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Yifan Zhao
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK

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Journal article
Published: 14 August 2021 in Sensors
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Pulsed thermography has been used significantly over the years to detect near and sub-surface damage in both metals and composites. Where most of the research has been in either improving the detectability and/or its applicability to specific parts and scenarios, efforts to analyse and establish the level of uncertainty in the measurements have been very limited. This paper presents the analysis of multiple uncertainties associated with thermographic measurements under multiple scenarios such as the choice of post-processing algorithms; multiple flash power settings; and repeat tests on four materials, i.e., aluminium, steel, carbon-fibre reinforced plastics (CFRP) and glass-fibre reinforced plastics (GFRP). Thermal diffusivity measurement has been used as the parameter to determine the uncertainty associated with all the above categories. The results have been computed and represented in the form of a relative standard deviation (RSD) ratio in all cases, where the RSD is the ratio of standard deviation to the mean. The results clearly indicate that the thermal diffusivity measurements show a large RSD due to the post-processing algorithms in the case of steel and a large variability when it comes to assessing the GFRP laminates.

ACS Style

Sri Addepalli; Yifan Zhao; John Ahmet Erkoyuncu; Rajkumar Roy. Quantifying Uncertainty in Pulsed Thermographic Inspection by Analysing the Thermal Diffusivity Measurements of Metals and Composites. Sensors 2021, 21, 5480 .

AMA Style

Sri Addepalli, Yifan Zhao, John Ahmet Erkoyuncu, Rajkumar Roy. Quantifying Uncertainty in Pulsed Thermographic Inspection by Analysing the Thermal Diffusivity Measurements of Metals and Composites. Sensors. 2021; 21 (16):5480.

Chicago/Turabian Style

Sri Addepalli; Yifan Zhao; John Ahmet Erkoyuncu; Rajkumar Roy. 2021. "Quantifying Uncertainty in Pulsed Thermographic Inspection by Analysing the Thermal Diffusivity Measurements of Metals and Composites." Sensors 21, no. 16: 5480.

Journal article
Published: 27 July 2021 in Measurement
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Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components.

ACS Style

Haochen Liu; Gen Li; Yifan Zhao. A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure. Measurement 2021, 184, 109926 .

AMA Style

Haochen Liu, Gen Li, Yifan Zhao. A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure. Measurement. 2021; 184 ():109926.

Chicago/Turabian Style

Haochen Liu; Gen Li; Yifan Zhao. 2021. "A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure." Measurement 184, no. : 109926.

Original article
Published: 12 July 2021 in Chinese Journal of Mechanical Engineering
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The remaining useful life (RUL) of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators. Recently, different deep learning (DL) techniques have been used for RUL prediction and achieved great success. Because the data is often time-sequential, recurrent neural network (RNN) has attracted significant interests due to its efficiency in dealing with such data. This paper systematically reviews RNN and its variants for RUL prediction, with a specific focus on understanding how different components (e.g., types of optimisers and activation functions) or parameters (e.g., sequence length, neuron quantities) affect their performance. After that, a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance. The result suggests that the variant methods usually perform better than the original RNN, and among which, Bi-directional Long Short-Term Memory generally has the best performance in terms of stability, precision and accuracy. Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately. It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance .

ACS Style

Youdao Wang; Yifan Zhao; Sri Addepalli. Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction. Chinese Journal of Mechanical Engineering 2021, 34, 1 -20.

AMA Style

Youdao Wang, Yifan Zhao, Sri Addepalli. Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction. Chinese Journal of Mechanical Engineering. 2021; 34 (1):1-20.

Chicago/Turabian Style

Youdao Wang; Yifan Zhao; Sri Addepalli. 2021. "Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction." Chinese Journal of Mechanical Engineering 34, no. 1: 1-20.

Journal article
Published: 09 July 2021 in Sustainability
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Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year.

ACS Style

Jennifer Ross; Grant Bigg; Yifan Zhao; Edward Hanna. A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland. Sustainability 2021, 13, 7705 .

AMA Style

Jennifer Ross, Grant Bigg, Yifan Zhao, Edward Hanna. A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland. Sustainability. 2021; 13 (14):7705.

