• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.101-113
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• 2015

The Monte-Carlo (MC) technique is very efficient solution for statistical problem. Various MC methods can easily be applied to statistical circuit performance analysis. Recently, as the number of process parameters and their impact, has increasingly affected circuit performance, a sufficient sample size is required in order to consider high dimensionality, profound nonlinearity, and stringent accuracy requirements. Also, it is important to identify the performance of circuit as soon as possible. In this paper, Fast MC method is proposed for efficient analysis of circuit performance. The proposed method analyzes performance using enhanced-precision Latin Hypercube Sampling Monte Carlo (LHSMC). To increase the accuracy of the analysis, we calculate the effective dimension for the low discrepancy value on critical parameters. This will guarantee a robust input vector for the critical parameters. Using a 90nm process parameter and OP-AMP, we verified the accuracy and reliability of the proposed method in comparison with the standard MC, LHS and Quasi Monte Carlo (QMC).

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.45-51
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• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.235-240
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• 2006

The paper will give an overview of the mission of GalTeC and then concentrate on two main aspects. The first more detailed aspect, is the analysis of the key performance parameters for the Galileo system services and presenting a technical overview of methods and algorithms used. The second more detailed aspect, is the service volume prediction including service dimensioning using the Prediction tool. In order to monitor and validate the Galileo SIS performance for Open Service (OS) and Safety Of Life services (SOL) regarding the key performance parameters, different analyses in the SIS domain and User domain are considered. In the SIS domain, the validation of Signal-in-Space Accuracy SISA and Signal-in-Space Monitoring Accuracy SISMA is performed. For this purpose first of all an independent OD&TS and Integrity determination and processing software is developed to generate the key reference performance parameters named as SISRE (Signal In Space Reference Errors) and related over-bounding statistical information SISRA (Signal In Space Reference Accuracy) based on raw measurements from independent sites (e.g. IGS), Galileo Ground Sensor Stations (GSS) or an own regional monitoring network. Secondly, the differences of orbits and satellite clock corrections between Galileo broadcast ephemeris and the precise reference ephemeris generated by GalTeC will also be compared to check the SIS accuracy. Thirdly, in the user domain, SIS based navigation solution PVT on reference sites using Galileo broadcast ephemeris and the precise ephemeris generated by GalTeC are also used to check key performance parameters. In order to demonstrate the GalTeC performance and the methods mentioned above, the paper presents an initial test result using GPS raw data and GPS broadcast ephemeris. In the tests, some Galileo typical performance parameters are used for GPS system. For example, the maximum URA for one day for one GPS satellite from GPS broadcast ephemeris is used as substitution of SISA to check GPS ephemeris accuracy. Using GalTeC OD&TS and GPS raw data from IGS reference sites, a 10 cm-level of precise orbit determination can be reached. Based on these precise GPS orbits from GalTeC, monitoring and validation of GPS performance can be achieved with a high confidence level. It can be concluded that one of the GalTeC missions is to provide the capability to assess Galileo and general GNSS performance and prediction methods based on a regional and global monitoring networks. Some capability, of which first results are shown in the paper, will be demonstrated further during the planned Galileo IOV phase, the Full Galileo constellation phase and for the different services particularly the Open Services and the Safety Of Life services based on the Galileo Integrity concept.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.2
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• pp.184-189
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• 2013

This paper proposes a method for improving the performance of a position sensorless control system for a slotless surface permanent magnet synchronous motor (SPMSM) in a very low-speed region. In position sensorless control based on a motor model, accurate motor parameters are required because parameter errors would affect position estimation accuracy. Therefore, online parameter identification is applied in the proposed system. The error between the reference voltage and the voltage applied to the motor is also affect position estimation accuracy and stability, thus it is compensated to ensure accuracy and stability of the sensorless control system. In this study, two voltage error compensation methods are used, and the effects of the compensation methods are discussed. The performance of the proposed sensorless control method is evaluated by experimental results.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.11a
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• pp.181-186
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• 1997

Transcoding is the key technique to further reduce the bit-rate of a previously compressed video. The performance of the transcoding is evaluated by the two factors, the accuracy on the target bit-rate and the complexity of the implementation. In this paper, were propose a new rate control algorithm which has very accurate bit-rate control performance and much smaller computational complexity. For the accuracy problem, we empirically observe the relationship between the quantization step size and generated bits in requantization process and then find that the relationship can be characterized as the new piece-wise linear model. For the complexity problem, we reduce the role of feedback rate control. The simulation results show that the proposed method gives the better performance in the accuracy with the same picture quality than conventional rat control algorithm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.164-169
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• 2000

Recently, High accuracy and precision are required in various industrial field especially, semiconductor manufacturing apparatus, Ultra precision positioning apparatus, Information field and so on. Positioning technology is a very important one among them. As such technology has been rapidly developed, this field needs the positioning accuracy as high as submicron. It is expected that the accuracy of 10nm and 1nm is required in precision work and ultra precision work field, respectively by the beginning of 2000s. High speed and low vibration are also needed. This work deals with the design method and control system of Ultra precision positioning apparatus. Control performance and stability analysis were performed in advance by modeling and designing the controller with Simulink.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.617-620
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• 2000

Recently, High accuracy and precision are required in various industrial field especially, semiconductor manufacturing apparatus, Ultra precision positioning apparatus, Information field and so on. Positioning technology is a very important one among them. Is such technology has been rapidly developed, this field needs the positioning accuracy as high as submicron. It is expected that the accuracy of 10nm and 1nm is required in precision work and ultra precision work field, respectively by the beginning of 2000s. High speed and low vibration are also needed. This work deals with the design method and control system of Ultra precision positioning apparatus. We will examine the control performance and stability before manufacturing the real apparatus by using MATLAB SIMULINK based or pre-designed controller and system modeling.