• 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 Korea Society of Computer and Information
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• v.26 no.12
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• pp.69-75
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• 2021

Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.668-674
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• 2022

Machine learning techniques using visual data have high usability in fields of industry and service such as scene recognition, fault detection, security and user analysis. Among these, user analysis through the videos from CCTV is one of the practical way of using vision data. Also, many studies about lightweight artificial neural network have been published to increase high usability for mobile and embedded environment so far. In this study, we propose the network combining the object detection and classification for mobile graphic processing unit. This network detects pedestrian and face, classifies age and gender from detected face. Proposed network is constructed based on MobileNet, YOLOv2 and skip connection. Both detection and classification models are trained individually and combined as 2-stage structure. Also, attention mechanism is used to improve detection and classification ability. Nvidia Jetson Nano is used to run and evaluate the proposed system.

• The Plant Pathology Journal
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• v.38 no.6
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• pp.593-602
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• 2022

Anthracnose, caused by the ascomycete fungus Colletotrichum scovillei, is a destructive disease in pepper. The fungus germinates and develops an infection structure called an appressorium on the plant surface. Several signaling cascades, including cAMP-mediated signaling and mitogen-activated protein kinase (MAPK) cascades, are involved in fungal development and pathogenicity in plant pathogenic fungi, but this has not been well studied in the fruit-infecting fungus C. scovillei. Ste50 is an adaptor protein interacting with multiple upstream components to activate the MAPK cascades. Here, we characterized the CsSTE50 gene of C. scovillei, a homolog of Magnaporthe oryzae MST50 that functions in MAPK cascades, by gene knockout. The knockout mutant ΔCsste50 had pleiotropic phenotypes in development and pathogenicity. Compared with the wild-type, the mutants grew faster and produced more conidia on regular agar but were more sensitive to osmotic stress. On artificial and plant surfaces, the conidia of the mutant showed significantly reduced germination and failed to form appressoria. The mutant was completely non-pathogenic on pepper fruits with or without wounds, indicating that pre-penetration and invasive growth were both defective in the mutant. Our results show that the adaptor protein CsSTE50 plays a role in vegetative growth, conidiation, germination, appressorium formation, and pathogenicity in C. scovillei.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.47-57
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• 2021

Neuromorphic technology is proposed to complement the shortcomings of existing artificial intelligence technology by mimicking the human brain structure and computational process with hardware. NA-IDE has also been proposed for developing neuromorphic hardware-based IoT applications. To implement an SNN model in NA-IDE, commonly used input data must be transformed for use in the SNN model. In this paper, we implemented a neural coding method encoder component that converts image data into a spike train signal and uses it as an SNN input. The decoder component is implemented to convert the output back to image data when the SNN model generates a spike train signal. If the decoder component uses the same parameters as the encoding process, it can generate static data similar to the original data. It can be used in fields such as image-to-image and speech-to-speech to transform and regenerate input data using the proposed encoder and decoder.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.99-105
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• 2023

In this paper, an intelligent CCTV was designed and experimentally developed by using an IOT device, Raspberry Pi, and artificial intelligence technology. Object Detection technology was used to detect the number of people on the CCTV screen, and Action Detection technology provided by OpenPose was used to detect emergency situations. The proposed system has a structure of CCTV, server and client. CCTV uses Raspberry Pi and USB camera, server uses Linux, and client uses iPhone. Communication between each subsystem was implemented using the MQTT protocol. The system developed as a prototype could transmit images at 2.7 frames per second and detect emergencies from images at 0.2 frames per second.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.9-15
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• 2023

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.