift response is by using modal parameters identified under natural excitations. Although the modal parameters of shear structures cannot be identified accurately in the real environment, the identification error has little impact on the estimation when measurements from several floors are used. However, the estimation accuracy falls dramatically when there is only one accelerometer. This paper describes the susceptibility of single sensor identification to modelling error and simulations that preliminarily verified this characteristic. To make a robust evaluation from measurements of one floor of shear structures based on imprecisely identified parameters, a novel scheme is devised to approximately correct the mode shapes with respect to fictitious frequencies generated with a genetic algorithm; in particular, the scheme uses constrained minimization to take both the mathematical aspect and the realistic aspect of the mode shapes into account. The algorithm was validated by using a full-scale shear building. The differences between single-sensor and multiple-sensor estimations were analyzed. It was found that, as the number of accelerometers decreases, the error rises due to insufficient data and becomes very high when there is only one sensor. Moreover, when measurements for only one floor are available, the proposed method yields more precise and appropriate mode shapes, leading to a better estimation on the drift angle of the lower floors compared with a method designed for multiple sensors. As well, it is shown that the reduction in space complexity is offset by increasing the computation complexity.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.216-221
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• 2024

Ion cyclotron range of frequency (ICRF) heating system is an important auxiliary heating method in the experimental Advanced Superconducting Tokamak (EAST). In EAST, several megawatts of power are transmitted with coaxial transmission lines and coupled to the plasma. For the long pulse and high power operation of the ICRF waves heating system, it is very important to effectively control the power and initial phase of the ICRF signals. In this paper, a power and phase feedback control system is described based on field programmable gate array (FPGA) devices, which can realize complicated algorithms with the advantages of fast running and high reliability. The transmitted power and antenna phase are measured by a power and phase detector and digitized. The power and phase feedback control algorithms is designed to achieve the target power and antenna phase. The power feedback control system was tested on a dummy load and during plasma experiments. Test results confirm that the feedback control system can precisely control ICRF power and antenna phase and is robust during plasma variations.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of Global Scholars of Marketing Science
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• v.12
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• pp.99-119
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• 2003

A model is proposed for the role of emotional intensity of a web site, and the moderating influence of product involvement, in the Integrative Framework of persuasion (Meyers-Levy and Malvaiya 1999). The model also appropriately operationalizes the constructs emotional intensity of a web site and product involvement The three routes to persuasion, Central, Peripheral, and Experiential correspond to high, moderate, and low involvement (Meyers-Levy and Malaviya 1999). The involvement construct is measured from message recipients using the Personal Product Inventory (Pill, which was developed to capture the concept of product involvement (Zaichkowsky 1985). The conceptualization of the Personal Product Inventory is a contextrree measure that also has robust psychometric properties when applied to advertisements (Zaichkowsky 1994). The propositions highlight the expected importance of emotional intensity of a web site. The moderating influence of product involvement is also proposed. Specifically, what this work proposes is that the emotional intensity of a product site has a larger impact on attitude change under low product involvement, as opposed to moderate product involvement. Support for this reasoning can be found in the persuasion literature (Petty et al 1986). The Petty et al (1986) frame work is a dual process descriptive and predictive frame work in the area of altitude formation and change. Recently, Myers Levy and Malaviya (1999) have proposed a tri-process framework. This is in tum based on the dual process model of Petty et al. (1986). The study outlined in this paper aims to deepen the Meyers Levy and Malaviya (1999) and frame work. The propositions outlined in the model are empirically tested using a repeated measures experimental design. The emotional intensity is measured using a scale that is based on experts judgments. Using a paired comparison t-test two sites are determined to be of high and low emotional intensity. The model is tested using a repeated measures experimental design. The first independent variable Emotional Intensity of the site is manipulated. The Second independent variable, Personal Product Inventory is measured. While, the dependent variable, product altitude change will also be measured. Utilizing Analysis of Variance (ANOVA) the data is analyzed using SPSS. The results suggest that besides the rational content of messages their emotional content can also influence attitude change. Specifically, it is proposed that the manipulation of emotional intensity of a product Web site has a greater impact on product altitudes under high and low product involvement conditions, rather than moderate product involvement. However, the results for product involvement as a continuous variable has a p value of 0.09. Further, the results for three levels of product involvement were far from significant. For two levels of product involvement also, the results were insignificant, the p value approached 0.20. This evidence indicates that it is premature to conclude that there are three routes to persuasion. A caveat, however, must be added, in that the manipulations may not have been strong enough to test the proposed hypotheses. Further, undoubtedly, there is unequivocal evidence the emotional intensity of a product Web site, as measured here, has a direct impact on product attitudes.