• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.28-36
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• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.1-8
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• 2006

This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Science of Emotion and Sensibility
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• v.12 no.3
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• pp.341-350
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• 2009

Archer's capability of attention and relaxation control during shooting process was evaluated using EEG technology. Attention and meditation algorithms were used to represent the levels of mental concentration and relaxation levels. Elite, mid-level, and novice archers were tested for short and long distance shootings in the archery field. Single channel EEG was recorded on the forehead (Fp1) during the shooting process, and attention and meditation levels were computed by real time. Four types of variations were defined based on the increasing and decreasing patterns of attention and meditation levels during shooting process. Elite archers showed increases in both attention and relaxation while mid-level archers showed increased attention but decreased relaxation. Elite archers also showed higher levels of attention at the release than mid-level and novice archers. Levels of attention and relaxation and their variation patterns were useful to categorize archers and to provide feedback in training.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.115-120
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• 2011

This paper is concerned with the performance comparison of CR-CMA (Coordinate Reduction-CMA) and CM-CMA (Constellation Matching-Constant Modulus Algorithm) that is used for improving the convergence characteristic and residual intersymbol interference which are used as the performance index for an adaptive equalizer. The equalizer is used to reduce the distortion caused by the intersymbol interference on the wireless and the wired band-limited channel, and the blind method which does not need for extra bandwidth by the training sequence of digital code are researched. Recently, by using the merit of simple operation in the CMA, the performance improvement is obtained by the modifying the cost function of it. In this paper, the new algorithm, CR-CMA and CM-CMA, the performance analysis are performed and compared by computer simulation. The CR-CMA has a superior equalization characteristics in the recovered constellation, convergence speed and residual intersymbol interference than the CM-CMA by computer simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3658-3665
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• 2010

As the population of elderly people and disabled people are increased, various demands for human welfare using robot system are raised. Especially autonomous rehabilitation system using robot could reduce the human effort while maintaining the its intrinsic efficacy. This study deals with mobile gait rehabilitation system which combined with BWS (Body Weight Support) for training of elderly and handicapped people who suffer the muscle force weakness of lower extremity. BWS which is designed by kinematic analysis of body lifting characteristics and walking guide system are integrated with main control system and wheeled platform. This mobile platform is operated by UCS (User Command System) and autonomous trajectory planning algorithm. Finally, through the EMG (Electromyography) signal measuring and its analysis for subject, performance and feasibility of developed system is verified.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.491-496
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• 2004

The paper describes measurement techniques for an acoustic transfer characteristic of ship's bridge stimulated by a whistle sound The response sounds, according to the opening-shutting conditions of bridge doors for Training Ship ‘SAENURI’, are measured by B＆K 2260D equipment, and then the frequency responses are extracted by B＆K 7830 software. To evaluate the measured transfer characteristic, the 128th order FIR (Finite Impulse Response) filters, containing the different frequency characteristics, are constructed based on the frequency sampling-based design method Using evaluation indexes with six scales, psychological assessments by five subjects are carried out with test sounds which are obtained from convolving the source signal with FIR filters. As results of tests, the test sounds gives $A_S$ 3.3∼4.7 which means the psychological sense of ‘it is almost similar sound as original ones in a real world’, and thus it is clearly seen that the proposed method can be used for the measurement of an acoustic transfer characteristic of ship’s bridge.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.153-161
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• 2007

Stroke is the second most significant disease leading to death in Korea. The conventional therapeutic approach is mainly based on physical training, however, it usually provides the limited degree of recovery of the normal brain function. The electric stimulation therapy is a novel and candidate approach with high potential for stroke recovery. The feasibility was validated by preliminary rat experiments in which the motor function was recovered up to 80% of the normal performance level. It is thought to improve the neural plasticity of the nerve tissues around the diseased area in the stroked brain. However, there are not so much research achievements in the electric stimulation for stroke recovery as for the Parkinson's disease or Epilepsy. This study aims at the developments of a wireless variable pulse generator using ZigBee communication for future implantation into human brain. ZigBee is widely used in wireless personal area network (WPAN) and home network applications due to its low power consumption and simplicity. The developed wireless pulse generator controlled by ZigBee can generate various electric stimulations without any distortion. The electric stimulation includes monophasic and biphasic pulse with the variation of shape parameters, which can affect the level of recovery. The developed system can be used for the telerehabilitation of stroke patient by remote control of brain stimulation via ZigBee and internet. Furthermore, the ZigBee connection used in this study provides the potential neural signal transmission method for the Brain-Machine Interface (BMI).