Chicago/Turabian Style

Jennifer Ross; Grant Bigg; Yifan Zhao; Edward Hanna. 2021. "A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland." Sustainability 13, no. 14: 7705.

Journal article
Published: 24 May 2021 in Composite Structures
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Impact induced combined damage in composite laminates attracts great attention due to its significant degradation of the structural integrity. However, the provision of the quantitative analysis of each damage portion is challenging due to its bare visibility and structural mixture complexity, so-called barely visible impact damage (BVID), which is referred to as inter-laminar delamination, and is inherently coupled with in-plane transverse and matrix damage also known as combined damage. Instead of focusing on one type of damage in most of the existing studies, this paper proposes a decomposition and targeted enhancement technique based on Stationary Wavelet Transform (SWT) for such coupled BVID in composite laminates using laser-line scanning thermography. Firstly, a combined damage model composed of in-plane damage and inter-laminar delamination is established by finite element numerical modelling to predict the thermal response pattern in the laser scanning thermography. Then, a feature separation and targeted enhancement strategy based on SWT in the frequency domain is proposed to improve the contrast of the matrix crack and delamination in combined damage scenarios induced by low-velocity rigid impact via drop-tower tests, meanwhile eliminating noise and suppressing the laser pattern background. The enhanced images of in-plane damage and delamination are furtherly processed by Random Sample Consensus (RANSAC) method and confidence map algorithms to calibrate the damage profile. The proposed technique is validated through inspecting a group of unidirectional carbon fibre-reinforced polymer composite samples, impacted by a variety of energy levels, in fibre-parallel (0°), 45° and orthogonal scanning modes. The results demonstrate that the proposed technique can pertinently isolate, enhance and characterise the inspected in-plane crack and inter-laminates delamination in a flexible manner. The proposed methodology paves the way towards automated infrared thermography data analysis for quantitative dissection of actual combined damage in composite laminates.

ACS Style

Haochen Liu; Weixiang Du; Hamed Yazdani Nezhad; Andrew Starr; Yifan Zhao. A dissection and enhancement technique for combined damage characterisation in composite laminates using laser-line scanning thermography. Composite Structures 2021, 271, 114168 .

AMA Style

Haochen Liu, Weixiang Du, Hamed Yazdani Nezhad, Andrew Starr, Yifan Zhao. A dissection and enhancement technique for combined damage characterisation in composite laminates using laser-line scanning thermography. Composite Structures. 2021; 271 ():114168.

Chicago/Turabian Style

Haochen Liu; Weixiang Du; Hamed Yazdani Nezhad; Andrew Starr; Yifan Zhao. 2021. "A dissection and enhancement technique for combined damage characterisation in composite laminates using laser-line scanning thermography." Composite Structures 271, no. : 114168.

Journal article
Published: 17 May 2021 in Sensors
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The existing deep learning-based Personal Protective Equipment (PPE) detectors can only detect limited types of PPE and their performance needs to be improved, particularly for their deployment on real construction sites. This paper introduces an approach to train and evaluate eight deep learning detectors, for real application purposes, based on You Only Look Once (YOLO) architectures for six classes, including helmets with four colours, person, and vest. Meanwhile, a dedicated high-quality dataset, CHV, consisting of 1330 images, is constructed by considering real construction site background, different gestures, varied angles and distances, and multi PPE classes. The comparison result among the eight models shows that YOLO v5x has the best mAP (86.55%), and YOLO v5s has the fastest speed (52 FPS) on GPU. The detection accuracy of helmet classes on blurred faces decreases by 7%, while there is no effect on other person and vest classes. And the proposed detectors trained on the CHV dataset have a superior performance compared to other deep learning approaches on the same datasets. The novel multiclass CHV dataset is open for public use.

ACS Style

Zijian Wang; Yimin Wu; Lichao Yang; Arjun Thirunavukarasu; Colin Evison; Yifan Zhao. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches. Sensors 2021, 21, 3478 .

