Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
A calibration method for the five essential parameters is proposed. Using the calibration results, the three dimensional (3D) reconstruction can be performed directly. The five essential parameters include the distanc...A calibration method for the five essential parameters is proposed. Using the calibration results, the three dimensional (3D) reconstruction can be performed directly. The five essential parameters include the distance between the camera and the projector, the distance between the reference plane and the camera, the fundamental frequency of the fringe pattern, the scale factor from the image coordinates to the world coordinate system in X axis direction and that in Y axis direction. The proposed calibration method is implemented and tested in our 3D reconstruction system. The mean calibration error is found to be 0.0215 mm over a volume of 400 mm (H)×300 mm (V)×500 mm (D). The proposed calibration method is accurate and useful for the 3D reconstruction system.展开更多
The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating condi...The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.展开更多
Aiming at the low speed of traditional scale-invariant feature transform(SIFT) matching algorithm, an improved matching algorithm is proposed in this paper. Firstly, feature points are detected and the speed of featur...Aiming at the low speed of traditional scale-invariant feature transform(SIFT) matching algorithm, an improved matching algorithm is proposed in this paper. Firstly, feature points are detected and the speed of feature points matching is improved by adding epipolar constraint; then according to the matching feature points, the homography matrix is obtained by the least square method; finally, according to the homography matrix, the points in the left image can be mapped into the right image, and if the distance between the mapping point and the matching point in the right image is smaller than the threshold value, the pair of matching points is retained, otherwise discarded. Experimental results show that with the improved matching algorithm, the matching time is reduced by 73.3% and the matching points are entirely correct. In addition, the improved method is robust to rotation and translation.展开更多
This paper discusses the future power system consisting of distributed generations connected to local loads in the form of micro-grid systems.The benefits of having energy storage systems and the role of power electro...This paper discusses the future power system consisting of distributed generations connected to local loads in the form of micro-grid systems.The benefits of having energy storage systems and the role of power electronics in micro-grid systems are presented.This paper also examines how micro-grids have a key role to play in the development of the smart grid.展开更多
In this paper,we propose a model predictive control(MPC)strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints.Some special co...In this paper,we propose a model predictive control(MPC)strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints.Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation.Then,recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions.Moreover,the local optimality of the tracking MPC is achieved for unreachable output reference signals.By comparing to traditional tracking MPC,the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.展开更多
Gait energy image(GEI)is composed of static body silhouette and dynamic frequency information of human gait.To achieve fast and efficient gait recognition,combined with the accurate description of the information of d...Gait energy image(GEI)is composed of static body silhouette and dynamic frequency information of human gait.To achieve fast and efficient gait recognition,combined with the accurate description of the information of details and directions in image by Curvelet transform,a gait recognition method using GEI and Curvelet(GEIC)is presented.Firstly,to gain the gait energy images,the gait cycle is selected according to the aspect ratio.Secondly,Curvelet energy coefficients of the GEI,which are used as gait feature vector,are extracted by Curvelet transform in different scales and different directions.Finally,the gait recognition is accomplished by the K nearest neighbor(KNN)classifier.The experimental results demonstrate that GEIC performs well on CASIA(B)database,with the average accuracy of 86.83%.Compared with GEI+KPCA,GEI+W(2D)2PCA and GEI+(2D)~2PCA,the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.展开更多
Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,ha...Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.展开更多
Electrical transformers are vital components found virtually in most power-operated equipments. These transformers spontaneously radiate heat in both operation and steady-state mode. Should this thermal radiation inhe...Electrical transformers are vital components found virtually in most power-operated equipments. These transformers spontaneously radiate heat in both operation and steady-state mode. Should this thermal radiation inherent in transformers rises above allowable threshold a reduction in efficiency of operation occurs. In addition, this could cause other components in the system to malfunction. The aim of this work is to detect the remote causes of this undesirable thermal rise in transformers such as oil distribution transformers and ways to control this prevailing thermal problem. Oil transformers consist of these