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Ye Sun
Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, USA

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Original paper
Published: 25 August 2021 in Geotechnical and Geological Engineering
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This study investigates image-data-driven deep learning in the stability analysis of geosystems. For the purpose, the recent breakthrough in computer vision represented by the Convolutional Neural Network (CNN), which was later used as a core technique in developing Google’s AlphaGo, was studied for its capacity in assessing the stability of retaining walls. The concept used in the famous Dogs vs. Cats Kaggle challenge, in which machine learning algorithms are used to classify whether an image contains a dog or a cat, was employed. A CNN was used to analyze images for retaining walls to tell whether a wall is “cat” (safe) or “dog” (failed). For quantitative analysis, 2D images for retaining walls, organized as datasets of sizes from 500 to 200,000, were generated using a stochastic method and labeled using a traditional mechanistic method. An accuracy of 97.94% was achieved for predicting whether the retaining wall is safe via binary classifications with the CNN. Testing via the analysis of 20,000 additional images, which were independent and identically distributed, confirmed the results. Further investigations into the dataset sizes and computational power yielded quantitative insights into the influence of data and computing resources on the application of deep learning in the stability analysis of geosystems. The study, for the first time, proves the feasibility of stability analysis of geosystems with image data and provides a potential big data solution for geotechnical engineering as well as other civil engineering areas.

ACS Style

Zhen Liu; Shiyan Hu; Ye Sun; Behnam Azmoon. An Exploratory Investigation into Image-Data-Driven Deep Learning for Stability Analysis of Geosystems. Geotechnical and Geological Engineering 2021, 1 -16.

AMA Style

Zhen Liu, Shiyan Hu, Ye Sun, Behnam Azmoon. An Exploratory Investigation into Image-Data-Driven Deep Learning for Stability Analysis of Geosystems. Geotechnical and Geological Engineering. 2021; ():1-16.

Chicago/Turabian Style

Zhen Liu; Shiyan Hu; Ye Sun; Behnam Azmoon. 2021. "An Exploratory Investigation into Image-Data-Driven Deep Learning for Stability Analysis of Geosystems." Geotechnical and Geological Engineering , no. : 1-16.

Conference paper
Published: 27 June 2021 in Inventive Computation and Information Technologies
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Recent advances in autonomous and semi-autonomous vehicles have resulted in more efficient vehicle maneuvers, which causes drivers to be less involved in operational driving, thus leading to increased drivers’ engagement in other non-driving related activities. High levels of automation arise concerns such as a decrease in situational awareness (SA) and driving performance as drivers are likely to undertake a non-driving related task during vehicle maneuvers. For cars with conditional or higher automation (SAE Level 3 or up), there is a need to study and monitor drivers’ level of engagement/involvement with the vehicles. Considering the key factors for safety when drivers perform a non-driving related task (NDRT) during conditional automation, issues concerning drivers’ SA and takeover performance in absence or failure of automation need to be addressed. In this study, we designed and conducted driving simulator experiments to investigate drivers’ SA while they were performing a NDRT during the conditional automated driving. We also investigated drivers’ takeover time and takeover quality in different NDRTs. We found that drivers’ vigilance levels are different while performing different NDRTs during semi-automation. Our results show that the response time was typically between 5–8 s and the deviation of distance to the center of the lane ranged from −0.66 m to −0.3 m which might suggest the variability in takeover quality during different NDRTs.

ACS Style

Shruti Amre; Ye Sun. Effects of Non-driving Task Related Workload and Situational Awareness in Semi-autonomous Vehicles. Inventive Computation and Information Technologies 2021, 247 -254.

AMA Style

Shruti Amre, Ye Sun. Effects of Non-driving Task Related Workload and Situational Awareness in Semi-autonomous Vehicles. Inventive Computation and Information Technologies. 2021; ():247-254.

Chicago/Turabian Style

Shruti Amre; Ye Sun. 2021. "Effects of Non-driving Task Related Workload and Situational Awareness in Semi-autonomous Vehicles." Inventive Computation and Information Technologies , no. : 247-254.

Journal article
Published: 12 October 2019 in ACM Transactions on Cyber-Physical Systems
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Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

ACS Style

Hui Huang; Shiyan Hu; Ye Sun. Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition. ACM Transactions on Cyber-Physical Systems 2019, 3, 1 -19.

AMA Style

Hui Huang, Shiyan Hu, Ye Sun. Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition. ACM Transactions on Cyber-Physical Systems. 2019; 3 (4):1-19.

Chicago/Turabian Style

Hui Huang; Shiyan Hu; Ye Sun. 2019. "Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition." ACM Transactions on Cyber-Physical Systems 3, no. 4: 1-19.

