• Proceedings of the KIEE Conference
• /
• 1998.07f
• /
• pp.2051-2053
• /
• 1998

In spite of nonlinear or step-changing load and line disturbances, the inverter for the UPS must provide the pure sinusoidal output voltage with low THD(Total Harmonics Distortion). This paper proposes an inverter controller for the UPS which has a good dynamic response characteristic and robustness for applying industrial world directly. The inverter output voltage is controlled instantaneously with a double regulation loop by a TMS320C31 Digital Signal Processor so that it has very good dynamic response for nonlinear or step-changing load and line disturbances. To improve the voltage utilization and response characteristics, the Space Vector Modulation(SVM) technique is adapted for the switching method of this system. The characteristics of the proposed control system were verified by simulations and experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.27 no.2
• /
• pp.189-194
• /
• 2017

An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1287-1292
• /
• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• Proceedings of the KIPE Conference
• /
• 2002.07a
• /
• pp.308-312
• /
• 2002

In this paper, EPLD(Erasable Programmable Logic Device) based induction motor drives with a SRP-PWM(Separatley Randomized Pulse Position PWM) is proposed. In the proposed RPWM (Random PWM), each of three phase pulses is located randomly in each switching interval. Based on the space vector modulation technique, the duty ratio of the pulses is calculated. To verify the validity of the proposed RPWM, the experimental study was tried. Along with the randomization of PWM pulses, the space vector modulation is also executed in the TMS320C31 DSP(Digital Signal Processor). The experimental results show that the voltage and switching noise harmonics are spread to a wide band area. Also, the performance of the proposed SRP-PWM and the conventional SVM-PWM are nearly the same from the viewpoing of the v/f constant control.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.638-642
• /
• 2005

This paper proposes a modified space-vector pulse width modulation (PWM) strategy which can restrict the common-mode voltage for three-phase to three-phase matrix converter and still keep sinusoidal input and output waveforms and unity power factor at the input side. The proposed control method has been developed based on contributing the appropriate space vectors instead of using zero space vectors. The advantages of this proposed method is to reduce the peak value of common-mode voltage to 42% beside the lower high harmonic components as compared to the conventional SVM method. Hence, the new table is also presented with the new space vector rearrangement. Furthermore, the voltage transfer ratio is unaffected by the proposed method. A simulation of the overall system has been carried out to validate the advantages of the proposed method.

• Journal of Sensor Science and Technology
• /
• v.29 no.3
• /
• pp.194-200
• /
• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1530-1537
• /
• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.585-588
• /
• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.667-675
• /
• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Structural Monitoring and Maintenance
• /
• v.9 no.2
• /
• pp.179-200
• /
• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.