學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 12

Gamma-Ray Irradiation Effect on Ferroelectric Devices with Hafnium Aluminum Oxides
(2019-12-01)
Liu, Chien
;
Tung, Yi-Chun
;
Wu, Tian-Li
;
Cheng, Chun-Hu
;
Tseng, Chih-Yang
Herein, the gamma-ray irradiation effect on ferroelectric HfAlO(x)capacitors for biomedical and space applications is investigated. The experimental results focus on the observation of ferroelectric polarization characteristics in support of different defect mechanisms induced by annealing, endurance cycling, and gamma-ray irradiation. It is found that the HfAlO(x)with low Al doping of 6.5% become more vulnerable to radiation exposure, but a remarkable improvement in 9%-Al-doped HfAlO(x)due to less oxygen vacancies is demonstrated. In addition, the results also confirm that the fatigue property of ferroelectric HfAlO(x)capacitor is determined by the electric-stress-induced defect during endurance cycling, not mainly contributed by irradiation damage, which shows the potential for memory applications in rigorous irradiation environment.
Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task
(2020-06-01)
Ko, Li-Wei
;
Komarov, Oleksii
;
Lai, Wei-Kai
;
Liang, Wei-Gang
;
Jung, Tzyy-Ping
Objective. A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth (R) module. Main results. The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 +/- 0.019 s, and the r(2) is 0.885 +/- 0.057 according to a within-session leave-one-trial-out cross-validation. Significance. The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.
Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
(2020-04-01)
Komarov, Oleksii
;
Ko, Li-Wei
;
Jung, Tzyy-Ping
;
交大名義發表
;
生物資訊及系統生物研究所
University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments. Often these factors result in long-term issues with daytime sleepiness, emotional instability, and mental exhaustion, which may lead to difficulties in the educational process. This study introduces the Daily Sampling System (DSS) implemented as a smartphone application, which combines a set of self-assessment scales for evaluating variations in the emotional state and sleep quality throughout a full academic term. In addition to submitting the daily sampling scores, the participants regularly filled in the Depression, Anxiety, and Stress Scales (DASS) reports and took part in resting-state EEG data recording immediately after report completion. In total, this study collected 1835 daily samples and 94 combined DASS with EEG datasets from 18 university students (aged 23-27 years), with 79.3 +/- 15.3% response ratio in submitting the daily reports during an academic semester. The results of pairwise testing and multiple regression analysis demonstrate that the daily level of self-perceived fatigue correlates positively with stress, daytime sleepiness, and negatively with alertness on awakening, self-evaluated sleep quality, and sleep duration. The spectral analysis of the EEG data reveals a significant increase in the resting-state spectral power density across the theta and low-alpha frequency bands associated with increased levels of anxiety and stress. Additionally, the state of depression was accompanied by an intensification of high-frequency EEG activity over the temporal regions. No significant differences in prefrontal alpha power asymmetry were observed under the described experimental conditions while comparing the states of calmness and emotional arousal of the participants for the three conditions of depression, anxiety, and stress.
The effects of different fatigue levels on brain-behavior relationships in driving
(1970-01-01)
Huang, Kuan-Chih
;
Chuang, Chun-Hsiang
;
Wang, Yu-kai
;
Hsieh, Chi-Yuan
;
King, Jung-Tai
Background In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain-behavior relationships. Methods A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. Results Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. Conclusion Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators.
Development of Fatigue Test System for Small Composite Wind Turbine Blades
(2011-01-01)
Lai, Feng-Min
;
Yang, Shin-Han
;
Wu, Jia-Hroung
;
Hsueh, Chun-Teng
;
Yang, Chao-Chieh
The research was to develop a small blade fatigue test system which met the criterion of the IEC (TR 61400-23) for the small composite wind turbine blades (SCWTBs). The fatigue test machine consisted of the servo motor, linear guide, transmission, controlling system and clamping apparatuses. These components were used to balance the weights loaded on the blades. Weights were added to change the different mass at each saddle for the loading tests. Amplitude of fatigue can be manipulated by rotating the eccentric holes of the flywheel. The kinetic power of the SCWTBs was translated from the circular motion to linear motion. The fatigue test system was able to test 1m-3m SCWTBs with single or multiple samples tested simultaneously. Because vibration frequency of the fatigue test system was limited to 3-6 Hz, this research also tested the repaired samples after breaking them intentionally for the tests. Finally, the paper drew the linear formulation of the Goodman diagram. The experimentally determined S-N curve for R= -1 was used to define the intercepts of the constant life curves of the vertical axis of the SCWTBs. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]
Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data
(2018-11-12)
Fonseca, Andre
;
Kerick, Scott
;
King, Jung-Tai
;
Lin, Chin-Teng
;
Jung, Tzyy-Ping
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving
(2018-03-27)
Chuang, Chun-Hsiang
;
Cao, Zehong
;
King, Jung-Tai
;
Wu, Bing-Syun
;
Wang, Yu-Kai
Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.
An EEG-Based Fatigue Detection and Mitigation System
(2016-06)
Huang, Kuan-Chih
;
Huang, Teng-Yi
;
Chuang, Chun-Hsiang
;
King, Jung-Tai
;
Wang, Yu-Kai
Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual\'s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant\'s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject\'s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
Developing an EEG-based on-line closed-loop lapse detection and mitigation system
(2014-10-13)
Wang, Yu-Te
;
Huang, Kuan-Chih
;
Wei, Chun-Shu
;
Huang, Teng-Yi
;
Ke, Li-Wei
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects\' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.
Electrical properties of metal-ferroelectric-insulator-semiconductor using sol-gel derived SrBi2Ta2O9 film and ultra-thin Si3N4 buffer layer
(2002-12-02)
Huang, CH
;
Tseng, TY
;
Chien, CH
;
Yang, MJ
;
Leu, CC
The electrical properties of the metal-ferroelectric-insulator-silicon memories with stacked gate configuration of Pt/ SrBi2Ta2O9 (SBT)/Si3N4/p-Si (1 0 0) were investigated. In an attempt to operate at low voltage with sufficient large memory window, various ultra-thin Si3N4 buffer layers in thickness of 3.5, 2, and 0.9 nm were employed. From the results of C-V measurements, the memory window can be as large as 0.8 V at the bias amplitude of 5 V for the sample with 0.9 nm SixNy buffer layer. Well-crystallized perovskite structures have been further confirmed by the spectra of X-ray diffraction measurements. The leakage current, which plays a very important role in the data retention, of Pt/SBT (245 nm)/Si3N4 (0.9 nm)/p-Si (10 0) can be as low as 2.5 X 10(-8) A/cm(2) at 200 kV/cm. Excellent fatigue-free performance with up to 10(10) read/write cycles and good retention time of > 2 h have been obtained. Optimization and scaling of SBT thin films are believed to be effective in pursuing extremely low voltage operation, high-density and liable 1T nonvolatile ferroelectric random access memories. (C) 2002 Elsevier Science B.V. All rights reserved.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results