components: windings usually made of copper or aluminum conductor, the core normally made of silicon steel, the heat radiators, and the dielectric materials such as transformer oil, cellulose insulators and other peripherals. The Resistor-Inductor-Capacitor Thermal Network (RLCTN) model at architectural level identifies with these components to have ensemble operational mode as oil transformer. The Inductor represents the windings, the Resistor representing the core and the Capacitor represents the dielectrics. Thermography of transformer under various loading conditions was analyzed base on Infrared thermal gradient. Mathematical, experimental, and simulation results gotten through RLCTN with respect to time and thermal image analysis proved that the capacitance of the dielectric is inversely proportional to the thermal rise.展开更多
The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address th...The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.展开更多
A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect...A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.展开更多
A high-precision vision detection and measurement system using mobile robot is established for the industry field detection of motorcycle frame hole and its diameter measurement. The robot path planning method is rese...A high-precision vision detection and measurement system using mobile robot is established for the industry field detection of motorcycle frame hole and its diameter measurement. The robot path planning method is researched, and the non-contact measurement method with high precision based on visual digital image edge extraction and hole spatial circle fitting is presented. The Canny operator is used to extract the edge of captured image, the Lagrange interpolation algorithm is utilized to determine the missing image edge points and calculate the centroid, and the least squares fitting method is adopted to fit the image edge points. Experimental results show that the system can be used for the high-precision real-time measurement of hole on motorcycle frame. The absolute standard deviation of the proposed method is 0.026 7 mm. The proposed method can not only improve the measurement speed and precision, but also reduce the measurement error.展开更多
To quickly obtain accurate 3D data of dental cast model, this paper proposes a 3D reconstruction method for dental cast model based on structured light. This method combines the structured light with the motor turntab...To quickly obtain accurate 3D data of dental cast model, this paper proposes a 3D reconstruction method for dental cast model based on structured light. This method combines the structured light with the motor turntable to obtain a group of 3D data for the dental cast model from multiple angles, and automatically registers the dental 3D data from multiple angles through the ball calibration of turntable. Compared with the real data of the dental cast model, the maximum error of the 3D reconstruction results in this paper is 0.115 mm. The reconstruction time of this process is about 130s. The experimental results show that the method has high precision and high scanning speed for the 3D reconstruction of the dental cast model.展开更多
In order to realize the online measurement of lamp dimension,the bulb image dimension measurement based on vision(BIDMV)is proposed.The image of lamp is obtained by camera.After image processing,such as Otsu algorithm...In order to realize the online measurement of lamp dimension,the bulb image dimension measurement based on vision(BIDMV)is proposed.The image of lamp is obtained by camera.After image processing,such as Otsu algorithm,median filter,ellipse fitting and envelope rectangle fitting,the dimension of lamp can be calculated.Based on this method,a non-contact real-time measurement system of the lamp’s dimension is developed.The precision of the proposed method is 0.07 mm,and it can satisfy the tolerance of the National Standard GB15766.1-2008.The experiment results show that the proposed method has a faster measuring speed and a higher precision compared with other measurement methods.展开更多
This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural n...This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural network(RBFNN).The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible.展开更多
Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular netwo...Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.展开更多
To achieve accurate measurements, the creating a fitting hole for internal diameter(CFHID) measurement method and the establishing multi-sectional curve for external diameter(EMCED) measurement method are proposed in ...To achieve accurate measurements, the creating a fitting hole for internal diameter(CFHID) measurement method and the establishing multi-sectional curve for external diameter(EMCED) measurement method are proposed in this paper, which are based on computer vision principle and three-dimensional(3D) reconstruction. The methods are able to highlight the 3D characteristics of the scanned object and to achieve the accurate measurement of 3D data. It can create favorable conditions for realizing the reverse design and 3D reconstruction of scanned object. These methods can also be applied to dangerous work environment or the occasion that traditional contact measurement can not meet the demands, and they can improve the security in measurement.展开更多
