The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study t...The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study the characteristics of stomata, trichomes and dermal cell, etc.. The results showed that stoma exists only on the lower epidermis and its distribution is irregular, and leaf epidermis consist of epidermis cells, stoma complexes and bushy trichomes including glandular hair and non-glandular hair. On the upper epidermis, anticlinal wall caves in sinuous groove to countercheck the transpiration. Evidences from leaf morphological structures serve as another proof on drought-resistant mechanisms. Some strumaes distributing regularly are hypothesized as oxalic calcium on the lower epidermis under laser scanning confocal microscopy （LSCM） with Fluo-3/AM, which can increase their endurance to drought stress. Therefore, the above characteristics of Flos Lonicerae can reduce the loss of water and make Japanese honeysuckle and Wild Honeysuckle adapt to the droughty environment at Karst area in southwest China. However, there is some difference of the two species. From the SEM （Scanning Electron Microscopy） result, it is shown that on the upper epidermis, some glandular hair regularly present along the midrib of Japanese honeysuckle, but Wild Honeysuckle has no glandular hair on the upper epidermis, which can verify the relationships of Flos Lonicerae species and provide the significance for classification of Flos Lonicerae.展开更多
Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable t...Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.展开更多
CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, how...CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, however, has not kept pace with the levels of people＇s English. Every foreigner who has traveled or lived in China will have a story to tell about an English- language sign that has been lost in translation.展开更多
Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many ...Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many foreign ESL(English as a second language)instructors often feel confused when they first step into a Chinese classroom.Chinese ESL students are different because their展开更多
confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the m...confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the man and his teachings.展开更多
The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop ba...The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop based on their initial feeding experiences after birth.Infants accustomed to the maternal nipple often resist bottle-feeding;conversely,those accustomed to bottle-feeding may reject the maternal nipple.This confusion is particularly common among infants receiving mixed feeding.展开更多
Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specif...Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specific breastfeeding habits after birth,based on their initial feeding experience.For babies who are accustomed to their mother’s nipple,they tend to show resistance to bottle feeding;on the contrary,those who have adapted to bottle feeding may refuse to accept their mother’s nipple.This confusion is particularly common among mixed-feeding babies.展开更多
With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information...With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.展开更多
Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-pub...Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-publicity, tyrannical hess and destructiveness. Whereas the author＇s subjective suspicion is visible in democratic development of African countries. Indeed, in a country development, it is quite necessary to depend on its citizen, observing public feelings; controlling a state with science; benefiting citizen; utilizing resources; constructing homestead, intending creation with a harmonious and stable way.展开更多
1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTI...1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTIONS.The years from1910 to 1930 are often called the Era of Moder-nism,for there seems to have been in both展开更多
The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas...The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.展开更多
This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic ...This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic content,compaction of soil(soil toughness),plant root strength,crop biomass,tree diameter at knee height,Shannon Wiener Index(SWI)for trees and herbs was assembled from field tests at two historic landslide locations:Aranayaka and Kurukudegama,Sri Lanka.An economical,finer resolution database was obtained as the field tests were not cost-prohibitive.The logistic regression(LR)analysis showed that soil moisture content,compaction of soil,SWI for trees and herbs were statistically significant at P<0.05.The variance inflation factors(VIFs)were computed to test for multicollinearity.VIF values(<2)confirmed the absence of multicollinearity between four independent variables in the LR model.Receiver Operating Characteristics(ROC)curve and Confusion Metrix(CM)methods were used to validate the model.In ROC analysis,areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%,respectively,demonstrating the model’s excellent compatibility and predictability.According to the CM,the model demonstrated a 79.6% accuracy,63.6% precision,100% recall,and a F-measure of 77.8%.The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics.Finally,the susceptibility map,which was then classified as low,medium,and highly susceptible areas based on the natural breaks(Jenks)method,was generated using geographical information systems(GIS)techniques.All the historic landslide locations fell into the high susceptibility areas.Thus,validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.展开更多
Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is graduall...Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.展开更多
Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them....Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.展开更多
Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as i...Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.展开更多
Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challen...Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challenges in real-world conditions,i.e.,illumination changes,large pose variations and partial or full occlusions.Those challenges lead to different face areas with different degrees of sharpness and completeness.Inspired by this fact,we focus on the authenticity of predictions generated by different<emotion,region>pairs.For example,if only the mouth areas are available and the emotion classifier predicts happiness,then there is a question of how to judge the authenticity of predictions.This problem can be converted into the contribution of different face areas to different emotions.In this paper,we divide the whole face into six areas:nose areas,mouth areas,eyes areas,nose to mouth areas,nose to eyes areas and mouth to eyes areas.To obtain more convincing results,our experiments are conducted on three different databases:facial expression recognition+(FER+),real-world affective faces database(RAF-DB)and expression in-the-wild(ExpW)dataset.Through analysis of the classification accuracy,the confusion matrix and the class activation map(CAM),we can establish convincing results.To sum up,the contributions of this paper lie in two areas:1)We visualize concerned areas of human faces in emotion recognition;2)We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.展开更多
The inclusion of more potentially correct words in the candidate sets is important to improve the accuracy of Large Vocabulary Continuous Speech Recognition （LVCSR）. A candidate expansion algorithm based on the Weig...The inclusion of more potentially correct words in the candidate sets is important to improve the accuracy of Large Vocabulary Continuous Speech Recognition （LVCSR）. A candidate expansion algorithm based on the Weighted Syllable Confusion Matrix （WSCM） is proposed. First, WSCM is derived from a confusion network. Then, the reeognised candidates in the confusion network is used to conjeeture the most likely correct words based on WSCM, after which, the conjectured words are combined with the recognised candidates to produce an expanded candidate set. Finally, a combined model having mutual information and a trigram language model is used to rerank the candidates. The experiments on Mandarin film data show that an improvement of 9.57% in the character correction rate is obtained over the initial recognition performance on those light erroneous utterances.展开更多
In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learni...In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.展开更多
The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibrat...The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.展开更多
The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the ...The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.展开更多
基金This study was supported by the Ministry of Sciences and Technology of China (No.2005DIB3J067)the National Science Foundation of China (No.40572107, No.40231008, No.40672165 and No.30600074)+2 种基金the Chongqing Science & Technology Commission (No.2005AB7006)the Open Fund and Key Subject of Physical Geog-raphy, Southwest Normal University of China (No.250-411110)the Open Fund of Key Laboratory of Chinese Academy of Geological Sci-ences (No.KL05-20).
文摘The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study the characteristics of stomata, trichomes and dermal cell, etc.. The results showed that stoma exists only on the lower epidermis and its distribution is irregular, and leaf epidermis consist of epidermis cells, stoma complexes and bushy trichomes including glandular hair and non-glandular hair. On the upper epidermis, anticlinal wall caves in sinuous groove to countercheck the transpiration. Evidences from leaf morphological structures serve as another proof on drought-resistant mechanisms. Some strumaes distributing regularly are hypothesized as oxalic calcium on the lower epidermis under laser scanning confocal microscopy （LSCM） with Fluo-3/AM, which can increase their endurance to drought stress. Therefore, the above characteristics of Flos Lonicerae can reduce the loss of water and make Japanese honeysuckle and Wild Honeysuckle adapt to the droughty environment at Karst area in southwest China. However, there is some difference of the two species. From the SEM （Scanning Electron Microscopy） result, it is shown that on the upper epidermis, some glandular hair regularly present along the midrib of Japanese honeysuckle, but Wild Honeysuckle has no glandular hair on the upper epidermis, which can verify the relationships of Flos Lonicerae species and provide the significance for classification of Flos Lonicerae.
基金supported by the National Natural Science Foundation of China (No.62072127,No.62002076,No.61906049)Natural Science Foundation of Guangdong Province (No.2023A1515011774,No.2020A1515010423)+3 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Scientific research project for Guangzhou University (No.RP2022003).
文摘Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.
文摘CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, however, has not kept pace with the levels of people＇s English. Every foreigner who has traveled or lived in China will have a story to tell about an English- language sign that has been lost in translation.
文摘Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many foreign ESL(English as a second language)instructors often feel confused when they first step into a Chinese classroom.Chinese ESL students are different because their
文摘confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the man and his teachings.
文摘The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop based on their initial feeding experiences after birth.Infants accustomed to the maternal nipple often resist bottle-feeding;conversely,those accustomed to bottle-feeding may reject the maternal nipple.This confusion is particularly common among infants receiving mixed feeding.
文摘Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specific breastfeeding habits after birth,based on their initial feeding experience.For babies who are accustomed to their mother’s nipple,they tend to show resistance to bottle feeding;on the contrary,those who have adapted to bottle feeding may refuse to accept their mother’s nipple.This confusion is particularly common among mixed-feeding babies.
基金Supported by Financial Support of China Geological Survey(1212010916048)the Fundamental Research Funds for the Central Universities(200903046)~~
文摘With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.
文摘Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-publicity, tyrannical hess and destructiveness. Whereas the author＇s subjective suspicion is visible in democratic development of African countries. Indeed, in a country development, it is quite necessary to depend on its citizen, observing public feelings; controlling a state with science; benefiting citizen; utilizing resources; constructing homestead, intending creation with a harmonious and stable way.
文摘1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTIONS.The years from1910 to 1930 are often called the Era of Moder-nism,for there seems to have been in both
基金supported by the Shandong Provincial Natural Science Foundation(No.ZR2019MEE096)the Key National Natural Science Foundation of China(No.U1733201)。
文摘The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.
