Urban Agglomeration(UA)is regarded as an emerging complex urban system in China.The development of UA demands a reasonable scale structure,which can be investigated by Zipf’s law.However,few studies have been conduct...Urban Agglomeration(UA)is regarded as an emerging complex urban system in China.The development of UA demands a reasonable scale structure,which can be investigated by Zipf’s law.However,few studies have been conducted to quantify the optimal scale of UA and how its development deviates from the optimal scale.With the continuous urban expansion,the problem of UAs’scale structure has received increasing attention.In this study,we propose a method based on Zipf’s law for estimating the theoretical optimal scale of UAs in China and assessing the deviation rate from their optimal scales.Twelve typical UAs in China are selected,and their development is assessed via urban impervious surface data from 2000 to 2018.The results show that the average deviation rate of the investigated UAs decreased from 3.40%in 2000 to 2.32%in 2018,demonstrating that these UAs are on a positive evolution trajectory.Furthermore,according to the development stage,we make recommendations on“large cities vs.medium/small-sized cities and promoting vs.restraining”to each UA based on its size.The conceptual and analytical knowledge,as well as the results from this study,are expected to offer valuable insights and new references for regulating and managing UAs’development in China.展开更多
With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driv...With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.展开更多
It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experi...It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.展开更多
In order to study the influence of the traffic characteristics on traffic accidents in extra long tunnel, the main measurement indicators of traffic flow during the time of traffic accidents are matched with the accid...In order to study the influence of the traffic characteristics on traffic accidents in extra long tunnel, the main measurement indicators of traffic flow during the time of traffic accidents are matched with the accident information to form a data set of the number of traffic accidents and the hourly traffic flow of the accident. Vehicle ratio and the number of accidents are mainly used as the characteristic indicators of traffic flow. At the same time, the longitudinal distribution law of the average speed of traffic flow and the number of traffic accidents in the extra long tunnel is studied. Based on the superposition principle, the extra long tunnel is divided into 5 traffic safety zones. This paper analyzes the distribution of time, morphology, cause of accident, and other characteristics in different traffic safety zones, finding that the shape of traffic accidents in extra long tunnel is mainly rear-end collisions. Improper operation and illegal lane changes are the main causes of accidents.展开更多
For Automatic Optical Inspection (AOI) machines that were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated. Images captured by old AO...For Automatic Optical Inspection (AOI) machines that were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated. Images captured by old AOI machines are not easy to be recognized by typical optical character recognition (OCR) algorithms, especially for dark silk. How to effectively increase silk recognition accuracy is indispensable for improving overall production efficiency in SMT plant. This paper uses fine tuned Character Region Awareness for Text Detection (CRAFT) method to build model for dark silk recognition. CRAFT model consists of a structure similar to U-net, followed by VGG based convolutional neural network. Continuous two-dimensional Gaussian distribution was used for the annotation of image segmentation. CRAFT model is good at recognizing different types of printed characters with high accuracy and transferability. Results show that with the help of CRAFT model, accuracy for OK board is 95% (error rate is 5%), and accuracy for NG board is 100% (omission rate is 0%).展开更多
In Electronics Manufacturing Services (EMS) industry, Printed Circuit Board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for...In Electronics Manufacturing Services (EMS) industry, Printed Circuit Board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for uneven heating in reflow soldering process. Conventional computer vision technique based on OpenCV or Halcon usually cause false positive call for originally good soldering point on PCB because OpenCV or Halcon use the pre-defined threshold in color proportion for deciding whether the specific soldering point is OK or NG (not good). However, soldering point forms are various after heating in reflow soldering process. This paper puts forward a VGG structure deep convolutional neural network, which is named SolderNet for processing soldering point after reflow heating process to effectively inspect soldering point status, reduce omission rate and error rate, and increase first pass rate. SolderNet consists of 11 hidden convolution layers and 3 densely connected layers. Accuracy reports are divided into OK point recognition and NG point recognition. For OK soldering point recognition, 92% is achieved. For NG soldering point recognition, 99% is achieved. The dataset is collected from KAGA Co. Ltd Plant in Suzhou. First pass rate at KAGA plant is increased from 25% to 80% in general.展开更多
