• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.99-108
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• 1994

In this paper an image compression technique based on fractal theory using iterated contractive transformation is analysed and an improved image coder is suggested. Existing methods used the classifier proposed by Ramamurthi and Gersho which utilize the properties of neighboring pixels in the spatial domain. In this paper DCT-based classification is applied to 512$\times$512 images and PSNR improvement of 0.4~2.7 dB is obtained at lower bit rate over conventional algorithms. In addition the effect of varying the domain block size and quantization step size of the luminance shift parameter on the compression ratio and the image quality is compared and analysed.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.367-368
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• 2006

This paper presents an analytic derivation of phase measurement error. The analysis derives the measurement error caused by the finite-bit quantization of both input signals and twiddle factors used in the recursive implementation of the phasor measurement algorithm. The derivation is based on the statistical exploration of the error dynamic equations. The effect of frequency deviation and the number of DFT points are also included in the study. The analysis results are verified with the data obtained from the computer simulation by widely varying the values of error causing factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.822-833
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• 1996

The video coding for very low bit rate has recently received considerable attention, but conventional block based transform coding schemes suffer from the blocking effect for the constraints of bit rates. In this paper, we present a video coding sysem suing multi-resolution motion estimation/compensation with variable size block(VMRME/C) and multi-resolution vector quantization(MRVQ) in wavelet transform domain for very low bit rate coding. It is shown that the presented scheme has better performance in the peak signal-to-nose ratio(RSNR) by 0.2-0.6 dB as well as subjective quality than that of conventional block based transform video coding techniques(especially, H. 263 which is DCT based video coding).

• Annual Conference of KIPS
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• 2021.05a
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• pp.373-376
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• 2021

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members' disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement implies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1208-1222
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• 2024

Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.454-454
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• 2011

Graphene shows unusual electronic properties, such as carrier mobility as high as 10,000 $cm^2$/Vs at room temperature and quantum electronic transport, due to its electronic structure. Carrier mobility of graphene is ten times higher than that of Silicon device. On the one hand, quantum mechanical studies have continued on graphene. One of them is quantum Hall effect which is observed in graphene when high magnetic field is applied under low temperature. This is why two dimension electron gases can be formed on Graphene surface. Moreover, quantum Hall effect can be observed in room temperature under high magnetic field and shows fractional quantization values. Quantum Hall effect is important because quantized Hall resistances always have fundamental value of h/$e^2$ ~ 25,812 Ohm and it can confirm the quantum mechanical behaviors. The value of the quantized Hall resistance is extremely stable and reproducible. Therefore, it can be used for SI unit. We study to measure quantum Hall effect in CVD graphene. Graphene devices are made by using conventional E-beam lithography and RIE. We measure quantum Hall effect under high magnetic field at low temperature by using He4 gas closed loop cryostat.

• ETRI Journal
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• v.38 no.3
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• pp.417-424
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• 2016

In this paper, an accurate characterization of a fabricated X-band transmit/receive module is described with the process of generating control data to correct amplitude and phase deviations in an active electronically scanned array antenna unit. In the characterization, quantization errors (from both a digitally controlled attenuator and a phase shifter) are considered using not theoretical values (due to discrete sets of amplitude and phase states) but measured values (of which implementation errors are a part). By using the presented procedure for the characterization, each initial control bit of both the attenuator and the phase shifter is closest to the required value for each array element position. In addition, each compensated control bit for the parasitic cross effect between amplitude and phase control is decided using the same procedure. Reduction of the peak sidelobe level of an array antenna is presented as an example to validate the proposed procedure.

• Speech Sciences
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• v.12 no.4
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• pp.7-16
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• 2005

We investigate the effect of membership concentration on the performance of the speaker-independent recognition system by FVQ/HMM. For the membership function, we adopt the result obtained from the objective function approach by Bezdek. Membership concentration is done by varying the exponent in the membership function. The number of selected clusters is constrained to two for the sake of cheap computational cost. Experimental results showed that the recognition rate has its maximum value when the membership function was taken to be inversely proportional to the distance of the input vector from the cluster centroid. When the membership concentration was two weak or too strong, the performance was found to be relatively poor as expected. Except these extreme cases, the membership concentration was not shown to affect the recognition rate significantly. This is in accordance with the general observation that the fuzzy system is not much sensitive. to the detailed shape of the membership function as long as it is overlapped over multiple classes.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• Proceedings of the KAIS Fall Conference
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• 2011.12a
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• pp.62-66
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• 2011

TPM 활동은 도입준비 단계의 활동으로 TPM의 기본 방침 및 목표의 설정과 이를 달성하기 위한 TPM 활동성과를 측정하는 지표를 통한 성과의 정량화 등이 필요하다. 본 연구에서는 선행 연구 결과를 통해 TPM 활동의 측정 요소를 분석하고, BSC 기반의 TPM 성과 측정 방법과 정성적 성과의 정량화를 통한 지표의 경제적 효과 측정 방법을 제시하였다.