AMA Style

Zijian Wang, Yimin Wu, Lichao Yang, Arjun Thirunavukarasu, Colin Evison, Yifan Zhao. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches. Sensors. 2021; 21 (10):3478.

Chicago/Turabian Style

Zijian Wang; Yimin Wu; Lichao Yang; Arjun Thirunavukarasu; Colin Evison; Yifan Zhao. 2021. "Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches." Sensors 21, no. 10: 3478.

Journal article
Published: 28 April 2021 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.

ACS Style

Xiaocai Shan; Shoudong Huo; Lichao Yang; Jun Cao; Jiaru Zou; Liangyu Chen; Ptolemaios Georgios Sarrigiannis; Yifan Zhao. A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2021, 29, 841 -851.

AMA Style

Xiaocai Shan, Shoudong Huo, Lichao Yang, Jun Cao, Jiaru Zou, Liangyu Chen, Ptolemaios Georgios Sarrigiannis, Yifan Zhao. A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2021; 29 ():841-851.

Chicago/Turabian Style

Xiaocai Shan; Shoudong Huo; Lichao Yang; Jun Cao; Jiaru Zou; Liangyu Chen; Ptolemaios Georgios Sarrigiannis; Yifan Zhao. 2021. "A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, no. : 841-851.

Journal article
Published: 25 March 2021 in Pattern Recognition
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For a Level 3 automated vehicle, according to the SAE International Automation Levels definition (J3016), the identification of non-driving activities (NDAs) that the driver is engaging with is of great importance in the design of an intelligent take-over interface. Much of the existing literature focuses on the driver take-over strategy with associated Human-Machine Interaction design. This paper proposes a dual-camera based framework to identify and track NDAs that require visual attention. This is achieved by mapping the driver's gaze using a nonlinear system identification approach, on the object scene, recognised by a deep learning algorithm. A novel gaze-based region of interest (ROI) selection module is introduced and contributes about a 30% improvement in average success rate and about a 60% reduction in average processing time compared to the results without this module. This framework has been successfully demonstrated to identify five types of NDA required visual attention with an average success rate of 86.18%. The outcome of this research could be applicable to the identification of other NDAs and the tracking of NDAs within a certain time window could potentially be used to evaluate the driver's attention level for both automated and human-driving vehicles.

ACS Style

Lichao Yang; Kuo Dong; Yan Ding; James Brighton; Zhenfei Zhan; Yifan Zhao. Recognition of visual-related non-driving activities using a dual-camera monitoring system. Pattern Recognition 2021, 116, 107955 .

AMA Style

Lichao Yang, Kuo Dong, Yan Ding, James Brighton, Zhenfei Zhan, Yifan Zhao. Recognition of visual-related non-driving activities using a dual-camera monitoring system. Pattern Recognition. 2021; 116 ():107955.

Chicago/Turabian Style

Lichao Yang; Kuo Dong; Yan Ding; James Brighton; Zhenfei Zhan; Yifan Zhao. 2021. "Recognition of visual-related non-driving activities using a dual-camera monitoring system." Pattern Recognition 116, no. : 107955.

Journal article
Published: 16 February 2021 in Soil Systems
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This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model.

ACS Style

Rebecca Whetton; Yifan Zhao; Said Nawar; Abdul Mouazen. Modelling the Influence of Soil Properties on Crop Yields Using a Non-Linear NFIR Model and Laboratory Data. Soil Systems 2021, 5, 12 .

AMA Style

Rebecca Whetton, Yifan Zhao, Said Nawar, Abdul Mouazen. Modelling the Influence of Soil Properties on Crop Yields Using a Non-Linear NFIR Model and Laboratory Data. Soil Systems. 2021; 5 (1):12.

Chicago/Turabian Style

Rebecca Whetton; Yifan Zhao; Said Nawar; Abdul Mouazen. 2021. "Modelling the Influence of Soil Properties on Crop Yields Using a Non-Linear NFIR Model and Laboratory Data." Soil Systems 5, no. 1: 12.

Journal article
Published: 18 December 2020 in IFAC-PapersOnLine
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Data-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction.

