Objective:To compare the clinical efficacy of mifepristone-misoprostol medical management versus surgical curettage for first-trimester missed miscarriage,and to establish evidence-based sonographic cutoff values pred...Objective:To compare the clinical efficacy of mifepristone-misoprostol medical management versus surgical curettage for first-trimester missed miscarriage,and to establish evidence-based sonographic cutoff values predictive of incomplete abortion requiring surgical intervention.Methods:We retrospectively analyzed a cohort of 702 women diagnosed with first-trimester missed miscarriage between January 2020 and May 2023.Demographic characteristics and ultrasound parameters were systematically recorded.Receiver operating characteristic(ROC)curve analysis was performed to establish optimal sonographic cutoff values for predicting incomplete abortion requiring surgical intervention.Results:146 patients received medical treatment(mifepristone and misoprostol)and 556 underwent surgical curettage.At the 1-month follow-up,the medical group showed significantly greater endometrial thickness and longer postoperative bleeding duration than the surgical group(P<0.05).The menstrual volume reduction rate(23.56%)was significantly lower in the medical group than in the surgical group.The incomplete abortion rate was higher in the medical group(17.12%,25/146)than in the surgical group(2.88%,16/556).Among the medical group,14 patients(9.59%)required curettage due to incomplete abortion,while 11 cases resolved spontaneously after prolonged medication.ROC curve analysis identified two cut-off values indicating the need for surgical intervention:endometrial thickness>1.21 cm at 24 h post-medical abortion,and residual mass diameter>0.95 cm at 7 days post-medical abortion.Conclusions:Medical management of first-trimester missed miscarriage using mifepristone-misoprostol demonstrates comparable efficacy to surgical curettage.An endometrial thickness>1.21 cm at 24 h or residual tissue diameter>0.95 cm at 7 days post-medical abortion should prompt consideration of incomplete abortion.展开更多
Asia’s unhealed wounds and incomplete justice of WWII.WHILE Europe commemorated Nazi Germany’s defeat during the World War II in May 1945,few acknowledged that the war raged on for several more months in Asia,claimi...Asia’s unhealed wounds and incomplete justice of WWII.WHILE Europe commemorated Nazi Germany’s defeat during the World War II in May 1945,few acknowledged that the war raged on for several more months in Asia,claiming millions more lives before Japan officially signed the instrument of surrender on September 2,1945.展开更多
When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can...When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can be used to divide the receiving sys-tem into several interconnected sub-partitions and improve the voltage support capability of the receiving system.Compared with asyn-chronous interconnection,which completely separates the receiving systems with VSC-HVDC,incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids.To analyze the voltage support capability of the VSC in incomplete segmentation,a micro-incremental model of the VSC was established,the operating impedance of the VSC was calculated,and the voltage support function of the VSC was quantified.The effect of the fault on the system short-circuit capacity was analyzed,and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained.A VSC-segmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform,and the validity of the micro-increment model and accuracy of the proposed conclusions were verified.展开更多
The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limi...The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
Post-colonoscopic colorectal cancer(PCCRC),also known as interval CRC,is defined as CRC diagnosed more than six months after a colonoscopy in which no cancer was detected.It typically arises from missed lesions or inc...Post-colonoscopic colorectal cancer(PCCRC),also known as interval CRC,is defined as CRC diagnosed more than six months after a colonoscopy in which no cancer was detected.It typically arises from missed lesions or incomplete resections and is now recognized as one of the most reliable quality indicators for assessing colonoscopy performance.With an incidence rate of 3.6%to 9.3%,PCCRC remains a significant concern,highlighting the limitations of colonoscopy in CRC screening—not only in terms of diagnostic accuracy but also in its preventive role and effectiveness in treating lesions.A range of clinical,endoscopic,and biological factors has been associated with an increased risk of PCCRC.Identifying these factors can help stratify high-risk patients,enabling earlier detection and improving preventive strategies for interval CRC.Reducing PCCRC should be a top priority for every endoscopy unit.While technological advancements will enhance polyp detection,minimize missed lesions,prevent incomplete resections,and improve overall procedural quality,the most impactful strategy remains internal self-assessment within each unit.This review should evaluate key performance metrics,including cecal intubation rate,adenoma detection rate,withdrawal time,PCCRC incidence,and incomplete resections—both at the individual endoscopist level and across the entire unit.展开更多
The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is ...The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.展开更多