Journal article
Published: 12 August 2019 in IEEE Transactions on Power Electronics
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In this article, a synchronized switching harvesting on inductor (SSHI)-based rectifier for triboelectric energy harvesting is reported for the first time. In the recent few years, triboelectric energy harvesters (TEs), also called triboelectric nanogenerators (TENGs), are emerging and extensively studied, which provide promising solutions for converting mechanical energy to electricity as power resources for electronics. To practically power electronics from TENGs, a power management module with high energy efficiency is also essential. The state of the art of power management circuits for triboelectric energy harvesting is mainly focused on increasing dc-dc power efficiency technically. In this article, we report an SSHI rectifying strategy associated with TEH design and provide a new perspective on designing TEHs or TENGs by considering their capacitance concurrently. A new theoretical model is developed for electricity generation from triboelectric energy harvesting considering the introduced pairing capacitance and the impact force under practical condition. We also demonstrate that ultralow-cost, easy-fabricated TEHs can also generate a reasonable amount of power. The experimental results show that the proposed SSHI rectifier increases the harvested power by 242.83% when compared with a full-wave bridge rectifier for a newly designed low-cost TEH. The proposed SSHI interface provides a promising strategy of rectifier design to enhance ac-dc energy efficiency for triboelectric energy harvesting.

ACS Style

Xian Li; Ye Sun. An SSHI Rectifier for Triboelectric Energy Harvesting. IEEE Transactions on Power Electronics 2019, 35, 3663 -3678.

AMA Style

Xian Li, Ye Sun. An SSHI Rectifier for Triboelectric Energy Harvesting. IEEE Transactions on Power Electronics. 2019; 35 (4):3663-3678.

Chicago/Turabian Style

Xian Li; Ye Sun. 2019. "An SSHI Rectifier for Triboelectric Energy Harvesting." IEEE Transactions on Power Electronics 35, no. 4: 3663-3678.

Journal article
Published: 04 April 2019 in Sensors
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Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.

ACS Style

Hui Huang; Shiyan Hu; Ye Sun. A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising. Sensors 2019, 19, 1617 .

AMA Style

Hui Huang, Shiyan Hu, Ye Sun. A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising. Sensors. 2019; 19 (7):1617.

Chicago/Turabian Style

Hui Huang; Shiyan Hu; Ye Sun. 2019. "A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising." Sensors 19, no. 7: 1617.

Conference paper
Published: 01 October 2018 in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
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Biosignals often require high data transmission in real-time monitoring and visualization. Low-power techniques are always desirable for designing sustainable wireless sensor nodes. Signal compression techniques provide a promising solution in developing low-power wireless sensor nodes as it can significantly reduce the amount of data transmitted via power-demanding wireless transmission and thus greatly lower the energy consumption of sensor nodes. In this study, we develop a new approach for ECG signal compression on low-power ECG sensor nodes by leveraging sparse features of ECG signals in frequency domain. The experimental results show that our method has better compression performance which achieves the average compression ratio (CR) of 65.91 with the comparable RMSE of no more than 5% than the state-of-the-art that can achieve the CR of around 40 with the same level error rate. The promising compression performance of the proposed method provides a feasible solution to achieve ultra-low power consumption for wireless ECG sensor node design.

ACS Style

Hui Huang; Shiyan Hu; Ye Sun. ECG Signal Compression for Low-power Sensor Nodes Using Sparse Frequency Spectrum Features. 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) 2018, 1 -4.

AMA Style

Hui Huang, Shiyan Hu, Ye Sun. ECG Signal Compression for Low-power Sensor Nodes Using Sparse Frequency Spectrum Features. 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). 2018; ():1-4.

Chicago/Turabian Style

Hui Huang; Shiyan Hu; Ye Sun. 2018. "ECG Signal Compression for Low-power Sensor Nodes Using Sparse Frequency Spectrum Features." 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) , no. : 1-4.

