For program behavior-based anomaly detection, the only way to ensure accurate monitoring is to construct an efficient and precise program behavior model. A new program behavior-based anomaly detection model, called co...For program behavior-based anomaly detection, the only way to ensure accurate monitoring is to construct an efficient and precise program behavior model. A new program behavior-based anomaly detection model, called combined pushdown automaton (CPDA) model was proposed, which is based on static binary executable analysis. The CPDA model incorporates the optimized call stack walk and code instrumentation technique to gain complete context information. Thereby the proposed method can detect more attacks, while retaining good performance.展开更多
The objective of this research was to develop an uncut crop edge detection system for a combine harvester.A laser rangefinder(LF)was selected as a primary sensor,combined with a pan-tilt unit(PTU)and an inertial measu...The objective of this research was to develop an uncut crop edge detection system for a combine harvester.A laser rangefinder(LF)was selected as a primary sensor,combined with a pan-tilt unit(PTU)and an inertial measurement unit(IMU).Three-dimensional field information can be obtained when the PTU rotates the laser rangefinder in the vertical plane.A field profile was modeled by analyzing range data.Otsu’s method was used to detect the crop edge position on each scanning profile,and the least squares method was applied to fit the uncut crop edge.Fundamental performance of the system was first evaluated under laboratory conditions.Then,validation experiments were conducted under both static and dynamic conditions in a wheat field during harvesting season.To verify the error of the detection system,the real position of the edge was measured by GPS for accuracy evaluation.The results showed an average lateral error of±12 cm,with a Root-Mean-Square Error(RMSE)of 3.01 cm for the static test,and an average lateral error of±25 cm,with an RMSE of 10.15 cm for the dynamic test.The proposed laser rangefinder-based uncut crop edge detection system exhibited a satisfactory performance for edge detection under different conditions in the field,and can provide reliable information for further study.展开更多
In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, d...In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.展开更多
文摘For program behavior-based anomaly detection, the only way to ensure accurate monitoring is to construct an efficient and precise program behavior model. A new program behavior-based anomaly detection model, called combined pushdown automaton (CPDA) model was proposed, which is based on static binary executable analysis. The CPDA model incorporates the optimized call stack walk and code instrumentation technique to gain complete context information. Thereby the proposed method can detect more attacks, while retaining good performance.
基金the fund of China Scholarship Council,Chinese Universities Scientific Fund(ZD2013015)the research Fund for the Doctoral Program of Higher Education of China(20130204110020).
文摘The objective of this research was to develop an uncut crop edge detection system for a combine harvester.A laser rangefinder(LF)was selected as a primary sensor,combined with a pan-tilt unit(PTU)and an inertial measurement unit(IMU).Three-dimensional field information can be obtained when the PTU rotates the laser rangefinder in the vertical plane.A field profile was modeled by analyzing range data.Otsu’s method was used to detect the crop edge position on each scanning profile,and the least squares method was applied to fit the uncut crop edge.Fundamental performance of the system was first evaluated under laboratory conditions.Then,validation experiments were conducted under both static and dynamic conditions in a wheat field during harvesting season.To verify the error of the detection system,the real position of the edge was measured by GPS for accuracy evaluation.The results showed an average lateral error of±12 cm,with a Root-Mean-Square Error(RMSE)of 3.01 cm for the static test,and an average lateral error of±25 cm,with an RMSE of 10.15 cm for the dynamic test.The proposed laser rangefinder-based uncut crop edge detection system exhibited a satisfactory performance for edge detection under different conditions in the field,and can provide reliable information for further study.
基金Supported by National Natural Science Foundation of China(Grant Nos.11501522,11101014,11001118 and11171012)National Statistical Research Projects(Grant No.2014LZ45)+2 种基金the Doctoral Fund of Innovation of Beijing University of Technologythe Science and Technology Project of the Faculty Adviser of Excellent PhD Degree Thesis of Beijing(Grant No.20111000503)the Beijing Municipal Education Commission Foundation(Grant No.KM201110005029)
文摘In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.
