• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4930-4951
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• 2018

The deep learning based speech enhancement has shown considerable success. However, it still suffers performance degradation under mismatch conditions. In this paper, an adaptation method is proposed to improve the performance under noise mismatch conditions. Firstly, we advise a noise aware training by supplying identity vectors (i-vectors) as parallel input features to adapt deep neural network (DNN) acoustic models with the target noise. Secondly, given a small amount of adaptation data, the noise-dependent DNN is obtained by using $L_2$ regularization from a noise-independent DNN, and forcing the estimated masks to be close to the unadapted condition. Finally, experiments were carried out on different noise and SNR conditions, and the proposed method has achieved significantly 0.1%-9.6% benefits of STOI, and provided consistent improvement in PESQ and segSNR against the baseline systems.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.230-236
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• 2002

A study on architecture of new parallel interference canceller which can be applied for reverse link of next generation mobile communication system supporting multi-rate is performed on this paper. The proposed method adopts new algorithm which can be applied for multi-rate system to reduce multiple access interference (MAI) which cause performance degradation of CDMA system and limit of channel capacity. The proposed system is evaluated by simulation results under various conditions. As a result, performance enhancement is achieved compared to existing conventional interference cancellers. Although the amount of calculation is increased, we can find that the performance is improved generally.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.114-117
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• 2015

이 논문에서는 소수 추출 방법인 Sieve of Eratosthenes 알고리즘을 병렬화하되 실행시간과 에너지 소모 면에서 개선된 효과를 얻고자 한다. 멀티코어 프로세서의 공유 메모리를 효율적으로 활용하도록 하이브리드 병렬 프로그래밍 모델을 적용하고, 부하 균등화를 정교하게 조절하도록 파이프라인 작업 방식을 도입하였다. 실험결과 이전 방식보다 연산속도가 향상되었고, 에너지 사용량도 감소함을 확인하였다.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.197-202
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• 2007

In this study, the cooling characteristics of a liquid cooler using thermoelectric module was experimentally investigated. The experiment was conducted for various inner structures of liquid cooler (4 cases), hot fluid flow rates (0.15-0.25 L/min), number of T.E module (2, 4, 6 set), and the cooling water flow rates (200-600 cc/min) for both parallel and counter flow types. Among the results, better cooling performance geometry was selected. And experiment was also carried out to examine further enhancement of cooling performance by inserting coils (pitches: 0.2, 3, 6 mm) into the hot-fluid channel. Present results showed that the short serpentine type(case2) indicated the best cooling performance. In the case of coil pitch of 3 mm, the best cooling performance was shown, more than 10% increase of the inlet and outlet temperature difference, compared with the case of the cooler without coil. Consequently, the inserted coil pitch should be properly selected to improve cooling performance.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.1888-1893
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• 2007

In this study, the microchannel plated heat exchanger were numerically studied for the enhancement of heat transfer in the channel configuration. Unit cold and hot fluid region with the microchannel were modeled and periodic boundary condition at the side wall was applied to continuously repeating geometry. The material of micro-structured plate is STS304 and working fluid is water. Triangular obstacles were placed in micro channel to enhance heat transfer. The performance of microchannel plated heat exchangers were numerically investigated with various obstacle configuration and Reynolds number under the parallel and counter flows. Heat transfer rate has increased about 18% compared with straight channel, but pressure drop also increased about 3.5 times. The main factor of increasing of pressure drop and heat transfer rate is considered that the momentum was lost to collide against obstacles, generation of secondary flow and boundary layer separation, wake and vortex forming phenomena.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1377-1383
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• 2019

