As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the...As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.展开更多
Electronic Medical Records(EMR) with unstructured sentences and various conceptual expressions provide rich information for medical information extraction. However, common Named Entity Recognition(NER)in Natural Langu...Electronic Medical Records(EMR) with unstructured sentences and various conceptual expressions provide rich information for medical information extraction. However, common Named Entity Recognition(NER)in Natural Language Processing(NLP) are not well suitable for clinical NER in EMR. This study aims at applying neural networks to clinical concept extractions. We integrate Bidirectional Long Short-Term Memory Networks(Bi-LSTM) with a Conditional Random Fields(CRF) layer to detect three types of clinical named entities. Word representations fed into the neural networks are concatenated by character-based word embeddings and Continuous Bag of Words(CBOW) embeddings trained both on domain and non-domain corpus. We test our NER system on i2b2/VA open datasets and compare the performance with six related works, achieving the best result of NER with F1 value 0.853 7. We also point out a few specific problems in clinical concept extractions which will give some hints to deeper studies.展开更多
In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,ha...In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.展开更多
High-efficiency electromagnetic interference(EMI)shielding materials are of great importance for electronic equipment reliability,information security and human health.In this work,bidirectional aligned Ti_(3)C_(2)T_(...High-efficiency electromagnetic interference(EMI)shielding materials are of great importance for electronic equipment reliability,information security and human health.In this work,bidirectional aligned Ti_(3)C_(2)T_(x)@Fe_(3)O_(4)/CNF aerogels(BTFCA)were firstly assembled by bidirectional freezing and freeze-drying technique,and the BTFCA/epoxy nanocomposites with long-range aligned lamellar structures were then prepared by vacuum-assisted impregnation of epoxy resins.Benefitting from the successful construction of bidirectional aligned three-dimensional conductive networks and electromagnetic synergistic effect,when the mass fraction of Ti_(3)C_(2)T_(x) and Fe_(3)O_(4) are 2.96 and 1.48 wt%,BTFCA/epoxy nanocomposites show outstanding EMI shield-ing effectiveness of 79 dB,about 10 times of that of blended Ti_(3)C_(2)T_(x)@Fe_(3)O_(4)/epoxy(8 dB)nanocomposites with the same loadings of Ti_(3)C_(2)T_(x) and Fe_(3)O_(4).Meantime,the corresponding BTFCA/epoxy nanocomposites also present excellent thermal stability(T_(heat-resistance index) of 198.7℃)and mechanical properties(storage modulus of 9902.1 MPa,Young’s modulus of 4.51 GPa and hardness of 0.34 GPa).Our fabricated BTFCA/epoxy nanocomposites would greatly expand the applications of MXene and epoxy resins in the fields of information security,aerospace and weapon manufacturing,etc.展开更多
A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar...A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.展开更多
The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention R...The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.展开更多
Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM,...Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.展开更多
Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activitie...Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.展开更多
Metasurfaces,which are the two-dimensional counterparts of metamaterials,have demonstrated unprecedented capabilities to manipulate the wavefront of electromagnetic waves in a single flat device.Despite various advanc...Metasurfaces,which are the two-dimensional counterparts of metamaterials,have demonstrated unprecedented capabilities to manipulate the wavefront of electromagnetic waves in a single flat device.Despite various advances in this field,the unique functionalities achieved by metasurfaces have come at the cost of the structural complexity,resulting in a time-consuming parameter sweep for the conventional metasurface design.Although artificial neural networks provide a flexible platform for significantly improving the design process,the current metasurface designs are restricted to generating qualitative field distributions.In this study,we demonstrate that by combining a tandem neural network and an iterative algorithm,the previous restriction of the design of metasurfaces can be overcome with quantitative field distributions.As proof-of-principle examples,metalenses predicted via the designed network architecture that possess multiple focal points with identical/orthogonal polarisation states,as well as accurate intensity ratios(quantitative field distributions),were numerically calculated and experimentally demonstrated.The unique and robust approach for the metasurface design will enable the acceleration of the development of devices with high-accuracy functionalities,which can be applied in imaging,detecting,and sensing.展开更多
Study of internal-wave and internal-tide deposits is a very young research field in deep-water sedimentology. It has been just twenty years since the first example of internal-wave and internal-tide deposits was ident...Study of internal-wave and internal-tide deposits is a very young research field in deep-water sedimentology. It has been just twenty years since the first example of internal-wave and internal-tide deposits was identified in the stratigraphic record. Since that time, Chinese scholars have made unremitting efforts and gained some significant research achievements in this field. This paper briefly outlines the history and main achievements of research of internal-wave and internal-tide deposits in China, describes depositional charac-teristics, sedimentary successions, types of lithofacies, and depositional models of internal-wave and internal-tide deposits identified mainly from ancient strata, and summarizes the existing problems in this research field. New advances in marine physics should be applied to research of the subject of internal-wave and internal-tide deposition, whereas the sedimentary characteristics of internal-wave and internal-tide deposits may be used to deduce the physical processes of their creation. Flume experiments on internal-wave and internal-tide deposition should also be put in practice as often as possible, so that the mechanisms of internal-wave and internal-tide deposition can be explored.展开更多
