• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.9
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• pp.800-807
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• 2006

To enhance the conversion speed more fast, we separate the determination process of MSB and LSB with the two independent ADC circuits of the Incremental Sigma Delta ADC. After the 1st Incremental Sigma Delta ADC conversion finished, the 2nd Incremental Sigma Delta ADC conversion start while the 1st Incremental Sigma Delta ADC work on the next input. By determining the MSB and the LSB independently, the ADC conversion speed is improved by two times better than the conventional Extended Counting Incremental Sigma Delta ADC. In processing the 2nd Incremental Sigma Delta ADC, the inverting sample/hold circuit inverts the input the 2nd Incremental Sigma Delta ADC, which is the output of switched capacitor integrator within the 1st Incremental Sigma Delta ADC block. The increased active area is relatively small by the added analog circuit, because the digital circuit area is more large than analog. In this paper, a 14 bit Extended Counting Incremental Sigma-Delta ADC is implemented in $0.25{\mu}m$ CMOS process with a single 2.5 V supply voltage. The conversion speed is about 150 Ksamples/sec at a clock rate of 25 MHz. The 1 MSB is 0.02 V. The active area is $0.50\;x\;0.35mm^{2}$. The averaged power consumption is 1.7 mW.

• ETRI Journal
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• v.34 no.4
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• pp.645-648
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• 2012

An incremental subspace learning scheme to recover lost speech segments online is presented. Our contributions in this work are twofold. First, the recovery problem is transformed into an interpolation problem of the time-varying gains via nonnegative matrix factorization. Second, incremental nonnegative matrix factorization is employed to allow online processing and track the evolution of speech statistics. The effectiveness of the proposed scheme is confirmed by the experiment results.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.3
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• pp.73-80
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• 2014

In this paper, we propose an incremental simulation method in event-driven HDL simulation to reduce the simulation execution time. In general, the simulation is repeated with a series of design changes. Incremental simulation is an efficient simulation method that shortens the simulation execution time for the following simulation by using the result of previous simulation. We have observed the effectiveness of the proposed approach through the experimentation with multiple real designs.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.77-82
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• 2020

Most of the navigation in the vehicle has been developed based on a complex structure of PSF(Physical Storage Format) files, making it difficult to support incremental map updates. DB-based navigation is drawing attention as a next-generation navigation method to solve this problem. In DB-based navigation that supports incremental map updates, data fragmentation due to continuous map data updates can increase data access costs, which can lead to a decrease in search performance. In this paper, as one of the performance enhancement methods of DB-based navigation that supports incremental map updates, data fragmentation prevention techniques were presented and the performance improvement effect was verified through actual implementation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• Transactions of Materials Processing
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• v.17 no.1
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• pp.27-34
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• 2008

In order to manufacture a doubly curved sheet metal, the incremental roll forming process which adopts advantages such as the flexibility of the incremental forming process and continuous bending deformation of the roll forming process has been experimentally investigated. An experimental equipment was developed which was named as unit roll set (URS) consisting of two pairs of support rolls and an upper center roll. The upper roll equipped with the servo control unit is motor-driven and can be positioned in the vertical direction according to the user's commands. Four support rolls are idle, and they freely rotate only along the axis so as to transfer the plate more stably in the tangential direction of the rotation of the driving roll. In the process, the plate is deformed incrementally as deformation proceeds simultaneously in longitudinal and transverse directions. Through the experiments using URS, information regarding to forming schedules is found out to fabricate curved hull plates. This study demonstrates the further application of the incremental roll forming process in shipbuilding industries.

• Journal of Information Processing Systems
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• v.5 no.2
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• pp.105-116
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• 2009

To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency, and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.797-806
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• 2006

In the data warehouse environment, materialized views are typically used to support efficient query processing. Materialized views need to be updated when source data change. Since the update of the views need impose a significant overhead, it is essential to update the views efficiently. Though various view maintenance strategies have been discussed in the past, the efficient maintenance of SPJ materialized views has not been sufficiently investigated. In this paper, we propose an efficient incremental view maintenance method for SPJ materialized views that minimizes the total accesses to data sources. The proposed method finds an optimal view maintenance strategy using a dynamic programming algorithm. We also present various experimental results that shows the efficiency of our proposed method.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.533-544
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• 2004

Reports have a significant meaning in time-constrained large transaction environments, such as airplane control systems or wargame simulations. This is due to the necessity of generating reports within a given time limit without restraining the operation performance of large transaction environments. In order to generate reports in large transaction environments while satisfying time-constrained requirements, this paper proposes a model which combines the incremental operation mechanism and materialized view mechanism using triggers and stored procedures. Further, the implementation and evaluation of the proposed model provides the Identification of the characteristics of the proposed model.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.