• 한국음향학회지
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• 제42권2호
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• pp.102-111
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• 2023

자동 음악 채보는 주어진 오디오에서 음표 정보를 추출하는 태스크로, 이 연구에서는 피아노 음악의 자동음악 채보 모델에서 지연 시간을 줄이는 방법을 소개한다. 신경망 기반 채보 모델이 피아노 채보에도 적용되어 높은 정확도를 기록하였고 이를 이용한 실시간 구현도 소개된 바 있지만, 채보를 위한 지연 시간이 길어 인터랙티브 시나리오에서 활용하기에 한계가 있었다. 이 문제를 해결하기 위해 본 연구는 Fast Fourier Transformation(FFT)에서 윈도우 크기와 홉 크기를 줄이거나 합성곱 레이어의 커널 크기를 수정하고 시간 축에서 레이블을 이동하여 모델이 시작을 더 일찍 예측하도록 훈련하는 등 피아노 전사를 위한 신경망의 내재적 지연 시간을 줄이는 몇 가지 기술을 제안한다. 실험 결과, 이러한 접근 방식을 결합하면 높은 전사 정확도를 유지하면서 지연 시간을 줄일 수 있음을 알 수 있었다. 기존 모델은 160 ms의 지연 시간을 가지고 음표 F1 점수는 93.43 %였으나 제안한 방법을 적용하면 96 ms와 64 ms의 지연 시간 동안 각각 92.67 %와 90.51 %의 F1 점수를 달성할 수 있었다. 이러한 결과는 향후 피아노 교육을 위한 실시간 피드백 제공 등 다양한 인터랙티브 시나리오를 위한 자동 채보 모델에 활용될 수 있을 것이다.

• 대한의용생체공학회:의공학회지
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• 제36권4호
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• pp.95-102
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• 2015

Epilepsy is a chronic neurological disease which produces repeated seizures. Over 30% of epileptic patients cannot be treated with anti-epileptic drugs, and surgical resection may cause loss of brain functions. Seizure suppression by electrical stimulation is currently being investigated as a new treatment method as clinical evidence has shown that electrical stimulation to brain could suppress seizure activity. In this paper, design of a real-time closed-loop neurostimulation system for epileptic seizure suppression is presented. The system records neural signals, detects seizures and delivers electrical stimulation. The system consists of a 6-channel electrode, front-end amplifiers, a data acquisition board by National Instruments, and a neurostimulator and Generic Osorio-Frei algorithm was applied for seizure detection. The algorithm was verified through simulation using electroencephalogram data, and the operation of whole system was verified through simulation and in- vivo test.

• The Korean Journal of Physiology and Pharmacology
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• 제11권6호
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• pp.247-251
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• 2007

Changes of single unit activity of CA1 hippocampus region were investigated in anesthetized Mongolian gerbils for six days following transient ischemia. Ischemia was produced immediately before the implantation of micro-wire recording electrodes. In control animals receiving pseudo-ischemic surgery, neither spontaneous neuronal activities ($5.70{\pm}0.4Hz$) nor the number of recorded neurons per animal changed significantly for six days. Correlative firings among simultaneously recorded neurons were weak (correlation coefficient > 0.6) in the control animals. Animals subjected to ischemia exhibited a significant elevation of neural firing at post-ischemic 12 hr ($9.95{\pm}0.9Hz$) and day 1 ($8.48{\pm}0.8Hz$), but a significant depression of activity at post-ischemic day 6 ($1.84{\pm}0.3Hz$) when compared to the activities of non-ischemic control animal. Ischemia significantly (correlation coefficient > 0.6) increased correlative firings among simultaneously recorded neurons, which were prominent especially during post-ischemic days 1, 2 and 6. Although the numbers of spontaneously active neurons recorded from control group varied within normal range during the experimental period, those from ischemic group changed in post-ischemic time-dependent manner. Temporal changes of the number of cells recorded per animal between control group and ischemic group were also significantly different (p = 0.0084, t = 3.271, df = 10). Cresyl violet staining indicated significant loss of CA1 cells at post-ischemic day 7. Overall, we showed post-ischemic time-dependent, differential changes of three characteristics, including spontaneous activity, network relationship and excitability of CA1 cells, suggesting sustained neural functions. Thus, histological observation of CA1 cell death till post-ischemic day 7 may not represent actual neuronal death.

