• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.272-281
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• 2009

In this paper, three-dimensional optimal evasive maneuver patterns for air-to-surface attack missiles against proportionally navigated anti-air defense missiles were investigated. An interception error of the defense missile is produced by an evasive maneuver of the attack missile. It is assumed that the defense missiles are continuously launched during the flight of attack missile. The performance index to be minimized is then defined as the negative square integral of the interception errors. The direct parameter optimization technique based on SQP and a co-evolution method based on the augmented Lagrangian formulation are adopted to get the attack missile's optimal evasive maneuver patterns. The overall shape of the resultant optimal evasive maneuver is represented as a deformed barrel-roll.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.576-584
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• 2009

Up to now, it has been said that no satisfactory computer solution has been found for synthesizing four-bar linkage based on the prescribed coupler link curve. In our study, an algorithm has been developed to improve the design synthesis of four bar linkage based on the 5 precision points method. The suggested algorithm generates the desired coupler curve by using NURBS, and then the generated curve approximates as closely as possible to the desired curve representing coupler link trajectory. Also, when comparing each generated curve by constructing the control polygon, rapid comparison is easily achieved by applying convex hull of the control polygon. Finally, an optimization process using ADS is incorporated into the algorithm based on the 5 precision point method to reduce the total optimization process time. As for examples, two four bar linkages were tested and the result well demonstrated the effectiveness of the algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.106-116
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• 2012

Optimal transfer trajectories based on the planar circular restricted three body problem are designed by using mixed impulsive and continuous thrust. Continuous and dynamic trajectory optimization is reformulated in the form of discrete optimization problem. This is done by the method of direct transcription and collocation. It is then solved by using nonlinear programming software. Two very different transfer trajectories can be obtained by the different combinations of the design parameters. Furthermore, it was found out that all designed trajectories permit a ballistic capture by the Moon's gravity. Finally, the required thrust profiles are presented and they are analyzed in detail.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1137-1145
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• 2007

Palletizing tasks are necessary to promote efficient storage and shipping of boxed products. These tasks, however, involve some of the most monotonous and physically demanding labor in the factory. Thus, many types of robot palletizing systems have been developed, although many robot motion commands still depend on the teach pendant. That is, the operator inputs the motion command lines one by one. This is very troublesome and, most importantly, the user must know how to type the code. We propose a new GUI(Graphic User Interface) for the palletizing system that is more convenient. To do this, we used the PLP "Fast Algorithm" and 3-D auto-patterning visualization. The 3-D patterning process includes the following steps. First, an operator can identify the results of the task and edit them. Second, the operator passes the position values of objects to a robot simulator. Using those positions, a palletizing operation can be simulated. We chose a widely used industrial model and analyzed the kinematics and dynamics to create a robot simulator. In this paper we propose a 3-D patterning algorithm, 3-D robot-palletizing simulator, and modified trajectory generation algorithm, an "overlapped method" to reduce the computing load.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.814-819
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• 2007

Palletizing task is well-known time consuming and laborious process in factory, hence automation is seriously required. To do this, artificial robot is generally used. These systems however, mostly user teaches the robot point to point and to avoid time-variable obstacle, robot is required to attach the vision camera. These system structures bring about inefficiency and additional cost. In this paper we propose task-oriented trajectory generation algorithm for palletizing. This algorithm based on $A^{*}$ algorithm and slice plane theory, and modify the object dealing method. As a result, we show the elapsed simulation time and compare with old method. This simulation algorithm can be used directly to the off-line palletizing simulator and raise the performance of robot palletizing simulator not using excessive motion area of robot to avoid adjacent components or vision system. Most of all, this algorithm can be used to low-level PC or portable teach pendent

• ETRI Journal
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• v.39 no.3
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• pp.353-363
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• 2017

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.961-968
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• 2013

In this paper, we propose an optimal design for starfish capturing manipulator module with four-bar linkage mechanism. A tool link with compliance is attached on the four-bar linkage, and the tool repeats detaching starfish from the ground and putting it into the storage box. Since the tool is not rigid and the manipulator is operating underwater, the trajectory of the tool tip is determined by its dynamics as well as kinematics. We analyzed the trajectory of the manipulator tool tip by quasi-static analysis considering both kinematics and dynamics. In optimization, the lengths of each link and the tool stiffness are considered as control variables. To maximize the capturing ability, capturing stroke of the four-bar manipulator trajectory is maximized. Reaction force and reaction moment, and other kinematic constraints were considered as inequality constraints.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.33-40
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• 2018

There are lots of literature about connected car system from industry and academia. The connected car is a smart car integrated with IT technology that is connected to people, vehicles and traffic management systems. It is important to V2I (vehicle to infrastructure) communication which is the connection between the vehicle and the infrastructure. CIM (cooperative intersection management) is a device to manage the communication between vehicle and infrastructure. In this paper, we analyze two intelligent vehicle control methods using CIM at non-signalized intersections. In the first method, a vehicle to pass through intersection needs to reserve a resource of intersection. In the second method, trajectory patterns on pre-planned vehicles are classified to pass through intersection. We analyze case studies of two methods to be implemented by DP(dynamic programming) and ACO(ant colony optimization) algorithms. The methods can be reasonably improved by placing importance on vehicles or controlling speeds of vehicles.