• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.108-114
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• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Journal of Broadcast Engineering
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• v.15 no.5
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• pp.672-680
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• 2010

This paper presents an adaptive quantization matrix selection scheme for H.264 video encoding. Conventional H.264 coding standard applies the same quantization matrix to the entire video sequence without considering local characteristics in each frame. In this paper, we propose block adaptive selection of quantization matrix according to edge directivity of each block. Firstly, edge directivity of each block is determined using intra prediction modes of its spatially adjacent blocks. If the block is decided as a directional block, new weighted quantization matrix is applied to the block. Otherwise, conventional quantization matrix is used for quantization of the non-directional block. Since the proposed weighted quantization is designed based on statistical distribution of transform coefficients in accordance with intra prediction modes, we can achieve high coding efficiency. Experimental results show that the proposed scheme can improve coding efficiency by about 2% in terms of BD bit-rate.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.149-154
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• 2017

The MCT-Adaboost algorithm chooses an optimal set of features in each rounds. On each round, it chooses the best feature by calculate minimizing error rate using feature index and MCT kernel distribution. The involved process of weak classification executed by a hard decision. This decision occurs some problems when it chooses ambiguous kernel feature. In this paper, we propose the modified MCT-Adaboost classification using soft decision. The typical MCT-Adaboost assigns a same initial weights to each datum. This is because, they assume that all information of database is blind. We assign different initial weights with our propose new algorithm using some statistical properties of involved features. In experimental results, we confirm that our method shows better performance than the traditional one.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.141-152
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• 2005

A new emotion-based image retrieval method is proposed in this paper. Query emotion descriptors called query color code and query gray code are designed based on the human evaluation on 13 emotions('like', 'beautiful', 'natural', 'dynamic', 'warm', 'gay', 'cheerful', 'unstable', 'light' 'strong', 'gaudy' 'hard', 'heavy') when 30 random patterns with different color, intensity, and dot sizes are presented. For emotion image retrieval, once a query emotion is selected, associated query color code and query gray code are selected. Next, DB color code and DB gray code that capture color and, intensify and dot size are extracted in each database image and a matching process between two color codes and between two gray codes are peformed to retrieve relevant emotion images. Also, a new relevance feedback method is proposed. The method incorporates human intention in the retrieval process by dynamically updating weights of the query and DB color codes and weights of an intra query color code. For the experiments over 450 images, the number of positive images was higher than that of negative images at the initial query and increased according to the relevance feedback.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.79-87
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• 2018

In this study, the evaluation index was developed to design the project evaluation model, and the weight was given through the AHP survey. project was evaluated for the first time by using the designed evaluation model for the field appraisal. As a result, it was revealed that the weight of the propriety index of the participant effect and the project content was the highest among 20 indexes. This study is significant in having built a base on which the evaluation system could be stabilized, by developing the integrative project evaluation model of . However, we have yet to adequately address the evaluator's acceptability and efficacy in evaluating indicators. The validity and continuity of the evaluation model will be secured through this.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.420-423
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• 2015

This paper proposes a method of non-linearity error detection and calibration for binary-weighted charge-driven DACs. In general, the non-linearity errors of DACs often occur due to the mismatch of layout designs or process variation, even when careful layout design methods and process calibration are adopted. Since such errors can substantially degrade the SNDR performance of DAC, it is crucial to accurately measure the errors and calibrate the design mismatches. The proposed method employs 2 identical DAC circuits. The 2 DACs are sweeped, respectively, by using 2 digital input counters with a fixed difference. A comparator identifies any non-linearity errors larger than an acceptable discrepancy. We also propose a calibration method that can fine-tune the DAC's capacitor sizes iteratively until the comparator finds no further errors. Simulations are presented, which show that the proposed method is effective to detect the non-linearity errors and calibrate the capacitor mismatches of a 12-bit DAC design of binary-weighted charge-driven structure.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.177-187
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• 2002

Spatial data mining is a process to discover interesting relationships and characteristics those exist implicitly in a spatial database. Many spatial clustering algorithms have been developed. But, there are few approaches that focus simultaneously on clustering spatial data and assigning weight to non-spatial attributes of objects. In this paper, we propose a new spatial clustering algorithm, called DBSCAN-W, which is an extension of the existing density-based clustering algorithm DBSCAN. DBSCAN algorithm considers only the location of objects for clustering objects, whereas DBSCAN-W considers not only the location of each object but also its non-spatial attributes relevant to a given application. In DBSCAN-W, each datum has a region represented as a circle of various radius, where the radius means the degree of the importance of the object in the application. We showed that DBSCAN-W is effective in generating clusters reflecting the users requirements through experiments.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.949-958
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• 2004

race detection using a boosting based algorithm requires a very large size of face and nonface data. In addition, the fact that there always occurs a need for adding additional training data for better detection rates demands an efficient incremental teaming algorithm. In the design of incremental teaming based classifiers, the final classifier should represent the characteristics of the entire training dataset. Conventional methods have a critical problem in combining intermediate classifiers that weight updates depend solely on the performance of individual dataset. In this paper, for the purpose of application to face detection, we present a new method to combine an intermediate classifier with previously acquired ones in an optimal manner. Our algorithm creates a validation set by incrementally adding sampled instances from each dataset to represent the entire training data. The weight of each classifier is determined based on its performance on the validation set. This approach guarantees that the resulting final classifier is teamed by the entire training dataset. Experimental results show that the classifier trained by the proposed algorithm performs better than by AdaBoost which operates in batch mode, as well as by ${Learn}^{++}$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1531-1536
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• 2022

This paper considers a problem to find a set of intervals containing all the points for the given n points and m intervals on a line, This is a special case of the set cover problem, well known as an NP-hard problem. As optimization criteria of the problem, there are minimizing the number of intervals to cover the points, maximizing the number of points each of which is covered by exactly one interval, and so on. In this paper, the intervals have weights and the sum of weights of intervals to cover a point is defined as a membership of the point. We will study the problem to find an interval cover minimizing the maximum of memberships of points. Using the dynamic programming method, we provide an O(m²)-time algorithm to improve the time complexity O(nm log n) given in the previous work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1637-1643
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• 2022

In recent years, the demand for image-processing technology in digital marketing has increased due to the expansion and diversification of the digital market, such as video, security, and machine intelligence. Noise-processing is essential for image-correction and reconstruction, especially in the case of sensitive noises, such as in CT, MRI, X-ray, and scanners. The two main salt and pepper noises have been actively studied, but the details and edges are still unsatisfactory and tend to blur when there is a lot of noise. Therefore, this paper proposes an algorithm that applies a weight-based Euclidean distance equation to the partial mask and uses only the non-noisy pixels that are the most similar to the original as effective pixels. The proposed algorithm determines the type of filter based on the state of the internal pixels of the designed partial mask and the degree of mask deterioration, which results in superior noise cancellation even in highly damaged environments.