• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.487-494
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• 2022

Purpose: This study proposes a Metaverse-based police education and training model that can efficiently improve the performance of various police activities according to changes in the environment of the times. Method: The structure of this system can generate Avatar Controller expressed using HMD and haptic technology, access the Network Interface, and educate and train individually or on a team basis through the command control module, education and training content module, and analysis module. Result: In the proposed model of this study, the command and control module was incorporated into individual or team-based education and training, enabling organic collaborative training among team members by monitoring the overall situation of terrorism or crime in real time. Conclusion: Metaverses-based individual or team-based police education and training can provide a more efficient and safe education and training environment based on immersion, interaction, and rapid judgment in various situations.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.400-405
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• 2022

Legislative approaches to regulating the digital sex industry are increasingly being debated at the international and national levels. There is a trend showing an increased interest in the decriminalization of sex work. At the same time, in many countries, activities related to digital prostitution remain criminalized. In this regard, it is important to analyze the international legal experience of the criminalization and decriminalization of digital prostitution, as well as to pay attention to the key problematic issues that arise during the criminalization and decriminalization of such an issue. The object of the study is the international experience of criminalization and decriminalization of digital prostitution. The subject of the study is social relations that arise, change, and cease during the criminalization and decriminalization of digital prostitution. The research methodology consists of such methods as philosophical, logical, special-legal, system analysis methods, and formal-dogmatic methods. Research results. As a result of the study of the international legal experience of criminalization and decriminalization of digital prostitution, it was concluded that the criminalization and/or decriminalization of digital prostitution is treated differently in different countries. Workers in this industry advocate decriminalization, not legalization, because decriminalization puts power directly in the hands of sex workers and creates no legal barriers. Countries that have decriminalized digital prostitution believe that sex work is real work and should be treated respectfully, and banning resources such as OnlyFans is not in favor of such workers. Regarding positions on the criminalization of prostitution, countries use different models of such criminalization, including the model of legalization of digital prostitution, which, on the one hand, allows prostitution, but establishes criminal liability for deviations from the rules established by the state.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.213-221
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• 2022

Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.1-7
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• 2024

The utilization of Unmanned Aerial Vehicles (UAVs) is expanding in various industries such as logistics, manufacturing, and transportation. However, to operate a large number of UAVs, it is imperative to first plan a secure and efficient self-configuring communication network for UAVs. In this study, we proposed a method for planning a secure and efficient UAV self-configuring communication network using Voronoi diagrams in the following three steps: 1) generating Voronoi diagrams using obstacles, 2) selecting obstacles to consider for path generation, and 3) planning the optimal path and outputting the path. The real-time feasibility of using the proposed method for planning optimal communication paths for a realistic number of UAVs was experimentally validated.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• IMMUNE NETWORK
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• v.21 no.3
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• pp.22.1-22.17
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• 2021

Chitinase-3-like-1 (CHI3L1) is known to induce inflammation in the progression of allergic diseases. Previous our studies revealed that 2-({3-[2-(1-cyclohexen-1-yl)ethyl]-6,7-dimethoxy-4-oxo-3,4-dihydro-2-quinazolinyl}sulfanyl)-N-(4-ethylphenyl)butanamide (K284-6111; K284), the CHI3L1 inhibiting compound, has the anti-inflammatory effect on neuroinflammation. In this study, we investigated that K284 treatment could inhibit the development of atopic dermatitis (AD). To identify the effect of K284, we used phthalic anhydride (5% PA)-induced AD animal model and in vitro reconstructed human skin model. We analyzed the expression of AD-related cytokine mediators and NF-κB signaling by Western blotting, ELISA and quantitative real-time PCR. Histological analysis showed that K284 treatment suppressed PA-induced epidermal thickening and infiltration of mast cells. K284 treatment also reduced PA-induced release of inflammatory cytokines. In addition, K284 treatment inhibited the expression of NF-κB activity in PA-treated skin tissues and TNF-α and IFN-γ-treated HaCaT cells. Protein-association network analysis indicated that CHI3L1 is associated with lactoferrin (LTF). LTF was elevated in PA-treated skin tissues and TNF-α and IFN-γ-induced HaCaT cells. However, this expression was reduced by K284 treatment. Knockdown of LTF decreased the expression of inflammatory cytokines in TNF-α and IFN-γ-induced HaCaT cells. Moreover, anti-LTF antibody treatment alleviated AD development in PA-induced AD model. Our data demonstrate that CHI3L1 targeting K284 reduces AD-like skin inflammation and K284 could be a promising therapeutic agent for AD by inhibition of LTF expression.