Predicting short-term traffic crashes is chal-lenging due to an imbalanced data set characterized by excessive zeros in noncrash counts,random crash occur-rences,spatiotemporal correlation in crash counts,and inherent...Predicting short-term traffic crashes is chal-lenging due to an imbalanced data set characterized by excessive zeros in noncrash counts,random crash occur-rences,spatiotemporal correlation in crash counts,and inherent heterogeneity.Existing models struggle to effec-tively address these distinct characteristics in crash data.This paper proposes a new joint model by combining the time-series generalized regression neural network(TGRNN)model and the binomially weighted convolutional neural network(BWCNN)model.The joint model aims to capture all these characteristics in short-term crash predic-tion.The model was trained and tested using real-world,highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019,along with crash data extracted from the UK National Accident Database for the same year.The short-term is defined as a 30-min interval,providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards.The year was segmented into 30-min intervals,resulting in a highly imbalanced data set with over 99.99%noncrash samples.The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min.The findings revealed that 75.3%of crashes and 81.6%of noncrash events were correctly predicted in the southbound direction.In the northbound direction,78.1%of crashes and 80.2%of noncrash events were accurately captured.Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes.The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.展开更多
Accurate monitoring of photovoltaic(PV)spatial distribution using remote sensing imagery is critical for understanding energy production dynamics.The integration of spatial and spectral features facilitates precise id...Accurate monitoring of photovoltaic(PV)spatial distribution using remote sensing imagery is critical for understanding energy production dynamics.The integration of spatial and spectral features facilitates precise identification of diverse PV installation scenarios.However,existing methods primarily depend on single-source multispectral or high-resolution imagery,limiting their ability to balance spatial detail and spectral richness.To address this,this paper proposes a spatial-spectral differential semantic fusion network named FusionPV to comprehensively map PV locations within complex geographical environments.First,a spatial-spectral differential semantic aware module(SDAM)is proposed to extract spatial and spectral features related to PV discrimination from multimodal images.Subsequently,a dual-domain adaptive cross-fusion module(DAFM)is designed to deeply aggregate and cross-focus multimodal information using a cross-attention mechanism.Furthermore,a local-global semantic aggregation module(LGAM)is introduced to construct global descriptors by locally encoding and aggregating images,thereby enhancing contextual comprehension of intricate scenes.We construct a multimodal PV dataset by integrating GF-2 and Sentinel-2 imagery,focusing on Hubei Province,China.Experimental results demonstrate that FusionPV outperforms five state-of-the-art methods,achieving Kappa coefficient improvements ranging from 3.78%to 7.23%.Additionally,a comparison with four existing PV products indicates that FusionPV is a superior solution for acquiring a high-quality,extensive database of PV locations.展开更多
Fatigue is an important cause of traffic crashes,and effective fatigue detection models can reduce these crashes.Research has found large differences in fatigued driving performance from driver to driver,as well as a ...Fatigue is an important cause of traffic crashes,and effective fatigue detection models can reduce these crashes.Research has found large differences in fatigued driving performance from driver to driver,as well as a significant cumulative effect of fatigue on a given driver over time.Both sources of variation can decrease the accuracy of detection systems,but previous studies have not done enough to evaluate these differences.The purpose of this study is therefore to develop a fatigue detection model that considers individual differ ences and the time cumulative effect of fatigue.Data on the lateral position of the car in its lane,steering wheel movement,speed,and eye movement were collected from 22 dri vers using a driving simulator with an eye-tracking system.Drivers’subjective fatigue scores were collected using the Karolinska Sleepiness Scale.State space models(SSMs)were built to detect fatigue in each driver,considering his or her individual features.As a time series model,the SSM can also address the time cumulative effect of fatigue,and it does not require a large dataset to achieve high levels of accuracy.The differences in SSM results confirm that diversity does exist among drivers’fatigued driving performance,so the ability of the SSM to take into account driver-specific information from each individ ual driver suggests that it is more suitable for fatigue detection than models that use aggre gated driver data.Results show that the fatigue detection accuracy of the SSM(77.73%)is higher than that of artificial neural network models(61.37%).The advantages of accuracy,high interpretability,and flexibility make the SSM a comprehensive and valuable individ ualized fatigue detection model for commercial use.展开更多
