FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively mode...Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively model these complexities.This paper presents a novel approach by transforming fault diagnosis into a graph recognition task,using sensor data represented as graph-structured data with the k-nearest neighbors(KNN)algorithm.A Graph Transformer is applied to extract node and graph features,with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations.Experiments on the TFF dataset show that Graph Transformer combined with consistency loss outperforms conventional methods in fault diagnosis accuracy,offering a promising solution for enhancing fault detection in industrial systems.展开更多
In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this stu...In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.展开更多
Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflict...Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflicts happen.Therefore a common synchronous mechanism is proposed to support consistency maintenance in the process of multi-view business modeling.Based on operation analysis on different views of models in the real-time collaborative editing system,detection of potential conflicts is realized by means of a decision-making tree.Then consistency maintenance provides a comprehensive and applicable conflicts detection and resolution for collaborative business modeling.Finally,a prototype of collaborative multi-view business modeling system is introduced to verify the approach.The point is that the mechanism proposes a comprehensive solution for collaborative multi-view business modeling.展开更多
Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER...Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.展开更多
Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the ...Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the convenience sampling method,patients with heart failure and their main caregivers in the Department of Cardiology of a tertiary hospital in Suzhou from May to November 2023 were selected as the research subjects.The HFSS scale was used for data collection.The paired t-test or paired Wilcoxon test was used to evaluate the differences in the binary symptom assessment scores of heart failure.The intraclass correlation coefficient was used to assess the consistency level of the binary symptom assessment.The Pearson correlation test was used to examine the correlation of the binary symptom assessment.Regression analysis was employed to explore the factors related to the consistency assessment.Results:A total of 103 pairs of valid questionnaires were collected.The consistency levels of symptom evaluations in patients with heart failure and their primary caregivers were statistically significant(P<0.05).The most frequently reported and severe symptom by patients with heart failure and their primary caregivers is shortness of breath during activity.Both have a high consensus on the severity and urgency of most heart failure symptoms.The patient’s gender,body mass index,number of children,history of diabetes,number of comorbidities,mean arterial pressure,LVEF,number of stents,whether a defibrillator was implanted,as well as the gender,marital status,education level,relationship with the patient,care time,whether they lived together,and communication and interaction situation of the main caregiver were the influencing factors for the consistency of binary symptom assessment of heart failure(P<0.05).Conclusion:The degree of consistency in binary symptom assessment between patients with heart failure and their primary caregivers was moderate or higher,which emphasizes the importance of including binary groups in clinical assessment.展开更多
Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds...Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds,besides captive parrots and songbirds,systemic and quantitative studies on human-made sound mimicry in wild birds remain scarce.In this study,we investigated the mimetic accuracy and consistency of electric moped sounds imitated by an urban bird,the Chinese Blackbird(Turdus mandarinus).We found that:(1)Only one type of electric moped sound was imitated,i.e.,13 of 26 males mimicked the first part of the antitheft alarm,a phrase containing a series of identical notes.(2)The mimicry produced by male Chinese Blackbirds had fewer notes and lower consistency within phrases compared to the model alarms.(3)The mimicry of male Chinese Blackbirds was imperfect,i.e.,most of the acoustic parameters differed from the model alarms.Additionally,mimetic notes were lower in frequency than the models.Mimetic notes from two areas were also different in acoustic structures,suggesting Chinese Blackbirds might learn mimicry mainly from conspecific neighbors within each area respectively rather than electric mopeds,namely the secondary mimicry.Imperfect mimicry of human-made sounds could result from cost and physical constraints,associated with high consistency,frequency,and repetitions.Consequently,Chinese Blackbirds copied a simplified version of electric moped alarms.We recommend further attention to mimic species inhabiting urban ecosystems to better understand vocal mimicry's adaptation to ongoing urbanization.展开更多
With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image...With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.展开更多
The 5E model includes Engagement,Exploration,Explanation,Elaboration,and Evaluation,with“Evaluation”at the end,conflicting with teaching learning-evaluation consistency.Thus,formative levaluation is integrated into ...The 5E model includes Engagement,Exploration,Explanation,Elaboration,and Evaluation,with“Evaluation”at the end,conflicting with teaching learning-evaluation consistency.Thus,formative levaluation is integrated into the first four stages,and a summative evaluation table is designed for the fifth,enabling students to self-evaluate and reflect.Elementary school English picture book teaching is used as an example to demonstrate the optimized model's application.展开更多