Journal article
Published: 25 September 2018 in Smart Health
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High medical expenditure on chronic diseases in recent years promotes proactive healthcare and health management. Wearable devices provide a good solution for long-term monitoring for both in-hospital and out-of-hospital healthcare; however, long-term contact may cause comfort issues. This paper introduces the design, manufacturing, and experimental validation of a button-like wearable system for long-term multiple biopotential monitoring using non-contact electrodes, namely NCMB-Button. The button-like system is capable of acquiring ECG signals through multiple layers of cloth without directly contacting human skin and other multiple biopotential signals, including EMG and EEG without using traditional wet gel. The detecting performance is achieved and enhanced by introducing a feedback loop into the skin to reduce motion artifacts. The system is packaged into a 39 mm × 32 mm × 17 mm 3D printed case, featuring low-weight and low-cost. The total weight of the system is 24.0 g. The detection performance is validated by testing in multiple motion scenarios through different types of cloth. The results show that ECG signals were able to be detected through the thickness of 2.60 mm of fabric cloth with fast recovery from motion artifacts. Two types of EMG signals were also detected during the validation experiments. The EEG signals were detected during sleepiness condition in which the alpha wave was visible as the indicator. The NCMB-Button demonstrates the feasibility of long-term biopotential monitoring for various applications without interrupting daily activities.

ACS Style

Xian Li; Ye Sun. A wearable button-like system for long-term multiple biopotential monitoring using non-contact electrodes. Smart Health 2018, 11, 2 -15.

AMA Style

Xian Li, Ye Sun. A wearable button-like system for long-term multiple biopotential monitoring using non-contact electrodes. Smart Health. 2018; 11 ():2-15.

Chicago/Turabian Style

Xian Li; Ye Sun. 2018. "A wearable button-like system for long-term multiple biopotential monitoring using non-contact electrodes." Smart Health 11, no. : 2-15.

Journal article
Published: 10 July 2018 in IEEE Journal of Biomedical and Health Informatics
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Human skin temperature mapping provides abundant information of physiological conditions of human body, which provides supplementary or alternative indicators for disease monitoring or diagnosis. The existing models of temperature mapping or temperature field distribution of human skin are generally established by finite element method. Due to the complexity of biological systems, it is challenging to achieve high accuracy mathematical models of temperature field of human skin. The goal of this study is to establish human skin temperature 3D mapping platform by integrating optical fibers and improved GA-BP neural network. The proposed data-driven method is capable of acquiring entire human skin temperature 3D mapping by simply measuring a few points on human skin. Multiple experiments were conducted to validate the proposed method on different areas of human skin in different ambient environments. In each experiment setting, the measured data and the model output data were compared. The results show that the mean absolute error (MAE) among all the validation experiments is 0.11°C, which is lower than that in the state of the art using physical modeling for skin temperature prediction and more close to clinical accuracy. Also the proposed approach is accurate and reliable, which may provide a platform technology for human skin temperature mapping that can be used in both medical and scientific studies as well as home monitoring.

ACS Style

Weixing Liu; Dagong Jia; Jing Zhao; Hongxia Zhang; Tiegen Liu; Yimo Zhang; Ye Sun. An Optical Fiber-Based Data-Driven Method for Human Skin Temperature 3-D Mapping. IEEE Journal of Biomedical and Health Informatics 2018, 23, 1141 -1150.

AMA Style

Weixing Liu, Dagong Jia, Jing Zhao, Hongxia Zhang, Tiegen Liu, Yimo Zhang, Ye Sun. An Optical Fiber-Based Data-Driven Method for Human Skin Temperature 3-D Mapping. IEEE Journal of Biomedical and Health Informatics. 2018; 23 (3):1141-1150.

Chicago/Turabian Style

Weixing Liu; Dagong Jia; Jing Zhao; Hongxia Zhang; Tiegen Liu; Yimo Zhang; Ye Sun. 2018. "An Optical Fiber-Based Data-Driven Method for Human Skin Temperature 3-D Mapping." IEEE Journal of Biomedical and Health Informatics 23, no. 3: 1141-1150.

Journal article
Published: 21 March 2018 in IEEE Internet of Things Journal
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Human physical activity recognition is widely used in medical diagnosis, well-being management, and rehabilitation treatment. In spite of various Internet of Things (IoT) designs available in the literature, power resources often limit the lifetime of IoT. Regarding this weakness, this paper develops a new motion sensor system in wearable IoT (WIoT) for human physical activity recognition without any signal conditioning circuits. The triboelectricity-based physical model is explored in designing the motion sensor. It enables to collect motion signals caused by physical activities without any power supply. In addition, the triboelectric structure can be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion and a relatively stable voltage when involving continuous activities. Such a new design lays the foundations for constructing the next generation self-powered WIoT systems. Our new design has been extensively evaluated, where most common activities including sitting and standing, walking, climbing upstairs and downstairs, and running are used. The experimental results demonstrate that our system can achieve similar comparable performance as the state of the art for physical activity recognition at an average successful accuracy of over 80%. At the same time, our system reduces more than 25% energy consumption of the entire sensing hardware system which includes the sensor, microcontroller, and corresponding circuits.

