• Advances in concrete construction
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• 제11권1호
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• 동의생리병리학회지
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• 제36권2호
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• pp.79-87
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• 2022

In this study, extracts of Boswellia serrata gum resin, Curcuma longa rhizome, and Terminalia chebula fruit were combined in different ratios, and their anti-osteoarthritis effects were compared to determine which combination had the best synergistic effect. B. serrata, C. longa, and T. chebula extracts in a 2:1:2 ratio exhibited higher antioxidative activity in scavenging DPPH radicals than did the individual extracts alone or the other extract combinations. Additionally, the 2:1:2 combination significantly improved the levels of enzymatic antioxidants and antioxidant-related proteins. Moreover, this same combination ratio decreased the protein levels of matrix metalloproteinase (MMP) 3 and MMP13 in interleukin-1β-stimulated human articular chondrocytes (HCHs) and increased those of aggrecan and collagen type II alpha 1 chain (COL2A1). Analysis of the underlying mechanisms revealed that the 2:1:2 combination significantly inhibited the phosphorylation of nuclear factor kappa B (NF-κB) p65, extracellular regulated protein kinase (ERK), and p38 mitogen-activated protein kinase (MAPK). Therefore, the 2:1:2 combination of these three plant extracts has the best potential for use as an effective dietary supplement for improving joint health compared with the individual extracts and their other combination ratios.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.353-361
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• 2022

Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.

• 한국재료학회지
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• 제32권11호
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• pp.458-465
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• 2022

Interest in the use of semiconductor-based photocatalyst materials for the degradation of organic pollutants in a liquid phase has grown, due to their excellent performance and response to the light source. Herein, we fabricated a NiO-SiC-TiO₂ ternary structured photocatalyst which had reduced bandgap energy, with strong activation under UV-light irradiation. The synthesized samples were examined using XRD, SEM, EDX, TEM, DRS, EIS techniques and photocurrent measurement. The results confirmed that the two types of metal oxides were well bonded to the SiC fiber surface. The junction of the new photocatalyst exhibited a large number of photoexcited electrons and holes. The holes tended to oxidize the water and form a hydroxyl radical, which promoted the decomposition of methylene blue. The close contact between the 2D SiC fiber and metal oxide semiconductors expanded the scope of absorption wavelength, and enhanced the usability of the ternary photocatalyst for the degradation of methylene blue. Among three synthesized samples, the NiO-SiC-TiO₂ showed the best photocatalytic effect, and was considered to have excellent photoelectron transfer due to the synergy effect between the metal oxide and SiC.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.59-74
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• 2022

During the last decade, the security of digital images has received considerable attention in various multimedia transmission schemes. However, many current cryptosystems tend to adopt a single-layer permutation or diffusion algorithm, resulting in inadequate security. A hierarchical bilateral diffusion architecture for color image encryption is proposed in response to this issue, based on a hyperchaotic system and DNA sequence operation. Primarily, two hyperchaotic systems are adopted and combined with cipher matrixes generation algorithm to overcome exhaustive attacks. Further, the proposed architecture involves designing pixelpermutation, pixel-diffusion, and DNA (deoxyribonucleic acid) based block-diffusion algorithm, considering system security and transmission efficiency. The pixel-permutation aims to reduce the correlation of adjacent pixels and provide excellent initial conditions for subsequent diffusion procedures, while the diffusion architecture confuses the image matrix in a bilateral direction with ultra-low power consumption. The proposed system achieves preferable number of pixel change rate (NPCR) and unified average changing intensity (UACI) of 99.61% and 33.46%, and a lower encryption time of 3.30 seconds, which performs better than some current image encryption algorithms. The simulated results and security analysis demonstrate that the proposed mechanism can resist various potential attacks with comparatively low computational time consumption.

• 한국소프트웨어감정평가학회 논문지
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• 제16권2호
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• pp.63-68
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• 2020

본 논문에서는 딥러닝 프레임워크 기반의 얼굴인증 시스템에 대하여 제안한다. 제안 시스템은 딥러닝 알고리즘을 활용하여 얼굴영역 검출과 얼굴 특징 추출을 수행하고, 결합베이시안 학습 모델을 이용하여 얼굴인증을 수행한다. 제안 얼굴인증 알고리즘에 대한 성능 평가는 다양한 얼굴 사진들로 구성된 데이터베이스를 이용하여 수행하였으며, 한 명에 대한 얼굴 영상은 2장으로 구성하였다. 또한 얼굴인증 실험은 딥 뉴럴 네트워크를 통한 2048차원의 특징과 그 유사성을 측정하기 위해 결합베이시안 알고리즘을 적용하였으며, 얼굴인증에 실패한 동일오율을 계산함으로써 성능평가를 수행하였다. 실험 결과, 딥러닝 특징과 결합베이시안 알고리즘을 사용한 제안 방법은 1.2%의 동일오율을 보였다.

• 한국보건간호학회지
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• 제35권3호
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• pp.415-429
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• 2021

Purpose: The purpose of this study was to explore the health-related factors of the elderly in rural village in-depth and comprehensively based on the socio-ecological model. Methods: The data were collected from 22 elderly people through four focus group interviews and analyzed by deductive content analysis using four themes of the socio-ecological model (SEM) as an analysis matrix. Results: A total of 10 categories corresponding to the four themes of SEM were derived as follows: Intrapersonal level, "Awareness of Aging and Health", "Inefficient practice of health behavior", and "Daunted self-efficacy", Interpersonal level, "Social relations maintenance", and "Changing sense of community", Community level, "Local resources requiring improvement", "Problems caused by regional characteristics", "Disadvantaged group", and "Leadership and residents participation", Public policy level, "Health-related facilities and programs". Conclusion: We proposed the development and application of intervention programs that combined individual activities to improve self-management capacity and group activities to enhance social support and solidarity for rural villagers.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2889-2896
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• 2023

The waste acceptance criteria for heavy metals in mixed waste should be developed by reflecting the leaching behaviors that could highly depend on the repository design and environment surrounding the waste. The current standards widely used to evaluate the leaching characteristics of heavy metals would not be appropriate for the silo-type repository since they are developed for landfills, which are more common than a silo-type repository. This research aimed to explore the leaching behaviors of cementitious waste with Pb, Cd, and Sb metallic and oxide powders in an environment simulating a silo-type radioactive waste repository. The Toxicity Characteristic Leaching Procedure (TCLP) and the ANS 16.1 standard were employed with standard and two modified solutions: concrete-saturated deionized and underground water. The compositions and elemental distribution of leachates and specimens were analyzed using an inductively coupled plasma optical emission spectrometer (ICP-OES) and energy-dispersive X-ray spectroscopy combined with scanning electron microscopy (SEM-EDS). Lead and antimony demonstrated high leaching levels in the modified leaching solutions, while cadmium exhibited minimal leaching behavior and remained mainly within the cement matrix. The results emphasize the significance of understanding heavy metals' leaching behavior in the repository's geochemical environment, which could accelerate or mitigate the reaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.