• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.611-616
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• 2018

RFID-based Smart cabinets can make a recognition error owing to the electromagnetic wave interference. This paper proposes and implements a smart cabinet system for inventory management using RFID, especially which can be applied to biological laboratories. We calculate the optimal value of partition distance for the higher recognition rate between RFID tags and the reader, and the optimal partition thickness for electromagnetic wave absorption to achieve the higher recognition rate, in which two kinds of the partitions have been tested, a pure steel partition with various thickness and a thin steel partition attached with electromagnetic waves absorber. The experimental results show that the most recommended partition structure for the smart cabinets is one with the partition distance of 30cm and the partition thickness of 1mm attached with the electromagnetic wave absorption tapes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.140-145
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• 2008

Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.65-70
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• 2007

A method to focus the object in camera system by applying the Hill climb algorithm from optical lens moving device (VCM; Voice coil motor) is proposed. The focusing algorithm from VCM is focus on the object but in these criteria is a well-known drawback; the focus is good only at same distance objects but the focus is bad (blur image) at different distance objects because of the DOF (Depth of focus) or DOF (Depth of field) at the optical characteristic. Here, the new camera system that describes the Reflector of free curvature systems (or Diffractive Fresnel lens) and the partition of focusing window area is proposed. The method to improve the focus in all areas (different distance objects) is proposed by new optical system (discrete auto in-focus) using the Reflector of free curvature systems (or Diffractive Fresnel lens) and by applying the partition of all areas. The proposal is able to obtain good focus in all areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5491-5505
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• 2017

Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.455-460
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• 2003

A new cluster validity index is proposed that determines the optimal partition and optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. The overlap measure is obtained by computing an inter-cluster overlap. The separation measure is obtained by computing a distance between fuzzy clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index and nine previously formulated indexes on well-known data sets showed the superior effectiveness and reliability of the proposed index in comparison to other indexes.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.35-50
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• 2007

In a data centric sensor network, a sensor node to store data is determined by the measured data value of each sensor node. Therefore, if the same data occur frequently, the energy of the sensor node to store the data is exhausted quickly due to the concentration of loads. And if the sensor network is extended, the communication cost for storing data and processing queries is increased, since the length of the routing path for them is usually in the distance. However, the existing researches that generally focus on the efficient management of data storing can not solve these problems efficiently. In this paper, we propose a NUNS(Non-Uniform Network Split) method that can distribute loads of sensor nodes and decrease the communication cost caused by the sensor network extension. By dividing the sensor network into non-uniform partitions that have the minimum difference in the number of sensor nodes and the splitted area size and storing the data which is occurred in a partition at the sensor nodes within the partition, the NUNS can distribute loads of sensor nodes and decrease the communication cost efficiently. In addition, by dividing each partition into non-uniform zones that have the minimum difference in the splitted area size as many as the number of the sensor nodes in the partition and allocating each of them as the processing area of each sensor node, the NUNS can protect a specific sensor node from the load concentration and decrease the unnecessary routing cost.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.169-180
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• 2013

The modification of conditional Fuzzy C-Means (CFCM) with Gaussian weights (CFCM_GW) is accomplished for blind equalization of channels in this paper. The proposed CFCM_GW can deal with both of linear and nonlinear channels, because it searches for the optimal desired states of an unknown channel in a direct manner, which is not dependent on the type of channel structure. In the search procedure of CFCM_GW, the Bayesian likelihood fitness function, the Gaussian weighted partition matrix and the conditional constraint are exploited. Especially, in contrast to the common Euclidean distance in conventional Fuzzy C-Means(FCM), the Gaussian weighted partition matrix and the conditional constraint in the proposed CFCM_GW make it more robust to the heavy noise communication environment. The selected channel states by CFCM_GW are always close to the optimal set of a channel even when the additive white Gaussian noise (AWGN) is heavily corrupted. These given channel states are utilized as the input of the Bayesian equalizer to reconstruct transmitted symbols. The simulation studies demonstrate that the performance of the proposed method is relatively superior to those of the existing conventional FCM based approaches in terms of accuracy and speed.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.383-391
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• 2009

In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.3
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• pp.241-248
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• 1996

The relationship between width of line target and distance at the limit of discrimination was examined by means of the behavioral method, for filefish Stephanolepis cirrhifer from 11 to 15cm body length. Target distance was distance from beginning of partition board to target plate, and was varied from 50cm to 200cm. The target plate was made of white acrylic resin with a vertical black line in the center. The width of line target was varied from 0.2mm to 8.0mm. Fish were trained to respond to a line target and the width of line target reduced until the minimum width required to elicit a response was established. Rate of success was expressed as the percentage of target choices in 90 trials. The line acuity of filefish was found to be 0.58 at a target distance of 50cm. The rate of success decreased slowly as line target width decreased from 8.0mm to 1.5mm, and decreased suddenly for target widths less than about 1.5mm. The width of the line target D(mm) at the limit of discrimination was shown to be an exponential function of the target distance L(cm) as follows : D=exp(9.947$\times$$10^-3$.L+0.146)