Abstract: The present invention relates to a parallel robot system, including: a control apparatus; a parallel robot, including a mounting base, a moving platform, and a driving apparatus arranged between the mounting base and the moving platform, where the driving apparatus is configured to drive the moving platform to make multi-degree-of-freedom movement relative to the mounting base, and the driving apparatus receives a control signal from the control apparatus; a tracer, arranged on the moving platform; a passive arm, where the mounting base of the parallel robot is connected to one end of the passive arm; and an optical positioning and tracking apparatus, configured to track a spatial position of the tracer in real time and to send spatial position data of the tracer to the control apparatus.

Abstract: A method and application of flash imaging lidar to measure the attenuation coefficient of water bodies are provided. In this novel method, the real time remote measurements are achieved on the attenuation coefficient of fresh water using flash imaging lidar based on the adjacent frame difference (AFD) method and water body backscattering model. The method solves the problems of low accuracy and high error of data obtained from real-time remote sensing measurement of attenuation coefficients of water bodies in the prior art; the slow speed of measurement data obtained when measuring water bodies, and the poor feasibility and reliability effect of the remotely measured attenuation coefficients of water bodies obtained. The experimental and theoretical analyses fully illustrate the feasibility of real-time remote sensing measurement of the attenuation coefficient of water bodies using the AFD method, which has a strong practical value.

Abstract: The present invention relates to a robotic arm, including: an upper arm and a front arm; a first joint structure, by which the upper arm and the front arm are articulated together; a second joint structure, arranged at an end of the upper arm and/or an end of the front arm; a first driving mechanism, arranged in the upper arm; and a second driving mechanism, arranged in the front arm, where the first joint structure includes a first force amplification apparatus and a first locking element, the second joint structure includes a second force amplification apparatus and a second locking element, and the first driving mechanism and the second driving mechanism apply an acting force to the first locking element and/or the second locking element by the first force amplification apparatus and/or the second force amplification apparatus.

Abstract: The present disclosure relates to the technical field of fiber-optic gas sensing and laser photoacoustic spectroscopy, and reliability of a gas detection system is improved by actively selecting a working frequency of low noise interference combined with an optical fiber photoacoustic sensing probe capable of isolating high-frequency noise. A gas enters a photoacoustic microcavity through gaps on a sound-sensitive diaphragm after diffusing into a miniature air chamber through a plurality of micropores. Photoacoustic excitation light is incident into the photoacoustic microcavity through a fiber-optic collimator and then excited to generate a photoacoustic pressure wave to cause the sound-sensitive diaphragm to vibrate periodically. An end face of a single-mode optical fiber and the sound-sensitive diaphragm constitutes a fiber-optic Fabry-Perot interferometer. The interferometer measures a deflection of the diaphragm and inverts a concentration of the to-be-measured gas.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: A display device and a manufacturing method thereof, an electronic device, and a light control panel are provided. The display device includes a light control panel and a display liquid crystal panel. The display liquid crystal panel is on a light-emitting side of the light control panel; the light control panel includes a light control region, and the light control region is configured to provide adjusted backlight to the display liquid crystal panel; the display liquid crystal panel includes a display region, and the display region is configured to receive the adjusted backlight to perform display; and a distance between two opposite edges of the light control region in at least one direction is greater than a distance between two opposite edges of the display region in the at least one direction.

Abstract: Please replace the abstract originally filed with the following: A display panel and a display device. Because the pixel distribution density in a second display subarea (A12) is lower than that in a first display subarea (A11), the quantities of sub-pixels (pix) connected to scanning lines are not completely the same; a sub-pixel row having the greatest quantity of sub-pixels (pix) in the display panel is taken as a reference sub-pixel row, the quantity of the sub-pixels (pix) of the reference sub-pixel row is taken as a reference value, a sub-pixel row having the quantity of sub-pixels (pix) smaller than the reference value is taken as a compensation sub-pixel row, and a scanning line connected to the compensation sub-pixel row is taken as a first scanning line; the display panel is provided with load compensation units corresponding to at least part of the first scanning lines, and the load compensation units are located in the second display subarea.

Abstract: In certain aspects, a memory device includes a memory string including a drain select gate (DSG) transistor, a plurality of memory cells, and a source select gate (SSG) transistor, and a peripheral circuit coupled to the memory string. The peripheral circuit is configured to in response to an interrupt during a program operation on a select memory cell of the plurality of memory cells, turn on at least one of the DSG transistor or the SSG transistor. The peripheral circuit is also configured to suspend the program operation after turning on the at least one of the DSG transistor or the SSG transistor.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. The behavior signature includes at least one time series feature for an application used by the IoT device. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: An uplink data parsing method, an electronic device, and a storage medium are provided. The method includes: in an uplink scheduling process, a PHY layer of a baseband processing unit receiving uplink configuration information transmitted by a MAC layer; the PHY layer receiving uplink baseband signals obtained from a plurality of physical cells; the PHY layer determining, according to a first UE record, whether uplink data of a UE to be parsed is correctly parsed in the uplink scheduling process; if yes, the uplink data of the UE to be parsed being not parsed any more in the uplink scheduling process. The first UE record includes an uplink data parsing result of the UE of which uplink data needs to be parsed in the uplink scheduling process.

Abstract: Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: A method includes obtaining a given web page, parsing the given web page to identify one or more frame tags for one or more inline frames of the given web page, and extracting a set of features of a given inline frame from a given one of the identified frame tags in the given web page, the extracted set of features comprising one or more style features, one or more destination features and one or more context features of the given identified frame tag. The method also includes classifying the given inline frame as one of a malicious frame type and a benign frame type utilizing at least one model and at least a portion of the extracted set of features, and controlling access by one or more client devices associated with an enterprise to the given web page responsive to classifying the given inline frame as the malicious frame type.

Abstract: A method includes obtaining a given web page, parsing the given web page to identify one or more frame tags for one or more inline frames of the given web page, and extracting a set of features of a given inline frame from a given one of the identified frame tags in the given web page, the extracted set of features comprising one or more style features, one or more destination features and one or more context features of the given identified frame tag. The method also includes classifying the given inline frame as one of a malicious frame type and a benign frame type utilizing at least one model and at least a portion of the extracted set of features, and controlling access by one or more client devices associated with an enterprise to the given web page responsive to classifying the given inline frame as the malicious frame type.