In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others&#...In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.展开更多
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
Efficient cellular fusion of mononuclear precursors is the prerequisite for the generation of fully functional multinucleated bone-resorbing osteoclasts.However,the exact molecular factors and mechanisms controlling o...Efficient cellular fusion of mononuclear precursors is the prerequisite for the generation of fully functional multinucleated bone-resorbing osteoclasts.However,the exact molecular factors and mechanisms controlling osteoclast fusion remain incompletely understood.Here we identify RANKL-mediated activation of caspase-8 as early key event during osteoclast fusion.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in...For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.展开更多
This paper presents a 16-bit,18-MSPS(million samples per second)flash-assisted successive-approximation-register(SAR)analog-to-digital converter(ADC)utilizing hybrid synchronous and asynchronous(HYSAS)timing control l...This paper presents a 16-bit,18-MSPS(million samples per second)flash-assisted successive-approximation-register(SAR)analog-to-digital converter(ADC)utilizing hybrid synchronous and asynchronous(HYSAS)timing control logic based on an on-chip delay-locked loop(DLL).The HYSAS scheme can provide a longer settling time for the capacitive digital-to-analog converter(CDAC)than the synchronous and asynchronous SAR ADC.Therefore,the issue of incomplete settling or ringing in the DAC voltage for cases of either on-chip or off-chip reference voltage can be solved to a large extent.In addition,the fore-ground calibration of the CDAC’s mismatch is performed with a finite-impulse-response bandpass filter(FIR-BPF)based least-mean-square(LMS)algorithm in an off-chip FPGA(field programmable gate array).Fabricated in 40-nm CMOS process,the proto-type ADC achieves 94.02-dB spurious-free dynamic range(SFDR),and 75.98-dB signal-to-noise-and-distortion ratio(SNDR)for a 2.88-MHz input under 18-MSPS sampling rate.展开更多
This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the l...This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.展开更多
At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under...At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.展开更多
Since the inclusion of the low-altitude economy in the government work report during the National People’s Congress and the Chinese People’s Political Consultative Conference in March,many cities have sought to ente...Since the inclusion of the low-altitude economy in the government work report during the National People’s Congress and the Chinese People’s Political Consultative Conference in March,many cities have sought to enter this area first to seize the benefits of the low-altitude economy.According to incomplete statistics,17 provinces(municipalities,autonomous regions)in China have currently written the low-altitude economy into their 2024 government work report.In addition,cities such as Shenzhen,Guangzhou,Chengdu,Suzhou,Zhuhai,and Ganzhou have also included the low-altitude economy in their government work reports.More than 20 provincial and municipal governments,represented by Shenzhen,Hefei,Guangzhou and Chengdu,have introduced also policies to support the development of the low-altitude economy and its business ecosystem.展开更多
Crossing conjugate normal faults(CCNFs)are extensively developed in many hydrocarbon-producing basins,generally existing in the form of incomplete CCNFs.Nevertheless,the effect of the non-conjugate zone of the CCNFs o...Crossing conjugate normal faults(CCNFs)are extensively developed in many hydrocarbon-producing basins,generally existing in the form of incomplete CCNFs.Nevertheless,the effect of the non-conjugate zone of the CCNFs on the conjugate relay zone post late tectonic action has not been previously studied.We use 3D elastic-plastic modeling to investigate the influence of incomplete(i.e.,partially intersecting)CCNFs on the pattern of deformation of strata in the intersection region.A series of model simulations were performed to examine the effects of horizontal tectonic extension,fault size,and fault depth on the deformation of conjugate relay zones of incomplete CCNFs.Our analyses yielded the following results.(1)The model of incomplete conjugation predicts a convex-up style of deformation in the conjugate graben region superimposed on overall subsidence under applied horizontal tectonic extension.(2)The degree of convex-up deformation of the conjugate graben depends on the influence of the non-conjugate zone on the conjugate relay zone,which varies with the amount of horizontal tectonic extension,fault size,and fault burial depth.(3)Our results indicate that incomplete CCNFs can form convex-up deformation,similar to that in the Nanpu Sag area and provide a sound understanding of hydrocarbon migration and accumulation.展开更多
Dear Editor,Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention.In particular, incomplete-information games(IIG) have gradually become a n...Dear Editor,Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention.In particular, incomplete-information games(IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences.展开更多
基金supported by National Natural Science Foundation of China(Project approval number 82201825).
