The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the targe...The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.展开更多
应变-旋转(Strain-Rotation,S-R)和分解定理为分析几何非线性问题提供了合理可靠的理论基础,但用有限元求解时会遇到大变形发生后的网格畸变问题。近年提出的虚单元法(Virtual element method,VEM)适用于一般的多边形网格,因此,该文尝...应变-旋转(Strain-Rotation,S-R)和分解定理为分析几何非线性问题提供了合理可靠的理论基础,但用有限元求解时会遇到大变形发生后的网格畸变问题。近年提出的虚单元法(Virtual element method,VEM)适用于一般的多边形网格,因此,该文尝试使用一阶虚单元求解基于S-R和分解定理的二维几何非线性问题,以克服网格畸变的影响。基于重新定义的多项式位移空间基函数,推演获得一阶虚单元分析线弹性力学问题时允许位移空间向多项式位移空间的投影表达式;按照虚单元法双线性格式的计算规则,分析处理基于更新拖带坐标法和势能率原理的增量变分方程;进而建立离散系统方程及其矩阵表达形式,并编制MATLAB求解程序;采用常规多边形网格和畸变网格,应用该文算法分析均布荷载下的悬臂梁和均匀内压下的厚壁圆筒变形。结果与已有文献和ANSYS软件的对比表明:该文算法在两种网格中均可有效执行且具备足够数值精度。总体该文算法为基于S-R和分解定理的二维几何非线性问题求解提供了一种鲁棒方法。展开更多
The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis(DEA)model and Shannon’s ent...The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis(DEA)model and Shannon’s entropy.This company operates 37 main routes on the backbone roads.Thus,it plays a significant role in public transportation in the city.According to bus industry norms,an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands.For passenger satisfaction,passenger waiting time and passenger-crowding degree are considered,and they are undesirable indicators.To describe such indicators,a superefficient DEA model is constructed.With this model,by using actual data,efficiency is evaluated for each bus route.Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results.Also,sensitivity analysis is presented.Therefore,the results are meaningful for the company to improve its operations and management.展开更多
Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with...Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.展开更多
Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgrat...Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgratz T,Cramer M and Plenio M B Phys.Rev.Lett.113140401(2014)].In the present paper,under the BCP framework we provide two classes of coherence measures based on the sandwiched Rényi relative entropy.We also prove that we cannot get a new coherence measure f(C(·))by a function f acting on a given coherence measure C.展开更多
The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s o...The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.展开更多
Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information the...Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information theory and the geometrical learning. In this paper, we carry out a bold exploration in this field, raise a new approach on face recognition, the intrinsic α-Rényi entropy of the face image attained from manifold learning is used as the characteristic measure during recognition. The new algorithm is tested on ORL face database, and the ex-periments obtain the satisfying results.展开更多
基金This work was supported by the National Natural Science Foundation of China(62071475,61890541,62171447).
文摘The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.
文摘应变-旋转(Strain-Rotation,S-R)和分解定理为分析几何非线性问题提供了合理可靠的理论基础,但用有限元求解时会遇到大变形发生后的网格畸变问题。近年提出的虚单元法(Virtual element method,VEM)适用于一般的多边形网格,因此,该文尝试使用一阶虚单元求解基于S-R和分解定理的二维几何非线性问题,以克服网格畸变的影响。基于重新定义的多项式位移空间基函数,推演获得一阶虚单元分析线弹性力学问题时允许位移空间向多项式位移空间的投影表达式;按照虚单元法双线性格式的计算规则,分析处理基于更新拖带坐标法和势能率原理的增量变分方程;进而建立离散系统方程及其矩阵表达形式,并编制MATLAB求解程序;采用常规多边形网格和畸变网格,应用该文算法分析均布荷载下的悬臂梁和均匀内压下的厚壁圆筒变形。结果与已有文献和ANSYS软件的对比表明:该文算法在两种网格中均可有效执行且具备足够数值精度。总体该文算法为基于S-R和分解定理的二维几何非线性问题求解提供了一种鲁棒方法。
基金supported in part by the Science and Technology Development Fund(FDCT),Macao SAR(0017/2019/A1,0002/2020/AKP)in part by the National Natural Science Foundation of China(61803397)。
文摘The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis(DEA)model and Shannon’s entropy.This company operates 37 main routes on the backbone roads.Thus,it plays a significant role in public transportation in the city.According to bus industry norms,an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands.For passenger satisfaction,passenger waiting time and passenger-crowding degree are considered,and they are undesirable indicators.To describe such indicators,a superefficient DEA model is constructed.With this model,by using actual data,efficiency is evaluated for each bus route.Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results.Also,sensitivity analysis is presented.Therefore,the results are meaningful for the company to improve its operations and management.
基金funded by the deanship of scientific research at princess Nourah bint Abdulrahman University through the fast-track research-funding program.
文摘Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.
基金Project supported by the China Scholarship Council(Grant No.201806305050)
文摘Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgratz T,Cramer M and Plenio M B Phys.Rev.Lett.113140401(2014)].In the present paper,under the BCP framework we provide two classes of coherence measures based on the sandwiched Rényi relative entropy.We also prove that we cannot get a new coherence measure f(C(·))by a function f acting on a given coherence measure C.
基金This work was supported by Universiti Sains Malaysia under external grant(Grant Number 304/PNAV/650958/U154).
文摘The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.
文摘Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information theory and the geometrical learning. In this paper, we carry out a bold exploration in this field, raise a new approach on face recognition, the intrinsic α-Rényi entropy of the face image attained from manifold learning is used as the characteristic measure during recognition. The new algorithm is tested on ORL face database, and the ex-periments obtain the satisfying results.