Histogram equalization is the most frequently used algorithm for image enhancement. Its hardware implementation significantly outperforms in time its software version. The overall performance of FPGA-based implementation of histogram equalization can be improved by applying pipelining in the design and by exploiting the multipliers and a lot of SRAM blocks which are embedded in recent FPGAs. This work proposes how to implement a fast histogram equalization circuit for 8-bit gray level images. The proposed design contains a FIFO to perform equalization on an image while the histogram for next image is being calculated. Because of some overlap in time for histogram equalization, embedded multipliers and pipelined design, the proposed design can perform histogram equalization on a pixel nearly at a clock. And its dual parallel version outperforms in time almost two times over the original one.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.41-47
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• 2014

In this paper, we implemented a CUDA Fortran parallel program to run the CFD_NIMR model on GP-GPU's, which simulates air diffusion on urban terrains. A GP-GPU is graphic processing unit in the form of a PCI card, and a general calculation accelerator to perform a large amount of high speed calculations with low cost and electric power. The GP-GPU gives performance enhancement of speed by 15 times to compare the Nvidia Tesla C1060 GPU with Intel XEON 2.0 GHz CPU. In addition, the program on a GP-GPU shows efficient performance compared to an MPI parallel program on multiple CPU's. It is expected that a proposed programming method on the GP-GPU parallel program can be used for numerical models with a similar structure.

• KSBB Journal
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• v.22 no.6
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• pp.387-392
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• 2007

Recently, microfluidic biochips for DNA microarray are providing a number of advantages such as, reduction in reagent volume, high-throughput parallel sample screening, automation of processing, and reduction in hybridization time. Particularly, the enhancement of target probe hybridization by decrease of hybridization time is an important aspect highlighting the advantage of microfluidic DNA microarray platform. Fundamental issues to overcome extremely slow diffusion-limited hybridization are based on physical, electrical or fluidic dynamical mixing technology. So far, there have been some reports on the enhancement of the hybridization with the microfluidic platforms. In this review, their principle, performance, and outreaching of the technology are overviewed and discussed for the implementation into many bio-applications.

• The Journal of the Acoustical Society of Korea
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• v.32 no.3
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• pp.252-261
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• 2013

The common speech recognition system displays higher recognition performance in a quiet environment, while its performance declines sharply in a real environment where there are noises. To implement a speech recognizer that is robust in different speech settings, this study suggests the method of Parallel Model Combination adaptation using frequency-variant based on environment-awareness (FV-PMC), which uses variants in frequency; acquires the environmental data for speech recognition; applies it to upgrading the speech recognition model; and promotes its performance enhancement. This FV-PMC performs the speech recognition with the recognition model which is generated as followings: i) calculating the average frequency variant in advance among the readily-classified noise groups and setting it as a threshold value; ii) recalculating the frequency variant among noise groups when speech with unknown noises are input; iii) regarding the speech higher than the threshold value of the relevant group as the speech including the noise of its group; and iv) using the speech that includes this noise group. When noises were classified with the proposed FV-PMC, the average accuracy of classification was 56%, and the results from the speech recognition experiments showed the average recognition rate of Set A was 79.05%, the rate of Set B 79.43%m, and the rate of Set C 83.37% respectively. The grand mean of recognition rate was 80.62%, which demonstrates 5.69% more improved effects than the recognition rate of 74.93% of the existing Parallel Model Combination with a clear model, meaning that the proposed method is effective.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.48-61
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• 2017

An IaaS service of a cloud computing environment makes itself attractive for running large scale parallel application thanks to its innate characteristics that a user can utilize a desired number of high performance virtual machines without maintenance cost. The total execution time of a parallel application on a high performance computing environment depends on a task scheduling algorithm. Most studies on task scheduling algorithms on cloud computing environment try to reduce a user cost, and studies on task scheduling algorithms that try to reduce total execution time are rarely carried out. In this paper, we propose a task scheduling algorithm called an HAGD and a performance enhancement method called a group task duplication method of which the HAGD utilizes. The group task duplication method simplifies previous task duplication method, and the HAGD uses the group task duplication method or a task insertion method according to the characteristics of a computing environment and an application. We found that the proposed algorithm provides superior performance regardless of the characteristics in terms of normalized total execution time through performance evaluations.