文摘Objective:To compare the clinical efficacy of mifepristone-misoprostol medical management versus surgical curettage for first-trimester missed miscarriage,and to establish evidence-based sonographic cutoff values predictive of incomplete abortion requiring surgical intervention.Methods:We retrospectively analyzed a cohort of 702 women diagnosed with first-trimester missed miscarriage between January 2020 and May 2023.Demographic characteristics and ultrasound parameters were systematically recorded.Receiver operating characteristic(ROC)curve analysis was performed to establish optimal sonographic cutoff values for predicting incomplete abortion requiring surgical intervention.Results:146 patients received medical treatment(mifepristone and misoprostol)and 556 underwent surgical curettage.At the 1-month follow-up,the medical group showed significantly greater endometrial thickness and longer postoperative bleeding duration than the surgical group(P<0.05).The menstrual volume reduction rate(23.56%)was significantly lower in the medical group than in the surgical group.The incomplete abortion rate was higher in the medical group(17.12%,25/146)than in the surgical group(2.88%,16/556).Among the medical group,14 patients(9.59%)required curettage due to incomplete abortion,while 11 cases resolved spontaneously after prolonged medication.ROC curve analysis identified two cut-off values indicating the need for surgical intervention:endometrial thickness>1.21 cm at 24 h post-medical abortion,and residual mass diameter>0.95 cm at 7 days post-medical abortion.Conclusions:Medical management of first-trimester missed miscarriage using mifepristone-misoprostol demonstrates comparable efficacy to surgical curettage.An endometrial thickness>1.21 cm at 24 h or residual tissue diameter>0.95 cm at 7 days post-medical abortion should prompt consideration of incomplete abortion.
文摘Asia’s unhealed wounds and incomplete justice of WWII.WHILE Europe commemorated Nazi Germany’s defeat during the World War II in May 1945,few acknowledged that the war raged on for several more months in Asia,claiming millions more lives before Japan officially signed the instrument of surrender on September 2,1945.
基金supported by the State Grid Science and Technology Project 5108-202218280A-2-87-XG.
文摘When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can be used to divide the receiving sys-tem into several interconnected sub-partitions and improve the voltage support capability of the receiving system.Compared with asyn-chronous interconnection,which completely separates the receiving systems with VSC-HVDC,incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids.To analyze the voltage support capability of the VSC in incomplete segmentation,a micro-incremental model of the VSC was established,the operating impedance of the VSC was calculated,and the voltage support function of the VSC was quantified.The effect of the fault on the system short-circuit capacity was analyzed,and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained.A VSC-segmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform,and the validity of the micro-increment model and accuracy of the proposed conclusions were verified.
文摘The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
文摘Post-colonoscopic colorectal cancer(PCCRC),also known as interval CRC,is defined as CRC diagnosed more than six months after a colonoscopy in which no cancer was detected.It typically arises from missed lesions or incomplete resections and is now recognized as one of the most reliable quality indicators for assessing colonoscopy performance.With an incidence rate of 3.6%to 9.3%,PCCRC remains a significant concern,highlighting the limitations of colonoscopy in CRC screening—not only in terms of diagnostic accuracy but also in its preventive role and effectiveness in treating lesions.A range of clinical,endoscopic,and biological factors has been associated with an increased risk of PCCRC.Identifying these factors can help stratify high-risk patients,enabling earlier detection and improving preventive strategies for interval CRC.Reducing PCCRC should be a top priority for every endoscopy unit.While technological advancements will enhance polyp detection,minimize missed lesions,prevent incomplete resections,and improve overall procedural quality,the most impactful strategy remains internal self-assessment within each unit.This review should evaluate key performance metrics,including cecal intubation rate,adenoma detection rate,withdrawal time,PCCRC incidence,and incomplete resections—both at the individual endoscopist level and across the entire unit.
文摘The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.