The registration of point cloud is important for large object measurement.A measurement method for coordinate system transformation based on robot is proposed in this paper.Firstly,for obtaining extrinsic parameters,t...The registration of point cloud is important for large object measurement.A measurement method for coordinate system transformation based on robot is proposed in this paper.Firstly,for obtaining extrinsic parameters,the robot moves to three different positions to capture the images of three targets.Then the transformation matrix X between camera and tool center point(TCP) coordinate systems can be calculated by using the known parameters of robot and the extrinsic parameters,and finally the multi-view coordinate system can be transformed into robot coordinate system by the transformation matrix X.With the help of robot,the multi-view point cloud can be easily transformed into a unified coordinate system.By using robot,the measurement doesn't need any mark.Experimental results show that the method is effective.展开更多
This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system...This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.展开更多
The sensor virus is a serious threat,as an attacker can simply send a single packet to compromise the entire sensor network.Epidemics become drastic with link additions among sensors when the small world phenomena occ...The sensor virus is a serious threat,as an attacker can simply send a single packet to compromise the entire sensor network.Epidemics become drastic with link additions among sensors when the small world phenomena occur.Two immunization strategies,uniform immunization and temporary immunization,are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses.With the former strategy,the infection extends exponentially,although the immunization effectively reduces the contagion speed.With the latter strategy,recurrent contagion oscillations occur in the small world when the spatial-temporal dynamics of the epidemic are considered.The oscillations come from the small-world structure and the temporary immunization.Mathematical analyses on the small world of the Cayley tree are presented to reveal the epidemic dynamics with the two immunization strategies.展开更多
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
基金supported by the National Natural Science Foundation of China (Nos.60808020 and No. 61078041)the Tianjin Research Program of Application Foundation and Advanced Technology (No.10JCYBJC07200)
文摘A calibration method for the five essential parameters is proposed. Using the calibration results, the three dimensional (3D) reconstruction can be performed directly. The five essential parameters include the distance between the camera and the projector, the distance between the reference plane and the camera, the fundamental frequency of the fringe pattern, the scale factor from the image coordinates to the world coordinate system in X axis direction and that in Y axis direction. The proposed calibration method is implemented and tested in our 3D reconstruction system. The mean calibration error is found to be 0.0215 mm over a volume of 400 mm (H)×300 mm (V)×500 mm (D). The proposed calibration method is accurate and useful for the 3D reconstruction system.
基金the financial support from the National Natural Science Foundation of China(52207229)the financial support from the China Scholarship Council(202207550010)。
文摘The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.60808020 and 61078041)the National Science and Technology Support(No.2014BAH03F01)+1 种基金the Tianjin Research Program of Application Foundation and Advanced Technology(No.10JCYBJC07200)the Technology Program of Tianjin Municipal Education Commission(No.20130324)
文摘Aiming at the low speed of traditional scale-invariant feature transform(SIFT) matching algorithm, an improved matching algorithm is proposed in this paper. Firstly, feature points are detected and the speed of feature points matching is improved by adding epipolar constraint; then according to the matching feature points, the homography matrix is obtained by the least square method; finally, according to the homography matrix, the points in the left image can be mapped into the right image, and if the distance between the mapping point and the matching point in the right image is smaller than the threshold value, the pair of matching points is retained, otherwise discarded. Experimental results show that with the improved matching algorithm, the matching time is reduced by 73.3% and the matching points are entirely correct. In addition, the improved method is robust to rotation and translation.
基金funded by the ARC Linkage Grant LP100100618,Country Energy and the University of Wollongong
文摘This paper discusses the future power system consisting of distributed generations connected to local loads in the form of micro-grid systems.The benefits of having energy storage systems and the role of power electronics in micro-grid systems are presented.This paper also examines how micro-grids have a key role to play in the development of the smart grid.
基金the National Natural Science Foundation of China(61773345)the Zhejiang Provincial Major Projects Foundation of China(2020C03056).
文摘In this paper,we propose a model predictive control(MPC)strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints.Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation.Then,recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions.Moreover,the local optimality of the tracking MPC is achieved for unreachable output reference signals.By comparing to traditional tracking MPC,the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.
