Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological con...Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological condition. This study examined use of new VFM and flow field for assessment of left ventricular （LV） systolic hemodynamics in patients with simple hyperthyroidism （HT）. Thirty-seven simple HT patients were enrolled as HT group, and 38 gender- and age-matched healthy volunteers as control group. VFM model was used to analyze LV flow field at LV apical long-axis view. The follow- ing flow parameters were measured, including peak systolic velocity （Vs）, peak systolic flow （Fs）, total systolic negative flow （SQ） in LV basal, middle and apical level, velocity gradient from the apex to the aortic valve （AV）, and velocity according to half distance （V1/2）. The velocity vector in the LV cavity, stream line and vortex distribution in the two groups were observed. The results showed that there were no significant differences in the conventional parameters such as left ventricular ejection fraction （LVEF）, left ventricular end-diastolic diameter （LVEDD） and left atrium diameter （LAD） between HT group and control group （P〉0.05）. Compared with the control group, a brighter flow and more vortexes were detected in HT group. Non-uniform distribution occurred in the LV flow field, and the stream lines were discontinuous in HT group. The values of Vs and Fs in three levels, SQ in middle and basal levels, AV and V1/2 were higher in HT group than in control group （P〈0.01）. AV was positively correlated with serum free thyroxin （FT4） （r=0.48, P〈0.01）. Stepwise multiple regression analysis showed that LVEDD, FT4, and body surface area （BSA） were the influence factors of △V. The unstable left ventricular sys- tolic hydrodynamics increased in a compensatory manner in simple PIT patients. The present study in- dicated that VFM may be used for early detection of abnormal ventricle contraction in clinical settings.展开更多
Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in text...Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image,a new color texture enhancement algorithm based on the Line Integral Convolution(LIC)for the vector field data is proposed,which combines the HSV color mapping and cumulative distribution function calculation of vector field data.This algorithm can be summarized as follows:firstly,the vector field data is convoluted twice by line integration to get the gray texture image.Secondly,the method of mapping vector data to each component of the HSV color space is established.And then,the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image.Thirdly,the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values.Finally,both the gray texture image and the color image are fused to get the color texture.The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data.Furthermore,the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.展开更多
Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with...Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained展开更多
Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The m...Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The mapping discovery between two ontologies is achieved by computing the similarity between the concepts of two ontologies.Every concept consists of four features of concept name,property,instance and structure.First,the algorithms of calculating four individual similarities corresponding to the four features are given.Secondly,the similarity vectors consisting of four weighted individual similarities are built,and the weights are the linear function of harmony and reliability.The similarity vector is used to represent the similarity relation between two concepts which belong to different fuzzy ontolgoies.Lastly,Support Vector Machine(SVM) is used to get the mapping concept pairs by the similarity vectors.Experiment results are satisfactory.展开更多
In this paper, we introduce and study a class of generalized vector quasivariational-like inequality problems, which includes generalized nonlinear vector variational inequality problems, generalized vector variationa...In this paper, we introduce and study a class of generalized vector quasivariational-like inequality problems, which includes generalized nonlinear vector variational inequality problems, generalized vector variational inequality problems and generalized vector variational-like inequality problems as special cases. We use the maximal element theorem with an escaping sequence to prove the existence results of a solution for generalized vector quasi-variational-like inequalities without any monotonicity conditions in the setting of locally convex topological vector space.展开更多
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
文摘背景:腰椎小关节炎是引起下腰痛的一个主要原因,目前主要依靠MRI进行初步定性诊断,但仍有一定漏诊、误诊的概率发生,因此MR T2^(*)mapping成像技术有望成为定量检查腰椎小关节炎软骨损伤的重要检测手段。目的:探讨MR T2^(*)mapping成像技术在定量分析腰椎小关节炎软骨损伤退变中的应用价值。方法:收集南京医科大学第四附属医院2020年4月至2022年3月门诊或住院合并下腰痛共110例患者,设为病例组;同时招募无症状志愿者80例,设为对照组。对所有纳入对象L1-S1的小关节行3.0 T MR扫描,获取T2^(*)mapping横断位图像和T2WI图像,分别对所有小关节软骨进行Weishaupt分级及T2^(*)值测量,收集数据并行统计学分析。不同小关节Weishaupt分级之间小关节软骨T2^(*)值比较采用单因素方差分析。结果与结论:①经统计分析发现,病例组腰椎小关节软骨T2^(*)值(17.6±1.5)ms明显较对照组(21.4±1.3)ms降低,差异有显著性意义(P<0.05);②在病例组中,随着腰椎小关节Weishaupt分级增加,小关节软骨T2^(*)值也呈逐渐下降趋势,且这种差异有显著性意义(P<0.05);③提示T2^(*)mapping能够较好地显示腰椎小关节软骨损伤的早期病理变化,腰椎小关节软骨的T2^(*)值能够定量评估腰椎小关节的软骨损伤程度;T2^(*)mapping成像技术能为影像学诊断腰椎小关节炎软骨早期损伤提供很好的理论依据,具有重要的临床应用价值。
