• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.293-300
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• 2014

This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. Since an insulation defect, such as one resulting from PD, would have a corresponding particular pattern, pattern recognition of PD is significant means to discriminate insulation conditions of high-voltage electrical apparatus. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of cast resin current transformer (CRCT) models. These tests used artificial defects created in order to produce the common PD activities of CRCTs by using feature vectors of field-test PD patterns. The significant features are extracted by using nonlinear principal component analysis (NLPCA) method. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach is efficient and reliable.

• 한국IT서비스학회지
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• 제7권2호
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• pp.149-164
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• 2008

As the computing landscape is shifting to ubiquitous computing environments, there is increasingly growing the demand for a variety of device controls that react to user's implicit activities without excessively drawing user attentions. We developed an EMG-based car interface that enables the physically handicapped to drive a car using their functioning peripheral nerves. Our method extracts electromyogram signals caused by wrist movements from four places in the user's forearm and then infers the user's intent from the signals using multi-layered neural nets. By doing so, it makes it possible for the user to control the operation of car equipments and thus to drive the car. It also allows the user to enter inputs into the embedded computer through a user interface like an instrument LCD panel. We validated the effectiveness of our method through experimental use in a car built with the EMG-based interface.

• International Journal of Ocean System Engineering
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• 제3권3호
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• 대한의용생체공학회:의공학회지
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• 제39권3호
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• pp.124-133
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• 2018

We tried to investigate the changes in cortical activities according to emotional valence states during watching video clips. We examined the neural basis of two emotional states (positive and negative) using spectral power analysis and brain functional connectivity analysis of cortical current density time-series reconstructed from high-density electroencephalograms (EEGs). Fifteen healthy participants viewed a series of thirty-two 2 min emotional video clips. Sixty-four channel EEGs were recorded. Distributed cortical sources were reconstructed using weighted minimum norm estimation. The temporal and spatial characteristics of spectral source powers showing significant differences between positive and negative emotion were examined. Also, correlations between gamma-band activities and affective valence ratings were determined. We observed the changes of cortical current density time-series according to emotional states modulated by video clip. Gamma-band activities showed significant difference between emotional states for thirty seconds at the middle and the latter half of the video clip, mainly in prefrontal area. It was also significantly anti-correlated with the self-ratings of emotional valence. In addition, the gamma-band activities in frontal and temporal areas were strongly phase-synchronized, more strongly for negative emotional states. Cortical activities in frontal and temporal areas showed high spectral power and inter-regional phase synchronization in gamma-band during negative emotional states. It is inferred that the higher amygdala activation induced by negative stimuli resulted in strong emotional effects and caused strong local and global synchronization of neural activities in gamma-band in frontal and temporal areas.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2000년도 제31회 춘계학술대회
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• pp.68-75
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• 2000

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining system. A major topic relevant to metal-cutting operations is monitoring tool wear, which affects process efficiency and product quality, and implementing automatic tool replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. Cutting force components are divided into static and dynamic components in this paper, and the static components of cutting force have been used to detect flank wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the force modeling is performed for various cutting conditions. The normalized force disparities are defined in this paper, and the relationships between normalized disparity and flank wear are established. Finally, Artificial neural network is used to learn these relationships and detect tool wear. According to the proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 말소리와 음성과학
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• 제3권2호
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• pp.23-28
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• 2011

Electromagnetometers (EMA) have been widely used in articulatory studies as their temporal resolution can capture most speech activities and the fleshpoint information allows one to readily quantify and analyze tongue shape. However, the drawback is that the data lacks details of activity in the pharyngeal region. Several studies have attempted to estimate the unknown pharyngeal shape of the tongue, but few studies are based on unimodal data containing both front and back regions of the tongue at the same time. We use Stone's ball bearing method to obtain fleshpoint data as well as tongue shape. We further introduce a novel way of connecting balls and attaching them onto the tongue to ensure accurate tracking. An Artificial Neural Network is applied to build a map between observable flesh-points, unknown tongue shape, and pharyngeal region and is optimized to efficiently address nonlinearity.

• International Journal of Contents
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• 제6권1호
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• pp.53-58
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• 2010

This study details the neuroscientific concept of somatosensation, general sensory stimulation therapy and virtual reality therapy. Somatosensation is a method that the human body uses to accept information from the inner and outer parts of the body. A traditional sensory stimulation therapy was designed to maximize neural recovery, but the neural recovery is most effective when the therapeutic environment is similar to real life. The virtual reality provides natural environment that users may perceive as meaningful and even participants with significant impairment can perform some of the activities of their daily lives within the virtual environment. The virtual reality will become a complementary part of somatosensory rehabilitation.

• Asia pacific journal of information systems
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• 제11권4호
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• pp.175-186
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• 2001

Though people frequently rely on intuition in managing activities, they rarely use it in developing effective decision-making support systems. In this report, we investigate the correlations between characteristics of cognition and emotion and judgmental time-series forecasting accuracy, and compare their strengths by using a self-supervised adaptive neural network. Through the experiments, we hope to help find a desirable atmosphere for decision-making. Our experiments showed that both cognition characteristics and emotion characteristics had correlations with the time-series forecasting accuracy, and that cognition characteristics had larger correlation than emotion characteristics. We also found that conceptual style had larger correlation than behavioral or analytical styles with the accuracy.