Eco-friendly tin sulfide(SnS)has attracted increasing attention in the thermoelectric community because of its elemental abundance and analogous crystal structure to SnSe as a new thermoelectric material.However,so fa...Eco-friendly tin sulfide(SnS)has attracted increasing attention in the thermoelectric community because of its elemental abundance and analogous crystal structure to SnSe as a new thermoelectric material.However,so far no high dimensionless thermoelectric figure of merit ZT>1 was reported in SnS polycrystals.This work found an effective strategy for enhancing the thermoelectric performance of ptype polycrystalline SnS by Ag doping and vacancy engineering,leading to three orders of magnitude increase in carrier concentration and optimized effective mass and carrier mobility.As a result of the enhanced electrical conductivity,three times higher power factor ~3.85 μW/cm K^(2) at 877 K is realized in Sn_(0.995)Ag_(0.005)S sample.Interestingly,nanostructuring with Ag nano-precipitates were formed in the Agdoped SnS sample.Moreover,with introducing Sn vacancies in the crystal structure of Sn_(0.995-vac)Ag_(0.005)S,the power factor further enhanced to~4.25 μW/cm K^(2).In addition to the low-frequency phonons scattering by Ag nano-precipitates,dislocations strengthens the scattering of mid-frequency phonon,leading to an ultralow lattice thermal conductivity <0.5 W/m K above 800 K and a record high ZT up to 1.1 at 877 K in Sn_(0.99)Ag_(0.005)S polycrystals.展开更多
Despite an effective p-type dopant for PbTe, the low solubility of Na limits the fully optimization of thermoelectric properties of Na-doped PbTe. In this work, Na-doped PbTe was synthesized under high pressure. The f...Despite an effective p-type dopant for PbTe, the low solubility of Na limits the fully optimization of thermoelectric properties of Na-doped PbTe. In this work, Na-doped PbTe was synthesized under high pressure. The formation of the desired rocksalt phase with substantially increased Na content leads to a high carrier concentration of 3.2×10^20 cm^-3 for Na0.03Pb0.97Te. Moreover, dense in-grain dislocations are identified from the microstructure analysis. Benefited from the improved power factor and greatly suppressed lattice thermal conductivity, the maximal ZT of 1.7 is achieved in the optimal Na0.03Pb0.97Te. Current work thus designates the advantage of high pressure in synthesizing PbTe-based thermoelectric materials.展开更多
Isobaric specific heat capacity(Cp)is an important parameter not only in physics but also for most materials.Its accurate measurement is particularly critical for performance evaluation of thermoelectric materials,but...Isobaric specific heat capacity(Cp)is an important parameter not only in physics but also for most materials.Its accurate measurement is particularly critical for performance evaluation of thermoelectric materials,but the experiments by differential scanning calorimetry(DSC)often lead to large uncertainties in the measurements,especially at elevated temperatures.In this study,we propose a simple method to determine Cp by measuring the sound velocity(υ)based on lattice vibration and expansion theory.The relative standard error of theυis smaller than 1%,showing good accuracy and repeatability.The calculated Cp at elevated temperature(>300 K)increases slightly with increasing temperature due to the lattice expansion,which is more reasonable than the Dulong–Petit value.展开更多
Fabrication of nanoparticle-dispersed composites is an effective strategy for enhancing the performance of thermoelectric materials,and in particular SiC nanoparticles have been often used to create composites with Bi...Fabrication of nanoparticle-dispersed composites is an effective strategy for enhancing the performance of thermoelectric materials,and in particular SiC nanoparticles have been often used to create composites with Bi_(2)Te_(3)-based applied thermoelectric materials.However,the effect of particle size on the thermoelectric performance is unclear.This work systematically investigated the electrical and thermal properties of a series of(Bi,Sb)_(2)Te_(3)-based nanocomposites containing dispersed SiC nanoparticles of different sizes.It was found that particle size has a significant impact on the electrical properties with smaller SiC nanoparticles giving rise to higher electrical conductivity.Even though the dispersed SiC nanoparticles enhanced the Seebeck coefficient,no apparent