During the initial phases of operation following the construction or renovation of existing buildings,the availability of historical power usage data is limited,which leads to lower accuracy in load forecasting and hi...During the initial phases of operation following the construction or renovation of existing buildings,the availability of historical power usage data is limited,which leads to lower accuracy in load forecasting and hinders normal usage.Fortunately,by transferring load data from similar buildings,it is possible to enhance forecasting accuracy.However,indiscriminately expanding all source domain data to the target domain is highly likely to result in negative transfer learning.This study explores the feasibility of utilizing similar buildings(source domains)in transfer learning by implementing and comparing two distinct forms of multi-source transfer learning.Firstly,this study focuses on the Higashita area in Kitakyushu City,Japan,as the research object.Four buildings that exhibit the highest similarity to the target buildings within this area were selected for analysis.Next,the two-stage TrAdaBoost.R^(2) algorithm is used for multi-source transfer learning,and its transfer effect is analyzed.Finally,the application effects of instance-based(IBMTL)and feature-based(FBMTL)multi-source transfer learning are compared,which explained the effect of the source domain data on the forecasting accuracy in different transfer modes.The results show that combining the two-stage TrAdaBoost.R^(2) algorithm with multi-source data can reduce the CV-RMSE by 7.23%compared to a single-source domain,and the accuracy improvement is significant.At the same time,multi-source transfer learning,which is based on instance,can better supplement the integrity of the target domain data and has a higher forecasting accuracy.Overall,IBMTL tends to retain effective data associations and FBMTL shows higher forecasting stability.The findings of this study,which include the verification of real-life algorithm application and source domain availability,can serve as a theoretical reference for implementing transfer learning in load forecasting.展开更多
This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time...This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.展开更多
Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,whi...Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,which adapts the pre-trained model to the target domain.In real scenarios,the availability of labels for target data is rare thus resulting in unsupervised domain adaptation.Herein,we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks(GANs)are integrated to improve the performance of computer vision or robotic vision-based systems in our study.Cosine Generative Adversarial Network(CosGAN)is developed as a GAN that uses cosine embedding loss to handle issues associated with unsupervised source-relax domain adaptations.For less complex architecture,the CosGAN training process has two steps that produce results almost comparable to other state-of-the-art techniques.The efficiency of CosGAN was compared by conducting experiments using benchmarked datasets.The approach was evaluated on different datasets and experimental results show superiority over existing state-of-the-art methods in terms of accuracy as well as generalization ability.This technique has numerous applications including wheeled robots,autonomous vehicles,warehouse automation,and all image-processing-based automation tasks so it can reshape the field of robotic vision with its ability to make robots adapt to new tasks and environments efficiently without requiring additional labeled data.It lays the groundwork for future expansions in robotic vision and applications.Although GAN provides a variety of outstanding features,it also increases the risk of instability and over-fitting of the training data thus making the data difficult to converge.展开更多
On the production and understanding of the idioms, cognitive linguists put forward totally different opinions from the traditional linguistic theory. One enduring belief about the arbitrariness of English idioms is th...On the production and understanding of the idioms, cognitive linguists put forward totally different opinions from the traditional linguistic theory. One enduring belief about the arbitrariness of English idioms is that they are non-componential, because their idiomatic meanings are not deducible from the meaning of their individual parts. According to"conceptual metaphor"theory, which are proposed by cognitive linguists Lakoff and Johnson: Metaphor is a mapping that from one more familiar, more easily understanding source domain to a less familiar, more difficult target domain. The paper studies on the idiomaticity and the motivation of the idioms based on the"conceptual metaphor"theory from the perspective of cognitive linguistics.展开更多
OBJECTIVE: The association of spleen system including both spleen and stomach with earth, one of the five elements, is a part of the theory of five elements. Practitioners of Traditional Chinese Medicine (TCM) used th...OBJECTIVE: The association of spleen system including both spleen and stomach with earth, one of the five elements, is a part of the theory of five elements. Practitioners of Traditional Chinese Medicine (TCM) used the theory as a reasoning tool to illustrate the Zang-Fu organs' physiological functions and the interaction among them.The exploration of how the theory of that spleen system is associated with earth was created may provide insights into how five-element theory is applied to TCM practice. METHODS: Using analogism as a method to explore the relationship between earth and spleen system inTCM. RESULTS: Chinese ancestors experienced and observed the features of earth from agricultural practice and used the knowledge for the explanation ofspleen system functions including physiological functions, pathological characteristics and for the treatment of related illnesses. CONCLUSION: The theory of the five elements in TCM is a kind of metaphor, which depends on observation and exploration of the natural world and experience of human beings.展开更多
Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning applications.It typically benefits the model training of unlabeled target domain by leveraging knowledge from lab...Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning applications.It typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source domain.For this purpose,the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing work.Nevertheless,for the sake of privacy preservation,the source domain is usually not provided with training data but trained predictor(e.g.,classifier).This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are incalculable.To this end,this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning,namely Sample Transport Domain Adaptation(STDA).Specifically,STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target domain.Then,it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence,and consequently generates labels for the target domain.We train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner,and eventually achieve knowledge adaptation to the target domain and attain confident labels for it.Finally,evaluation results have validated effectiveness and superiority of the proposed method.展开更多
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarci...Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.展开更多
In classical theories of language,metaphor is viewed as a linguistic phenomenon.In the new theoretical framework of metaphor studies,in everyday conventional language conceptual metaphor has functions of structuring c...In classical theories of language,metaphor is viewed as a linguistic phenomenon.In the new theoretical framework of metaphor studies,in everyday conventional language conceptual metaphor has functions of structuring conceptual system,conceptualizing abstract concepts in terms of the comprehensible ones and providing a new understanding of our experience.展开更多
Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper...Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper. First, the discrete time vector for interpolation and the amplitude restoration formula is derived based on the moving relationship and the Morse acoustic theory, then the amplitude weights are corrected and the distortion signal is interpolated. Every point of the discrete signal is operated separately in time domain. Compared with the existing frequency domain methods, this method does not need to know the characteristic frequency beforehand and would not be influenced by the blending of the frequency band. Hence, this method can be employed to correct multiple frequency signals and it is also a simple and effective Doppler effect reduction method.展开更多
Cognitive linguistics has gained more and more attention over the years since it provides new horizon to understand language in relation to human experience. At present, it is of prevalence to study education from the...Cognitive linguistics has gained more and more attention over the years since it provides new horizon to understand language in relation to human experience. At present, it is of prevalence to study education from the linguistic perspective. This paper tends to center on education discourses, both written texts and educational practices, and understand their underlying cognitive or cultural models by resorting to the metaphorical theory in cognitive linguistics.展开更多
In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly differen...In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.展开更多
基金This research was supported by the National Key Research and Development Program of China(No.2023YFC3807102).
文摘During the initial phases of operation following the construction or renovation of existing buildings,the availability of historical power usage data is limited,which leads to lower accuracy in load forecasting and hinders normal usage.Fortunately,by transferring load data from similar buildings,it is possible to enhance forecasting accuracy.However,indiscriminately expanding all source domain data to the target domain is highly likely to result in negative transfer learning.This study explores the feasibility of utilizing similar buildings(source domains)in transfer learning by implementing and comparing two distinct forms of multi-source transfer learning.Firstly,this study focuses on the Higashita area in Kitakyushu City,Japan,as the research object.Four buildings that exhibit the highest similarity to the target buildings within this area were selected for analysis.Next,the two-stage TrAdaBoost.R^(2) algorithm is used for multi-source transfer learning,and its transfer effect is analyzed.Finally,the application effects of instance-based(IBMTL)and feature-based(FBMTL)multi-source transfer learning are compared,which explained the effect of the source domain data on the forecasting accuracy in different transfer modes.The results show that combining the two-stage TrAdaBoost.R^(2) algorithm with multi-source data can reduce the CV-RMSE by 7.23%compared to a single-source domain,and the accuracy improvement is significant.At the same time,multi-source transfer learning,which is based on instance,can better supplement the integrity of the target domain data and has a higher forecasting accuracy.Overall,IBMTL tends to retain effective data associations and FBMTL shows higher forecasting stability.The findings of this study,which include the verification of real-life algorithm application and source domain availability,can serve as a theoretical reference for implementing transfer learning in load forecasting.
基金supported by the National Natural Science Foundation of China(61072120)
文摘This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.
文摘Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,which adapts the pre-trained model to the target domain.In real scenarios,the availability of labels for target data is rare thus resulting in unsupervised domain adaptation.Herein,we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks(GANs)are integrated to improve the performance of computer vision or robotic vision-based systems in our study.Cosine Generative Adversarial Network(CosGAN)is developed as a GAN that uses cosine embedding loss to handle issues associated with unsupervised source-relax domain adaptations.For less complex architecture,the CosGAN training process has two steps that produce results almost comparable to other state-of-the-art techniques.The efficiency of CosGAN was compared by conducting experiments using benchmarked datasets.The approach was evaluated on different datasets and experimental results show superiority over existing state-of-the-art methods in terms of accuracy as well as generalization ability.This technique has numerous applications including wheeled robots,autonomous vehicles,warehouse automation,and all image-processing-based automation tasks so it can reshape the field of robotic vision with its ability to make robots adapt to new tasks and environments efficiently without requiring additional labeled data.It lays the groundwork for future expansions in robotic vision and applications.Although GAN provides a variety of outstanding features,it also increases the risk of instability and over-fitting of the training data thus making the data difficult to converge.
