After towing rope connecting a barge to a tug was subdivided into multiple finite elements, then those dynamic models was established using Newton's second law and considering the external force and moment such as tension, drag, Coriolis force, gravity, buoyancy, and impact due to free surface acting on each element. While the previous research on the model of towing rope considered only translation, five-degree-of-freedom equations of motion except roll based on the body-fixed frame were established in this paper. All elements are connected by a spring and a damper, and the stiffness of the spring was set as the equivalent value of the real rope. In order to confirm the established multiple finite element model, various scenarios such as freely falling of towing rope in the air and above the free surface, accelerating of a tug which tows a barge connected by towing rope, and sinusoidal moving of a tug were set up and simulated. As the results, the trajectories of the tug, the barge, and the towing rope showed good tendencies to the ones of real expected situations.
Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
Journal of Intelligence and Information Systems
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v.25
no.1
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pp.163-177
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2019
As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.
Based on the fact that a human being walks naturally and stably with consuming a minimum energy, this paper proposes a new method of generating a natural gait of 5-link biped robot like human by analyzing a COG (Center Of Gravity) trajectory of human's gait. In order to generate a natural gait pattern for 5-link biped robot, it considers the COG trajectory measured from human's gait images on the sagittal and frontal plane. Although the human and 5-link biped robot are similar in the side of the kinematical structure, numbers of their DOFs(Degree Of Freedom) are different. Therefore, torques of the human's joints cannot are applied to robot's ones directly. In this paper, the proposed method generates the gait pattern of the 5-link biped robot from the GA algorithm which utilize human's ZMP trajectory and torques of all joints. Since the gait pattern of the 5-link biped robot model is generated from human's ones, the proposed method creates the natural gait pattern of the biped robot that minimizes an energy consumption like human. In the side of visuality and energy efficiency, the superiority of the proposed method have been improved by comparative experiments with a general method that uses a inverse kinematics.
Tower crane's wall tie is generally used for extending of mast height according to rising of lifting height. In order to get wall tie member force this problem, this study concerning wall tie is based on load data described in manual book of 290HC model. This study made the equation of wall tie member force and computer programming for calculating wall tie member force and then get ${\theta}-P$ curves(angle-wall tie force). After considering the ${\theta}-P$ curves, optimum angle range ($48.4^{\circ}{\sim}77.2^{\circ}$) about wall ties (A), (C) members was obtained. Member force of wall tie (B) was changed from tension to compression or from compression to tension at $74^{\circ}$ in service and $54^{\circ}$ in out of service. When both horizontal force($H_A$) and torsional moment ($M_D$) were varied from (+) to (-), wall tie force(A, B, C) were changed almost symmetrically about ${\theta}$-axis. Because this study was based on wall tie analysis conditions, wall tie members in symmetric and ideal geometry shape used for analizing wall tie of tower crane, it is necessary to have more careful verification in order to apply generally the results of this study.
The end plate connection is applied to beam-column moment connections in various forms. Such end plate connection displays changes in the behavioral characteristics, strength and stiffness, and energy dissipation capacity based on the thickness and length of the end plate, the number and diameter of the high strength bolt, the gauge distance of the high strength bolt, prying action force of the high strength bolt, and dimensions and length of the welds. Accordingly, this study has apprehended the axial tensile stiffness and prying action force of the high strength bolt connected on the tensile side based on the difference in thickness of the end plate, and was conducted to propose an analysis model for the prediction of such variables that affect the operating properties of the end plate. To achieve this, this study has conducted a three-dimensional non-linear finite-element analysis of the unstiffened expanding end plate connection by selecting only the thickness of the end plate as the variable.
In this paper, large-deformation elasto-plastic analysis of space frames that considersjoint connection properties is presented. This method is based on the large-deformation formula with finite rotation, which was developed initially for elastic systems, and is extended herein to include the elasto-plastic effect and the member joint connection properties of semi-rigid what?. The analytical method was derived from the Eulerian concept, which takes into consideration the effects of large joint translations and rotations. The localmember force-deformation relationships were obtained from the beam-column approach, and the change caused by the axial strain in the member chord lengths and flexural bowing were taken into account. The effect of the axial force of the member on bending and torsional stiffness, and on the plastic moment capacity, is included in the analysis. The material is assumed to be ideally elasto-plastic, and yielding is considered concentrated at the member ends in the form of plastic hinges. The semi-rigid properties of the member joint connection are considered based on the power or linear model. The arc length method is usedto trace the post-buckling range of the elastic and elasto-plastic problems with the semi-rigid connection. A sample non-linear buckling analysis was carried out with the proposed space frame formulations to demonstrate the potential of the developed method in terms of its accuracy and efficiency.
