• Journal of Information Technology Services
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• v.17 no.2
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• pp.179-190
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• 2018

This study is about the influence of authors' centrality on research outcomes in a large-scale collaborative research network. Using the social network analysis method, five types of centralities were derived. Six research outcomes of individual researchers were also derived through bibliographic information of the social science field for the last 10 years. A multivariate regression analysis was conducted to examine the causal relationship between the centrality and research outcome, and the effect of centrality on research outcomes was found to be statistically significant. The result of this study shows that the revised citation and H-index significantly influenced the authors' centrality. This result can imply that the centrality of the researcher can expect a considerable influence of the thesis as well as a certain level of productivity. The meaning of this study is to analyze the effect of centrality on the research outcomes of the large-scale collaborative research network in the past decade, and is carefully to suggest a guideline in order to support new research information services for active researchers and the advancement of collaborative research. This study has its limitation for interpreting the diverse academic fields of the social sciences in a uniform way. In future study, it is necessary to conduct studies using various weighted indices for network centrality in order to measure the influence of research.

• Biomaterials and Biomechanics in Bioengineering
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• v.3 no.1
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• pp.47-57
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• 2016

Results from multiple high profile experiments on the parameters influencing the impacts that cause skull fractures to the frontal, temporal, and parietal bones were gathered and analyzed. The location of the impact as a binary function of frontal or lateral strike, the velocity, the striking area of the impactor, and the force needed to cause skull fracture in each experiment were subjected to statistical analysis using the JMP statistical software pack. A novel neural network model predicting skull fracture threshold was developed with a high statistical correlation ($R^2=0.978$) and presented in this text. Despite variation within individual studies, the equation herein proposes a 3 kN greater resistance to fracture for the frontal bone when compared to the temporoparietal bones. Additionally, impacts with low velocities (<4.1 m/s) were more prone to cause fracture in the lateral regions of the skull when compared to similar velocity frontal impacts. Conversely, higher velocity impacts (>4.1 m/s) showed a greater frontal sensitivity.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.5
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• pp.17-26
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• 2011

Drying behavior and physical properties of HwBKP, SwBKP, and OCC handsheets depending on kneading, refining and wet pressing were analyzed. The maximum drying shrinkage velocity was newly adopted to verify the effect of mechanical treatment of pulps by evaluating drying behavior according to varying the kneading, refining and wet pressing treatments. Those various treatments were changed to evaluate the relationship between the maximum drying shrinkage velocity and handsheets properties. When the drying shrinkage and the maximum drying velocity increased by refining and wet-pressing, handsheets strength was increased. The maximum drying shrinkage velocity showed higher correlation with physical properties of paper than WRV at different refining loads at SwBKP and mixed pulp. At high wet-web dryness, drying shrinkage, the maximum drying shrinkage velocity and strength properties of handsheet were increased. It meant that drying shrinkage behavior was highly affected by not only fibers' shrinkage but also fiber bonding. Kneading pre-treatment for KOCC and SwBKP effectively modified fiber properties and increasing paper strength and drying shrinkage. The effect of kneading pre-treatment was also confirmed by the maximum drying shrinkage velocity. Strength properties of mixed pulp handsheets were not increased by the kneading pre-treatment, although the maximum drying shrinkage velocity and WRV was increased. It meant that fibers network bonding of HwBKP was limited because of ves sels and ray cells' interference for bonding. Therefore in order to improve paper strengths containing HwBKP by mechanical treatments, interference of vessels and ray cells for fiber bondings should be carefully controlled.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.614-623
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• 2016

Grid(pipe network) design is an important element of Smart Water Grid, which essential to estimate hydraulic parameters such as the pressure, friction factor, friction velocity, head loss and energy slope. Especially, friction velocity in a grid is an important factor in conjunction with energy gradient, friction coefficient, pressure and head loss. However, accurate estimation friction head loss, friction velocity and friction factor are very difficult. The empirical friction factor is still estimated by using theory and equation which were developed one hundred years ago. Therefore, in this paper, new equation from maximum velocity and friction velocity is developed by using integration relationship between Darcy-Weisbach's friction head loss equation and Schlichting equation and regression analysis. To prove the developed equation, smooth pipe data areis used. Proposed equation shows high accuracy compared to observed data. Study results are expected to be used in stability improvements and design in a grid.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.987-994
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• 2000

A Linear Pulse Motor (LPM) is a direct drive motor that has good performance in terms of accuracy, velocity and acceleration compared to the conventional rotating system with toothed belts and ball screws. However, since an LPM needs supporting devices which maintain constant air-gap and has strong nonlinearity caused by leakage magnetic flux, friction and cogging, etc., there are many difficulties in improvement on accuracy with conventional control theory. Moreover, when designing the position controller of LPM, the modeling error and load variations has not been considered. In order to compensate these components, the neural network with conventional feedback controller is introduced. This neural network of feedback error learning type changes the current commands to improve position accuracy. As a result of experiments, we observes that more accurate position control is possible compared to conventional controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1624-1627
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• 2005

We proposed a design technique for auto-landing guidance and control system. This technique utilizes linear controller and neural network. Main features of this technique is to use conventional linear controller and compensate for the error coming from the model uncertainties and/or reference model mismatch. In this study, the multi-perceptron neural network with single hidden layer is adopted to compensate for the errors. Glide-slope capture logic for auto-landing guidance and control system is designed in this technique. From the simulation results, it is observed that the responses of velocity and pitch angle to commands are fairly good, which are directly related to control inputs of throttle and elevator, respectively.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.244-247
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• 1993

In this paper, Fuzzy-Neural Network Control system that has the characteristic of fuzzy control to be controlled easily end the good characteristic of a artificial neural network to control the plant due to its learning is presented. A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which ere created by a expert. To adaptivity, the more precise modeling is implemented by error beck-propagation learning of adjusting the link-weight of fuzzy membership function in Fuzzy-Neural Network. The more classified fuzzy rule is used to include the property of Dual Mode Method. To test the effectiveness of the algorithm presented above, the simulation for position end velocity of DC servo motor is implemented.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.15-24
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• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.3
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• pp.141-151
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• 1993

For navigation of a mobile robot, it is one of the essential tasks to find out its current position. Dead reckonining is the most frequently used method to estimate its position. Hpwever conventional dead reckoner is prone to give us false information on the robot position especially when the wheels are slipping. This paper proposes an improved dead reckoning scheme using neural networks. The network detects the instance of wheel slopping and estimates the linear velocity of the wheel; thus it calculates current position and heading angle of a mobile robot. The structure and variables of the nerual network are chosen in consideration of slip motion characteristics. A series of experiments are performed to train the networks and to investigate the performance of the improved dead reckoning system.