基金supported by the Aeronautical Science Foundation of China(20220001057001)an Open Project of the National Key Laboratory of Air-based Information Perception and Fusion(202437)
文摘In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金supported by the Bayerische Forschungsstiftung to B.K.the Deutsche Forschungsgemeinschaft (CRC1181 to G.K.and G.S.+3 种基金SCHE 2062/1-1 to C.S.)funded by the Wellcome Trust Investigator Award (107964/Z/15/Z)the UK Dementia Research Institutefunded by BBSRC Discovery Fellowship (BB/T009543/1)。
文摘Efficient cellular fusion of mononuclear precursors is the prerequisite for the generation of fully functional multinucleated bone-resorbing osteoclasts.However,the exact molecular factors and mechanisms controlling osteoclast fusion remain incompletely understood.Here we identify RANKL-mediated activation of caspase-8 as early key event during osteoclast fusion.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
基金supported by the National Natural Science Foundation of China (62173333, 12271522)Beijing Natural Science Foundation (Z210002)the Research Fund of Renmin University of China (2021030187)。
文摘For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
基金supported by Qingdao Hi-image Technologies Co., Ltdin part by the NSFC of China under Grant 62174149, 61974118, 62004156the National Key R&D Program of China under Grant 2022YFC2404902
文摘This paper presents a 16-bit,18-MSPS(million samples per second)flash-assisted successive-approximation-register(SAR)analog-to-digital converter(ADC)utilizing hybrid synchronous and asynchronous(HYSAS)timing control logic based on an on-chip delay-locked loop(DLL).The HYSAS scheme can provide a longer settling time for the capacitive digital-to-analog converter(CDAC)than the synchronous and asynchronous SAR ADC.Therefore,the issue of incomplete settling or ringing in the DAC voltage for cases of either on-chip or off-chip reference voltage can be solved to a large extent.In addition,the fore-ground calibration of the CDAC’s mismatch is performed with a finite-impulse-response bandpass filter(FIR-BPF)based least-mean-square(LMS)algorithm in an off-chip FPGA(field programmable gate array).Fabricated in 40-nm CMOS process,the proto-type ADC achieves 94.02-dB spurious-free dynamic range(SFDR),and 75.98-dB signal-to-noise-and-distortion ratio(SNDR)for a 2.88-MHz input under 18-MSPS sampling rate.
基金supported by the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation (USCAST2022-11)Aeronautical Science Foundation of China (20220001057001)。
文摘This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.
基金This researchwas supported by the National Natural Science Foundation of China(Nos.51767017 and 51867015)the Basic Research and Innovation Group Project of Gansu(No.18JR3RA133)the Natural Science Foundation of Gansu(No.21JR7RA258).
文摘At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.
文摘Since the inclusion of the low-altitude economy in the government work report during the National People’s Congress and the Chinese People’s Political Consultative Conference in March,many cities have sought to enter this area first to seize the benefits of the low-altitude economy.According to incomplete statistics,17 provinces(municipalities,autonomous regions)in China have currently written the low-altitude economy into their 2024 government work report.In addition,cities such as Shenzhen,Guangzhou,Chengdu,Suzhou,Zhuhai,and Ganzhou have also included the low-altitude economy in their government work reports.More than 20 provincial and municipal governments,represented by Shenzhen,Hefei,Guangzhou and Chengdu,have introduced also policies to support the development of the low-altitude economy and its business ecosystem.
基金supported by the National Key Research and Development Program of China(No.2022YFF0800703)the National Natural Science Foundation of China(No.42230309).
文摘Crossing conjugate normal faults(CCNFs)are extensively developed in many hydrocarbon-producing basins,generally existing in the form of incomplete CCNFs.Nevertheless,the effect of the non-conjugate zone of the CCNFs on the conjugate relay zone post late tectonic action has not been previously studied.We use 3D elastic-plastic modeling to investigate the influence of incomplete(i.e.,partially intersecting)CCNFs on the pattern of deformation of strata in the intersection region.A series of model simulations were performed to examine the effects of horizontal tectonic extension,fault size,and fault depth on the deformation of conjugate relay zones of incomplete CCNFs.Our analyses yielded the following results.(1)The model of incomplete conjugation predicts a convex-up style of deformation in the conjugate graben region superimposed on overall subsidence under applied horizontal tectonic extension.(2)The degree of convex-up deformation of the conjugate graben depends on the influence of the non-conjugate zone on the conjugate relay zone,which varies with the amount of horizontal tectonic extension,fault size,and fault burial depth.(3)Our results indicate that incomplete CCNFs can form convex-up deformation,similar to that in the Nanpu Sag area and provide a sound understanding of hydrocarbon migration and accumulation.
基金partially supported by the National Natural Science Foundation of China (62073079, 62173308)the Natural Science Foundation of Zhejiang Province of China (LZ24F030009, LR20F030001)。
文摘Dear Editor,Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention.In particular, incomplete-information games(IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences.
