• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.93-103
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• 2007

Ship identification systems currently employed provide the underwater sound analysis, analyzed data saving and user interface with comparison function. But final analysis and identification depend only on experts. Therefore, the reliability of these identification systems relies on user's ability on information recognition. This paper presents the method of recognition for the purpose of providing the basic data for an automatic ship class identification. we get the underwater sounds using the PC. We use Matlab in order to reduce ambient noises, take out an acoustic characteristics using the pattern recognition, and classify the ships.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• 재활복지
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• v.17 no.4
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• pp.371-382
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• 2013

Recently, the number of those who were transplanted cochlear implants increased as health insurance increases has expanded. Last six years between 2005 to 2009, patients who received a cochlear implant surgery were about 3,300 and number of cochlear implants in adults of them have shown growing aspects. In the case of young children, they actively participated auditory training program after cochlear implant surgery and the studies related to auditory training in child are many, but the studies related to auditory training in adults is insufficient. In this study, we perform the auditory training for the female adult (age 54) received cochlear implant after language acquisition used Ling 6 sounds test, standardized consonants, vowels and sentences listening test and word recognition and confirmation test. As a result after auditory training for 10 weeks, she identified all phonemes in Ling 6 sound test and performed close to 100% in standardized consonants, vowels and sentences listening tests. Also, she improved the ability of real-world environmental sound and real-world words identifications by 57-95%. The results of this study showed the need of auditory training program with systematic and effective planning and considering the characteristics of the individual for adults.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.395-401
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• 2023

Sound Event Detection (SED) aims to identify not only sound category but also time interval for target sounds in an audio waveform. It is a critical technique in field of acoustic surveillance system and monitoring system. Recently, various models have introduced through Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4. This paper explored how to design optimal parameters of DenseNet based model, which has led to outstanding performance in other recognition system. In experiment, DenseRNN as an SED model consists of DensNet-BC and bi-directional Gated Recurrent Units (GRU). This model is trained with Mean teacher model. With an event-based f-score, evaluation is performed depending on parameters, related to model architecture as well as model training, under the assessment protocol of DCASE task4. Experimental result shows that the performance goes up and has been saturated to near the best. Also, DenseRNN would be trained more effectively without dropout technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• Journal of the Korean Institute of Gas
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• v.21 no.2
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• pp.58-63
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• 2017

Currently, the safety monitoring process in a complex plant environment is proceeded by human. Sometimes, human error that may occur in a filed causes an severe problem. This paper introduces new method of safety monitoring system using sound information and fuzzy theory that is one of intelligent theories, in order to recognize the status of plant environment. In this paper, the filling station of high pressure gas will be used as a test plant. The result system will be widely applied for more complex plant environments.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.99-106
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• 1994

In this paper, we develop an algorithm which recognizes Ilpa-style shorthand characters by on-line. It discriminates the structure of characters using coordinates which are measured by tablet board, then it outputs the recognized characters using the fuzzy inference rules. Shorthand characters have several forms, in which an initial or a middle sound depends on angle and length while a last sound is treated as a hook. We apply fuzzy inference rules to the discrimination of the length, the angle, the curve, and the straight line. We also built up a set of standard character codes in order to reduce the processing time.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.215-220
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• 2005

Nowadays, various speech processing systems are being introduced in the fields of robotics. However, real-time processing and high performances are required to properly implement speech processing system for the autonomous robots. Achieving these goals requires advanced hardware techniques including intelligent software algorithms. For example, we need nonlinear amplifier boards which are able to adjust the compression radio (CR) via computer programming. And the necessity for noise reduction, double-buffering on EPLD (Erasable programmable logic device), simultaneous multi-channel AD conversion, distant sound localization will be explained in this paper. These ideas can be used to improve distant and omni-directional speech recognition. This speech processing system, based on embedded Linux system, is supposed to be mounted on the new home service robot, which is being developed at KIST (Korea Institute of Science and Technology)

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1421-1424
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• 2006

Recently Requirement of indoor environment is increased in APT. Among indoor noises of APT, noise of plumbing system in bathroom is very serious problem except of floor impact noise. Plumbing system with wan hanging unit make a good grade and recognition in rating noise of bathroom in grade of house rating. But it is hard to find a data which are measured in APT built. In this study, the effect of noise reduction is checked by measuring the noise of plumbing system with wall hanging unit that is built. As result the upper household's Peak sound level is measured 80dB(A), the under household's peak sound level is measured 40dB(A).