• Tribology and Lubricants
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• v.20 no.1
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• pp.7-13
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• 2004

High strength steels are widely used as tribo-materials in the field. Previous study revealed that for mild steel, the states of strain on the worn surface measured by X-ray diffraction has a good relationship with the state of wear. The objective of this study is to identify the relationship between the state of strain on the worn surface and the state of wear in high strength steels. Sliding wear tests were carried out using several hardened steels. X-ray diffraction tests were conducted to analyze the state of strain on the worn surface during wear. The experimental results indicated that the state of strain on worn surface in the hardened steel shows the same tendency as in the mild steel. It is clear that change of half value width on the worn surface as a function of sliding speeds is broadly similar in shape to wear characteristics curve and its magnitude has a good relationship with the wear rate at two different wear modes in the hardened steel.

• Design & Manufacturing
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• v.6 no.1
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• pp.84-89
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• 2012

Silicone is recently used for LED chip encapsulment due to its good thermal stability and optical transmittance. In order to predict residual stress which causes optical briefringence and mechanical warpage of silicone, finite element analysis was conducted for both curing and cooling process during silicone molding. For analysis of curing process, a cure kinetics model was derived based on the differential scanning calorimetry(DSC) test and applied to the material properties for finite element analysis. Finite element simulation result showed that the curing as well as the cooling process should be designed carefully so as to reduce the residual stress although the cooling process plays the bigger role than curing process in determining the final residual stress state. In addition, birefringence experiment was carried out in order to observe residual stress distribution. Experimental results showed that cooling-induced birefringence was larger than curing-induced birefringence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4418-4437
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• 2017

In this paper a three-phase secure compressive sensing (CS) and received signal strength (RSS) based target localization approach is proposed to mitigate the effect of malicious node attack. RSS measurements are first arranged into a group of subsets where the same measurement can be included in multiple subsets. Intermediate target position estimates are then produced using individual subsets of RSS measurements and the CS technique. From the intermediate position estimates, the residual error vector and residual error square vector are formed. The least median of residual error square is utilized to define a verifier parameter. The selected residual error vector is utilized along with a threshold to determine whether a node or measurement is under attack. The final target positions are estimated by using only the attack-free measurements and the CS technique. Further, theoretical analysis is performed for parameter selection and computational complexity evaluation. Extensive simulation studies are carried out to demonstrate the advantage of the proposed CS-based secure localization approach over the existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.53-58
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• 2020

This paper is concerned with the performance analysis of MSAGF-MMA with variable step size whose step size varies according to input power signal and decision-directed error signal. The proposed algorithm is made to change according to the input power signal which can reliably increase the convergence speed to the steady state by making the step size less affected by the fluctuation of the input signal in the MMA having the binary flag obtained from the modified Stop-and-Go algorithm. At the same time, the step size can be varied according to the decision-directed error signal so that the residual error can be reduced in the steady state. As a result of computer simulations, it is confirmed that the proposed algorithm has a very good performance in the evaluation of residual ISI and averaged-MSE in steady state as well as in terms of convergence speed to steady state compared to MMA and MSAGF-MMA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• Transactions of Materials Processing
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• v.25 no.1
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• pp.56-60
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• 2016

Precise prediction and control of the strip profile is crucial for automatic process set-up and operation of a hot strip mill. In the current study, we present the effect of post-deformation on the steady-state strip profile. The process was simulated by a 3-D elastic-plastic finite element (FE) analysis. Comparisons are made between the strip profile measured at the roll exit and the steady-state strip profile. The results raised an issue with regard to the importance of taking into account the effect of post-deformation.

• Journal of the Korean Geographical Society
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• v.30 no.4
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• pp.333-351
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• 1995

Trend surface analysis (TSA) was selected to estimate a natural trend in precipitation and to examine urban influences on precipitation over five urban areas (Houston, Dallas, and San Antonio, TX; New Orleans, LA; and Memphis, TN) in the southern United States. TSA was applied to monthly, seasonal and annual normal precipitation data for the period of 1961-1990. Winter and spring have more trends than summer and fall and the period of November through March have more marked trends than the period of April through October in all study areas except the Houston area. Residual maps for Houston, Dallas and San Antonio have positive residuals in the city and downwind during summer indicating that urban effects on precipitation enhancement in these areas do exist during these seasons after eliminating the natural precipitation variations. Summer residual maps for New Orleans and Memphis have no distinct precipitation increases due to urban effects. The June residual map in New Orleans and the July residual map in Memphis have positive values in the city, but the magnitude of values is smaller than other cities.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.91-98
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• 2013

The rigorous requirements of modern spacecraft missions necessitate a precise attitude determination strategy. This paper mainly researches that, based on three space-borne attitude sensors: 3-axis rate gyros, 3-antenna GPS receiver and star-sensor. To obtain global attitude estimation after an information fusion process, a feedback-involved Federated Kalman Filter (FKF), consisting of two subsystem Kalman filters (Gyros/GPS and Gyros/Star-sensor), is established. In these filters, the state equation is implemented according to the spacecraft's kinematic attitude model, while the residual error models of GPS and star-sensor observed attitude are utilized, to establish two observation equations, respectively. Taking the sensors' different update rates into account, these two subsystem filters are conducted under a variable step size state prediction method. To improve the fault tolerant capacity of the attitude determination system, this paper designs malfunction warning factors, based on the principle of ${\chi}^2$ residual verification. Mathematical simulation indicates that the information fusion strategy overwhelms the disadvantages of each sensor, acquiring global attitude estimation with precision at a 2-arcsecs level. Although a subsystem encounters malfunction, FKF still reaches precise and stable accuracy. In this process, malfunction warning factors advice malfunctions correctly and effectively.