• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.7
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• pp.938-945
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• 2011

Unsteady flow simulations through a transonic turbine vane were carried out for an isentropic Mach number of 1.02 and a Reynolds number of $10^6$. The main objective of the study is to investigate the effect of unsteadiness due to vortex shedding on the flow in transonic regime. The steady and the time-averaged unsteady results by employing three different turbulence models: shear stress transport (SST), k-${\omega}$, and ${\omega}$ Reynolds stress models were compared. The comparisons were emphasized on the isentropic Mach number along the blade and total pressure loss at the cascade exit. The results showed that both steady and unsteady calculations have good agreement with experimental data along the blade surface. However, at cascade exit, the unsteady calculations have much better agreement with experimental data than steady calculations. Based on these, we conclude that the unsteady flow calculations are essential for these types of problems.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.82-83
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• 2003

Plants have evolved differently from animals having mobile activities. Thus, plants should have developed unique defense mechanisms against biotic/abiotic stresses to which plants are differently exposed, according to seasons. Most organisms have an conserved signaling network using mitogen-activated protein kinase (MAPK) cascade(s). The phenomenon implied that they are functionally very important in all organisms. In fact, they constitute one of the major components of signaling pathways involved in regulating a wide range of cellular activities from growth and development to cell death. Recently, complete MAPK cascade was first characterized in Arabidopsis from the receptor kinase (FLS2) through fellowing MEKKI -MKK4/MKK5-MPK3/MPK6-WRKY22/MRKY29 pathway. Whereas, MAPK cascade signaling pathway in monocot plant including rice (0ryza sativa L.), the most important of all food crops and an established monocot plant research model, MAPKinase kinase kinases (MAPKKK) of rice are the first upstream component of the MAPK cascade, but MAPKKK has been first identified and characterized in our lab and designated as, OsEDRl based on its homology with the Arabidopsis EDRI. The Arabidopsis EDRl was regarded as a negative regulator of defense response and the role of rice OsEDRl was analyzed. Transcriptional regulation of OsEDRl was detected under various stresses and immunoblotting analysis is going on to detect the level of OsEDRl protein in the mutants showing unique phenotype. We also introduced the constitutively active and the dominant negative forms of the OsEDRl for characterizing biological function.

• Journal of Hydrogen and New Energy
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• v.35 no.3
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• pp.300-309
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• 2024

Hydrogen utilization in the transportation sector, which relies on fossil fuels, can significantly reduce greenhouse gas by using to hydrogen fuel cell vehicles, and its adoption depends performance of hydrogen refueling station. The present study developed a model to simulate the back-to-back filling process of heavy duty hydrogen fuel cell vehicles at hydrogen refueling stations using a cascade method. And its quantitatively evaluated hydrogen refueling station performance by simulating various mass flow rates and storage tank capacity combinations, analyzing vehicle state of charge (SOC) of vehicles. In the cascade refueling system, the capacity of the high-pressure storage tank was found to have the greatest impact on the reduction of filling time and improvement of efficiency.

• Smart Media Journal
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• v.3 no.1
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• pp.9-15
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• 2014

In these days, along with the extreme popularity of online social network services, it becomes an important problem understanding the role of social network in the research of message dissemination. Past studies of message dissemination over online social network services mostly consider the coverage of message dissemination and the methods to maximize it. But, these works lack of the consideration of the impact of multi channel social network, which has multiple communication channel with distinct properties of message transfer and various users with distinct channel preferences. In this paper, the new message dissemination model over multi-modal multi-channel social network, the Delay Weighted Independent Cascade Model, is proposed. The proposed model considers various channels including online social network service, email, SMS messaging, phone and mouth-to-mouth and their distinct message transfer properties. In order to consider the various user properties, the different value of probability of forwarding a message and the different preference of communication channel is considered. Moreover, the proposed model considers the distribution of user location and allows to analyze the properties of message dissemination under various scenarios. Based on the proposed model, a message dissemination simulator is generated and the message disseminations on various scenarios are analyzed.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.18-25
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• 2021

