• Journal of the Korean Society for Precision Engineering
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• v.31 no.12
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• pp.1127-1132
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• 2014

Fast tool servo (FTS) is an enabling technology to fabricate various shapes of functional surface geometries in a precise and controllable manner. FTS can be also employed as a straightforward and efficient surface treatment way of making such products more durable. In this work, process characteristics using high-precision FTS-based surface texturing were qualitatively and quantitatively investigated to provide a class of surface design rule. The morphologies of surfaces processed with different conditions were first examined by observing the resultant 2D/3D surface profiles. In addition, the effects of the surface treatment using FTS on hardness and wear properties were characterized and compared to those without treatment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1023-1041
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• 2010

In cognitive radio networks, cooperative spectrum sensing schemes are proposed to improve the performance of detecting licensees by secondary users. Commonly, the cooperative sensing can be realized by means of hard decision fusion (HDF) or soft decision fusion (SDF) schemes. The SDF schemes are superior to the HDF ones in terms of the detection performance whereas the HDF schemes are outperforming the SDF ones when the traffic overhead is taken into account. In this paper, a hybrid SFD-HDF cluster-based approach is developed to jointly exploit the advantages of SFD and HDF schemes. Different SDF schemes have been proposed and compared within a given cluster whereas the OR-rule base HDF scheme is applied to combine the decisions reported by cluster headers to a common receiver or base station. The computer simulations show promising results as the performance of the proposed scenario of hybridizing soft and hard fusion schemes is significantly outperforming other different combinations of conventional SDF and HDF schemes while it noticeably reduces the network traffic overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.58-73
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• 2014

As is well known, cooperative sensing can significantly improve the sensing accuracy as compared to local sensing in cognitive radio networks (CRNs). However, a large number of cooperative secondary users (SUs) reporting their local detection results to the fusion center (FC) would cause much overhead, such as sensing delay and energy consumption. In this paper, we propose a fast cooperative sensing scheme, called double threshold fusion (DTF), to reduce the sensing overhead while satisfying a given sensing accuracy requirement. In DTF, FC respectively compares the number of successfully received local decisions and that of failed receptions with two different thresholds to make a final decision in each reporting sub-slot during a sensing process, where cooperative SUs sequentially report their local decisions in a selective fashion to reduce the reporting overhead. By jointly considering sequential detection and selective reporting techniques in DTF, the overhead of cooperative sensing can be significantly reduced. Besides, we study the performance optimization problems with different objectives for DTF and develop three optimum fusion rules accordingly. Simulation results reveal that DTF shows evident performance gains over an existing scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9A
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• pp.719-726
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• 2009

CR systems performs spectrum sensing operation to detect the appearance of primary users. However, since it is not feasible to do spectrum sensing and data transmission simultaneously, they typically operate alternatively in a time domain. There have been an effort(8) to investigate the optimal spectrum sensing duration for maximum throughput for the scheme with cooperative spectrum sensing. This is based on an assumption that the communication channels between each secondary user and the fusion center are ideal and does not consider the effects of transmission error. Motivated by this, we here model the channels as binary symmetric channels and examined its effect on the maximum throughput and the associated optimal sensing duration. Analysis shows that the performance degradation due to the transmission error is smaller for the case of using the AND fusion rule than for the OR fusion rule.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.75-87
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• 2009

We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, comparative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks. Performance of the proposed technique applied to image fusion experiments, is characterized in terms of Mutual Information (MI) as the output quality measure. The method is tested with C=2 input images. The results of the proposed scheme indicate a practical and attractive alternative to current multi-focus image fusion techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.1
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• pp.19-27
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• 2014

As frequency resource has taken on greater importance, Cognitive Radio(CR) technology has been considered as the solution to improve spectrum utilization by allowing a secondary user to utilize a licensed band when the primary user is absent. So spectrum sensing is significant part of CR for high performance. Recently, cooperative spectrum sensing that secondary users share each sensing results is proposed to improve spectrum sensing accuracy. In this paper, OR rule based cooperative spectrum sensing scheme using reporting error probability which occurs in user to fusion center(FC) channel The simulation results show that proposed scheme mitigates false alarm probability limitation which appears in existing cooperative spectrum sensing scheme by restricting the number of cooperating users using reporting error probability.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.49-57
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• 1999

The fusion of fuzzy set theory and neural networks technologies have concentrated on applying neural networks to obtain the optimal rule bases of fuzzy logic system. Unfortunately, this is very hard to achieve due to limited learning capabilities of neural networks. To overcome this difficulty, we propose a new approach in which rough set theory and neuro-fuzzy fusion are combined to obtain the optimal rule base from input/output data. Compared with conventional FNN, the proposed algorithm is considerably more realistic because it reduces overlapped data when construction a rule base. This results are applied to the construction of inference rules for controlling the temperature at specified points in a refrigerator.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1597-1600
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• 1991

In this paper, we describe a notion of sensor modeling method in multisensor data fusion using fuzzy set theory. Each sensor module is characterized by its fuzzy constraints to specific features of environment. These sensor fuzzy constraints can be imposed on multisensory data to verify their degree of truth and compatibility toward the final decision making. In comparison with other sensor modeling methods, such as probabilistic models or rule-based models, the proposed method is very simple and can be easily implemented in intelligent robot systems.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.304-309
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• 1999

In this paper, we presents a new disparity map refinement algorithm using statistical characteristics of disparity map and edge information. The proposed algorithm generate a refined disparity map using disparity maps which are obtained from area and feature-based Stereo Matching by selecting a disparity value of edge point based on the statistics of both disparity maps. Experimental results on aerial stereo image show the better results than conventional fusion algorithms in the disparity error. This algorithm can be applied to the reconstruction of building image from the high resolution remote sensing data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.296-301
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• 1998

The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.