• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.10
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• pp.168-176
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• 1996

This paper presetns a 2-D DCT architecture adopting accurac y compensator for reducing the hardware complexity and increasing processing speed in VL\ulcornerSI implementation. In the application fields such as moving pictures experts group (MPEG) and joint photographic experts group (JPEG), 2-D DCT processor must be implemented precisely enough to meet the accuracy specifications of the ITU-T H.261. Almost all of 2-D DCT processors have been implemented using many multiplications and accumulations of matrices and vectors. The number of multiplications and accumulations seriously influence on comlexity and speed of 20D DCT processor. In 2-D DCT with fixed-point calculations, the computation bit width must be sufficiently large for the above accuracy specifications. It makes the reduction of hardware complexity hard. This paper proposes the accuracy compensator which compensates the accuracy of the finite word length calculation. 2-D DCT processor with the proposed accuracy compensator shows fairly reduced hardware complexity and improved processing speed.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.529-532
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• 2002

This research treated development of real time pitch tracer for training of musical tune of speech signal and pre-processing and post-processing technics were proposed to get higher accuracy in extraction of pitch. Autocorrelation Function was used to get pitch frequency from 64Hz to 980Hz in real time. Half Rectifier method and Envelop extraction method as a pre-processing was used to get higher accuracy in pitch detection, and improved results were obtained on noised speech signal. Post-processing method using periodicity of Autocorrelation was proposed to get higher accuracy in the high frequency region.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.12
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• pp.88-96
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• 2015

This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• Journal of the Korean Society of Visualization
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• v.13 no.3
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• pp.15-19
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• 2015

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2679-2691
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• 2012

In this paper, a high accuracy stereo reconstruction method for surgery instruments positioning is proposed. Usually, the problem of surgical instruments reconstruction is considered as a basic task in computer vision to estimate the 3-D position of each marker on a surgery instrument from three pairs of image points. However, the existing methods considered the 3-D reconstruction of the points separately thus ignore the structure information. Meanwhile, the errors from light variation, imaging noise and quantization still affect the reconstruction accuracy. This paper proposes a method which takes the structure information of surgical instruments as constraints, and reconstructs the whole markers on one surgical instrument together. Firstly, we calibrate the instruments before navigation to get the structure parameters. The structure parameters consist of markers' number, distances between each markers and a linearity sign of each instrument. Then, the structure constraints are added to stereo reconstruction. Finally, weighted filter is used to reduce the jitter. Experiments conducted on surgery navigation system showed that our method not only improve accuracy effectively but also reduce the jitter of surgical instrument greatly.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.967-985
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• 2019

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Korean Journal of Geomatics
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• v.2 no.2
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• pp.99-105
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• 2002

The accuracy of the Digital Photologging System, designed for the construction of the road Facility Database, highly depends on the positions and attitudes of the cameras from GPS/INS integration. In this paper, the development of a loosely coupled GPS/INS is presented. The performance of the system is verified through a simulation as well as a real test data processing. Since the IMU used in this study shows large systematic errors, the possible accuracy of the positions and attitudes of this low-performance IMU when combined with precise GPS positions are assigned. Currently, the integrated system shows the positional accuracy better than 5cm in real data processing. Although the accuracy of attitude based on real test could not be assigned at this time, it is expected that better than 0.5 degrees and 1.8 degrees for horizontal and down component are achievable according to the simulation result.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.