ACS Style

Hui Huang; Xian Li; Si Liu; Shiyan Hu; Ye Sun. TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting. IEEE Internet of Things Journal 2018, 5, 4441 -4453.

AMA Style

Hui Huang, Xian Li, Si Liu, Shiyan Hu, Ye Sun. TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting. IEEE Internet of Things Journal. 2018; 5 (6):4441-4453.

Chicago/Turabian Style

Hui Huang; Xian Li; Si Liu; Shiyan Hu; Ye Sun. 2018. "TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting." IEEE Internet of Things Journal 5, no. 6: 4441-4453.

Journal article
Published: 17 November 2017 in Sensors
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In this paper, we report the design, experimental validation and application of a scalable, wearable e-textile triboelectric energy harvesting (WearETE) system for scavenging energy from activities of daily living. The WearETE system features ultra-low-cost material and manufacturing methods, high accessibility, and high feasibility for powering wearable sensors and electronics. The foam and e-textile are used as the two active tribomaterials for energy harvester design with the consideration of flexibility and wearability. A calibration platform is also developed to quantify the input mechanical power and power efficiency. The performance of the WearETE system for human motion scavenging is validated and calibrated through experiments. The results show that the wearable triboelectric energy harvester can generate over 70 V output voltage which is capable of powering over 52 LEDs simultaneously with a 9 × 9 cm2 area. A larger version is able to lighten 190 LEDs during contact-separation process. The WearETE system can generate a maximum power of 4.8113 mW from hand clapping movements under the frequency of 4 Hz. The average power efficiency can be up to 24.94%. The output power harvested by the WearETE system during slow walking is 7.5248 µW. The results show the possibility of powering wearable electronics during human motion.

ACS Style

Xian Li; Ye Sun. WearETE: A Scalable Wearable E-Textile Triboelectric Energy Harvesting System for Human Motion Scavenging. Sensors 2017, 17, 2649 .

AMA Style

Xian Li, Ye Sun. WearETE: A Scalable Wearable E-Textile Triboelectric Energy Harvesting System for Human Motion Scavenging. Sensors. 2017; 17 (11):2649.

Chicago/Turabian Style

Xian Li; Ye Sun. 2017. "WearETE: A Scalable Wearable E-Textile Triboelectric Energy Harvesting System for Human Motion Scavenging." Sensors 17, no. 11: 2649.

Journal article
Published: 30 June 2017 in IEEE Transactions on Biomedical Engineering
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In this paper, we report the design and experimental validation of a novel optical sensor for radial artery pulse measurement based on fiber Bragg grating (FBG) and lever amplification mechanism. Pulse waveform analysis is a diagnostic tool for clinical examination and disease diagnosis. High fidelity radial artery pulse waveform has been investigated in clinical studies for estimating central aortic pressure, which is proved to be predictors of cardiovascular diseases. As a three-dimensional cylinder, the radial artery needs to be examined from different locations to achieve optimal pulse waveform for estimation and diagnosis. The proposed optical sensing system is featured as high sensitivity and immunity to electromagnetic interference for multilocation radial artery pulse waveform measurement. The FBG sensor can achieve the sensitivity of 8.236 nm/N, which is comparable to a commonly used electrical sensor. This FBG-based system can provide high accurate measurement, and the key characteristic parameters can be then extracted from the raw signals for clinical applications. The detecting performance is validated through experiments guided by physicians. In the experimental validation, we applied this sensor to measure the pulse waveforms at various positions and depths of the radial artery in the wrist according to the diagnostic requirements. The results demonstrate the high feasibility of using optical systems for physiological measurement and using this FBG sensor for radial artery pulse waveform in clinical applications.

ACS Style

Dagong Jia; Jing Chao; Shuai Li; Hongxia Zhang; Yingzhan Yan; Tiegen Liu; Ye Sun. A Fiber Bragg Grating Sensor for Radial Artery Pulse Waveform Measurement. IEEE Transactions on Biomedical Engineering 2017, 65, 839 -846.

AMA Style

Dagong Jia, Jing Chao, Shuai Li, Hongxia Zhang, Yingzhan Yan, Tiegen Liu, Ye Sun. A Fiber Bragg Grating Sensor for Radial Artery Pulse Waveform Measurement. IEEE Transactions on Biomedical Engineering. 2017; 65 (4):839-846.

Chicago/Turabian Style

Dagong Jia; Jing Chao; Shuai Li; Hongxia Zhang; Yingzhan Yan; Tiegen Liu; Ye Sun. 2017. "A Fiber Bragg Grating Sensor for Radial Artery Pulse Waveform Measurement." IEEE Transactions on Biomedical Engineering 65, no. 4: 839-846.

