• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.302-305
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• 1997

Treeing due to partial discharge(PD) is one of the main causes of breakdown of the insulating materials and reduction of tile insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation. using statically operator such as the center of gravity (G). the gradient of the discharge distribution(C), we have analyzed far tole prediction of life which we can be obtained the time, occurred of many pulse of small discharge amplitude.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.53-63
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• 2016

We consider simulation study combining static and mobile decoys for survivability of a surface warship against torpedo attack. It is assumed that an enemy torpedo is a passive acoustic homing torpedo and detects a target within its maximum target detection range and search beam angle by computing signal excess via passive sonar equation, and a warship conducts an evasive maneuvering with deploying static and mobile decoys simultaneously to counteract a torpedo attack. Suggesting the four different decoy deployment plans to achieve the best plan, we analyze an effectiveness for a warship's survival probability through Monte Carlo simulation, given a certain experimental environment. Furthermore, changing the speed and the source level of decoys, the maximum torpedo detection range of warship, and the maximum target detection range of torpedo, we observe the corresponding survival probabilities, which can provide the operational capabilities of an underwater defense system.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.572-577
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• 2009

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. To prune the redundant speech segments in a large speech segment DB, we can utilize a decision-tree based triphone clustering algorithm widely used in speech recognition area. But, the conventional methods have problems in representing the acoustic transitional characteristics of the phones and in applying context questions with hierarchic priority. In this paper, we propose a new clustering algorithm to downsize the speech DB. Firstly, three 13th order MFCC vectors from first, medial, and final frame of a phone are combined into a 39 dimensional vector to represent the transitional characteristics of a phone. And then the hierarchically grouped three question sets are used to construct the triphone trees. For the performance test, we used DTW algorithm to calculate the acoustic similarity between the target triphone and the triphone from the tree search result. Experimental results show that the proposed method can reduce the size of speech DB by 23% and select better phones with higher acoustic similarity. Therefore the proposed method can be applied to make a small sized TTS.

• Journal of Pharmacopuncture
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• v.17 no.3
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• pp.57-61
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• 2014

Objectives: When a person speaks, voice problems usually include pain or discomfort and/or difficulties in terms of the pitch, the loudness and the quality of the voice. When patients with voice problems induced by stroke, Parkinson's disease, and systemic diseases involving the voice are examined, generally, of the Four Diagnoses (四診), a Diagnosis of Hearing can be used in current Korean medicine. The effects of acupuncture and herb medicine on voice problems have been reported for over 20 years. However, when it comes to improvements, objective and subjective evaluation methods need to be explained. Methods: Subjective methods for evaluating voice were studied through a literature search of old medicinal books containing Korean medicine diagnostics, and an objective evaluation method using Praat software is presented. Results: Korean medicine doctors analyze the patient's voice in clinical settings unconsciously on a daily basis. However, most voice diagnoses depend on the doctor's subjective evaluation. Voice qualities can be evaluated by using the Eight Principles (八綱), including Yin-Yang; the Five Elements (Phases); the Grade, Roughness, Breathy, Asthenic, Strained (GRBAS) score, and the Visual Analogue Scale (VAS) as subjective methods, and an acoustic analysis using the Praat program can be used as an objective method. Conclusion: A more complete voice examination can be achieved by using subjective and objective methods at the same time. For an objective explanation and management of patient's voice problems or systemic disorders, an objective method should be used in Korean medicine, which already has many subjective diagnostic methods. More research needs to be conducted, and more clinical evidence needs to be collected in the future.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.6
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• pp.766-772
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• 2003

The pseudospectral method for stability analysis was used to find the most influential disturbance mode for transition of plane channel flows and Blasius flow at their critical Reynolds numbers. A number of various oblique disturbance waves were investigated for their pseudospectra and resolvent norm contours in each flow, and an exhaustive search method was employed to find the disturbing waves to which the flows become most unstable. In plane Poiseuille flow an oblique disturbance with a wavelength of 3.59h (where h is the half channel width) at an angle $28.7^{\circ}$ was found to be the most influential for the flow transition to turbulence, and in plane Couette flow it is an oblique wave with a wavelength of 3.49h at an angle of $19.4^{\circ}$. But in Blasius flow it was found that the most influential mode is a normal wave with a wavelength of $3.44{\delta}_{999}$. These results imply that the most influential disturbance mode is closely related to the fundamental acoustic wave with a certain shear sheltering in the respective flow geometry.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.1
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• pp.5-26
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• 2013

Since the optimized use of sonar systems for target tracking is a practical problem for naval operations, the measure of mission achievability is needed for preparing efficient sonar-maneuver tactic. In order to quantify the mission achievability or Measure Of Effectiveness(MOE) for given sonar-maneuver tactics, we developed and tested a simulation algorithm. The proposed algorithm for tracking is based on Measure Of Performance(MOP) for localization and tracking system of sonar against target. Probability of Detection(PD) using steering beam patterns referenced to the aspect angle of sonar is presented to consider the tracking-performance of sonar. Also, the integrated software package, named as Optimal Acoustic Search Path Planning(OASPP) is used for generating sonar-maneuver patterns and vulnerability analysis for a given scenario. Through simulation of a simple case for which the intuitive solution is known, the proposed algorithm is verified.

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.498-504
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• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.

• 재활복지
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• v.18 no.4
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• pp.237-255
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• 2014

Idiopathic Parkinson disease(IPD) is an neurodegenerative disease caused by the loss of dopamine cells in the substantia nigra, a region of midbrain. Its major symptoms are muscular rigidity, bradykinesia, resting tremor, and postural instability. An estimated 70~90% of patients with IPD also have hypokinetic dysarthria. Subthalamic nucleus deep brain stimulation (STN-DBS) has been reported to be successful in relieving the core motor symptoms of IPD in the advanced stages of the disease. However, data on the effects of STN-DBS on speech performance are inconsistent. A medline literature search was done to retrieve articles published from 1987 to 2012. The results were narrowed down to focus on speech performance under STN-DBS based perceptual, acoustic, and/or aerodynamic analyses. Among the 32 publications which dealt with speech performance after STN-DBS indicated improvement(42%), deterioration(29%), mixed results(26%), or no change(3%). The most favorite method was found to be based upon acoustic analysis by using a vowel prolongation and Unified Parkinson's Disease Rating Scale(UPDRS). For the purpose of verifying the effect of the STN-DBS, speech evaluation should be undertaken on all speech components such as articulation, resonance, phonation, respiration, and prosody by using a contextual speech task.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.