基金supported by Independent Innovation Fund of Huazhong University of Science and Technology for Clinical Skills,China(No.2015-01-18-53028)
文摘Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological condition. This study examined use of new VFM and flow field for assessment of left ventricular （LV） systolic hemodynamics in patients with simple hyperthyroidism （HT）. Thirty-seven simple HT patients were enrolled as HT group, and 38 gender- and age-matched healthy volunteers as control group. VFM model was used to analyze LV flow field at LV apical long-axis view. The follow- ing flow parameters were measured, including peak systolic velocity （Vs）, peak systolic flow （Fs）, total systolic negative flow （SQ） in LV basal, middle and apical level, velocity gradient from the apex to the aortic valve （AV）, and velocity according to half distance （V1/2）. The velocity vector in the LV cavity, stream line and vortex distribution in the two groups were observed. The results showed that there were no significant differences in the conventional parameters such as left ventricular ejection fraction （LVEF）, left ventricular end-diastolic diameter （LVEDD） and left atrium diameter （LAD） between HT group and control group （P〉0.05）. Compared with the control group, a brighter flow and more vortexes were detected in HT group. Non-uniform distribution occurred in the LV flow field, and the stream lines were discontinuous in HT group. The values of Vs and Fs in three levels, SQ in middle and basal levels, AV and V1/2 were higher in HT group than in control group （P〈0.01）. AV was positively correlated with serum free thyroxin （FT4） （r=0.48, P〈0.01）. Stepwise multiple regression analysis showed that LVEDD, FT4, and body surface area （BSA） were the influence factors of △V. The unstable left ventricular sys- tolic hydrodynamics increased in a compensatory manner in simple PIT patients. The present study in- dicated that VFM may be used for early detection of abnormal ventricle contraction in clinical settings.
基金The National Natural Science Foundation of China under contract Nos 61702455,61672462 and 61902350the Natural Science Foundation of Zhejiang Province,China under contract No.LY20F020025。
文摘Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image,a new color texture enhancement algorithm based on the Line Integral Convolution(LIC)for the vector field data is proposed,which combines the HSV color mapping and cumulative distribution function calculation of vector field data.This algorithm can be summarized as follows:firstly,the vector field data is convoluted twice by line integration to get the gray texture image.Secondly,the method of mapping vector data to each component of the HSV color space is established.And then,the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image.Thirdly,the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values.Finally,both the gray texture image and the color image are fused to get the color texture.The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data.Furthermore,the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.
文摘Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained
基金supported by the Natural Science Foundation of Beijing City under Grant No.4123094the Science and Technology Project of Beijing Municipal Commission of Education under Grants No.KM201110028020,No.KM201010028019+1 种基金the National Nature Science Foundation under Grants No.61100205,No.60873001,No.60863011,No.61175068the Fundamental Research Funds for the Central Universities under Grant No.2009RC0212
文摘Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The mapping discovery between two ontologies is achieved by computing the similarity between the concepts of two ontologies.Every concept consists of four features of concept name,property,instance and structure.First,the algorithms of calculating four individual similarities corresponding to the four features are given.Secondly,the similarity vectors consisting of four weighted individual similarities are built,and the weights are the linear function of harmony and reliability.The similarity vector is used to represent the similarity relation between two concepts which belong to different fuzzy ontolgoies.Lastly,Support Vector Machine(SVM) is used to get the mapping concept pairs by the similarity vectors.Experiment results are satisfactory.
基金The NSF(60804065) of Chinathe Foundation(11A029,11A028) of China West Normal University+2 种基金the Fundamental Research Funds(13D016) of China West Normal Universitythe Key Project(211163) of Chinese Ministry of EducationSichuan Youth Science and Technology Foundation(2012JQ0032)
文摘In this paper, we introduce and study a class of generalized vector quasivariational-like inequality problems, which includes generalized nonlinear vector variational inequality problems, generalized vector variational inequality problems and generalized vector variational-like inequality problems as special cases. We use the maximal element theorem with an escaping sequence to prove the existence results of a solution for generalized vector quasi-variational-like inequalities without any monotonicity conditions in the setting of locally convex topological vector space.