The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engin...The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engineering projects.During the collection process of JRC samples,the redundancy or insufficiency of representative rock joint surface topography(RJST) information in serial length JRC samples is the essential reason that affects the reliability of the scale effect results.Therefore,this paper proposes an adaptive sampling method,in which we use the entropy consistency measure Q(a) to evaluate the consistency of the joint morphology information contained in adjacent JRC samples.Then the sampling interval is automatically adjusted according to the threshold Q(at) of the entropy consistency measure to ensure that the degree of change of RJST information between JRC samples is the same,and ultimately makes the representative RJST information in the collected JRC samples more balanced.The application results of actual cases show that the proposed method can obtain the scale effect in the JRC efficiently and reliably.展开更多
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu...The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.展开更多
Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewpriv...Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.展开更多
Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ...Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.展开更多
Concentrating Solar Power(CSP)is one of the most promising solar technologies for sustainable power generation in countrieswith high solar potential,likeChad.Identifying suitable sites is of great importance for deplo...Concentrating Solar Power(CSP)is one of the most promising solar technologies for sustainable power generation in countrieswith high solar potential,likeChad.Identifying suitable sites is of great importance for deploying solar power plants.This work focuses on the identification of potential sites for the installation of solar power plants in Chad as well as a comparative analysis using the Analytical Hierarchy Process(AHP),Fuzzy Analytical Hierarchy Process(FAHP),and Full Consistency Method(FUCOM).The results show that 35%of the Chadian territory,i.e.,an area of 449,400 km2,is compatible with the implementation of Concentrating Solar Power.The North,North,East,Southeast,and East zones are the most suitable.The main criteria for influence are direct normal irradiation,the soil slope,and the water resource.FUCOM gave a weight of 41.9%for Direct Normal Irradiation(DNI)compared to 32.71%and 31.81%for AHP and FAHP.This method can be applied to other renewable energy technologies such as photovoltaics,wind power,and biomass.Combining its different analyses will be a valuable tool for planning any renewable energy project in Chad.This work should also facilitate the techno-economic analysis of future CSP plants in Chad.展开更多
Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equ...Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.展开更多
Dear Editor,This letter proposes a novel dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency.Inspired by biological vision,dynamic vision data are collected by ...Dear Editor,This letter proposes a novel dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency.Inspired by biological vision,dynamic vision data are collected by the event camera,which is able to capture the micro-vibration information of mechanical equipment,due to the significant advantage of extremely high temporal sampling frequency.展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very ...The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very important. In this paper, we present a new inversion algorithm and approach based on the in-depth analysis of the causes in their accuracy differences. With this new method, the inversion accuracy of the three parameters is improved synchronously by reasonable approximations and mutual constraint among the parameters. Theoretical model calculations and actual data applications with this method indicate that the three elastic parameters all have high inversion accuracy and maintain consistency, which also coincides with the theoretical model and actual data. This method has good application prospects.展开更多
The rheological behavior of low consistency thermomechanical pulp of Chinese fir harvested by intermediate thinning was analyzed. The results show that the apparent viscosity of pulp changed along with the beating deg...The rheological behavior of low consistency thermomechanical pulp of Chinese fir harvested by intermediate thinning was analyzed. The results show that the apparent viscosity of pulp changed along with the beating degree, pulp consistency and shearing velocity. With the increasing of pulp consistency, the apparent viscosity of pulp increased gradually. Beating degree of pulp had an effect on microstructure of pulp. The apparent viscosity of pulp declined as beating degree of pulp increased, and the apparent viscosity of pulp fell along with the shearing velocity increasing. Based on the results, the rheological models are set up. The models showed that the fluid types of the low consistency pulp could be described as pseudoplastics fluids （non-Newtonian fluids）.展开更多
On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent grow...On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.展开更多
基金supported by the Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金supported by the National Natural Science Foundation of China under Grants Nos.62573292,62206199 and 62476192National Key Laboratory of Marine Engine Science and Technology under Grant No.LAB-2024-04-WD+2 种基金Young Elite Scientist Sponsorship Program under Grant No.YESS20220409the Hainan Province Science and Technology Special Fund under Grant No.ZDYF2024GXJS003the Natural Science Foundation of Tianjin under Grant No.23JCQNJC02010.
