• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.51-60
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• 2004

Due to the improvements in circuit design and manufacturing technique, the complexity of a circuit is growing. Since the complexity of a circuit causes high frequency of faults, it is very important to locate faults for improvement of yield and reduction of production cost. But unfortunately it takes a long time to find sites of defects by e-beam proving if the physical level. A fault diagnosis algorithm in the Sate level has meaning to reduce diagnosis time by limiting fault sites. In this paper, we propose an efficient fault diagnosis algorithm in the logical level. Our method is hybrid fault diagnosis algorithm using a new fault dictionary and additional fault simulation which minimizes memory consumption and simulation time.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2785-2799
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• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.156-165
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• 1998

In this paper, the hybrid algorithm for the scene change detection of MPEG-based compressed video data is proposed. There have been two methods to detect scene changes of video data compressed using algorithms such as MPEG or motion-JPEG: analyzing the compressed data directly, and analyzing from the retrieved data. The former has the advantage of taking less time, while the latter can obtain detail results at the expense of time and memory. Thus by combining each algorithm we detect cuts from compressed sequence, retrieve data for some selected region, and detect gradual scene changes. Simulation results verify the superiorities of the proposed algorithm in analyzing time and accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2675-2680
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• 2010

Computation steps for 3D graphic processing consist of two stages - fixed operation stage and programming required stage. Using this characteristic of 3D pipeline, a hybrid structure between graphics hardware designed by fixed structure and programmable hardware based on instructions, can handle graphic processing more efficiently. In this paper, fragment Shader is designed under this hybrid structure. It also supports OpenGL ES 2.0. Interior interface is optimized to reduce the delay of entire pipeline, which may be occurred by data I/O between the fixed hardware and the Shader. Interior register group of the Shader is designed by an interleaved structure to improve the register space and processing speed.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.161-166
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• 2012

When a car accident happened on a highway, the accident vehicle should broadcast a safe message to its neighbors in order to prevent a chain-reaction collision. Also, there is a problem that the estimation accuracy is low because of the memory limit from increasing the sampling count. In this paper, we proposes a HVC systems using a back-off algorithm applied to a gaussian model. And we proposes a MAC protocol preventing the communication delay by separating the neighbor count collection channel, data channel, and RSU communication channel. As a result, we show the frame reception success rate of our protocol improved about 10% than the previous protocol.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.429-437
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• 2003

Over the last decades, improvements in CPU speed have greatly exceeded those in memory and disk speeds by orders of magnitude and this enabled the use of compression techniques to reduce the database size as well as the query cost. Although compression techniques are employed in various database researches, there is little work on compressing multi-dimensional index structures. In this paper, we propose an efficient compression method called the hybrid encoding method (HEM) that is tailored to multi-dimensional indexing structures. The HEM compression significantly reduces the query cost and the size of multi-dimensional index structures. Through mathematical analyses and extensive experiments, we show that the HEM compression outperforms an existing method in terms of the index size and the query cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1590-1609
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• 2021

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.