ACS Style

Youdao Wang; Sri Addepalli; Yifan Zhao. Recurrent Neural Networks and its variants in Remaining Useful Life prediction. IFAC-PapersOnLine 2020, 53, 137 -142.

AMA Style

Youdao Wang, Sri Addepalli, Yifan Zhao. Recurrent Neural Networks and its variants in Remaining Useful Life prediction. IFAC-PapersOnLine. 2020; 53 (3):137-142.

Chicago/Turabian Style

Youdao Wang; Sri Addepalli; Yifan Zhao. 2020. "Recurrent Neural Networks and its variants in Remaining Useful Life prediction." IFAC-PapersOnLine 53, no. 3: 137-142.

Journal article
Published: 18 December 2020 in IFAC-PapersOnLine
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Accurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.

ACS Style

Anna Zaporowska; Haochen Liu; Zakwan Skaf; Yifan Zhao. A clustering approach to detect faults with multi-component degradations in aircraft fuel systems. IFAC-PapersOnLine 2020, 53, 113 -118.

AMA Style

Anna Zaporowska, Haochen Liu, Zakwan Skaf, Yifan Zhao. A clustering approach to detect faults with multi-component degradations in aircraft fuel systems. IFAC-PapersOnLine. 2020; 53 (3):113-118.

Chicago/Turabian Style

Anna Zaporowska; Haochen Liu; Zakwan Skaf; Yifan Zhao. 2020. "A clustering approach to detect faults with multi-component degradations in aircraft fuel systems." IFAC-PapersOnLine 53, no. 3: 113-118.

Journal article
Published: 18 December 2020 in IFAC-PapersOnLine
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With the increase of the functionalisation, integration and complexity of industrial components and systems, deploying Non-Destructive Testing (NDT) devices for ‘in-situ’ inspection has become a major challenge for high-value assets. Due to the mismatching of size and volume between the existing inspection unit and the targeted complex object, inaccessibility and inapplicability have limited the applicability of NDT techniques. To address this challenge, this paper introduces a novel miniaturised active thermography system based on a commercial thermal imaging sensor featured with small size and low cost. Combining with different excitation sources, its detection performance on different types of defect of carbon fibre reinforced polymer (CFRP) is investigated and compared with an existing system. The results show that the proposed system can work with laser and flash effectively for degradation assessment although the detectability is compromised. Such a technique will play a unique role in the in-situ inspection where the space to deploy the device is limited.

ACS Style

Weixiang Du; Haochen Liu; Adisorn Sirikham; Sri Addepalli; Yifan Zhao. A miniaturised active thermography system for in-situ inspections. IFAC-PapersOnLine 2020, 53, 66 -71.

AMA Style

Weixiang Du, Haochen Liu, Adisorn Sirikham, Sri Addepalli, Yifan Zhao. A miniaturised active thermography system for in-situ inspections. IFAC-PapersOnLine. 2020; 53 (3):66-71.

Chicago/Turabian Style

Weixiang Du; Haochen Liu; Adisorn Sirikham; Sri Addepalli; Yifan Zhao. 2020. "A miniaturised active thermography system for in-situ inspections." IFAC-PapersOnLine 53, no. 3: 66-71.

Journal article
Published: 16 September 2020 in Infrared Physics & Technology
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Counterfeit Electronic Components (CECs) pose a serious threat to all intellectual properties and bring fatal failure to the key industrial systems. This paper initiates the exploration of the prospect of CEC detection using pulsed thermography (PT) by proposing a detectability evaluation method for material and structural anomalies in CECs. Firstly, a numerical Finite Element Modelling (FEM) simulation approach of CEC detection using PT was established to predict the thermal response of electronic components under the heat excitation. Then, by experimental validation, FEM simulates multiple models with attribute deviations in mould compound conductivity, mould compound volumetric heat capacity and die size respectively considering experimental noise. Secondly, based on principal components analysis (PCA), the gradients of the 1st and 2nd principal components are extracted and identified as two promising classification features of distinguishing the deviation models. Thirdly, a supervised machine learning-based method was applied to classify the features to identify the range of detectability. By defining the 90% of classification accuracy as the detectable threshold, the detectability ranges of deviation in three attributes have been quantitively evaluated respectively. The promising results suggest that PT can act as a concise, operable and cost-efficient tool for CECs screening which has the potential to be embedded in the initial large scale screening stage for anti-counterfeit.