dependence of the enhancement on the particle size was observed.It was also found that smaller SiC nanoparticles scatter phonons to some extent while the larger nanoparticles contribute to increased thermal conductivity.Eventually,the highest ZT value of 1.12 was obtained in 30 nm-SiC dispersed sample,corresponding to an increase by 18%from 0.95 for the matrix made from commercial scraps,and then the ZT was further boosted to 1.33 by optimizing the matrix composition and expelling excess Te during the optimized spark plasma sintering process.This work proves that the dispersion of smaller SiC nanoparticles in p-type(Bi,Sb)_(2)Te_(3) materials is more effective than the dispersion of larger nanoparticles.In addition,it is revealed that additional compositional and/or processing optimization is vital and effective for obtaining further performance enhancement for nanocomposites of SiC nanoparticles dispersed in(Bi,Sb)_(2)Te_(3).展开更多
Extremely low lattice thermal conductivity is always the pursuit of thermoelectric materials research.In this work,we reported an exceptional effect of Ag2S addition in MnTe,an emerging promising midtemperature thermo...Extremely low lattice thermal conductivity is always the pursuit of thermoelectric materials research.In this work,we reported an exceptional effect of Ag2S addition in MnTe,an emerging promising midtemperature thermoelectric material,to enable the realization of minimum lattice thermal conductivity,namely-0.4 Wm^(-1) K^(-1).Such a low lattice thermal conductivity is guaranteed by the incorporation of in-situ formed Ag rich phase(Ag2Te)with ultralow lattice thermal conductivity and further scattering of phonons from the partial doping effects induced point defects and boundaries between various phases.Apart from the dramatically decreased lattice thermal conductivity,the partial doping of Ag and S simultaneously enhance the electrical conductivity,further contributing to enhanced thermoelectric performance.Meanwhile,an inverse sign of Seebeck and Hall coefficient was observed and rationalized by the influence of highly electron-conductive Ag_(2)Te phase.Thanks to the synergetic modulation of electrical and thermal transport properties by in-situ formed composite,a high ZT value of 1.1 was achieved in MnTe based thermoelectric materials,which also demonstrates the importance of compositing approaches to design state-of-the-art thermoelectric materials.展开更多
基金supported in part by the National Natural Science Foundation of China[Grants number 42090012]03 Special Research and 5G Project of Jiangxi Province in China[Grants number 20212ABC03A09]Zhuhai Industry University Research Cooperation Project of China[Grants number ZH22017001210098PWC].
文摘Urban Agglomeration(UA)is regarded as an emerging complex urban system in China.The development of UA demands a reasonable scale structure,which can be investigated by Zipf’s law.However,few studies have been conducted to quantify the optimal scale of UA and how its development deviates from the optimal scale.With the continuous urban expansion,the problem of UAs’scale structure has received increasing attention.In this study,we propose a method based on Zipf’s law for estimating the theoretical optimal scale of UAs in China and assessing the deviation rate from their optimal scales.Twelve typical UAs in China are selected,and their development is assessed via urban impervious surface data from 2000 to 2018.The results show that the average deviation rate of the investigated UAs decreased from 3.40%in 2000 to 2.32%in 2018,demonstrating that these UAs are on a positive evolution trajectory.Furthermore,according to the development stage,we make recommendations on“large cities vs.medium/small-sized cities and promoting vs.restraining”to each UA based on its size.The conceptual and analytical knowledge,as well as the results from this study,are expected to offer valuable insights and new references for regulating and managing UAs’development in China.
文摘With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.
文摘It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.
文摘In order to study the influence of the traffic characteristics on traffic accidents in extra long tunnel, the main measurement indicators of traffic flow during the time of traffic accidents are matched with the accident information to form a data set of the number of traffic accidents and the hourly traffic flow of the accident. Vehicle ratio and the number of accidents are mainly used as the characteristic indicators of traffic flow. At the same time, the longitudinal distribution law of the average speed of traffic flow and the number of traffic accidents in the extra long tunnel is studied. Based on the superposition principle, the extra long tunnel is divided into 5 traffic safety zones. This paper analyzes the distribution of time, morphology, cause of accident, and other characteristics in different traffic safety zones, finding that the shape of traffic accidents in extra long tunnel is mainly rear-end collisions. Improper operation and illegal lane changes are the main causes of accidents.
