This research focused on the social roles of women in Al-Abassiya locality,South Kordofan State,examining the obstacles they face and the impact on their participation in peace building.Using qualitative methods,data ...This research focused on the social roles of women in Al-Abassiya locality,South Kordofan State,examining the obstacles they face and the impact on their participation in peace building.Using qualitative methods,data were collected through various means,revealing that conflicts have significantly affected women’s lives,particularly in rural areas.Women play key roles in family,education,health services,and humanitarian aid,but their involvement in conflict resolution and peace building activities is limited.Obstacles include cognitive,economic,social,and institutional barriers.Recommendations include providing education and training programs,strengthening economic status,ensuring women’s representation in decision-making bodies,advocating for legal reforms promoting gender equality,improving access to information technologies,educating communities on women’s involvement in peace building,and providing support services for women affected by conflicts.展开更多
With the aim of enhancing the value of local building materials, the subject of this paper is a thermophysical study of natural Chadian clay from the eastern region of Chad, “Abeche”. Samples were taken from a brick...With the aim of enhancing the value of local building materials, the subject of this paper is a thermophysical study of natural Chadian clay from the eastern region of Chad, “Abeche”. Samples were taken from a brickwork in Abeche from a depth of 1 m, then using a clay brick-making press, 4 × 5 × 8 cm3 clay test tubes were made with 2%, 4%, 6% and 8% cow dung, and a 100% clay sample was used as a control. These samples underwent thermophysical characterization using the hot-wire method with a hot-plane option, yielding results that could improve thermophysical performance. The thermal conductivity of the test sample is in the order of 0.715 to 0.420 W/m. K, at 8% for cow dung, so the addition of cow dung as a percentage in the clay matrix enabled us to obtain various satisfactory thermal parameters.展开更多
Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in da...Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.展开更多
In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor anal...In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.展开更多
We present the analogous inequalities of Bell's inequality for N-qubit system predicted respectively by realistic theory, quantum mechanics, local theory, local realistic theory, and local quantum theory on the sa...We present the analogous inequalities of Bell's inequality for N-qubit system predicted respectively by realistic theory, quantum mechanics, local theory, local realistic theory, and local quantum theory on the same Belltype joint experiment. It is shown that quantum mechanics can be interpreted by hidden-variable theories while being incompatible to any local theory. A necessary condition for the separability of N-qubit system is derived.展开更多
A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, e...A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.展开更多
Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance de...Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.展开更多
Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recog...Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.展开更多
Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machi...Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.展开更多
Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many v...Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many vastly different dynamic systems can be treated in this manner,and to answer why a detailed,accurate,and global mathematical model is unnecessary,is the target of this paper.Driven by a motivating example,the notions of normality and locality are introduced.Normality shows that,in ADRC,the plant is normalized to an integrator chain,which is called local nominal model and locally describes the plant’s frequency response in the neighborhood of the expected gain crossover frequency.Locality interprets why ADRC can design the controller only with the local information of the plant.With normality and locality,ADRC can be effective and robust,and obtain operational stability discussed by T.S.Tsien.Then viewing proportional-integral-derivative(PID)control as a low-frequency approximation of second-order linear ADRC,the above results are extended to PID control.A controller design framework is proposed to obtain the controller in three steps:(1)choose an integrator chain as the local nominal model of the plant;(2)select a controller family corresponding to the local nominal model;and(3)tune the controller to guarantee the gain crossover frequency specification.The second-order linear ADRC and the PID control are two special cases of the framework.展开更多
For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring st...For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.展开更多
Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SS...Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.展开更多
Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-...Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.展开更多
A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest vi...A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.展开更多
With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource m...With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.展开更多
Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-p...Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.展开更多
Measurement-induced nonlocality(MIN),a quantum correlation measure for bipartite systems,is an indicator of maximal global effects due to locally invariant von Neumann projective measurements.It is originally defined ...Measurement-induced nonlocality(MIN),a quantum correlation measure for bipartite systems,is an indicator of maximal global effects due to locally invariant von Neumann projective measurements.It is originally defined as the maximal square of the Hilbert-Schmidt norm of the difference between pre-and post-measurement states.In this article,we propose a new form of MIN based on affinity.This quantity satisfies all the criteria of a bona fide measure of quantum correlation measures.This quantity is evaluated for both arbitrary pure and 2×n dimensional(qubit-qudit)mixed states.The operational meaning of the proposed quantity is interpreted in terms of the interferometric power of the quantum state.We apply these results on two-qubit mixed states,such as the Werner,isotropic and Bell diagonal states.展开更多