ACS Style

Haochen Liu; Lawrence Tinsley; Sri Addepalli; Xiaochen Liu; Andrew Starr; Yifan Zhao. Detectability evaluation of attributes anomaly for electronic components using pulsed thermography. Infrared Physics & Technology 2020, 111, 103513 .

AMA Style

Haochen Liu, Lawrence Tinsley, Sri Addepalli, Xiaochen Liu, Andrew Starr, Yifan Zhao. Detectability evaluation of attributes anomaly for electronic components using pulsed thermography. Infrared Physics & Technology. 2020; 111 ():103513.

Chicago/Turabian Style

Haochen Liu; Lawrence Tinsley; Sri Addepalli; Xiaochen Liu; Andrew Starr; Yifan Zhao. 2020. "Detectability evaluation of attributes anomaly for electronic components using pulsed thermography." Infrared Physics & Technology 111, no. : 103513.

Journal article
Published: 06 September 2020 in iScience
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Summary In this work, pattern recognition and characterization of the neuromuscular dynamics of driver upper limb during naturalistic driving were studied. During the human-in-the-loop experiments, two steering tasks, namely, the passive and active steering tasks, were instructed to be completed by the subjects. Furthermore, subjects manipulated the steering wheel with two distinct postures and six different hand positions. The neuromuscular dynamics of subjects' upper limb were measured using electromyogram signals, and the behavioral data, including the steering torque and steering angle, were also collected. Based on the experimental data, patterns of muscle activities during naturalistic driving were investigated. The correlations, amplitudes, and responsiveness of the electromyogram signals, as well as the smoothness and regularity of the steering torque were discussed. The results reveal the mechanisms of neuromuscular dynamics of driver upper limb and provide a theoretical foundation for the design of the future human-machine interface for automated vehicles.

ACS Style

Yang Xing; Chen Lv; Yifan Zhao; Yahui Liu; Dongpu Cao; Sadahiro Kawahara. Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions. iScience 2020, 23, 1 .

AMA Style

Yang Xing, Chen Lv, Yifan Zhao, Yahui Liu, Dongpu Cao, Sadahiro Kawahara. Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions. iScience. 2020; 23 (9):1.

Chicago/Turabian Style

Yang Xing; Chen Lv; Yifan Zhao; Yahui Liu; Dongpu Cao; Sadahiro Kawahara. 2020. "Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions." iScience 23, no. 9: 1.

Journal article
Published: 03 September 2020 in Sensors
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Unverified or counterfeited electronic components pose a big threat globally because they could lead to malfunction of safety-critical systems and reduced reliability of high-hazard assets. The current inspection techniques are either expensive or slow, which becomes the bottleneck of large volume inspection. As a complement of the existing inspection capabilities, a pulsed thermography-based screening technique is proposed in this paper using a digital twin methodology. A FEM-based simulation unit is initially developed to simulate the internal structure of electronic components with deviations of multiple physical properties, informed by X-ray data, along with its thermal behaviour under exposure to instantaneous heat. A dedicated physical inspection unit is then integrated to verify the simulation unit and further improve the simulation by taking account of various uncertainties caused by equipment and samples. Principle component analysis is used for feature extraction, and then a set of machine learning-based classifiers are employed for quantitative classification. Evaluation results of 17 chips from different sources successfully demonstrate the effectiveness of the proposed technique.

ACS Style

Haochen Liu; Lawrence Tinsley; Wayne Lam; Sri Addepalli; Xiaochen Liu; Andrew Starr; Yifan Zhao. A Novel Inspection Technique for Electronic Components Using Thermography (NITECT). Sensors 2020, 20, 5013 .

AMA Style

Haochen Liu, Lawrence Tinsley, Wayne Lam, Sri Addepalli, Xiaochen Liu, Andrew Starr, Yifan Zhao. A Novel Inspection Technique for Electronic Components Using Thermography (NITECT). Sensors. 2020; 20 (17):5013.