In this paper, total lightning data observed by SAFIR3000 3-D Lightning Locating System was combined with radar data to analyze characteristics of the lightning activity and electric structure of a hailstorm that occu...In this paper, total lightning data observed by SAFIR3000 3-D Lightning Locating System was combined with radar data to analyze characteristics of the lightning activity and electric structure of a hailstorm that occurred in Beijing on 31 May 2005. The results indicated that there were two active periods for the lightning activity during the hailstorm process. The hail shooting was found in the first period. After the end of the hail shooting, lightning frequency decreased suddenly. However, more active lightning activities occurred in the second period with lots of them appearing in the cloud anvil region. The peak of the lightning frequency came about 5 min prior to the hail shooting. Only 6.16% of the total lightning was cloud-to-ground （CG） lightning, among which 20% had positive polarity. This percentage was higher than that in normal thunderstorms. In addition, heavier positive CG lightning discharge occurred before rather than after the hail shooting. In the stage of the hail shooting, the electric structure of the hailstorm was inverted, with the main negative charge region located around the -40℃ level and the main positive charge region around the -15℃ level. In addition, a weak negative charge region existed below the positive charge region transitorily. After the hail shooting, the electric structure underwent fast and persistent adjustments and became a normal tripole, with positive charge in the upper and lower levels and negative charge in the middle levels. However, the electric structure was tilted under the influence of the westerly wind in the middle and upper levels. The lightning activity and electric structure were closely related to the dynamic and microphysical processes of the hailstorm. It was believed that severe storms with stronger updrafts were more conducive to an inverted tripolar electric structure than normal thunderstorms, and the inverted distribution could then facilitate more positive CG lightning in the severe storms.展开更多
The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved fr...The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.展开更多
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(pICRI-IACVq)
文摘As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
基金the National Social Science Foundation of China(No.17BYY047)
文摘Electronic Medical Records(EMR) with unstructured sentences and various conceptual expressions provide rich information for medical information extraction. However, common Named Entity Recognition(NER)in Natural Language Processing(NLP) are not well suitable for clinical NER in EMR. This study aims at applying neural networks to clinical concept extractions. We integrate Bidirectional Long Short-Term Memory Networks(Bi-LSTM) with a Conditional Random Fields(CRF) layer to detect three types of clinical named entities. Word representations fed into the neural networks are concatenated by character-based word embeddings and Continuous Bag of Words(CBOW) embeddings trained both on domain and non-domain corpus. We test our NER system on i2b2/VA open datasets and compare the performance with six related works, achieving the best result of NER with F1 value 0.853 7. We also point out a few specific problems in clinical concept extractions which will give some hints to deeper studies.
基金Supported by the Science and Technological Tackling Project of Heilongjiang Province(GB06A106)
文摘In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.
基金The authors are grateful for the supports from the National Natural Science Foundation of China(U21A2093 and 52203100)Y.L.Zhang would like to thank the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(CX2021107)。
文摘High-efficiency electromagnetic interference(EMI)shielding materials are of great importance for electronic equipment reliability,information security and human health.In this work,bidirectional aligned Ti_(3)C_(2)T_(x)@Fe_(3)O_(4)/CNF aerogels(BTFCA)were firstly assembled by bidirectional freezing and freeze-drying technique,and the BTFCA/epoxy nanocomposites with long-range aligned lamellar structures were then prepared by vacuum-assisted impregnation of epoxy resins.Benefitting from the successful construction of bidirectional aligned three-dimensional conductive networks and electromagnetic synergistic effect,when the mass fraction of Ti_(3)C_(2)T_(x) and Fe_(3)O_(4) are 2.96 and 1.48 wt%,BTFCA/epoxy nanocomposites show outstanding EMI shield-ing effectiveness of 79 dB,about 10 times of that of blended Ti_(3)C_(2)T_(x)@Fe_(3)O_(4)/epoxy(8 dB)nanocomposites with the same loadings of Ti_(3)C_(2)T_(x) and Fe_(3)O_(4).Meantime,the corresponding BTFCA/epoxy nanocomposites also present excellent thermal stability(T_(heat-resistance index) of 198.7℃)and mechanical properties(storage modulus of 9902.1 MPa,Young’s modulus of 4.51 GPa and hardness of 0.34 GPa).Our fabricated BTFCA/epoxy nanocomposites would greatly expand the applications of MXene and epoxy resins in the fields of information security,aerospace and weapon manufacturing,etc.
