• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.9-16
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• 2003

We proposed a method of a virtual robot arm controlled by the EMG pattern recognition using an improved SOFM method. The proposed method is simple in that the EMG signals are used as SOFM's input directly without preprocessing but nevertheless input patterns are reliably classified and then used for fuzzy logic systems to automatically tune the neighborhood and the learning rate. In order to verify the effectiveness of the proposed method, we experimented on EMG pattern recognition of 6 movements from the shoulder, wrist, and elbow. Experimental results show that the proposed SOFM method has 21.7% higher recognition rate than the general SOFM method, the average number of learning iterations has been decreased, and then the virtual robot arm is controlled by EMG pattern recognition.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.521-525
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• 2009

In this paper, we introduce fire detection system and software learning algorithm that recognize fire patterns. Flame patterns means that periodical and consistent pattern about general conception of fire, and to process it with the definition. Learning algorithm for flame pattern recognition that we propose is the method which is faster and more exactly than existing algorithm. Also, we trying to elicit the method through experiment result and by applying it, we show the validity of an early fire warning system.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.207-209
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• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.115-123
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• 2015

The incremental learning method performs well in face face tracking. However, it has a drawback in that it is sensitive to the tracking error in the previous frame due to the environmental changes. In this paper, we propose a selective incremental learning method to track a face more reliably under various conditions. The proposed method is robust to illumination variation by using the LBP(Local Binary Pattern) features for each individual frame. We select patches to be used in incremental learning by using Staggered Multi-Scale LBP, which prevents the propagation of tracking errors occurred in the previous frame. The experimental results show that the proposed method improves the face tracking performance on the videos with environmental changes such as illumination variation.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Journal of Digital Convergence
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• v.12 no.3
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• pp.383-390
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• 2014

In recent years, the convergence of IT and IT sightseeing tour has emerged as a fusion of academic disciplines in the future. Convergence study of social data analysis, raising the heat. Social Network Services (SNS) being utilized in many areas of marketing and to apply the case study is also increasing. This study is based u-smart tourist information systems for mobile learning content design. This is the pattern of things in the template library for things to increase the effectiveness of the learning content to mobile learning content to be converted to a. Design of mobile learning content using u-smart things smart phone app (App) and XMI to go through the design process of utilizing the heat. Future through the design process by implementing a mobile learning content to meet information quality tourist information content to create mobile learning content and learning things that can be content to live it up advantage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.2
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• pp.128-136
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• 1991

A neural network of multi layer perception type, learned by error back propagation learning rule, is generally used for the verification or clustering of similar type of patterns. When learning is completed, the network has a constant value of output depending on a pattern. This paper shows that the intensity of neuron's out put can be controlled by a function which intensifies the excitatory interconnection coefficients or the inhibitory one between neurons in output layer and those in hidden layer. In this paper the value of factor in the function to control the output is derived from the know values of the neural network after learning is completed And also this paper show that the amount of an increased neuron's output in output layer by arbitary value of the factor is derived. For the applications increased recognition performance of a pattern than has distortion is introduced and the output of partially damaged neurons are first managed and this paper shows that the reduced recognition performance can be recovered.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.175-181
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• 2005

This paper describes the development of on-line computer vision laboratories to teach the detailed image processing and pattern recognition techniques. The computer vision laboratories include distant image acquisition method, basic image processing and pattern recognition methods, lens and light, and communication. This study introduces a case study that teaches computer vision in distance learning environment. It shows a schematic of a distant loaming workstation and contents of laboratories with image processing examples. The study focus more on the contents of the vision Labs rather than internet application method. The study proposes the ways to improve the on-line computer vision laboratories and includes the further research perspectives