Chicago/Turabian Style

Haochen Liu; Lawrence Tinsley; Wayne Lam; Sri Addepalli; Xiaochen Liu; Andrew Starr; Yifan Zhao. 2020. "A Novel Inspection Technique for Electronic Components Using Thermography (NITECT)." Sensors 20, no. 17: 5013.

Journal article
Published: 27 July 2020 in IEEE Access
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In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.

ACS Style

Barmak Honarvar Shakibaei Asli; Jan Flusser; Yifan Zhao; John Ahmet Erkoyuncu; Kajoli Banerjee Krishnan; Yasin Farrokhi; Rajkumar Roy. Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure. IEEE Access 2020, 8, 141342 -141357.

AMA Style

Barmak Honarvar Shakibaei Asli, Jan Flusser, Yifan Zhao, John Ahmet Erkoyuncu, Kajoli Banerjee Krishnan, Yasin Farrokhi, Rajkumar Roy. Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure. IEEE Access. 2020; 8 (99):141342-141357.

Chicago/Turabian Style

Barmak Honarvar Shakibaei Asli; Jan Flusser; Yifan Zhao; John Ahmet Erkoyuncu; Kajoli Banerjee Krishnan; Yasin Farrokhi; Rajkumar Roy. 2020. "Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure." IEEE Access 8, no. 99: 141342-141357.

Journal article
Published: 14 July 2020 in Procedia Manufacturing
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Counterfeit electronic components (CEC) are of great concern to governments and industry globally as they could lead to systems and mission failure, malfunctioning of safety critical systems, and reduced reliability of high-hazard assets. Depending on the cost of CEC going into the production line, some industries might look to have some sort of inspection capability in-house to screen critical components before they go to assembly. Although advanced counterfeit inspection methods have been developed for a variety of components, they generally exhibit a combination of disadvantages such as destructive, low throughput, high unit cost, or inaccessible to unskilled operator. This paper investigates the potential of pulsed thermography on detection of CEC in a fast and non-destructive manner. The second derivative of post-heat thermal response is used to construct a fingerprint to differentiate genuine and counterfeit components. Results successfully demonstrate the potential of the proposed solution.

ACS Style

Lawrence Tinsley; Haochen Liu; Sri Naga Pavan Addepalli; Wayne Lam; Yifan Zhao. Inspection of electronic component using pulsed thermography. Procedia Manufacturing 2020, 49, 132 -138.

AMA Style

Lawrence Tinsley, Haochen Liu, Sri Naga Pavan Addepalli, Wayne Lam, Yifan Zhao. Inspection of electronic component using pulsed thermography. Procedia Manufacturing. 2020; 49 ():132-138.

Chicago/Turabian Style

Lawrence Tinsley; Haochen Liu; Sri Naga Pavan Addepalli; Wayne Lam; Yifan Zhao. 2020. "Inspection of electronic component using pulsed thermography." Procedia Manufacturing 49, no. : 132-138.

Review
Published: 14 July 2020 in Procedia Manufacturing
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Data-driven techniques, especially on artificial intelligence (AI) such as deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the growth of industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of the industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper gives a brief introduction of RUL prediction and reviews the start-of-the-art DL approaches in terms of four main representative deep architectures, including Auto-encoder, Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It has been observed that DL techniques attract growing interests on RUL prediction that suggests a promising future of their applications in manufacturing.

ACS Style

Youdao Wang; Yifan Zhao; Sri Naga Pavan Addepalli. Remaining Useful Life Prediction using Deep Learning Approaches: A Review. Procedia Manufacturing 2020, 49, 81 -88.

AMA Style

Youdao Wang, Yifan Zhao, Sri Naga Pavan Addepalli. Remaining Useful Life Prediction using Deep Learning Approaches: A Review. Procedia Manufacturing. 2020; 49 ():81-88.

Chicago/Turabian Style

Youdao Wang; Yifan Zhao; Sri Naga Pavan Addepalli. 2020. "Remaining Useful Life Prediction using Deep Learning Approaches: A Review." Procedia Manufacturing 49, no. : 81-88.

