• Korean Journal of Computational Design and Engineering
• /
• v.11 no.3
• /
• pp.223-233
• /
• 2006

Layered manufacturing(LM) is emerging as a new technology that enables the fabrication of three dimensional heterogeneous objects such as Multi-materials and Functionally Gradient Materials (FGMs). Among various types of heterogeneous objects, more attention has recently paid on the fabrication of FGMs because of their potentials in engineering applications. The necessary steps for LM fabrication of FGMs include representation and process planning of material information inside an FGM. This paper introduces a new process planning algorithm that takes into account the processing of material information. The detailed tasks are discretization (i.e., decomposition-based approximation of volume fraction), orientation (build direction selection), and adaptive slicing of heterogeneous objects. In particular, this paper focuses on the discretization process that converts all of the material information inside an FGM into material features like geometric features. It is thus possible to choose an optimal build direction among various pre-selected ones by approximately estimating build time. This is because total build time depends on the complexity of features. This discretization process also allows adaptive slicing of heterogeneous objects to minimize surface finish and material composition error. In addition, tool path planning can be simplified into fill pattern generation. Specific examples are shown to illustrate the overall procedure.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.5
• /
• pp.269-277
• /
• 2018

NAND flash memory has widely been used because of non-volatility, low power consumption and fast access time. However, it suffers from inability to provide update-in-place and the erase cycle is limited. The unit of read/write operation is a page and the unit of erase operation is a block. Moreover erase operation is slower than other operations. We proposed the Adaptive Garbage Collection (called "AGC") policy which focuses on not only reducing garbage collection process time for real-time guarantee but also wear-leveling for a flash memory lifetime. The AGC performs better than Cost-benefit policy and Greedy policy. But the AGC does not consider the operation characteristics. So we proposed the Advanced Adaptive Garbage Collection (called "A-AGC") policy which considers the page write operation count and block erase operation count. The A-AGC reduces the write operations by considering the data update frequency and update data size. Also, it reduces the erase operations by considering the file fragmentation. We implemented the A-AGC policy and measured the performance compared with the AGC policy. Simulation results show that the A-AGC policy performs better than AGC, specially for append operation.

• The Journal of Korean Association of Computer Education
• /
• v.14 no.2
• /
• pp.75-82
• /
• 2011

Cloud computing is a computing paradigm that provides user's services anywhere, anytime in a virtualized form composed of large computing resources based on internet or intranet. In Cloud computing environments, reliability of system is impact factor because many applications handle large data. In this paper, we propose an adaptive scheduling technique based on reliability with fault tolerance that manages resource variable and resolves problems(change of user's requirement, failure occurrence) in Cloud computing environment. Futhermore, we verified the performance of the proposed scheduling through experiments in CloudSim Simulation.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.4
• /
• pp.485-490
• /
• 2003

RP(Rapid Prototyping) has been used in the various industrial applications. This paper presents the optimization techniques fur fabricated 3D model design using RP machine for the medical field. Once the original brain model data are obtained from 2D slices of MRI/CT machine, the data can be modeled as an optimal ellipse. The objective of this study includes optimization of fabrication time and surface roughness using the adaptive slicing method. It can reduce fabrication time without losing surface roughness quality by accumulating the slices with variable thickness. According to the parameter tuning and synthesis of its effect, more suitable parameter values can be obtained by enhanced 3D brain model fabrication. Therefore, accurate 3D brain model fabricated by RP machine can enable a surgeon to perform pre-operation. to make a decision for the operation sequence and to perceive the 3D positions in prototype, before delicate operation of actual surgery.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.338-340
• /
• 2012

In images, the edge is an important element to analyze characteristics of the image and has been used selectively at several applications. Even now, many researches to detect and take advantage of theses edges are underway and in initially to detect edges, methods using the relation of adjacent pixels are proposed. Characteristic of these methods is that the processing speed of the algorithms is fast, but the specific weighted values are applied to all the pixels regardless of the images equally. In recent years, the research of the edge detection algorithm to adapt according to the image has been actively underway, in order to complement the drawbacks of the existing methods. Therefore, in order to detect the edge excellent characteristics In this paper, we proposed algorithm using adaptive mask.

• Journal of Power Electronics
• /
• v.12 no.3
• /
• pp.468-476
• /
• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• Journal of Communications and Networks
• /
• v.16 no.5
• /
• pp.512-522
• /
• 2014

This paper proposes fast millimeter-wave (mm-wave) beam training protocols with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responder. However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams ($1^{\circ}$ in the worst case). To alleviate this problem, we propose two types of adaptive beam training protocols for fixed and adaptive modulation, respectively, which take into account the unique propagation characteristics of millimeter waves. For fixed modulation, the proposed protocol allows for interactive beam training, stopping the search when a local maximum of the power angular spectrum is found that is sufficient to support the chosen modulation/coding scheme. We furthermore suggest approaches to prioritize certain directions determined from the propagation geometry, long-term statistics, etc. For adaptive modulation, the proposed protocol uses iterative multi-level beam training concepts for fast link configuration that provide an exhaustive search with significantly lower complexity. Our simulation results verify that the proposed protocol performs better than traditional exhaustive search in terms of the link configuration speed for mobile wireless service applications.

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.2
• /
• pp.95-102
• /
• 2001

Fuzzy system is capable of uniformly approximating any nonlinear function over compact input space. The applications of fuzzy system, however, have been primarily limited by the need for large number of fuzzy rules, in particular, for the high-order nonlinear system. In this paper, we propose the reconstruction methods of fuzzy systems, parallel type and cascade, based on the decomposition of some classes of high-order nonlinear functions. Using the both types appropriately, we can reduce the number of fuzzy rules geometrically. It can be applied to the fuzzy system that has an online adaptive structure. Two examples of adaptive fuzzy sliding mode control are shown in the computer simulations to verify the validity of the proposed algorithm.

• Communications for Statistical Applications and Methods
• /
• v.18 no.6
• /
• pp.871-886
• /
• 2011

This paper proposes a powerful SVM-ASM filter, the adaptive switching median(ASM) filter based on support vector machines(SVMs), to effectively reduce impulse noise in corrupted images while preserving image details and features. The proposed SVM-ASM filter is composed of two stages: SVM impulse detection and ASM filtering. SVM impulse detection determines whether the pixels are corrupted by noise or not according to an optimal discrimination function. ASM filtering implements the image filtering with a variable window size to effectively remove the noisy pixels determined by the SVM impulse detection. Experimental results show that the SVM-ASM filter performs significantly better than many other existing filters for denoising impulse noise even in highly corrupted images with regard to noise suppression and detail preservation. The SVM-ASM filter is also extremely robust with respect to various test images and various percentages of image noise.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.145-152
• /
• 2009

Applications such as study guides and adaptive tutoring must rely on a fine grained student model to tailor their interaction with the user. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the information. I study how to design learner tailoring question process for recommendation. And this process can be applied the CAT and I use the formal language such as CSP in each process development for efficient process design. I use the item difficulty of item response theory for question recommendation process and learner can choice the difficulty step for learning change to control the difficulty of question in next learning. Finally, this method displayed the structural difference to compare between existent and this process.