基金The Graduate Education Steering Committee of National Engineering Professional Degree (2016-ZX-064)Natural Science Foundation of Tianjin of China (16JCYBJC 15400)
文摘Gait energy image(GEI)is composed of static body silhouette and dynamic frequency information of human gait.To achieve fast and efficient gait recognition,combined with the accurate description of the information of details and directions in image by Curvelet transform,a gait recognition method using GEI and Curvelet(GEIC)is presented.Firstly,to gain the gait energy images,the gait cycle is selected according to the aspect ratio.Secondly,Curvelet energy coefficients of the GEI,which are used as gait feature vector,are extracted by Curvelet transform in different scales and different directions.Finally,the gait recognition is accomplished by the K nearest neighbor(KNN)classifier.The experimental results demonstrate that GEIC performs well on CASIA(B)database,with the average accuracy of 86.83%.Compared with GEI+KPCA,GEI+W(2D)2PCA and GEI+(2D)~2PCA,the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.
基金supported by the research grant(SEED-CCIS-2024-166),Prince Sultan University,Saudi Arabia。
文摘Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.
文摘Electrical transformers are vital components found virtually in most power-operated equipments. These transformers spontaneously radiate heat in both operation and steady-state mode. Should this thermal radiation inherent in transformers rises above allowable threshold a reduction in efficiency of operation occurs. In addition, this could cause other components in the system to malfunction. The aim of this work is to detect the remote causes of this undesirable thermal rise in transformers such as oil distribution transformers and ways to control this prevailing thermal problem. Oil transformers consist of these components: windings usually made of copper or aluminum conductor, the core normally made of silicon steel, the heat radiators, and the dielectric materials such as transformer oil, cellulose insulators and other peripherals. The Resistor-Inductor-Capacitor Thermal Network (RLCTN) model at architectural level identifies with these components to have ensemble operational mode as oil transformer. The Inductor represents the windings, the Resistor representing the core and the Capacitor represents the dielectrics. Thermography of transformer under various loading conditions was analyzed base on Infrared thermal gradient. Mathematical, experimental, and simulation results gotten through RLCTN with respect to time and thermal image analysis proved that the capacitance of the dielectric is inversely proportional to the thermal rise.
文摘The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.
文摘A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.60808020 and 61078041)the Tianjin Research Program of Application Foundation and Advanced Technology(No.10JCYBJC07200)
文摘A high-precision vision detection and measurement system using mobile robot is established for the industry field detection of motorcycle frame hole and its diameter measurement. The robot path planning method is researched, and the non-contact measurement method with high precision based on visual digital image edge extraction and hole spatial circle fitting is presented. The Canny operator is used to extract the edge of captured image, the Lagrange interpolation algorithm is utilized to determine the missing image edge points and calculate the centroid, and the least squares fitting method is adopted to fit the image edge points. Experimental results show that the system can be used for the high-precision real-time measurement of hole on motorcycle frame. The absolute standard deviation of the proposed method is 0.026 7 mm. The proposed method can not only improve the measurement speed and precision, but also reduce the measurement error.
基金supported by the National Natural Science Foundation of China(Nos.61078041 and 51806150)the Natural Science Foundation of Tianjin(Nos.16JCYBJC15400,15JCYBJC51700 and 18JCQNJC04400)+2 种基金the State Key Laboratory of Precision Measuring Technology and Instruments(Tianjin University)(PIL1603)the Program for Innovative Research Team in University of Tianjin(No.TD13-5036)Tianjin Enterprise Science and Technology Commissioner Project(No.18JCTPJC61700)
文摘To quickly obtain accurate 3D data of dental cast model, this paper proposes a 3D reconstruction method for dental cast model based on structured light. This method combines the structured light with the motor turntable to obtain a group of 3D data for the dental cast model from multiple angles, and automatically registers the dental 3D data from multiple angles through the ball calibration of turntable. Compared with the real data of the dental cast model, the maximum error of the 3D reconstruction results in this paper is 0.115 mm. The reconstruction time of this process is about 130s. The experimental results show that the method has high precision and high scanning speed for the 3D reconstruction of the dental cast model.
