• Smart Media Journal
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• 제9권4호
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• pp.9-16
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• 2020

Recently, various studies are being conducted to integrate Image Segmentation into smart factory industries and autonomous driving fields. In particular, Image Segmentation systems using deep learning algorithms have been researched and developed enough to learn from large volumes of data with higher accuracy. In order to use image segmentation in the autonomous driving sector, sufficient amount of learning is needed with large amounts of data and the streaming environment that processes drivers' data in real time is important for the accuracy of safe operation through highways and child protection zones. Therefore, we proposed a novel DFCN algorithm that enhanced existing FCN algorithms that could be applied to various road environments, demonstrated that the performance of the DFCN algorithm improved 1.3% in terms of "loss" value compared to the previous FCN algorithms. Moreover, the proposed DFCN algorithm was applied to the existing U-Net algorithm to maintain the information of frequencies in the image to produce better results, resulting in a better performance than the classical FCN algorithm in the autonomous environment.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of the Society of Naval Architects of Korea
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• 제59권6호
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• pp.385-392
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• 2022

Naval ship could face a variety of threats in isolated seas. In particular, fires and flooding are defined as disasters that are very likely to cause irreparable damage to ships. These disasters have a very high risk of personal injury as well. Therefore, when a disaster occurs, it must be quickly suppressed, but if there are people in the disaster area, the protection of life must be given priority. In order to quickly evacuate the ship crew in case of a disaster, we would like to propose a plan to quickly explore the evacuation route even in urgent situations. Using commercial escape simulation software, we obtain the data for deep neural network learning with simulations according to aisle characteristics and the properties and number of evacuation person. Using the obtained data, the passage prediction model is trained with a deep learning, and the passage time is predicted through the learned model. Construct a numerical map of a naval ship and construct a distance matrix of the vessel using predicted passage time data. The distance matrix configured in one of the path search algorithms, the Dijkstra algorithm, is applied to explore the evacuation path of naval ship.

• Structural Engineering and Mechanics
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• 제90권1호
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Archives of Reconstructive Microsurgery
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• 제6권1호
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• pp.29-38
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• 1997

A peripheral nerve when approximation of the ends imparts tension at the anastomosis and with a relatively long segment defect after excision of neuroma and neurofibroma cannnot be repaired by early primary suture. The one of the optimistic reconstruction method of severed peripheral nerves is to restore tension-free continuity at the repair site putting an autogenous nerve graft into the neural gap despite of ancipating motor or sensory deficit of the donor nerve area. To overcome the deficit of the autogenous nerve graft, several other conduits supplying a metabolically active environment which is able to support axon regeneration and progression, providing protection against scar invasion, and guiding the regrowing axons to the distal stump of the nerve have been studied. An author have used ipsilateral femoral vein, ipsilateral femoral vein filled with fresh thigh muscle, and autogenous sciatic nerve for the sciatic nerve defect of around 10 mm in length to observe the regeneration pattern in rat by light and electron microscopy. The results were as follows. 1. Light microscopically regeneration pattern of nerve fibers in the autogenous graft group was more abundant than vein graft and vein filled with muscle group. 2. On ultrastructural findings, the proxial end of the graft in various groups showed similar regenerating features of the axons, myelin sheaths, and Schwann cells. The fascicular arrangement of the myelinated and unmyelinated fibers was same regardless of the type of conduits. There were more or less increasing tendency in the number and the diameter of myelinated fibers correlated with the regeneration time. 3. In the middle of the graft, myelinated nerve fibers of vein filled with muscle group were more in number and myelin sheath was thinner than in the venous graft, but the number of regenerating axons in autogenous nerve graft was superior to that in both groups of the graft. The amount of collagen fibrils and amorphous materials in the endoneurial space was increased to elapsed time. 4. There was no difference in regenerating patterns of the nerve fibers of distal end of the graft. The size and shape of the myelinated nerve fbers were more different than that of proximal and middle portion of the graft. From the above results, the degree of myelination and regenerating activity in autogenous nerve is more effective and active in other types of the graft and there were no morphological differences in either ends of the graft regardless of regeneration time.

• Biomolecules & Therapeutics
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• 제20권1호
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• pp.27-35
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• 2012

Oroxylin A is a flavone isolated from a medicinal herb reported to be effective in reducing the inflammatory and oxidative stresses. It also modulates the production of brain derived neurotrophic factor (BDNF) in cortical neurons by the transactivation of cAMP response element-binding protein (CREB). As a neurotrophin, BDNF plays roles in neuronal development, differentiation, synaptogenesis, and neural protection from the harmful stimuli. Adenosine $A2_A$ receptor colocalized with BDNF in brain and the functional interaction between $A2_A$ receptor stimulation and BDNF action has been suggested. In this study, we investigated the possibility that oroxylin A modulates BDNF production in cortical neuron through the regulation of $A2_A$ receptor system. As expected, CGS21680 ($A2_A$ receptor agonist) induced BDNF expression and release, however, an antagonist, ZM241385, prevented oroxylin A-induced increase in BDNF production. Oroxylin A activated the PI3K-Akt-GSK-$3{\beta}$ signaling pathway, which is inhibited by ZM241385 and the blockade of the signaling pathway abolished the increase in BDNF production. The physiological roles of oroxylin A-induced BDNF production were demonstrated by the increased neurite extension as well as synapse formation from neurons. Overall, oroxylin A might regulate BDNF production in cortical neuron through $A2_A$ receptor stimulation, which promotes cellular survival, synapse formation and neurite extension.