Journal article
Published: 29 June 2020 in IEEE Sensors Journal
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It is of great importance to monitor the driver’s status to achieve an intelligent and safe take-over transition in the level 3 automated driving vehicle. We present a camera-based system to recognise the non-driving activities (NDAs) which may lead to different cognitive capabilities for take-over based on a fusion of spatial and temporal information. The region of interest (ROI) is automatically selected based on the extracted masks of the driver and the object/device interacting with. Then, the RGB image of the ROI (the spatial stream) and its associated current and historical optical flow frames (the temporal stream) are fed into a two-stream convolutional neural network (CNN) for the classification of NDAs. Such an approach is able to identify not only the object/device but also the interaction mode between the object and the driver, which enables a refined NDA classification. In this paper, we evaluated the performance of classifying 10 NDAs with two types of devices (tablet and phone) and 5 types of tasks (emailing, reading, watching videos, web-browsing and gaming) for 10 participants. Results show that the proposed system improves the averaged classification accuracy from 61.0% when using a single spatial stream to 90.5%.

ACS Style

Lichao Yang; Ting-Yu Yang; Haochen Liu; Xiaocai Shan; James Brighton; Lee Skrypchuk; Alexandros Mouzakitis; Yifan Zhao. A Refined Non-Driving Activity Classification Using a Two-Stream Convolutional Neural Network. IEEE Sensors Journal 2020, 21, 15574 -15583.

AMA Style

Lichao Yang, Ting-Yu Yang, Haochen Liu, Xiaocai Shan, James Brighton, Lee Skrypchuk, Alexandros Mouzakitis, Yifan Zhao. A Refined Non-Driving Activity Classification Using a Two-Stream Convolutional Neural Network. IEEE Sensors Journal. 2020; 21 (14):15574-15583.

Chicago/Turabian Style

Lichao Yang; Ting-Yu Yang; Haochen Liu; Xiaocai Shan; James Brighton; Lee Skrypchuk; Alexandros Mouzakitis; Yifan Zhao. 2020. "A Refined Non-Driving Activity Classification Using a Two-Stream Convolutional Neural Network." IEEE Sensors Journal 21, no. 14: 15574-15583.

Journal article
Published: 16 April 2020 in Computers and Electronics in Agriculture
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The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig. In the EU for instance, this capability is required for certification purposes. Although the RFID technology has been gradually developed and widely applied for this task, chip implanting might still be time-consuming and costly for current practical applications. In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition. Firstly, pig faces and eyes are detected automatically by two Haar feature-based cascade classifiers and one shallow convolutional neural network to extra high-quality images. Secondly, face recognition is performed by employing a deep convolutional neural network. Additionally, class activation maps generated by grad-CAM and saliency maps are utilised to visually understand how the discriminating parameters have been learned by the neural network. By applying the proposed approach on 10 randomly selected pigs filmed in farm condition, the proposed method demonstrates the superior performance against the state-of-art method with an accuracy of 83% over 320 testing images. The outcome of this study will facilitate the real-application of AI-based animal identification in swine production.

ACS Style

Mathieu Marsot; Jiangqiang Mei; Xiaocai Shan; Liyong Ye; Peng Feng; Xuejun Yan; Chenfan Li; Yifan Zhao. An adaptive pig face recognition approach using Convolutional Neural Networks. Computers and Electronics in Agriculture 2020, 173, 105386 .

AMA Style

Mathieu Marsot, Jiangqiang Mei, Xiaocai Shan, Liyong Ye, Peng Feng, Xuejun Yan, Chenfan Li, Yifan Zhao. An adaptive pig face recognition approach using Convolutional Neural Networks. Computers and Electronics in Agriculture. 2020; 173 ():105386.

Chicago/Turabian Style

Mathieu Marsot; Jiangqiang Mei; Xiaocai Shan; Liyong Ye; Peng Feng; Xuejun Yan; Chenfan Li; Yifan Zhao. 2020. "An adaptive pig face recognition approach using Convolutional Neural Networks." Computers and Electronics in Agriculture 173, no. : 105386.