• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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• pp.210-217
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• 2003

We introduced a scheme for finding an optimal data association matrix that represents the relationships between the measurements and tracks in multi-target tracking (MIT). We considered the relationships between targets and measurements as Markov Random Field and assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space that may incorporate most of the important natural constraints. To find the minimizer of the energy function, we derived a new equation in closed form. By introducing Lagrange multiplier, we derived a compact equation for parameters updating. In this manner, a pair of equations that consist of tracking and parameters updating can track the targets adaptively in a very variable environments. For measurements and targets, this algorithm needs only multiplications for each radar scan. Through the experiments, we analyzed and compared this algorithm with other representative algorithm. The result shows that the proposed method is stable, robust, fast enough for real time computation, as well as more accurate than other method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.3-9
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• 2015

We investigate rate scheduling and power allocation problem for a delay constrained CDMA systems. Specifically, we determine an energy efficient scheduling policy, while each user maintains the short term (n time slots) average throughput. We consider a multirate CDMA system where multirate is achieved by multiple codes. Each code can be interpreted as a virtual user. The aim is to schedule the virtual users into each time slot, such that the sum of transmit energy in n time slots is minimized. We then show that the total energy minimization problem can be solved by a shortest path algorithm. We compare the performance of the optimum scheduling with that of TDMA-type scheduling.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.65-72
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• 2010

In ad-hoc network, the technique to efficiently consume the limited amounts of energy is an important issue since the wireless terminal node is operated on batteries as their energy resource. In order to extend the system lifetime, through a balanced energy consumption, we must delay the situation in which a particular terminal node's energy is depleted and results in system disconnection. Also, the link, which has low reliability due to the mobility of the node, should be avoided considering the key element when setting up the route. The proposed CMLR method in this paper enables to increase the efficiency of energy consumption with a new cost function considering the residue energy of node, error rate of link, and transmission energy consumption. This method is extending the network lifetime and increasing the energy efficiency by compromising the value between the minimization of the transmission energy consumption and maximization of the node's lifetime. Through the simulations the proposed CMLR algorithm was verified by showing better performance over the conventional methods in terms of network lifetime and path efficiency.

• Structural Engineering and Mechanics
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• v.62 no.6
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• pp.749-758
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• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.11-18
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• 2005

The traditional method of manipulation of knots and degrees gives poor quality of surface, if compatibility of input curves is not good enough. In this work, a new algorithm of multiple refitting of curves has been developed using minimum energy based formulation to get compatible curves for skinning. The present technique first reduces the number of control points and gives smoother surface for given accuracy and the surface obtained is then skinned by compatible curves. This technique is very useful to reduce data size when a large number of data have to be handled. Energy based technique is suitable for approximating the missing data. The volumetric information can also be obtained from the surface data for analysis.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.19-26
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• 2018

One of the distinct features of Wireless Sensor Networks (WSNs) is duty cycling mechanism, which is used to conserve energy and extend the network lifetime. Large duty cycle interval introduces lower energy consumption, meanwhile longer end-to-end (E2E) delay. In this paper, we introduce an energy consumption minimization problem for duty-cycled WSNs. We have applied Q-learning algorithm to obtain the maximum duty cycle interval which supports various delay requirements and given Delay Success ratio (DSR) i.e. the required probability of packets arriving at the sink before given delay bound. Our approach only requires sink to compute Q-leaning which makes it practical to implement. Nodes in the different group have the different duty cycle interval in our proposed method and nodes don't need to know the information of the neighboring node. Performance metrics show that our proposed scheme outperforms existing algorithms in terms of energy efficiency while assuring the required delay bound and DSR.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.1
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• pp.1-6
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• 2002

Cogging torque is often a principal source of vibration and acoustic noise in high precision spindle motor applications. In this paper, cogging torque is analytically calculated using energy method to show that Fourier spectra of airgap permeance function and airgap MMF function are the most important design parameters to control cogging torque. To control these functions, stator pole shoe pairing and magnet arc design are proposed to minimize cogging torque. As for optimization technique, genetic algorithm is applied to handle trade-off effects of design parameters. Results show that the proposed method can reduce the cogging torque effectively.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.726-729
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• 1991

This paper proposes a new operation algorithm for elevator by considering both better service for passengers and minimization of energy consumption for elevator operation. The main idea of the proposed operation algorithm is based on the assumption that passengers push the numbered buttons indicating their destination, one of the main differences of proposed operation mode from the conventional one is that the elevator may move to the opposite direction for a few floors according to the rescheduled operational pattern determined by some factors. Some examples by computer simulation show the efficiency of the proposed operation algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.52-53
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• 2012

Belief propagation (BP) is a commonly used global energy minimization algorithm for solving stereo matching problem in 3D reconstruction. However, it requires large memory bandwidth and data size. In this paper, we propose a novel memory-efficient algorithm of BP in stereo matching on the Graphics Processing Units (GPU). The data size and transfer bandwidth are significantly reduced by storing only a part of the whole message. In order to maintain the accuracy of the matching result, the local messages are reconstructed using shared memory available in GPU. Experimental result shows that there is almost an order of reduction in the global memory consumption, and 21 to 46% saving in memory bandwidth when compared to the conventional algorithm. The implementation result on a recent GPU shows that we can obtain 22.8 times speedup in execution time compared to the execution on CPU.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1838-1854
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• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.