基金supported by the National Natural Science Foundation of China(Nos.60808020 and 61078041)the National Science and Technology Support(No.2014BAH03F01)+1 种基金the Tianjin Research Program of Application Foundation and Advanced Technology(No.10JCYBJC07200)the Technology Program of Tianjin Municipal Education Commission(No.20130324)
文摘In order to realize the online measurement of lamp dimension,the bulb image dimension measurement based on vision(BIDMV)is proposed.The image of lamp is obtained by camera.After image processing,such as Otsu algorithm,median filter,ellipse fitting and envelope rectangle fitting,the dimension of lamp can be calculated.Based on this method,a non-contact real-time measurement system of the lamp’s dimension is developed.The precision of the proposed method is 0.07 mm,and it can satisfy the tolerance of the National Standard GB15766.1-2008.The experiment results show that the proposed method has a faster measuring speed and a higher precision compared with other measurement methods.
基金supported by the Natural Science Foundation of Tianjin(Nos.16JCQNJC02100,15JCYBJC51700 and 16JCYBJC15400)the National Key Scientific Instrument and Equipment Development Project of China(No.2012YQ0901670602)+1 种基金the State Key Laboratory of Precision Measuring Technology and Instruments(Tianjin University,No.PIL1603)the Program for Innovative Research Team in University of Tianjin(No.TD13-5036)
文摘This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural network(RBFNN).The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible.
基金supported by the Key Scientific Research Project in Colleges and Universities of Henan Province of China(Grant Nos.21A510003)Science and the Key Science and Technology Research Project of Henan Province of China(Grant Nos.222102210053).
文摘Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.
基金supported by the National Natural Science Foundation of China(Nos.60808020 and 61078041)the Tianjin Research Program of Application Foundation and Advanced Technology(No.10JCYBJC07200)
文摘To achieve accurate measurements, the creating a fitting hole for internal diameter(CFHID) measurement method and the establishing multi-sectional curve for external diameter(EMCED) measurement method are proposed in this paper, which are based on computer vision principle and three-dimensional(3D) reconstruction. The methods are able to highlight the 3D characteristics of the scanned object and to achieve the accurate measurement of 3D data. It can create favorable conditions for realizing the reverse design and 3D reconstruction of scanned object. These methods can also be applied to dangerous work environment or the occasion that traditional contact measurement can not meet the demands, and they can improve the security in measurement.
基金supported by the National Natural Science Foundation of China(Nos.60808020 and 61078041)the National Science and Technology Support Program(No.2014BAH03F01)+2 种基金the Tianjin Research Program of Application Foundation and Advanced Technology(No.10JCYBJC07200)the Tianjin Small and Medium Enterprise Innovation Fund(No.12ZXCXGX11800)the Technology Program of Tianjin Municipal Education Commission(No.20130324)
文摘The registration of point cloud is important for large object measurement.A measurement method for coordinate system transformation based on robot is proposed in this paper.Firstly,for obtaining extrinsic parameters,the robot moves to three different positions to capture the images of three targets.Then the transformation matrix X between camera and tool center point(TCP) coordinate systems can be calculated by using the known parameters of robot and the extrinsic parameters,and finally the multi-view coordinate system can be transformed into robot coordinate system by the transformation matrix X.With the help of robot,the multi-view point cloud can be easily transformed into a unified coordinate system.By using robot,the measurement doesn't need any mark.Experimental results show that the method is effective.
基金funded by the ARC Linkage Grant LP LP0991428a URC Research Partnerships Grants Scheme, from the University of Wollongong
文摘This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.
文摘The sensor virus is a serious threat,as an attacker can simply send a single packet to compromise the entire sensor network.Epidemics become drastic with link additions among sensors when the small world phenomena occur.Two immunization strategies,uniform immunization and temporary immunization,are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses.With the former strategy,the infection extends exponentially,although the immunization effectively reduces the contagion speed.With the latter strategy,recurrent contagion oscillations occur in the small world when the spatial-temporal dynamics of the epidemic are considered.The oscillations come from the small-world structure and the temporary immunization.Mathematical analyses on the small world of the Cayley tree are presented to reveal the epidemic dynamics with the two immunization strategies.