This paper presents the measurement study of locality-aware Peer-to-Peer solutions on Internet Autonomous System (AS) topology by reducing AS hop count and increase nearby source nodes in P2P applications. We evaluate...This paper presents the measurement study of locality-aware Peer-to-Peer solutions on Internet Autonomous System (AS) topology by reducing AS hop count and increase nearby source nodes in P2P applications. We evaluate the performance of topology-aware BT system called TopBT with BitTorrent (BT) by constructing AS graph and measure the hops between nodes to observe the impact of quality of service in P2P applications.展开更多
In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by ...In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.展开更多
In the era of the Internet and globalisation, more and more international academics focus their attention on how city governments compete for talent, capital, and technology through website marketing to promote their ...In the era of the Internet and globalisation, more and more international academics focus their attention on how city governments compete for talent, capital, and technology through website marketing to promote their economy and global status. However, 1) present research generally overlooks the importance of different types of elements in different marketing themes, 2) the combinations of marketing themes are still unknown, and 3) the presumption that the emphasised elements and specific combination of marketing themes on official websites differentiates cities requires more cases to be understood. In light of this background, this study collects homepage elements of 49 Alpha world cities' official websites and quantitatively analyses the frequency of different types of elements, the marketing content themes, and the dissimilarity of content of Chinese Alpha world cities. The results indicate that comprehensiveness and locality appear in the process of city marketing throughout official city websites. Overall, we make the following conclusions. 1) The importance of different kinds of elements significantly differs between 49 Alpha world cities. 2) Based on various combinations of elements, the marketing contents of Alpha world cities through official websites can be categorised into six themes of history and culture, government and information, construction and environment, government and living, construction and living, and general compound. 3) The marketing elements of five Chinese Alpha world cities, including Hong Kong, Beijing, Shanghai, Taipei and Guangzhou, are different than the other 44 Alpha world cities, and Chinese cities prefer to advertise their history and culture but rarely market citizens' activities. Moreover, Chinese cities' marketing mostly targets natives while the other 44 Alpha cities target external groups, and the locality of world cities' website marketing is reinforced especially on a native language edition website. This study ultimately finds that the Chinese edition websites of five Chinese cities place more focus on introducing local historical buildings, administrative services, and internal business information than the English edition websites do.展开更多
基金The researcher would like to thank Dr.Afag Mohamed Sadig as supervisor and Prof Hazel Barrett as co-supervisor for the paper。
文摘This research focused on the social roles of women in Al-Abassiya locality,South Kordofan State,examining the obstacles they face and the impact on their participation in peace building.Using qualitative methods,data were collected through various means,revealing that conflicts have significantly affected women’s lives,particularly in rural areas.Women play key roles in family,education,health services,and humanitarian aid,but their involvement in conflict resolution and peace building activities is limited.Obstacles include cognitive,economic,social,and institutional barriers.Recommendations include providing education and training programs,strengthening economic status,ensuring women’s representation in decision-making bodies,advocating for legal reforms promoting gender equality,improving access to information technologies,educating communities on women’s involvement in peace building,and providing support services for women affected by conflicts.
文摘With the aim of enhancing the value of local building materials, the subject of this paper is a thermophysical study of natural Chadian clay from the eastern region of Chad, “Abeche”. Samples were taken from a brickwork in Abeche from a depth of 1 m, then using a clay brick-making press, 4 × 5 × 8 cm3 clay test tubes were made with 2%, 4%, 6% and 8% cow dung, and a 100% clay sample was used as a control. These samples underwent thermophysical characterization using the hot-wire method with a hot-plane option, yielding results that could improve thermophysical performance. The thermal conductivity of the test sample is in the order of 0.715 to 0.420 W/m. K, at 8% for cow dung, so the addition of cow dung as a percentage in the clay matrix enabled us to obtain various satisfactory thermal parameters.
基金Supported by the National Natural Science Foundation of China (60973097)the Scientific Research Foundation of Liaocheng University(X0810029)~~
文摘Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.
文摘In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.
文摘We present the analogous inequalities of Bell's inequality for N-qubit system predicted respectively by realistic theory, quantum mechanics, local theory, local realistic theory, and local quantum theory on the same Belltype joint experiment. It is shown that quantum mechanics can be interpreted by hidden-variable theories while being incompatible to any local theory. A necessary condition for the separability of N-qubit system is derived.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.
基金Supported by the National Natural Science Foundation of China （61273160）, the Natural Science Foundation of Shandong Province of China （ZR2011FM014） and the Fundamental Research Funds for the Central Universities （10CX04046A）.
文摘Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
基金Sponsored by Program for New Century Excellent Talents in University of China(NCET-08-0726)Beijing Nova Program of China(2007B027)
文摘Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.
基金supported by Fundamental Research Funds for the Central Universities of China (Grant No. CDJZR10118801)
文摘Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.
