• 스마트미디어저널
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• 제12권2호
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• pp.56-65
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• 2023

뉴스와 취재 프로그램 같은 방송에서는 제보자의 신원 보호를 위해 음성을 변조한다. 음성 변조 방법으로 피치(pitch)를 조절하는 방법이 가장 많이 사용되는데, 이 방법은 피치를 재조절하는 방식으로 쉽게 원본 음성과 유사하게 음성 복원이 가능하다. 따라서 방송 음성 변조 방법은 화자의 신원 보호를 제대로 해줄 수 없고 보안상 취약하기 때문에 이를 대체하기 위한 새로운 음성 변조 방법이 필요하다. 본 논문에서는 Voice Privacy Challenge에서 비식별화 성능이 검증된 Lightweight 음성 비식별화 모델을 성능 비교 모델로 사용하여 피치 조절을 사용한 방송 음성변조 방법과 음성 비식별화 성능 비교 실험 및 평가를 진행한다. Lightweight 음성 비식별화 모델의 6가지 변조 방법 중 비식별화 성능이 좋은 3가지 변조 방법 McAdams, Resampling, Vocal Tract Length Normalization(VTLN)을 사용하였으며 한국어 음성에 대한 비식별화 성능을 비교하기 위해 휴먼 테스트와 EER(Equal Error Rate) 테스트를 진행하였다. 실험 결과로 휴먼 테스트와 EER 테스트 모두 VTLN 변조 방법이 방송 변조보다 더 높은 비식별화 성능을 보였다. 결과적으로 한국어 음성에 대해 Lightweight 모델의 변조 방법은 충분한 비식별화 성능을 가지고 있으며 보안상 취약한 방송 음성 변조를 대체할 수 있을 것이다.

• International Journal of Concrete Structures and Materials
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• 제2권2호
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• pp.123-136
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• 2008

A new plastic-damage constitutive model applicable to lightweight concrete (LWC) and normal weight concrete (NWC) is proposed in this paper based on both continuum damage mechanics and plasticity theories. Two damage variables are used to represent tensile and compressive damage independently. The effective stress is computed in the Drucker-Prager multi-surface plasticity framework. The stress is then computed by multiplication of the damaged part and the effective part. The proposed model is coded as a user material subroutine and incorporated in a finite element analysis software. The constitutive integration algorithm is implemented by adopting the operator split involving elastic predictor, plastic corrector and damage corrector. The numerical study shows that the algorithm is efficient and robust in the finite element analysis. Experimental investigation is conducted to verify the proposed model involving both static and dynamic tests. The very good agreement between the numerical results and experimental results demonstrates the capability of the proposed model to capture the behaviors of LWC and NWC structures for static and impact loading.

• 한국산업융합학회 논문집
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• 제25권6_3호
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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.

• 한국IT서비스학회지
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• 제8권1호
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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.

• 한국건설순환자원학회논문집
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• 제7권3호
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• pp.216-223
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• 2019

이 연구의 목적은 바텀애시 골재와 기포를 융합한 경량 콘크리트(bottom ash based lightweight concrete, LWC-BF)의 압축 응력-변형률 모델 제시이다. Yang 등이 제시한 응력-변형률 곡선식에서 LWC-BF 9 배합의 실험으로부터 얻은 탄성계수, 최대응력 시 변형률 그리고 최대응력 이후 최대응력의 50% 응력 시 변형률 값들을 이용하여 상승부와 하강부의 기울기를 결정하였다. 제시된 모델은 기포 혼입율의 증가와 함께 감소되는 초기 강성 및 증가되는 하강부 기울기를 잘 반영하면서 실험결과와 잘 일치하였다. 제시된 모델의 예측값과 실험값의 평균제곱근 오차로부터 결정된 평균값과 표준편차는 각각 0.19와 0.08로서 각각 1.23과 0.47 값을 보이는 fib 2010 모델에 비해 현저히 낮았다.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• 센서학회지
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• 제30권5호
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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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• 제18권4호
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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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• 제18권2호
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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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• 제12권4호
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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.