In this study, the lateral behavior of Pile-Bent structures subjected to lateral loading was evaluated by a load-transfer approach. An analytical method based on the Beam-Column model and nonlinear load transfer curve method was proposed to consider material non-linearity (elastic and yielding) and $P-{\Delta}$ effect. Special attention was given to the lateral deflection of Pile-Bent structures depending on different soil properties, lateral load, slenderness ratio based on pier length and reinforcing effect of casing. From the results of the parametric study, it is shown that the increase of lateral displacement in a pile is much less favorable for an inelastic analysis than for an elastic analysis. It is found that for inelastic analysis, the maximum bending moment is located within a depth approximately 3.5D(D: pile diameter) below ground surface, but within 1.5D when $P-{\Delta}$ effect is considered. It is also found that the magnitude and distribution of the lateral deflections and bending moments on a pile are highly influenced by the inelastic analysis and $P-{\Delta}$ effect, let alone soil properties around an embedded pile.
In this investigation, results of laboratory tests on four reinforced concrete flat plate interior connections with elongated rectangular column support which has been used widely in tall residential buildings are presented. The purpose of this study is to evaluate an effect of column aspect ratio (${\beta}_c={c_1}/{c_2}$=side length ratio of column section in the direction of lateral loading $(c_1)$ to the direction of perpendicular to $c_1$) on the hysteretic behavior under earthquake type loading. The aspect ratio of column section was taken as $0.5{\sim}3\;(c_1/c_2=1/2,\;1/1,\;2/1,\;3/1)$ and the column perimeter was held constant at 1200mm in order to achieve nominal vertical shear strength $(V_c)$ uniformly. Other design parameters such as flexural reinforcement ratio $(\rho)$ of the slab and concrete strength$(f_{ck})$ was kept constant as ${\rho}=1.0%$ and $f_{ck}=40MPa$, respectively. Gravity shear load $(V_g)$ was applied by 30 percent of nominal vertical shear strength $(0.3V_o)$ of the specimen. Experimental observations on punching failure pattern, peak lateral-load and story drift ratio at punching failure, stiffness degradation and energy dissipation in the hysteresis loop, and steel and concrete strain distributions near the column support were examined and discussed in accordance with different column aspect ratio. Eccentric shear stress model of ACI 318-05 was evaluated with experimental results. A fraction of transferring moment by shear and flexure in the design code was analyzed based on the test results.
KSCE Journal of Civil and Environmental Engineering Research
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v.33
no.3
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pp.875-888
/
2013
The researches have recently progressed toward the use of the superelastic shape memory alloys (SMAs) to develop new smart control systems that reduce permanent deformation occurring due to severe earthquake events and that automatically recover original configuration. The superelastic SMA materials are unique metallic alloys that can return to undeformed shape without additional heat treatments only after the removal of applied loads. Once the superelastic SMA materials are thus installed at the place where large deformations are likely to intensively occur, the structural system can make the best use of recentering capabilities. Therefore, this study is intended to propose new buckling-restrained braced frames (BRBFs) with superelastic SMA bracing systems. In order to verify the performance of such bracing systems, 6-story braced frame buildings were designed in accordance with the current design specifications and then nonlinear dynamic analyses were performed at 2D frame model by using seismic hazard ground motions. Based on the analysis results, BRBFs with innovative SMA bracing systems are compared to those with conventional steel bracing systems in terms of peak and residual inter-story drifts. Finally, the analysis results show that new SMA bracing systems are very effective to reduce the residual inter-story drifts.
The Journal of Korean Institute of Communications and Information Sciences
/
v.19
no.9
/
pp.1719-1729
/
1994
In this paper, an adaptive CAC(Connection Admission Control) method is proposed. The adaptive CAC uses traffic estimates derived from both traffic parameter specified by user and cell flow measurements. Traffic estimation using user-specified parameters is performed at every moment of connection request or connection release by recursive formula which makes real-time calculation possible. Traffic estimation using cell flow measurement is carried out when the number of connected calls does not change during a measurement reflection period-renewal period. The most import ant thing for the traffic estimation using cell flow measurement is the determination of the length of a renewal period to trace a real traffic flow with an allowable time lag and the measurement reflection ratio(MRR) both to reduce the portion of overestimation and to avoid underestimation of real traffic flow. To solve these problems, the adaptive CAC updates renewal period and MRR adaptively according to the number of connections and the elapsed time after last connection or release respectively. Performance analysis for the proposed method is evaluated in several aspects for the cases of both homogeneous and heterogeneous bursty traffic. Numerical examples show the adaptive CAC method has the better performance compared with conventional CAC method based on burst model from the both utilization and QOS point of view.
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