• Science of Emotion and Sensibility
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• v.2 no.2
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• pp.37-42
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• 1999

Long-term exposure to loud noise affects performance since it changes arousal level, distracts attention, and also is able to evoke subjective stress accompanied by negative emotional states. The purpose of the study was to analyze dynamics of subjective and physiological variables during a relatively long-lasting (30 min) exposure to white noise (85 dB[A]). Physiological signals were recorded on 15 college students during 30 min of intense auditory stimulation. Autonomic variables, namely skin conductance level , non-specific SCR number, inter-best intervals in ECG, heart rate variability index (HF/LF ratio of HRV), skin temperature, as well as respiration rate were analyzed on 5 min epoch basis. Psychological assessment (subjective rating of stress level) was also repeated every 5 min. Statistical analysis was employed to trace the time course of the dynamics of subjective and autonomic physiological variables and their relationships. Results showed that the intense noise evoked subjective stress as well as associated autonomic nervous system responses. However it was shown that physiological variables endured specific changes in the process of exposure to the loud white noise. Discussed were probable psychophysiological mechanisms mediating reactivity to long-term auditory stimulation of high intensity, namely short-term activation, followed by transient adaptation (with relatively stable autonomic balance) and then a subsequent wave of arousal due to tonic sympathetic dominance.

• Journal of Audiology & Otology
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• v.25 no.1
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• pp.22-26
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• 2021

Background and Objectives: Music listening has a concomitant effect on structural and functional organization of the brain. It helps in relaxation, mind training and neural strengthening. In relation to it, the present study was aimed to find the effect of Carnatic music listening training (MLT) on speech in noise performance in adults. Subjects and Methods: A total of 28 participants (40-70 years) were recruited in the study. Based on randomized control trial, they were divided into intervention and control group. Intervention group underwent a short-term MLT. Quick Speech-in-Noise in Kannada was used as an outcome measure. Results: Results were analysed using mixed method analysis of variance (ANOVA) and repeated measures ANOVA. There was a significant difference between intervention and control group post MLT. The results of the second continuum revealed no statistically significant difference between post training and follow-up scores in both the groups. Conclusions: In conclusion short-term MLT resulted in betterment of speech in noise performance. MLT can be hence used as a viable tool in formal auditory training for better prognosis.

• Korean Journal of Audiology
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• v.25 no.1
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• pp.22-26
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• 2021

Background and Objectives: Music listening has a concomitant effect on structural and functional organization of the brain. It helps in relaxation, mind training and neural strengthening. In relation to it, the present study was aimed to find the effect of Carnatic music listening training (MLT) on speech in noise performance in adults. Subjects and Methods: A total of 28 participants (40-70 years) were recruited in the study. Based on randomized control trial, they were divided into intervention and control group. Intervention group underwent a short-term MLT. Quick Speech-in-Noise in Kannada was used as an outcome measure. Results: Results were analysed using mixed method analysis of variance (ANOVA) and repeated measures ANOVA. There was a significant difference between intervention and control group post MLT. The results of the second continuum revealed no statistically significant difference between post training and follow-up scores in both the groups. Conclusions: In conclusion short-term MLT resulted in betterment of speech in noise performance. MLT can be hence used as a viable tool in formal auditory training for better prognosis.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.348-354
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• 1993

In this research, a multi-tree coder at 9.6Kbits/sec using a novel scheme for adaptation of the short-term coefficients is developed. The overall delay of the tree coder is maintained at 2.5 msec(16 samples at the 6.4KHz sampling frequency). This coder produces good quality speech over ideal channels, and it is very robust to channel errors up to a bit error rate (BER) of $10^{-3}$. This robustness is achieved by using a parallel adaptation scheme in combination with the use of a smoothed version of the received excitation sequence for adaptation of the short-term prediction coefficients. For the multi-tree coder, reconstructed output speech is evaluated using signal-to-quantization noise ratios (SNR), segmental SNRs, and informal listening tests.

• Journal of Oral Medicine and Pain
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• v.35 no.1
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• pp.49-59
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• 2010

The purpose of this study was to investigate short-term masticatory muscle reactions in response to simulated noise and music sound. Hypothesis of this study was that loud noise would cause increased stiffness and decreased elasticity of the masticatory muscles compared to low level of noise or identical sound level of music. Fifteen male volunteers were recruited for the study. The sound levels of noise and music used here were 60 dB and 100 dB. The experiment comprised 4 sessions, Session 1 with 100 dB of noise for the 1st day of experiment: Session 2 with 100 dB of music for the $2^{nd}$ day: Session 3 with 60 dB of noise for the $3^{rd}$ day: Session 4 with 60 dB of music for the $4^{th}$ day. Stiffness and elasticity on the anterior temporalis and superficial masseter muscles were measured with tactile sensor before and 2, 4 and 6 minutes after exposure of sound. The study indicated that, in short-term exposure of sound, there was no significant difference between noise and music at both 60 and 100 dB of sound level, but that there were partially significant differences between 60 and 100 dB of sound level regardless of sound type. This suggest that high level of sounds like 100 dB used in this study, in spite of short term exposure of several minutes, would lead to masticatory muscle contraction, especially in the masseter muscles.

• Korean Journal of Optics and Photonics
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• v.4 no.4
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• pp.487-492
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• 1993

A polarization maintaining fiber optic gyroscope was constructed having an optical loss of about 25 dB. Using the earth rotation rate as a reference, the long term stability of better than $3^{\circ}/hr$ and the rms short term noise of $0.6^{\circ}/hr/{\sqrt{Hz}}$ were obtained.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Journal of the military operations research society of Korea
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• v.10 no.2
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• pp.51-59
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• 1984

Military personnel encounter a variety of noise environments. During exercises, high intensity noise levels are often encountered. Twenty-four subjects were required to respond to symbols presented under two levels of task difficulty, two levels of presentation rate, two levels of display orientation, and three levels of noise intensity. The purpose of the experiment was to determine whether noise intensity and display orientation had any effect on a short-term memory task. Results showed that continuous white noise at intensity levels of 30, 85, and 105 db had no effect on the shortterm memory task. Presentation rate and task difficulty demonstrated a significant relationship with task performance as did their two-way interaction. This two-way interaction between presentation rate and task difficulty exhibited a different pattern for the two levels of display orientation.

• Water for future
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• v.20 no.4
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• pp.257-266
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• 1987

The existing stochastic models for the data with hydrologic persistence can be classified into two categories; the short-term and long-term models.For the present study, the Hurst coefficients which are the dominant parameter in the Fast Fractional Gaussian Noise(FFGN)model, one of the long-term models. are estimated with historical annual and monthly streamflows. In order to verify the applicability of these estimators the statistical properties of the generated annual streamflows by FFGN model are compared with those of the historical annual streamflows. Then the generated annual streamflows by FFGN model are disaggregated into the monthly streamflows by disaggregation model at two sites, i.e. Waekman and Jindong, in the Nakdong River Basin. On the other hand, the monthly stream flows at the two sites were also generated by the two-site Matalas model which is one of the short-term models. To evaluate the applicability of the above models and to select the better model the statistical properties of the generated monthly streamflows by two models were compared with those of the historicals, respectively.