基金the National Natural Science Foundation of China (Grant No.40730525)Knowledge Innovation Engineering Project of Chinese Academy of Sciences (Grant No.KZCX2-YW-313)China's Special Funds for Major State Basic Research Project (Grant No.2007CB714401)
文摘A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.
基金supported by the National Natural Science Foundation of China(Nos.62106283 and 72001214)。
文摘The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.
基金supported by the National Natural Science Foundation of China (No. 61572505)ChanXueYan Prospective Project of Jiangsu Province (No. BY201502305)
文摘Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.
基金Supported by the Zhejiang Provincial Natural Science Foundation(No.LQ16H180004)~~
文摘Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.
基金the National Key Research and Development Program of China(2017YFA0701005)National Natural Science Foundation of China(62271320,61871268)+1 种基金“Shuguang”Program of Shanghai Education Commission(19SG44)the 111 Project(D18014).
文摘Metasurfaces,which are the two-dimensional counterparts of metamaterials,have demonstrated unprecedented capabilities to manipulate the wavefront of electromagnetic waves in a single flat device.Despite various advances in this field,the unique functionalities achieved by metasurfaces have come at the cost of the structural complexity,resulting in a time-consuming parameter sweep for the conventional metasurface design.Although artificial neural networks provide a flexible platform for significantly improving the design process,the current metasurface designs are restricted to generating qualitative field distributions.In this study,we demonstrate that by combining a tandem neural network and an iterative algorithm,the previous restriction of the design of metasurfaces can be overcome with quantitative field distributions.As proof-of-principle examples,metalenses predicted via the designed network architecture that possess multiple focal points with identical/orthogonal polarisation states,as well as accurate intensity ratios(quantitative field distributions),were numerically calculated and experimentally demonstrated.The unique and robust approach for the metasurface design will enable the acceleration of the development of devices with high-accuracy functionalities,which can be applied in imaging,detecting,and sensing.
基金funded by the National Natural Science Foundation of China (No.40672071 and 41072086)the Research Fund for the Doctoral Program of Higher Education in China (No.20104220110002)
文摘Study of internal-wave and internal-tide deposits is a very young research field in deep-water sedimentology. It has been just twenty years since the first example of internal-wave and internal-tide deposits was identified in the stratigraphic record. Since that time, Chinese scholars have made unremitting efforts and gained some significant research achievements in this field. This paper briefly outlines the history and main achievements of research of internal-wave and internal-tide deposits in China, describes depositional charac-teristics, sedimentary successions, types of lithofacies, and depositional models of internal-wave and internal-tide deposits identified mainly from ancient strata, and summarizes the existing problems in this research field. New advances in marine physics should be applied to research of the subject of internal-wave and internal-tide deposition, whereas the sedimentary characteristics of internal-wave and internal-tide deposits may be used to deduce the physical processes of their creation. Flume experiments on internal-wave and internal-tide deposition should also be put in practice as often as possible, so that the mechanisms of internal-wave and internal-tide deposition can be explored.
基金Supported by the Natural Science Foundation of China under Grant No.40875003the National Basic Research Program of China under No.2004CB418306.
文摘In this paper, total lightning data observed by SAFIR3000 3-D Lightning Locating System was combined with radar data to analyze characteristics of the lightning activity and electric structure of a hailstorm that occurred in Beijing on 31 May 2005. The results indicated that there were two active periods for the lightning activity during the hailstorm process. The hail shooting was found in the first period. After the end of the hail shooting, lightning frequency decreased suddenly. However, more active lightning activities occurred in the second period with lots of them appearing in the cloud anvil region. The peak of the lightning frequency came about 5 min prior to the hail shooting. Only 6.16% of the total lightning was cloud-to-ground （CG） lightning, among which 20% had positive polarity. This percentage was higher than that in normal thunderstorms. In addition, heavier positive CG lightning discharge occurred before rather than after the hail shooting. In the stage of the hail shooting, the electric structure of the hailstorm was inverted, with the main negative charge region located around the -40℃ level and the main positive charge region around the -15℃ level. In addition, a weak negative charge region existed below the positive charge region transitorily. After the hail shooting, the electric structure underwent fast and persistent adjustments and became a normal tripole, with positive charge in the upper and lower levels and negative charge in the middle levels. However, the electric structure was tilted under the influence of the westerly wind in the middle and upper levels. The lightning activity and electric structure were closely related to the dynamic and microphysical processes of the hailstorm. It was believed that severe storms with stronger updrafts were more conducive to an inverted tripolar electric structure than normal thunderstorms, and the inverted distribution could then facilitate more positive CG lightning in the severe storms.
文摘The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.