文摘Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively model these complexities.This paper presents a novel approach by transforming fault diagnosis into a graph recognition task,using sensor data represented as graph-structured data with the k-nearest neighbors(KNN)algorithm.A Graph Transformer is applied to extract node and graph features,with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations.Experiments on the TFF dataset show that Graph Transformer combined with consistency loss outperforms conventional methods in fault diagnosis accuracy,offering a promising solution for enhancing fault detection in industrial systems.
文摘In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.
基金the National Natural Science Foundation of China(Nos.61373030 and 71171132)
文摘Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflicts happen.Therefore a common synchronous mechanism is proposed to support consistency maintenance in the process of multi-view business modeling.Based on operation analysis on different views of models in the real-time collaborative editing system,detection of potential conflicts is realized by means of a decision-making tree.Then consistency maintenance provides a comprehensive and applicable conflicts detection and resolution for collaborative business modeling.Finally,a prototype of collaborative multi-view business modeling system is introduced to verify the approach.The point is that the mechanism proposes a comprehensive solution for collaborative multi-view business modeling.
基金support from the National Natural Science Foundation of China(Grant Number 62477023).
文摘Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.
文摘Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the convenience sampling method,patients with heart failure and their main caregivers in the Department of Cardiology of a tertiary hospital in Suzhou from May to November 2023 were selected as the research subjects.The HFSS scale was used for data collection.The paired t-test or paired Wilcoxon test was used to evaluate the differences in the binary symptom assessment scores of heart failure.The intraclass correlation coefficient was used to assess the consistency level of the binary symptom assessment.The Pearson correlation test was used to examine the correlation of the binary symptom assessment.Regression analysis was employed to explore the factors related to the consistency assessment.Results:A total of 103 pairs of valid questionnaires were collected.The consistency levels of symptom evaluations in patients with heart failure and their primary caregivers were statistically significant(P<0.05).The most frequently reported and severe symptom by patients with heart failure and their primary caregivers is shortness of breath during activity.Both have a high consensus on the severity and urgency of most heart failure symptoms.The patient’s gender,body mass index,number of children,history of diabetes,number of comorbidities,mean arterial pressure,LVEF,number of stents,whether a defibrillator was implanted,as well as the gender,marital status,education level,relationship with the patient,care time,whether they lived together,and communication and interaction situation of the main caregiver were the influencing factors for the consistency of binary symptom assessment of heart failure(P<0.05).Conclusion:The degree of consistency in binary symptom assessment between patients with heart failure and their primary caregivers was moderate or higher,which emphasizes the importance of including binary groups in clinical assessment.
基金supported by the National Key Research and Development Program of China(2022YFC3202104)the Western LightKey Laboratory Cooperative Research Cross-Team Project of Chinese Academy of Sciences(xbzg-zdsys-202207)。
文摘Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds,besides captive parrots and songbirds,systemic and quantitative studies on human-made sound mimicry in wild birds remain scarce.In this study,we investigated the mimetic accuracy and consistency of electric moped sounds imitated by an urban bird,the Chinese Blackbird(Turdus mandarinus).We found that:(1)Only one type of electric moped sound was imitated,i.e.,13 of 26 males mimicked the first part of the antitheft alarm,a phrase containing a series of identical notes.(2)The mimicry produced by male Chinese Blackbirds had fewer notes and lower consistency within phrases compared to the model alarms.(3)The mimicry of male Chinese Blackbirds was imperfect,i.e.,most of the acoustic parameters differed from the model alarms.Additionally,mimetic notes were lower in frequency than the models.Mimetic notes from two areas were also different in acoustic structures,suggesting Chinese Blackbirds might learn mimicry mainly from conspecific neighbors within each area respectively rather than electric mopeds,namely the secondary mimicry.Imperfect mimicry of human-made sounds could result from cost and physical constraints,associated with high consistency,frequency,and repetitions.Consequently,Chinese Blackbirds copied a simplified version of electric moped alarms.We recommend further attention to mimic species inhabiting urban ecosystems to better understand vocal mimicry's adaptation to ongoing urbanization.
基金Supported by Science and Technology Department Major Innovation Special Fund of Hubei Province of China(2020BAB116)。
文摘With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.
基金This paper is funded by Project Information:2023 Guangdong Undergraduate Colleges and Universities Teaching Quality and Teaching Reform Project Construction Project,Project Name:Action Research on Whole-area Nurturing of English Reading Teaching in Universities,Secondary and Primary Schools under the Perspective of Discipline Nurturing.Project serial number:895.