文摘For Automatic Optical Inspection (AOI) machines that were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated. Images captured by old AOI machines are not easy to be recognized by typical optical character recognition (OCR) algorithms, especially for dark silk. How to effectively increase silk recognition accuracy is indispensable for improving overall production efficiency in SMT plant. This paper uses fine tuned Character Region Awareness for Text Detection (CRAFT) method to build model for dark silk recognition. CRAFT model consists of a structure similar to U-net, followed by VGG based convolutional neural network. Continuous two-dimensional Gaussian distribution was used for the annotation of image segmentation. CRAFT model is good at recognizing different types of printed characters with high accuracy and transferability. Results show that with the help of CRAFT model, accuracy for OK board is 95% (error rate is 5%), and accuracy for NG board is 100% (omission rate is 0%).
文摘In Electronics Manufacturing Services (EMS) industry, Printed Circuit Board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for uneven heating in reflow soldering process. Conventional computer vision technique based on OpenCV or Halcon usually cause false positive call for originally good soldering point on PCB because OpenCV or Halcon use the pre-defined threshold in color proportion for deciding whether the specific soldering point is OK or NG (not good). However, soldering point forms are various after heating in reflow soldering process. This paper puts forward a VGG structure deep convolutional neural network, which is named SolderNet for processing soldering point after reflow heating process to effectively inspect soldering point status, reduce omission rate and error rate, and increase first pass rate. SolderNet consists of 11 hidden convolution layers and 3 densely connected layers. Accuracy reports are divided into OK point recognition and NG point recognition. For OK soldering point recognition, 92% is achieved. For NG soldering point recognition, 99% is achieved. The dataset is collected from KAGA Co. Ltd Plant in Suzhou. First pass rate at KAGA plant is increased from 25% to 80% in general.
文摘Predicting short-term traffic crashes is chal-lenging due to an imbalanced data set characterized by excessive zeros in noncrash counts,random crash occur-rences,spatiotemporal correlation in crash counts,and inherent heterogeneity.Existing models struggle to effec-tively address these distinct characteristics in crash data.This paper proposes a new joint model by combining the time-series generalized regression neural network(TGRNN)model and the binomially weighted convolutional neural network(BWCNN)model.The joint model aims to capture all these characteristics in short-term crash predic-tion.The model was trained and tested using real-world,highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019,along with crash data extracted from the UK National Accident Database for the same year.The short-term is defined as a 30-min interval,providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards.The year was segmented into 30-min intervals,resulting in a highly imbalanced data set with over 99.99%noncrash samples.The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min.The findings revealed that 75.3%of crashes and 81.6%of noncrash events were correctly predicted in the southbound direction.In the northbound direction,78.1%of crashes and 80.2%of noncrash events were accurately captured.Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes.The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.
基金supported by the National Key Research and Development Program of China(2023YFB3906105)Yunnan key R&D plan(202403ZC380001)+3 种基金Fundamental Research Funds for the Central Universities(2042024kf0038)Sichuan Science and Technology Pro-gram(2022YFN0031)Stable support for scientific research projects in key laboratories(WDZC20245250203)Fundamental Research Fund Program of LIESMARS(4201-420100071).