In many developed countries, such as South Korea, efficiently maintaining the aging infrastructures is an important issue. Currently, inspectors visually inspect the infrastructure for maintenance needs, but this method is inefficient due to its high costs, long logistic times, and hazards to the inspectors. Thus, in this paper, a novel crack inspection approach for concrete bridges is proposed using integrated image processing techniques. The proposed approach consists of four steps: (1) training a deep learning model to automatically detect cracks on concrete bridges, (2) acquiring in-situ images using a drone, (3) generating orthomosaic images based on 3D modeling, and (4) detecting cracks on the orthmosaic image using the trained deep learning model. Cascade Mask R-CNN, a state-of-the-art instance segmentation deep learning model, was trained with 3235 crack images that included 2415 hard negative images. We selected the Tancheon overpass, located in Seoul, South Korea, as a testbed for the proposed approach, and we captured images of pier 34-37 and slab 34-36 using a commercial drone. Agisoft Metashape was utilized as a 3D model generation program to generate an orthomosaic of the captured images. We applied the proposed approach to four orthomosaic images that displayed the front, back, left, and right sides of pier 37. Using pixel-level precision referencing visual inspection of the captured images, we evaluated the trained Cascade Mask R-CNN's crack detection performance. At the coping of the front side of pier 37, the model obtained its best precision: 94.34%. It achieved an average precision of 72.93% for the orthomosaics of the four sides of the pier. The test results show that this proposed approach for crack detection can be a suitable alternative to the conventional visual inspection method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.87-91
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• 2005

The cascade-correlation (CC) learning algorithm of Fahlman and Lebiere is one of the most influential constructive algorithm in a neural network. Cascading the hidden neurons results in a network that can represent very strong nonlinearities. Although this power is in principle useful, it can be a disadvantage if such strong nonlinearity is not required to solve the problem. 3 models are presented and compared empirically. All of them are based on valiants of the cascade architecture and output neurons weights training of the CC algorithm. Empirical results indicate the followings： (1) In the pattern classification, the model that train only new hidden neuron to output layer connection weights shows the best predictive ability; (2) In the function approximation, the model that removed input-output connection and used sigmoid-linear activation function is better predictability than CasCor algorithm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.206-215
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• 2013

In this paper, a synchronization control technique of dual-servo motor driven press system is proposed. An independent cascade PID control technique has been applied to the conventional press system for advancement of control stability. However, it is not easy to reduce synchronous error using the independent cascade PID control technique when some different load disturbances are involved in each motor. The eccentric error of the slide caused by the problem degrade the control performance of the BDC(Bottom Dead Center). In order to achieve reduction of the synchronous error between two servo motors and accurate position control simultaneously, a new control scheme comprised with cascade PID control loop and cross-coupling loop is proposed. In simulation using Matlab SIMULINK, the AC servo system is designed. The control performance of proposed technique is compared with conventional control technique to the model of AC servo system. Also, the sub-scale model of dual-servo motor driven press system which can replicate the slide motion is constructed for experimental verification for the performance of the proposed control technique. The cross-coupling control technique reveals more precise and stable performances in the position and synchronization controls.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.8
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• pp.521-529
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• 2017

Three-dimensional flow characteristics within a high-acceleration first-stage turbine vane passage has been investigated in a newly-built vane cascade for propulsion. The result shows that there is a strong favorable pressure gradient on the vane pressure surface. On its suction surface, however, there exists not only a much stronger favorable pressure gradient than that on the pressure surface upstream of the mid-chord but also a subsequent adverse pressure gradient downstream of it. By employing two different oil-film methods with upstream coating and full-coverage coating, a four-vortex model horseshoe vortex system can be identified ahead of each leading edge in the cascade, and the separation line of inlet boundary layer flow as well as the separation line of re-attached flow is provided as well. In addition, basic flow data such as secondary flow, aerodynamic loss, and flow turning angle downstream of the cascade are obtained.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.