• 대한건축학회논문집:계획계
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• 제34권12호
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• 사상체질의학회지
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• 제33권4호
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• 대한의용생체공학회:의공학회지
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• 제44권2호
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• 한국인공지능학회지
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• 제11권4호
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• 한국정보전자통신기술학회논문지
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• 제17권1호
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• pp.38-46
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• 2024

일반적으로 EEG 신호 분석은 의료 진단 및 재활 공학에 적용하여 뇌-컴퓨터 인터페이스 연구에 널리 사용되는 뇌 자극을 기록하는 객관적인 모드를 제공할 수 있는 능력 때문에 여러 연구의 주제가 되어 왔습니다. 본 연구에서는 뇌전도 측정하기 위한 뇌파 수신 하드웨어 개발 및 처리 시스템 구현을 통해 서버와 데이터 처리로 분류하여 개발을 진행하였다. 뇌전도를 이용한 뇌-컴퓨터 인터페이스 구현의 중간단계 연구로 진행되었으며, 측정된 뇌전도 데이터에 따라 사용자의 팔의 움직임을 예측하는 형태로 구현되었다. 네 개의 전극으로부터의 입력을 아날로그-디지털 변환기를 통해 뇌전도 측정을 수행하였다. 이를 통신 과정을 거쳐 서버에 전송한 뒤, 서버에서 합성곱 신경망 모델로 뇌전도 입력을 분류하여 그 결과를 사용자 단말로 표시하는 시스템의 흐름을 설계하고 구현하였다.

• Journal of Ginseng Research
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• 제24권3호
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• pp.134-137
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• 2000

Spontaneous bursting activity was studied in rat thalamocortical slices using extracellular field potential recording to test the potential utilization of ginsenoside Rb$_1$ in controlling overactivated neural systems. In order to induce bursting activity, slices were perfused with Mg$\^$2+/-free artificial cerebrospinal fluid (ACSF). Two major types of spontaneous bursting activity, simple thalamocortical burst complexes (sTBCs) and complex thalamocortical burst complexes (cTBCs), were recorded in Mg$\^$2+/ -free ACSF. Ginsenoside Rb$_1$ selectively suppressed cTBCs. Duration and occurrence rate of cTBCs were reduced by 87.3${\pm}$10.2% and 85.3${\pm}$ 14.7% in the presence of 90 ${\mu}$M ginsenoside Rb$_1$ respectively, while amplitude and intraburst frequency were slightly changed by ginsenoside Rb$_1$. In contrast, ginsenoside Rb$_1$was much less effective in reducing duration and occurrence rate of sTBCs. We also tested effects of ginsenoside Rb$_1$ on bursting activity in the presence of a GABA$\sub$A/ receptor antagonist, bicuculline methiodide (BMI). Ginsenoside Rb$_1$ had no effect in suppressing BMI-induced bursting activities. These results suggest that ginsenoside Rbi may be useful in controlling seizure-like bursting activity under pathological conditions.

• Journal of Advanced Marine Engineering and Technology
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• 제36권5호
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• pp.645-661
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• 2012

Recently, pirates are infesting on the sea and they have been hijacking the several vessels for example Samho Dream and Samho Jewelry of Korea. One of the items to reduce the risk is to adopt the invader detection system. If the pirates break in to the ship, the detection system can monitor the pirates and then call the security alarm. The crew can gain time to hide to the safe room and the report can be automatically sent to the control room to cope with the situation. For the invader detection, an unmanned observation system was proposed using the image detection algorithm that extracts the invader image from the recording image. To detect the motion area, the difference value was calculated between the current image and the prior image of the invader, and the 'AND' operator was used in calculated image and edge line. The image noise was reduced based on the morphology operation and then the image was transformed into morphological information. Finally, a neural network model was applied to recognize the invader. In the experimental results, it was confirmed that the proposed approach can improve the performance of the recognition in the invader monitoring system.