文摘The 5E model includes Engagement,Exploration,Explanation,Elaboration,and Evaluation,with“Evaluation”at the end,conflicting with teaching learning-evaluation consistency.Thus,formative levaluation is integrated into the first four stages,and a summative evaluation table is designed for the fifth,enabling students to self-evaluate and reflect.Elementary school English picture book teaching is used as an example to demonstrate the optimized model's application.
基金supported by the National Natural Science Foundation of China(No.42207175)。
文摘The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engineering projects.During the collection process of JRC samples,the redundancy or insufficiency of representative rock joint surface topography(RJST) information in serial length JRC samples is the essential reason that affects the reliability of the scale effect results.Therefore,this paper proposes an adaptive sampling method,in which we use the entropy consistency measure Q(a) to evaluate the consistency of the joint morphology information contained in adjacent JRC samples.Then the sampling interval is automatically adjusted according to the threshold Q(at) of the entropy consistency measure to ensure that the degree of change of RJST information between JRC samples is the same,and ultimately makes the representative RJST information in the collected JRC samples more balanced.The application results of actual cases show that the proposed method can obtain the scale effect in the JRC efficiently and reliably.
文摘The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.
文摘Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.
基金supported in part by the National Nature Science Foundation of China(62273239,62103283).
文摘Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.
文摘Concentrating Solar Power(CSP)is one of the most promising solar technologies for sustainable power generation in countrieswith high solar potential,likeChad.Identifying suitable sites is of great importance for deploying solar power plants.This work focuses on the identification of potential sites for the installation of solar power plants in Chad as well as a comparative analysis using the Analytical Hierarchy Process(AHP),Fuzzy Analytical Hierarchy Process(FAHP),and Full Consistency Method(FUCOM).The results show that 35%of the Chadian territory,i.e.,an area of 449,400 km2,is compatible with the implementation of Concentrating Solar Power.The North,North,East,Southeast,and East zones are the most suitable.The main criteria for influence are direct normal irradiation,the soil slope,and the water resource.FUCOM gave a weight of 41.9%for Direct Normal Irradiation(DNI)compared to 32.71%and 31.81%for AHP and FAHP.This method can be applied to other renewable energy technologies such as photovoltaics,wind power,and biomass.Combining its different analyses will be a valuable tool for planning any renewable energy project in Chad.This work should also facilitate the techno-economic analysis of future CSP plants in Chad.
基金supported by the National Key R&D Program of China(2022YFB4301403).
文摘Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.
文摘Dear Editor,This letter proposes a novel dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency.Inspired by biological vision,dynamic vision data are collected by the event camera,which is able to capture the micro-vibration information of mechanical equipment,due to the significant advantage of extremely high temporal sampling frequency.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金sponsored by the National Major Program (No. 2011ZX05006-006)the 973 Program of China (No. 2011CB201104)Technical Research of Elastic Flooding Boundary and Well Network Optimization at the Development Late Stage of Low Permeable Oil Field (No. 2011ZX05009)
文摘The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very important. In this paper, we present a new inversion algorithm and approach based on the in-depth analysis of the causes in their accuracy differences. With this new method, the inversion accuracy of the three parameters is improved synchronously by reasonable approximations and mutual constraint among the parameters. Theoretical model calculations and actual data applications with this method indicate that the three elastic parameters all have high inversion accuracy and maintain consistency, which also coincides with the theoretical model and actual data. This method has good application prospects.
基金This study was sponsored by the Research Funding for Outstanding Young University Faculty of China Ministry of Education (No. 2001-39), Fujian Provincial Innovation Fundation for Young Science and Technology Talents (No. 2004J012), and the National Natural Science Funda-tion of China (No. 30571461)
文摘The rheological behavior of low consistency thermomechanical pulp of Chinese fir harvested by intermediate thinning was analyzed. The results show that the apparent viscosity of pulp changed along with the beating degree, pulp consistency and shearing velocity. With the increasing of pulp consistency, the apparent viscosity of pulp increased gradually. Beating degree of pulp had an effect on microstructure of pulp. The apparent viscosity of pulp declined as beating degree of pulp increased, and the apparent viscosity of pulp fell along with the shearing velocity increasing. Based on the results, the rheological models are set up. The models showed that the fluid types of the low consistency pulp could be described as pseudoplastics fluids （non-Newtonian fluids）.
文摘On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.