基金funded by the National Research Council,Sri Lanka[NRC 17-066]。
文摘This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic content,compaction of soil(soil toughness),plant root strength,crop biomass,tree diameter at knee height,Shannon Wiener Index(SWI)for trees and herbs was assembled from field tests at two historic landslide locations:Aranayaka and Kurukudegama,Sri Lanka.An economical,finer resolution database was obtained as the field tests were not cost-prohibitive.The logistic regression(LR)analysis showed that soil moisture content,compaction of soil,SWI for trees and herbs were statistically significant at P<0.05.The variance inflation factors(VIFs)were computed to test for multicollinearity.VIF values(<2)confirmed the absence of multicollinearity between four independent variables in the LR model.Receiver Operating Characteristics(ROC)curve and Confusion Metrix(CM)methods were used to validate the model.In ROC analysis,areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%,respectively,demonstrating the model’s excellent compatibility and predictability.According to the CM,the model demonstrated a 79.6% accuracy,63.6% precision,100% recall,and a F-measure of 77.8%.The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics.Finally,the susceptibility map,which was then classified as low,medium,and highly susceptible areas based on the natural breaks(Jenks)method,was generated using geographical information systems(GIS)techniques.All the historic landslide locations fell into the high susceptibility areas.Thus,validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.
基金Project(52161135301)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(202306370296)supported by China Scholarship Council。
文摘Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.
基金supported by the National Natural Science Foundation of China(Grant Nos.61203094 and 61305042)the Natural Science Foundation of the United States(Grant Nos.CNS-1253424 and ECCS-1202225)+3 种基金the Science and Technology Foundation of Henan Province,China(Grant No.152102210048)the Foundation and Frontier Project of Henan Province,China(Grant No.162300410196)the Natural Science Foundation of Educational Committee of Henan Province,China(Grant No.14A413015)the Research Foundation of Henan University,China(Grant No.xxjc20140006)
文摘Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.
基金This work was supported in part by Shandong Provincial Natural Science Foundation(ZR2019PF007)the National Key Research and Development Plan of China(2018YFB0803504)+2 种基金Basic Scientific Research Operating Expenses of Shandong University(2018ZQXM004)Guangdong Province Key Research and Development Plan(2019B010137004)the National Natural Science Foundation of China(U20B2046).
文摘Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.
基金supported by the National Key Research & Development Plan of China (No. 2017YFB1002804)National Natural Science Foundation of China (Nos. 61425017, 61773379, 61332017, 61603390 and 61771472)the Major Program for the 325 National Social Science Fund of China (No. 13&ZD189)
文摘Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challenges in real-world conditions,i.e.,illumination changes,large pose variations and partial or full occlusions.Those challenges lead to different face areas with different degrees of sharpness and completeness.Inspired by this fact,we focus on the authenticity of predictions generated by different<emotion,region>pairs.For example,if only the mouth areas are available and the emotion classifier predicts happiness,then there is a question of how to judge the authenticity of predictions.This problem can be converted into the contribution of different face areas to different emotions.In this paper,we divide the whole face into six areas:nose areas,mouth areas,eyes areas,nose to mouth areas,nose to eyes areas and mouth to eyes areas.To obtain more convincing results,our experiments are conducted on three different databases:facial expression recognition+(FER+),real-world affective faces database(RAF-DB)and expression in-the-wild(ExpW)dataset.Through analysis of the classification accuracy,the confusion matrix and the class activation map(CAM),we can establish convincing results.To sum up,the contributions of this paper lie in two areas:1)We visualize concerned areas of human faces in emotion recognition;2)We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.
基金supported by the National Natural Science Foundation of China under Grants No.61005004,No.61175011,No.61171193the Next-Generation Broadband Wireless Mobile Communications Network Technology Key Project under Grant No.2011ZX03002-005-01+2 种基金the One Church,One Family,One Purpose(111Project)under Grant No.B08004the Key Project of Ministry of Science and Technology of China under Grant No.2012ZX-03002019-002the National High Techni-cal Research and Development Program of China(863Program)under Grant No.2011A-A01A205
文摘The inclusion of more potentially correct words in the candidate sets is important to improve the accuracy of Large Vocabulary Continuous Speech Recognition （LVCSR）. A candidate expansion algorithm based on the Weighted Syllable Confusion Matrix （WSCM） is proposed. First, WSCM is derived from a confusion network. Then, the reeognised candidates in the confusion network is used to conjeeture the most likely correct words based on WSCM, after which, the conjectured words are combined with the recognised candidates to produce an expanded candidate set. Finally, a combined model having mutual information and a trigram language model is used to rerank the candidates. The experiments on Mandarin film data show that an improvement of 9.57% in the character correction rate is obtained over the initial recognition performance on those light erroneous utterances.
基金Acknowledgements This study is supported by the National Natural Science Foundation of China （60705019）, the National High-Tech Research and Development Plan of China （ 2006AA010102 and 2007AA01Z417）, the NOKIA project, and the 111 Project of China under Grant No. 1308004.
文摘In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.
文摘The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.
基金Science Research Project of Gansu Provincial Transportation Department(No.2017-012)
文摘The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.