文摘Accurate monitoring of photovoltaic(PV)spatial distribution using remote sensing imagery is critical for understanding energy production dynamics.The integration of spatial and spectral features facilitates precise identification of diverse PV installation scenarios.However,existing methods primarily depend on single-source multispectral or high-resolution imagery,limiting their ability to balance spatial detail and spectral richness.To address this,this paper proposes a spatial-spectral differential semantic fusion network named FusionPV to comprehensively map PV locations within complex geographical environments.First,a spatial-spectral differential semantic aware module(SDAM)is proposed to extract spatial and spectral features related to PV discrimination from multimodal images.Subsequently,a dual-domain adaptive cross-fusion module(DAFM)is designed to deeply aggregate and cross-focus multimodal information using a cross-attention mechanism.Furthermore,a local-global semantic aggregation module(LGAM)is introduced to construct global descriptors by locally encoding and aggregating images,thereby enhancing contextual comprehension of intricate scenes.We construct a multimodal PV dataset by integrating GF-2 and Sentinel-2 imagery,focusing on Hubei Province,China.Experimental results demonstrate that FusionPV outperforms five state-of-the-art methods,achieving Kappa coefficient improvements ranging from 3.78%to 7.23%.Additionally,a comparison with four existing PV products indicates that FusionPV is a superior solution for acquiring a high-quality,extensive database of PV locations.
基金This study was jointly sponsored by the National Key R&D Program of China(2018YFB0105202).
文摘Fatigue is an important cause of traffic crashes,and effective fatigue detection models can reduce these crashes.Research has found large differences in fatigued driving performance from driver to driver,as well as a significant cumulative effect of fatigue on a given driver over time.Both sources of variation can decrease the accuracy of detection systems,but previous studies have not done enough to evaluate these differences.The purpose of this study is therefore to develop a fatigue detection model that considers individual differ ences and the time cumulative effect of fatigue.Data on the lateral position of the car in its lane,steering wheel movement,speed,and eye movement were collected from 22 dri vers using a driving simulator with an eye-tracking system.Drivers’subjective fatigue scores were collected using the Karolinska Sleepiness Scale.State space models(SSMs)were built to detect fatigue in each driver,considering his or her individual features.As a time series model,the SSM can also address the time cumulative effect of fatigue,and it does not require a large dataset to achieve high levels of accuracy.The differences in SSM results confirm that diversity does exist among drivers’fatigued driving performance,so the ability of the SSM to take into account driver-specific information from each individ ual driver suggests that it is more suitable for fatigue detection than models that use aggre gated driver data.Results show that the fatigue detection accuracy of the SSM(77.73%)is higher than that of artificial neural network models(61.37%).The advantages of accuracy,high interpretability,and flexibility make the SSM a comprehensive and valuable individ ualized fatigue detection model for commercial use.
基金supported by the Basic Science Center Project of Natural Science Foundation of China(51788104)the National Key R&D Program of China(2018YFB0703603).
文摘Eco-friendly tin sulfide(SnS)has attracted increasing attention in the thermoelectric community because of its elemental abundance and analogous crystal structure to SnSe as a new thermoelectric material.However,so far no high dimensionless thermoelectric figure of merit ZT>1 was reported in SnS polycrystals.This work found an effective strategy for enhancing the thermoelectric performance of ptype polycrystalline SnS by Ag doping and vacancy engineering,leading to three orders of magnitude increase in carrier concentration and optimized effective mass and carrier mobility.As a result of the enhanced electrical conductivity,three times higher power factor ~3.85 μW/cm K^(2) at 877 K is realized in Sn_(0.995)Ag_(0.005)S sample.Interestingly,nanostructuring with Ag nano-precipitates were formed in the Agdoped SnS sample.Moreover,with introducing Sn vacancies in the crystal structure of Sn_(0.995-vac)Ag_(0.005)S,the power factor further enhanced to~4.25 μW/cm K^(2).In addition to the low-frequency phonons scattering by Ag nano-precipitates,dislocations strengthens the scattering of mid-frequency phonon,leading to an ultralow lattice thermal conductivity <0.5 W/m K above 800 K and a record high ZT up to 1.1 at 877 K in Sn_(0.99)Ag_(0.005)S polycrystals.
基金supported by the National Natural Science Foundation of China (51525205, 51421091, and 51722209)the Key Basic Research Project of Hebei (14961013D)
文摘Despite an effective p-type dopant for PbTe, the low solubility of Na limits the fully optimization of thermoelectric properties of Na-doped PbTe. In this work, Na-doped PbTe was synthesized under high pressure. The formation of the desired rocksalt phase with substantially increased Na content leads to a high carrier concentration of 3.2×10^20 cm^-3 for Na0.03Pb0.97Te. Moreover, dense in-grain dislocations are identified from the microstructure analysis. Benefited from the improved power factor and greatly suppressed lattice thermal conductivity, the maximal ZT of 1.7 is achieved in the optimal Na0.03Pb0.97Te. Current work thus designates the advantage of high pressure in synthesizing PbTe-based thermoelectric materials.