基金This work was supported by the National Nature Science Foundation of China(Grant No.61733017).
文摘Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many vastly different dynamic systems can be treated in this manner,and to answer why a detailed,accurate,and global mathematical model is unnecessary,is the target of this paper.Driven by a motivating example,the notions of normality and locality are introduced.Normality shows that,in ADRC,the plant is normalized to an integrator chain,which is called local nominal model and locally describes the plant’s frequency response in the neighborhood of the expected gain crossover frequency.Locality interprets why ADRC can design the controller only with the local information of the plant.With normality and locality,ADRC can be effective and robust,and obtain operational stability discussed by T.S.Tsien.Then viewing proportional-integral-derivative(PID)control as a low-frequency approximation of second-order linear ADRC,the above results are extended to PID control.A controller design framework is proposed to obtain the controller in three steps:(1)choose an integrator chain as the local nominal model of the plant;(2)select a controller family corresponding to the local nominal model;and(3)tune the controller to guarantee the gain crossover frequency specification.The second-order linear ADRC and the PID control are two special cases of the framework.
基金Supported by the National Natural Science Foundation of China (61074079)Shanghai Leading Academic Discipline Project (B054)
文摘For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.
文摘Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.
基金National Natural Science Foundation of China (60776793,60802043)National Basic Research Program of China (2010CB327900)
文摘Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.
基金supported by the Key Research and Development Program of Hubei Province(2020BAB113)。
文摘A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.
基金Supported by the High Technology Research andDevelopment Program of China (2003AA414210) and the NationalNatural Science Foundation of China (60173051)
文摘With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.
基金supported by the Sino-German Center (the National Natural Science Foundation of China and the German Science Foundation GZ1201)the Postgraduate Courses Project of Peking University (2014-40)
文摘Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.
文摘Measurement-induced nonlocality(MIN),a quantum correlation measure for bipartite systems,is an indicator of maximal global effects due to locally invariant von Neumann projective measurements.It is originally defined as the maximal square of the Hilbert-Schmidt norm of the difference between pre-and post-measurement states.In this article,we propose a new form of MIN based on affinity.This quantity satisfies all the criteria of a bona fide measure of quantum correlation measures.This quantity is evaluated for both arbitrary pure and 2×n dimensional(qubit-qudit)mixed states.The operational meaning of the proposed quantity is interpreted in terms of the interferometric power of the quantum state.We apply these results on two-qubit mixed states,such as the Werner,isotropic and Bell diagonal states.
文摘This paper presents the measurement study of locality-aware Peer-to-Peer solutions on Internet Autonomous System (AS) topology by reducing AS hop count and increase nearby source nodes in P2P applications. We evaluate the performance of topology-aware BT system called TopBT with BitTorrent (BT) by constructing AS graph and measure the hops between nodes to observe the impact of quality of service in P2P applications.
文摘In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.
基金National Natural Science Foundation of China(No.41320104001,41871140)the Scientific Specific Fund Project of Collaboration and Innovation Center of Coordinating Urban and Rural in Shanxi Province(No.SXCXCXZD2017-002)the Special Fund Project of the Basic Research Service of Sun Yat-sen University(No.17lgjc04)
文摘In the era of the Internet and globalisation, more and more international academics focus their attention on how city governments compete for talent, capital, and technology through website marketing to promote their economy and global status. However, 1) present research generally overlooks the importance of different types of elements in different marketing themes, 2) the combinations of marketing themes are still unknown, and 3) the presumption that the emphasised elements and specific combination of marketing themes on official websites differentiates cities requires more cases to be understood. In light of this background, this study collects homepage elements of 49 Alpha world cities' official websites and quantitatively analyses the frequency of different types of elements, the marketing content themes, and the dissimilarity of content of Chinese Alpha world cities. The results indicate that comprehensiveness and locality appear in the process of city marketing throughout official city websites. Overall, we make the following conclusions. 1) The importance of different kinds of elements significantly differs between 49 Alpha world cities. 2) Based on various combinations of elements, the marketing contents of Alpha world cities through official websites can be categorised into six themes of history and culture, government and information, construction and environment, government and living, construction and living, and general compound. 3) The marketing elements of five Chinese Alpha world cities, including Hong Kong, Beijing, Shanghai, Taipei and Guangzhou, are different than the other 44 Alpha world cities, and Chinese cities prefer to advertise their history and culture but rarely market citizens' activities. Moreover, Chinese cities' marketing mostly targets natives while the other 44 Alpha cities target external groups, and the locality of world cities' website marketing is reinforced especially on a native language edition website. This study ultimately finds that the Chinese edition websites of five Chinese cities place more focus on introducing local historical buildings, administrative services, and internal business information than the English edition websites do.