Journal article
Published: 16 March 2017 in Applied Sciences
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Entrained air voids can improve the freeze-thaw durability of concrete, and also affect its mechanical and transport properties. Therefore, it is important to measure the air void structure and understand its influence on concrete performance for quality control. This paper aims to measure air void structure evolution at both early-age and hardened stages with the ultrasonic technique, and evaluates its influence on concrete properties. Three samples with different air entrainment agent content were specially prepared. The air void structure was determined with optimized inverse analysis by achieving the minimum error between experimental and theoretical attenuation. The early-age sample measurement showed that the air void content with the whole size range slightly decreases with curing time. The air void size distribution of hardened samples (at Day 28) was compared with American Society for Testing and Materials (ASTM) C457 test results. The air void size distribution with different amount of air entrainment agent was also favorably compared. In addition, the transport property, compressive strength, and dynamic modulus of concrete samples were also evaluated. The concrete transport decreased with the curing age, which is in accordance with the air void shrinkage. The correlation between the early-age strength development and hardened dynamic modulus with the ultrasonic parameters was also evaluated. The existence of clustered air voids in the Interfacial Transition Zone (ITZ) area was found to cause severe compressive strength loss. The results indicated that this developed ultrasonic technique has potential in air void size distribution measurement, and demonstrated the influence of air void structure evolution on concrete properties during both early-age and hardened stages.

ACS Style

Shuaicheng Guo; Qingli Dai; Xiao Sun; Ye Sun; Zhen Liu. Ultrasonic Techniques for Air Void Size Distribution and Property Evaluation in Both Early-Age and Hardened Concrete Samples. Applied Sciences 2017, 7, 290 .

AMA Style

Shuaicheng Guo, Qingli Dai, Xiao Sun, Ye Sun, Zhen Liu. Ultrasonic Techniques for Air Void Size Distribution and Property Evaluation in Both Early-Age and Hardened Concrete Samples. Applied Sciences. 2017; 7 (3):290.

Chicago/Turabian Style

Shuaicheng Guo; Qingli Dai; Xiao Sun; Ye Sun; Zhen Liu. 2017. "Ultrasonic Techniques for Air Void Size Distribution and Property Evaluation in Both Early-Age and Hardened Concrete Samples." Applied Sciences 7, no. 3: 290.

Journal article
Published: 01 June 2016 in Construction and Building Materials
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The size distribution of air voids in concrete has significant impacts on freeze-thaw damage. This study presented a non-destructive ultrasonic scattering technique to determine the air void size distribution in hardened concrete samples. The ultrasonic scattering theory was applied to calculate the theoretical attenuation of concrete by including the effects of the viscoelastic matrix and different sizes of air voids and aggregates. The air void size distribution was determined by using an inverse analysis to minimize the difference between theoretical and experimental attenuation of concrete. The logarithm normal distribution for large-size air voids and normal distribution for small-size air voids were selected to better represent the air void size distribution in concrete. Both the large-size range and small-size air void distributions were obtained with ultrasonic scattering techniques for hardened concrete specimens. These results were favorably compared with the petrography-based ASTM C 457 method. The comparisons indicated that the developed ultrasonic scattering technique can measure the size distribution of air voids in concrete for the evaluation of freeze-thaw durability.

ACS Style

Shuaicheng Guo; Qingli Dai; Xiao Sun; Ye Sun. Ultrasonic scattering measurement of air void size distribution in hardened concrete samples. Construction and Building Materials 2016, 113, 415 -422.

AMA Style

Shuaicheng Guo, Qingli Dai, Xiao Sun, Ye Sun. Ultrasonic scattering measurement of air void size distribution in hardened concrete samples. Construction and Building Materials. 2016; 113 ():415-422.

Chicago/Turabian Style

Shuaicheng Guo; Qingli Dai; Xiao Sun; Ye Sun. 2016. "Ultrasonic scattering measurement of air void size distribution in hardened concrete samples." Construction and Building Materials 113, no. : 415-422.

Journal article
Published: 10 February 2014 in IEEE Journal of Biomedical and Health Informatics
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This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.

ACS Style

Ye Sun; Xiong Yu. An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE Journal of Biomedical and Health Informatics 2014, 18, 1932 -1939.

AMA Style

Ye Sun, Xiong Yu. An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE Journal of Biomedical and Health Informatics. 2014; 18 (6):1932-1939.

Chicago/Turabian Style

Ye Sun; Xiong Yu. 2014. "An innovative nonintrusive driver assistance system for vital signal monitoring." IEEE Journal of Biomedical and Health Informatics 18, no. 6: 1932-1939.