基金Basic Science Center Project of NSFC,Grant/Award Number:51788104National Key R&D Program of China,Grant/Award Number:2018YFB0703603。
文摘Isobaric specific heat capacity(Cp)is an important parameter not only in physics but also for most materials.Its accurate measurement is particularly critical for performance evaluation of thermoelectric materials,but the experiments by differential scanning calorimetry(DSC)often lead to large uncertainties in the measurements,especially at elevated temperatures.In this study,we propose a simple method to determine Cp by measuring the sound velocity(υ)based on lattice vibration and expansion theory.The relative standard error of theυis smaller than 1%,showing good accuracy and repeatability.The calculated Cp at elevated temperature(>300 K)increases slightly with increasing temperature due to the lattice expansion,which is more reasonable than the Dulong–Petit value.
基金supported by the Basic Science Center Project of the National Natural Science Foundation of China(51788104)the National Key R&D Program of China(2018YFB0703603)。
文摘Fabrication of nanoparticle-dispersed composites is an effective strategy for enhancing the performance of thermoelectric materials,and in particular SiC nanoparticles have been often used to create composites with Bi_(2)Te_(3)-based applied thermoelectric materials.However,the effect of particle size on the thermoelectric performance is unclear.This work systematically investigated the electrical and thermal properties of a series of(Bi,Sb)_(2)Te_(3)-based nanocomposites containing dispersed SiC nanoparticles of different sizes.It was found that particle size has a significant impact on the electrical properties with smaller SiC nanoparticles giving rise to higher electrical conductivity.Even though the dispersed SiC nanoparticles enhanced the Seebeck coefficient,no apparent dependence of the enhancement on the particle size was observed.It was also found that smaller SiC nanoparticles scatter phonons to some extent while the larger nanoparticles contribute to increased thermal conductivity.Eventually,the highest ZT value of 1.12 was obtained in 30 nm-SiC dispersed sample,corresponding to an increase by 18%from 0.95 for the matrix made from commercial scraps,and then the ZT was further boosted to 1.33 by optimizing the matrix composition and expelling excess Te during the optimized spark plasma sintering process.This work proves that the dispersion of smaller SiC nanoparticles in p-type(Bi,Sb)_(2)Te_(3) materials is more effective than the dispersion of larger nanoparticles.In addition,it is revealed that additional compositional and/or processing optimization is vital and effective for obtaining further performance enhancement for nanocomposites of SiC nanoparticles dispersed in(Bi,Sb)_(2)Te_(3).
基金supported by the National Key R&D Program of China(No.2018YFB0703603)。
文摘Extremely low lattice thermal conductivity is always the pursuit of thermoelectric materials research.In this work,we reported an exceptional effect of Ag2S addition in MnTe,an emerging promising midtemperature thermoelectric material,to enable the realization of minimum lattice thermal conductivity,namely-0.4 Wm^(-1) K^(-1).Such a low lattice thermal conductivity is guaranteed by the incorporation of in-situ formed Ag rich phase(Ag2Te)with ultralow lattice thermal conductivity and further scattering of phonons from the partial doping effects induced point defects and boundaries between various phases.Apart from the dramatically decreased lattice thermal conductivity,the partial doping of Ag and S simultaneously enhance the electrical conductivity,further contributing to enhanced thermoelectric performance.Meanwhile,an inverse sign of Seebeck and Hall coefficient was observed and rationalized by the influence of highly electron-conductive Ag_(2)Te phase.Thanks to the synergetic modulation of electrical and thermal transport properties by in-situ formed composite,a high ZT value of 1.1 was achieved in MnTe based thermoelectric materials,which also demonstrates the importance of compositing approaches to design state-of-the-art thermoelectric materials.
