• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1285-1291
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• 2022

In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.203-215
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• 2009

This paper illustrates the application of co-occurrence theory to generate lightweight ontologies semi-automatically. The proposed model includes three steps of a (Semi-) Automatic creation of Ontology; (they are conceptually named as) the Syntactic-based Ontology, the Semantic-based Ontology and the Ontology Refinement. Each of these three steps are designed to interactively work together, so as to generate Lightweight Ontologies. The Syntactic-based Ontology step includes generating Association words using co-occurrence in web documents. The Semantic-based Ontology step includes the Alignment large Association words with small Ontology, through the process of semantic relations by contextual terms. Finally, the Ontology Refinement step includes the domain expert to refine the lightweight Ontologies. We also conducted a case study to generate lightweight ontologies in specific domains(news domain). In this paper, we found two directions including (1) employment co-occurrence theory to generate Syntactic-based Ontology automatically and (2) Alignment large Association words with small Ontology to generate lightweight ontologies semi-automatically. So far as the design and the generation of big Ontology is concerned, the proposed research will offer useful implications to the researchers and practitioners so as to improve the research level to the commercial use.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.3
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• pp.216-223
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• 2019

The objective of this study is to propose a reliable stress-strain model in compression for lightweight concrete using bottom ash aggregates and air foam(LWC-BF). The slopes of the ascending and descending branches in the fundamental equation form generalized by Yang et al. were determined from the regression analyses of different data sets(including the modulus of elasticity and strains at the peak stress and 50% peak stress at the post-peak performance) obtained from 9 LWC-BF mixtures. The proposed model exhibits a good agreement with test results, revealing that the initial slope decreases whereas the decreasing rate in the stress at the descending branch increases with the increase in foam content. The mean and standard deviation of the normalized root-square mean errors calculated from the comparisons of experimental and predicted stress-strain curves are 0.19 and 0.08, respectively, for the proposed model, which indicates significant lower values when compared with those(1.23 and 0.47, respectively) calculated using fib 2010 model.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• Steel and Composite Structures
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• v.18 no.4
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• pp.1001-1021
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• 2015

Ultra-lightweight cement composite (ULCC) with a compressive strength of 60 MPa and density of $1,450kg/m^3$ has been developed and used in the steel-concrete-steel (SCS) sandwich structures. This paper investigates the structural performances of SCS sandwich composite beams with ULCC as filled material. Overlapped headed shear studs were used to provide shear and tensile bond between the face plate and the lightweight core. Three-dimensional nonlinear finite element (FE) model was developed for the ultimate strength analysis of such SCS sandwich composite beams. The accuracy of the FE analysis was established by comparing the predicted results with the quasi-static tests on the SCS sandwich beams. The FE model was also applied to the nonlinear analysis on curved SCS sandwich beam and shells and the SCS sandwich beams with J-hook connectors and different concrete core including ULCC, lightweight concrete (LWC) and normal weight concrete (NWC). Validations were also carried out to check the accuracy of the FE analysis on the SCS sandwich beams with J-hook connectors and curved SCS sandwich structure. Finally, recommended FE analysis procedures were given.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• Coupled systems mechanics
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• v.12 no.4
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• pp.315-335
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• 2023

The building sector has seen a huge increase in the use of lightweight concrete recently, which might result in saving in both cost and time. As a result, the study has been done on various types of concrete, including lightweight (LC), heavyweight (HC), and ordinary concrete (OC), to understand how they react to earthquake loads. The comparisons between their responses have also been taken into account in order to acquire the optimal reaction for various materials in building work. The findings demonstrate that LWC building models are more earthquake-resistant than the other varieties due to the reduction in building weight which can be a curial factor in the resistance of earthquake forces. Another crucial factor that was taken into study is the combination of various types of concrete [HC, LC, and OC] in the structural components. On the other hand, the bending moments and shear forces of LC had reduced to 17% and 19%, respectively, when compared to OC. Otherwise, the bending moment and shear force demand responses in the HC model reach their maximum values by more than 34% compared to the reference model OC. In addition, the results show that the LCC-OCR (light concrete column and ordinary concrete roof) and OCC-LCR (ordinary concrete for the column and light concrete for the roof) models' responses have fewer values than the other types.

• Structural Engineering and Mechanics
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• v.12 no.4
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• pp.363-376
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• 2001

Sandwich plates are widely used in lightweight design due to their high strength and stiffness to weight ratio. Due to the heterogeneous structure of sandwich plates, they can exhibit local instabilities (wrinkling), which lead to a sudden loss of stiffness in the structure. This paper presents an analytical solution to the wrinkling problem of sandwich plates. The solution is based on the Rayleigh-Ritz method, by assuming an appropriate deformation field. In contrast to the other approaches up to now, this model takes arbitrary and different orthotropic face layers, finite core thickness and orthotropic core material into account. This approach is the first to cover the wrinkling of unsymmetric sandwiches and sandwiches composed of orthotropic FRP face layers, which are most common in advanced lightweight design. Despite the generality of the solution, the computational effort is kept within bounds. The results have been verified using other analytical solutions and unit cell 3D FE calculations.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1093-1099
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• 2022

The number of buildings is increasing day by day. The next logical footstep is tackling challenges regarding scarcity of resources and sustainability, as well as shifting focus on existing building structures to renovate and retrofit. Many existing old and heritage buildings lack documentation, such as building models, despite their necessity. Technological advances allow us to use virtual reality, augmented reality, and mixed reality on mobile platforms in various aspects of the construction industry. For these purposes, having a BIM model or high detail 3D model is not always necessary, as a simpler model can serve the purpose within many mobile platforms. This paper streamlines a framework for generating a lightweight 3D model for mobile platforms. In doing so, we use an existing structure's site survey data for the foundation data, followed by mobile VR implementation. This research conducted a pilot study on an existing building. The study provides a process of swiftly generating a lightweight 3D model of a building with relative accuracy and cost savings.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.778-789
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• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.