文摘On the production and understanding of the idioms, cognitive linguists put forward totally different opinions from the traditional linguistic theory. One enduring belief about the arbitrariness of English idioms is that they are non-componential, because their idiomatic meanings are not deducible from the meaning of their individual parts. According to"conceptual metaphor"theory, which are proposed by cognitive linguists Lakoff and Johnson: Metaphor is a mapping that from one more familiar, more easily understanding source domain to a less familiar, more difficult target domain. The paper studies on the idiomaticity and the motivation of the idioms based on the"conceptual metaphor"theory from the perspective of cognitive linguistics.
基金Supported by National Science Foundation of China (No.30973971)Ph.D. Programs Foundation of Ministry of Education of China (No. 20090013110012)
文摘OBJECTIVE: The association of spleen system including both spleen and stomach with earth, one of the five elements, is a part of the theory of five elements. Practitioners of Traditional Chinese Medicine (TCM) used the theory as a reasoning tool to illustrate the Zang-Fu organs' physiological functions and the interaction among them.The exploration of how the theory of that spleen system is associated with earth was created may provide insights into how five-element theory is applied to TCM practice. METHODS: Using analogism as a method to explore the relationship between earth and spleen system inTCM. RESULTS: Chinese ancestors experienced and observed the features of earth from agricultural practice and used the knowledge for the explanation ofspleen system functions including physiological functions, pathological characteristics and for the treatment of related illnesses. CONCLUSION: The theory of the five elements in TCM is a kind of metaphor, which depends on observation and exploration of the natural world and experience of human beings.
基金This work was partially supported by the National Natural Science Foundation of China under Grant Nos.61702273 and 62076062the Natural Science Foundation of Jinangsu Province of China under Grant No.BK20170956+1 种基金the Open Projects Program of National Laboratory of Pattern Recognition under Grant No.20200007was also sponsored by Qing Lan Project.
文摘Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning applications.It typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source domain.For this purpose,the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing work.Nevertheless,for the sake of privacy preservation,the source domain is usually not provided with training data but trained predictor(e.g.,classifier).This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are incalculable.To this end,this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning,namely Sample Transport Domain Adaptation(STDA).Specifically,STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target domain.Then,it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence,and consequently generates labels for the target domain.We train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner,and eventually achieve knowledge adaptation to the target domain and attain confident labels for it.Finally,evaluation results have validated effectiveness and superiority of the proposed method.
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金Project(2301DH09002)supported by the Bureau of Planning and Natural Resources,Chongqing,ChinaProject(2022T3051)supported by the Science and Technology Service Network Initiative,ChinaProject(2018-ZL-01)supported by the Sichuan Transportation Science and Technology,China。
文摘Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.
文摘In classical theories of language,metaphor is viewed as a linguistic phenomenon.In the new theoretical framework of metaphor studies,in everyday conventional language conceptual metaphor has functions of structuring conceptual system,conceptualizing abstract concepts in terms of the comprehensible ones and providing a new understanding of our experience.
基金supported by the National Science Foundation of China(51075379)
文摘Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper. First, the discrete time vector for interpolation and the amplitude restoration formula is derived based on the moving relationship and the Morse acoustic theory, then the amplitude weights are corrected and the distortion signal is interpolated. Every point of the discrete signal is operated separately in time domain. Compared with the existing frequency domain methods, this method does not need to know the characteristic frequency beforehand and would not be influenced by the blending of the frequency band. Hence, this method can be employed to correct multiple frequency signals and it is also a simple and effective Doppler effect reduction method.
文摘Cognitive linguistics has gained more and more attention over the years since it provides new horizon to understand language in relation to human experience. At present, it is of prevalence to study education from the linguistic perspective. This paper tends to center on education discourses, both written texts and educational practices, and understand their underlying cognitive or cultural models by resorting to the metaphorical theory in cognitive linguistics.
基金supported by the National Natural Science Foundation of China(Nos.62225113,62176188,and 623B2080)the Innovative Research Group Project of Hubei Province(No.2024AFA017).
文摘In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.
