• Advances in nano research
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• v.13 no.3
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• pp.297-312
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• 2022

Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.906-913
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• 2022

In this paper, we propose a new deep network architecture using nearest neighbor kernel for the estimation of dense depth map from its sparse map and corresponding color information. First, we propose to decompose the depth map signal into the structure and details for easier prediction. We then propose two separate subnetworks for prediction of both structure and details using classification and regression approaches, respectively. Moreover, the nearest neighboring kernel method has been newly proposed for accurate prediction of structure signal. As a result, the proposed method showed better results than other methods quantitatively and qualitatively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1371-1376
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• 2019

In case of fire in a structure, it is difficult to suppress fire because it can not accurately grasp the location of fire in case of fire. In this paper, we propose a system algorithm that can guide the optimal evacuation route in case of deep learning-based (RNN) structure disaster. The present invention provides a service to transmit data detected by sensors to a server in real time by using installed sensor, to transmit and analyze information such as temperature, heat, smoke, toxic gas around the sensor, to identify the safest moving path within a set threshold, to transmit information to LED guide lights and direction indicators in a structure in real time to avoid risk factors. This is because the information of temperature, heat, smoke, and toxic gas in each area of the structure can be grasped, and it is considered that the optimal evacuation route can be guided in case of structure disaster.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• Ocean and Polar Research
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• v.26 no.2
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• pp.265-272
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• 2004

This study was conducted to investigate the community structure and distributional pattern of meiobenthos in the deep-sea bottom of the Clarion-Clipperton Fracture Zone of northeastern Pacific during July 2001. Examination of sediment samples collected on the eight survey station showed that there were 10 different types of meiobenthos. The most abundant meiobenthic animals were nematodes in all stations. Sarcomastigophorans, benthic harpacticoids were next abundant meiobenthos. Vertical distribution of meiobenthic animals showed the highest individual numbers in the surface sediment layers of 0-1 cm depth and showed more steep decreasing trend as sediment gets deeper on the stations of high latitude located in $16-17^{\circ}N$. Horizontal distribution of meiobenthic animal in the study area within CCFZ showed high densities of meiobenthos at the stations had few manganese nodules on their sediment surface in the site of low latitude. For size distribution analyses showed that animals which fit into the sieve mesh size of 0.063 mm were abundant.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.3
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• pp.79-87
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• 2011

Deep corrugated steel plate structure has more compressive force and flexibility in bending behavior than short span structure. Asymmetric earth pressure distribution has occurred during construction. Ultimate strength and moment in domestic area, having superior ability at bending strain has been examined in this study. Based on the result of the study preceded, performance of Deep corrugated steel plate specimen has been evaluated by comparing increase of strength according to the increase of reinforcement content in bolt connections and failure mode of specimen.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.3
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• pp.652-669
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• 2014

This paper concerns the kinematic characteristics of a coupling device in a deep-seabed mining system. This coupling device connects the buffer system and the flexible pipe. The motion of the buffer system, flexible pipe and mining robot are affected by the coupling device. So the coupling device should be considered as a major factor when this device is designed. Therefore, we find a stable kinematic device, and apply it to the design coupling device through this study. The kinematic characteristics of the coupling device are analyzed by multi-body dynamics simulation method, and finite element method. The dynamic analysis model was built in the commercial software DAFUL. The Fluid Structure Interaction (FSI) method is applied to build the deep-seabed environment. Hydrodynamic force and moment are applied in the dynamic model for the FSI method. The loads and deformation of flexible pipe are estimated for analysis results of the kinematic characteristics.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.3
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• pp.211-216
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• 2019

In order to utilize the marine environment in various fields such as renewable energy and offshore plant, it is necessary to utilize the far and deep ocean. However, there is still a limit to overcome and utilize the extreme deep-sea environment. Currently, the mooring system, which is the representative position control method of floating structure, has a structural and economic limit to expand the installation range to extreme deep-sea environment. Research has been conducted to utilize wave energy by developing floater using flapping foil as an alternative for station keeping in the deep sea by University of Ulsan. Based on the research, a model test was conducted for application to actual structures. In this study, we investigate how the floating body with passive flapping foils move in regular waves with different periods and study the condition of the model that can maintain its position within a certain range by overcoming the movement.