• Steel and Composite Structures
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• 제45권4호
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• pp.605-619
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• 2022

The tensile behavior of the Thread-fixed One-side Bolt (TOB) at high temperatures was studied using the Finite Element Modeling (FEM) to explore the structural responses that could not be measured in tests. The accuracy of the FEM was verified using the test results from the failure mode, load-displacement curve as well as yielding load. Three typical failure modes of TOB connected T-stubs were observed, which were the Flange Yielding (FY), the Bolt Failure (BF) and the Coupling Failure mode (CF). The influence of the flange thickness tb and the temperature θ on the tensile behavior of the T-stub were discussed. The initial stiffness and the yielding load decreased with the increase of the temperature. The T-stubs almost lost their resistance when the temperature exceeded 700℃. The failure modes of T-stubs were mainly decided by the flange thickness, which relates to the anchorage of the hole threads and the bending resistance of flange. The failure mode could also be changed by the high temperature. Design equations in EN 1993-1-8 were modified and verified by the FEM results. The results showed that these equations could predict the failure mode and the yielding load at different temperatures with satisfactory accuracy.

• 한국생산제조학회지
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• 제19권3호
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• pp.346-352
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• 2010

Recently, the high-tech industries, such as the aerospace industry, the auto industry, and the electronics industry, are growing up considerably. Because of that, high machining accuracy and productivity of precision parts have been required. The tooling system is important part in the machining center. HSK tooling system is more suitable than BT tooling system for that of high speed machining center. It is because static stiffness and machining accuracy of HSK tooling system are higher than those of BT tooling system. In this paper, static and dynamic behavior of the HSK tooling System is analyzed according to bearing characteristics and lightweight parts. In order that, three different models of the HSK tooling system are modelled by using a 3D modeling/design program. More stable one in the models of HSK tooling system can be selected by using the FEA(Finite Element Analysis).

• 정보보호학회논문지
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• 제27권2호
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• pp.361-371
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• 2017

스마트폰 기기 내에 저장되는 사용자 정보가 다양화되어 개인정보에 대한 위협도 함께 증가하고 있다. 패턴 잠금, 지문 인식 등 다양한 사용자 인증 기술이 스마트폰에 적용되어 있으나 사용자 의존적, 거부감 유발 등의 한계점을 보이고 있다. 최근 주목받고 있는 행위 기반 인증은 기기 사용과 동시에 인증이 가능하여 사용자에게 높은 편의성을 제공하나 타 인증 기술에 비해 정확도가 낮아 이를 개선하기 위한 연구가 꾸준히 수행되고 있다. 본 연구에서는 이전 연구에서 고려되지 않았던 앱 사용 정보를 새로운 인증 요소로 활용하는 방법을 제안한다. 또한 실제 앱 사용 상황을 고려한 데이터 수집 및 분석을 통해 제안 기술의 성능을 상세하게 분석한다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2003년도 가을 학술발표회 논문집
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• pp.388-391
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• 2003

In this study, the experimental results were simulated by using a nonlinear analysis programs IDARC 2D and RUAUMOKO 2D. These programs use a global Takeda-like model. The objectives of this study is to verify the correlation between the experimental and analytical responses of reinforced concrete (RC) frame and to provide the calibration to the available static inelastic analysis techniques. The evaluation of the accuracy of analytical simulation by IDARC 2D and RUAUMOKO 2D leads to the conclusion that the global behaviors can be, in general, simulated with limited accuracy in the linear analysis as detailing.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.288-292
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• 1997

Positioning accuracy largely depends on the variation of friction force in guide table, geometric accuracy of feed unit like as ballscrew and controllable accrecy of servo unit, in general. This paper deals with improvement of microstep resolution about hydrostatic table. Torque control mode have a advantage in microstep test, and more stable than velocity control mode in low velocity motion. Hydro static table have the elastic behavior within several .mu.m, so different character exist between the elastic motion and rolling motion. Integral gain is dominant than other gain in elastic motion. In order to improve response time in elastic motion,increasing gain is suggested within the stable region.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.179-187
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• 2013

Bates and Watts (1981) have discussed the problems of reparameterizing nonlinear models in obtaining accurate linear approximation confidence regions for the parameters. A similar problem exists with computing confidence curves for fitted values or predictions. The statistical behavior of fitted values does not depend on the parameterization. Thus, as long as the intrinsic curvature is small, standard Wald intervals for fitted values are likely to be sufficient. Accuracy of linear approximation for fitted values is investigated using confidence curves.

• Journal of Animal Science and Technology
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• 제64권6호
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• pp.1237-1244
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• 2022

Horse breeders suffer massive economic losses due to dystocia, abortion, and stillbirths. In Thoroughbred mares, breeders often miss the foaling process because approximately 86% of the foaling events occur from 19:00 to 7:00; consequently, breeders cannot assist mares experiencing dystocia. To solve this problem, various foaling alarm systems have been developed. However, there is a need to develop a new system to overcome the shortcomings of the existing devices and improve their accuracy. To this end, the present study aimed to (1) develop a novel foaling alarm system and (2) compare its accuracy with that of the existing Foalert^TM system. Specifically, eighteen Thoroughbred mares (11.9 ± 4.0 years old) were included. An accelerometer was used to analyze specific foaling behaviors. Behavioral data were transmitted to a data server every second. Depending on the acceleration value, behaviors were automatically classified by the server as categorized behaviors 1 (behaviors without change in body rotation), 2 (behaviors with sudden change in body rotation, such as rolling over), and 3 (behaviors with long-term change in body rotation, such as lying down laterally). The system was designed to alarm when the duration of categorized behaviors 2 and 3 was 12.9% and that of categorized behavior 3 was 1% during 10 min. The system measured the duration of each categorized behavior every 10 min and transmitted an alarm to the breeders when foaling was detected. To confirm its accuracy, the foaling detection time of the novel system was compared with that of Foalert^TM. The novel foaling alarm system and Foalert^TM alarmed foaling onset respectively 32.6 ± 17.9 and 8.6 ± 1.0 min prior to foal discharge, and the foaling detection rate of both systems was 94.4%. Therefore, the novel foaling alarm system equipped with an accelerometer can precisely detect and alert foaling onset.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.159-172
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• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 추계정기학술대회
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• pp.445-453
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• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.