• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.278-288
• /
• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Nuclear Engineering and Technology
• /
• v.50 no.8
• /
• pp.1306-1313
• /
• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• International conference on construction engineering and project management
• /
• 2007.03a
• /
• pp.337-345
• /
• 2007

Nowadays, it is convenient to use 3D modeling tools for general planning before construction. Normally, a 3D model is built with 3D CAD such as 3D Studio Max, Maya, etc. or simply with AutoCAD. All these software packages are effective in building 3D models but difficult to use, because many provided functions and tools require prior knowledge to build both 2D and 3D designs. Moreover, the traditional method of building 3D models is most time-consuming as experienced operators and manual input are required. Therefore, how to minimize the building time of 3D models and provide easy-to-use functions for users who are not familiar with 3D modeling becomes important. In this paper, the VisualLISP programming language is used to create a convenient tool for efficient generation of 3D components for the AutoCAD environment. This tool will be demonstrated with the generation of a 3D sluice, an artificial passage for water fitted with a valve or gate to stop or regulate water flow. With the tool, users only need to enter the parameters of a sluice in the edit box and the 3D model will be automatically generated in a few seconds. By changing parameters in the edit box and pressing the "OK" button, a new 3D sluice model will be generated in a short while.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.814-822
• /
• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.79-84
• /
• 2023

Social studies show that the number of separated families have lately increased due to different reasons. Despite the causes for family rift, many problems are resulted which affected the children physically and psychologically. This effect may cause them fail in their life especially at school. This paper focuses on the negative reaction of the parents' separation with other factors from the computer science prospective. Since the artificial intelligent field is the most common widespread in computer science, a predictive model is built to predict if a specific child whose parents separated, may complete the school successfully or fail to continue his education. This will be done using Decision Tree that have proved their effectiveness on the predication applications. As an experiment, a sample of individuals is randomly chosen and applied on our prediction model. As a result, this model shows that the separation may cause the child success at school if other factors are satisfied; the intelligent of the guardian, the relation between the parents after the separation, his age at the separation time, etc.

• International Journal of Advanced Culture Technology
• /
• v.11 no.2
• /
• pp.125-131
• /
• 2023

OVO is a digital platform that provides simple payments and smart financial services, as well as one of the largest digital payment platforms in Indonesia. It has wide coverage and security when making payments, and supports multiple settlement currencies. The purpose of this study is to explore the history, business model, and future strategic direction of OVO, an Indonesian e-wallet. To date, OVO has built its own mobile payment ecosystem covering a wide range of consumer scenarios including e-commerce, travel, offline shopping and finance. And it supports mobile banking, online banking, debit cards or selected partner merchants. Its three largest transaction categories are in the transportation, retail and e-commerce sectors. With over 110 million consumers and 1.3 million merchant users, it is one of the dominant e-wallets in Indonesian market and has become the country's e-payment market leader. OVO eWallet's 'One Card' model offers convenience and choice for users, thus contributing to the rapid growth of OVO eWallet. And OVO eWallet competes fiercely with other competitors, but OVO eWallet continues to grow in terms of the number of users and market share. Finally, this study analyzes the strategic goals and plans of OVO eWallet, predicts its future direction. OVO eWallet has a huge success, but there are still competition and challenges to face.

• The Journal of Information Systems
• /
• v.32 no.4
• /
• pp.149-162
• /
• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Journal of KIBIM
• /
• v.14 no.1
• /
• pp.30-38
• /
• 2024

This study aims to propose a smart city governance logic model that can accommodate more diverse information service systems by mixing hub-and-spoke and blockchain technologies as a data management model. Specifically, the research focuses on deriving the logic of an operating system that can work across smart city planning based on the two data governance technologies. The first step of the logic is the generation and collection of information, which is first divided into information that requires information protection and information that can be shared with the public, and the information that requires privacy is blockchainized, and the shared information is integrated and aggregated in a data hub. The next step is the processing and use of the information, which can actively use the blockchain technology, but for the information that can be shared other than the protected information, the governance logic is built in parallel with the hub-and-spoke type. Next is the logic of the distribution stage, where the key is to establish a service contact point between service providers and beneficiaries. Also, This study proposes the establishment of a one-to-one data exchange relationship between information providers, information consumers, and information processors. Finally, in order to expand and promote citizen participation opportunities through a reasonable compensation system in the operation of smart cities, we developed virtual currency as a local currency and designed an open operation logic of local virtual currency that can operate in the compensation dimension of information.

• Journal of Welding and Joining
• /
• v.17 no.1
• /
• pp.88-96
• /
• 1999

The effects of root gap on welding residual stress and deformation are dealt with the multi-pass weldment with three kinds(0, 6, 30mm) of root gap by F.E.M common code, and then compared with experiment data. In this analysis, an 100% ramp heat input model was used to avoid numerical convergence problem due to an instantaneous increase in temperature near the fusion zone, and the effect of a moving arc in a two dimensional plane was also included. During the analysis, a small time increment was applied in a period with instantaneous temperature fluctuation while a large time increment was used in the rest period. The residual stress is distributed as symmetric types and maximum value is also equivalent when the weldment with 0mm and 6mm root gap is welded. In the case of 30mm root gap welding, the distribution of the residual stress extends over a wide range as asymmetric types due to the built-up weld, and most of the residual stress is biased in the side of a built-up weld part. In case of 0mm gap welding and 6mm gap welding, a little angular distortion occurs, but the level of deformation is small. When the weldment with 30mm root gap is welded, the angular deformation of the asymmetric types, however, occurs larger than the other specimens. The experimental and the analytic results show good coincidence and indicate that the welding residual stress and deformation distribution of 30 mm root gap specimen may be asymmetric and the amplitude is larger than those of root gap specimen under standard.

• Structural Engineering and Mechanics
• /
• v.85 no.3
• /
• pp.381-391
• /
• 2023

The seismic performance of the tall building equipped with a tuned mass damper (TMD) considering soil-structure interaction (SSI) effects is well studied in the literature. However, these studies are performed on the nominal model of the seismic-excited structural system with SSI. Hence, the outcomes of the studies may not valid for the actual structural system. To address the study gap, the reliability theory as a useful and powerful method is utilized in the paper. The present study aims to carry out reliability analyses on tall buildings equipped with TMD under near-field pulse-like (NFPL) ground motions considering SSI effects using a subset simulation (SS) method. In the presence of uncertainties of the structural model, TMD device, foundation, soil, and near-field pulse-like ground motions, the numerical studies are performed on a benchmark 40-story building and the failure probabilities of the structures with and without TMD are evaluated. Three types of soils (dense, medium, and soft soils), different earthquake magnitudes (M_w = 7,0. 7,25. 7,5 ), different nearest fault distances (r = 5. 10 and 15 km), and three seismic performance levels of immediate occupancy (IO), life safety (LS), and collapse prevention (CP) are considered in this study. The results show that tall buildings built near faults and on soft soils are more affected by uncertainties of the structural and ground motion models. Hence, ignoring these uncertainties may result in an inaccurate estimation of the maximum seismic responses. Also, it is found the TMD is not able to reduce the failure probabilities of the structure in the IO seismic performance level, especially for high earthquake magnitudes and structures built near the fault. However, TMD is significantly effective in the reduction of failure probability for the LS and CP performance levels. For weak earthquakes and long fault distances, the failure probabilities of both structures with and without TMD are near zero, and the efficiency of the TMD in the reduction of failure probabilities is reduced by increasing earthquake magnitudes and the reduction of fault distance. As soil softness increases, the failure probability of structures both with and without TMD often increases, especially for severe near-fault earthquake motion.