• East Asian mathematical journal
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• v.22 no.1
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• pp.1-16
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• 2006

Some new reverses of the Schwarz inequality in inner product spaces and applications for vector-valued sequnces and integrals are given.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.4
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• pp.773-782
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• 2001

This study was to investigate the value changes in Korean women by analyzing the ads of womens magazines. The contents of ads were classified into two types of values: general and consumption values. The general values, composed of inner-directedness and outer-directrdness, might predict consumer behavior in general. The consumption values, utilitarians and hedonic, were expected to affect the consumer purchasing attitude to a specific product. Factors affecting the cultural values were per capita income and womens social status. Total 2969 illustrated ads with verbal theme from 32 Korean womens magazines were used. The content analysis, chi-square test, logistic regression were done for the analysis. The results showed that inner-directedness was dominant general value in Korean womens culture and increased over time. Younger consumers were more inner-directed than older ones were, and inner-directed values increased with income growth. For the consumption values, utilitarian values were higher than hedonic ones and went up over time. For the product types, utilitarian ads were frequent in cosmetics while hedonic ads were high for apparels. Those results implied that Korean womens culture became more inner-directed with increased income. Also consumption values were likely to differ between product groups. Per capita income was shown to increase inner-directedness while womens social status was to increase outer-directedness.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.102-111
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• 1990

In this thesis, an optical bidirectional inner-product associative memory model using liquid crystal television is proposed and analyzed theoretically and realized experimentally. The LCTV is used as a SLM(spatial light modulator), which is more practical than conventional SLMs, to produce image vector in terms of computer and CCD camera. Memory and input vectors are recorded into each LCTV through the video input connectors of it by using the image board. Two multi-focus hololenses are constructed in order to perform optical inner-product process. In forward process, the analog values of inner-products are measured by photodetectors and are converted to digital values which are enable to control the weighting values of the stored vectors by changing the gray levels of the pixels of the LCTV. In backward process, changed stored vectors are used to produce output image vector which is used again for input vector after thresholding. After some iterations, one of the stored vectors is retrieved which is most similar to input vector in other words, has the nearest hamming distance. The experimental results show that the proposed inner-product associative memory model can be realized optically and coincide well with the computer simulation.

• Communications of the Korean Mathematical Society
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• v.20 no.3
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• pp.487-503
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• 2005

Some companions of Gruss inequality in 2-inner product spaces and applications for determinantal integral inequalities are given.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.279-292
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• 2006

A reverse of Bessel's inequality in 2-inner product spaces and companions of $Gr\ddot{u}ss$ inequality with applications for determinantal integral inequalities are given.

• Journal of the Korean Mathematical Society
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• v.34 no.2
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• pp.265-277
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• 1997

Let E be a real, non-degenerate, indefinite inner product space with dim $E \geq 3$. It is shown that any bijection of E which preserves the light cones is an affine map.

• East Asian mathematical journal
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• v.23 no.1
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• pp.59-73
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• 2007

A new reverse of the generalised triangle inequality that complements the classical results of Diaz and Metcalf is obtained. Applications for inner product spaces and for complex numbers are provided.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.129-137
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• 2001

Matching pursuit algorithm was successfully demonstrated useful in low bit-rate video coding, However, one of the practical concerns related to applying the matching pursuit algorithm to application is its massive computation required for finding bases whose weighted sum best approximates the given input image. The main contribution of this paper is that we provide a new method that can drastically reduce the computational load without any degradation of image quality. Its main idea is based on reducing the number of inner product calculation required for finding best bases because the complexity of matching pursuit algorithm is due to the exhaustive local inner product calculation. As the first step, we compute a matrix which is the 1-D inner product of the given motion-compensated error input image with the 1-D vertical Gabor functions using the separable property of Gabor bases. In the second step, we calculate length of each vector in the matrix that corresponds to 1-D horizontal Gabor function, and compare the length with the current maximum absolute inner product value so far. According to the result of this comparison, one can decide whether or not to calculate the inner product. Since most of them do not need to calculate the inner product value, one can significantly reduce the computational load. Experimental results show that proposed method reduces about 70% of inner product calculation compared to the Neff's fast algorithm without any degradation of image quality.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.61-70
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• 2004

The operation mode of neural network is divided into learning and recognition process. Learning is updating process of weight until neural network archives target result from input pattern. Recognition is arithmetic process of input pattern and weight. Traditional inner product process is focused to improve processing speed and hardware complexity. There is no hardware architecture to distinguish between loaming and recognition mode of neural network. In this paper we propose the new architecture of low power inner product processor for reconfigurable neural network. The proposed architecture is similar with bit-serial inner product processor on learning mode. It have several advantages which are fast processing base on bit-level, suitability of hardware implementation and pipeline architecture to compute data. And proposed architecture minimizes active units and reduces consumption power on recognition mode. Result of simulation shows that active units is depend on bit representation of weight, but we can reduce active units about 50 precent.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.124-129
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• 2013

This paper proposes a high-performance vector inner product calculation circuit for real-time object recognition based on SVM algorithm. SVM algorithm shows a higher detection rate than other object recognition algorithms. However, it requires a huge amount of computational efforts. Since vector inner product calculation is one of the major operations of SVM algorithm, it is important to implement a high-performance vector inner product calculation circuit for real-time object recognition capability. The proposed circuit adopts the pipeline architecture with six stages to increase the operating speed and makes it possible to recognize objects in real time based on SVM. The proposed circuit was described in Verilog HDL at RTL. For silicon verification, an MPW chip was fabricated using TSMC 180nm standard cell library. The operation of the implemented MPW chip was verified on the test board with test application software developed for the chip verification.