114results about How to "Improve the detection effect" patented technology

The invention provides a real-time human body detection method based on an AdaBoost frame and colour of a head, comprising training and detecting steps: (1) extracting multi-scale HOG (histograms of oriented gradients) characteristics according to a template; (2) training a human body detection model by adopting an AdaBoost-Boosting method; (3) extracting histogram characteristics of the colour of the head; (4) training a head discrimination model by adopting an AdaBoost method; (5) detecting a human body based on a sliding window method; (6) for each detection window, firstly extracting the HOG characteristics, and judging whether the detected object is a human body or not according the human body detection model; and (7) for the window which is determined to be a human body, extracting the histogram characteristics of the head, judging whether the head is contained or not; and determining the window containing the head to be a window containing a human body, and drawing a rectangle at a corresponding position in an image. In the invention, the characteristic unit, namely a Block of the original HOG characteristics is adopted, the Block is of multi-scale type, and a characteristic template is combined, thus the detection effect is improved; and the head characteristic discrimination is added, thus the detection rate is improved.

The invention relates to an automatic voice summation tone detection method based on a deep neural network. The method comprises a step of training a deep neural network model for summation tone detection, wherein an input layer of the deep neural network model is characteristic information of voice and an output layer is probability output values for three states which are summation tone voice, voice of a single person and non-speech sound. The depth neural network model is used to perform summation tone detection on the automatic voice.

The invention discloses a face anti-counterfeiting detection method of a double-channel convolutional neural network based on an attention model, and the method comprises the steps: constructing a training set which comprises a training sample composed of an RGB face image and an MSR face image; constructing a face anti-counterfeiting detection network which comprises a feature extraction unit used for extracting RGB feature vectors and MSR feature vectors, a feature fusion unit used for fusing the RGB feature vectors and the MSR feature vectors, and a feature classification unit used for classifying the fused feature vectors; training the face anti-counterfeiting detection network by using the training set to obtain a face anti-counterfeiting detection model; during application, preprocessing a to-be-detected RGB face picture and then inputting the preprocessed face image into the face anti-counterfeiting detection model, and outputting a detection result, namely a true face or a false face, through calculation. According to the method, the influence of illumination on detection and rich texture information of the RGB picture can be reduced by using the MSR picture at the same time, so that the detection result is accurate and has certain generalization.

The invention provides a bad webpage detection method and device. The bed webpage detection method comprises the following steps of: acquiring suspected bad webpages corresponding to bad keywords according to the bad keywords, acquiring an original address corresponding to each suspected bad webpage and generating a bed website list containing the original addresses; analyzing the suspected bad webpages to generate an analysis result; and when vicious skips existing in the bad webpages are identified according to the analysis result, deleting the original addresses corresponding to the suspected bad webpages from the bad website list. According to the bad webpage detection method and device provided by the invention, because the suspected bad webpages acquired according to the bad keywords are further analyzed, the webpages with vicious skips are deleted, and the detection effect on the bad webpages is improved.

The invention discloses an assessment device for a fillet weld of an end flange of a GIS (gas insulated switchgear) and a barrel and a lossless assessment method, aiming at solving the problem that an existing detection method cannot effectively achieve lossless assessment on the fillet weld of the end flange of the GIS and the barrel. The device is characterized in that an ultrasonic probe is installed on a wedge block, both the ultrasonic probe and a encoder are connected with a scanning rack so as to run synchronously, the encoder is connected with an ultrasonic wave phased array detector through a data line, the ultrasonic probe is connected with the ultrasonic wave phased array detector through the data line, and the surface of the ultrasonic probe is coated with a coupling agent and is connected with the wedge block through bolts. The assessment device can realize lossless assessment of total cross section of the structure, can detect both surface defect and interior defect, can detect both the outer fillet weld of the end flange of the GIS in service and the barrel and the inner fillet weld, and can comprehensively assess the quality of the structure.

The invention provides a railway foreign matter invasion detection method based on railway monitoring. The method comprises the following steps: training YOLOv3 by utilizing an original image containing foreign matter invasion of a monitoring video in a known railway scene; obtaining a trained model weight; acquiring a monitoring video of a railway scene to be monitored, extracting a series of to-be-monitored original images from a monitoring video, processing the to-be-monitored original images through an existing Gaussian mixture model, and sequentially performing morphological processing, threshold adaptation and non-maximum suppression algorithm processing on output binary images to obtain an image area containing foreign matter invasion; and inputting the image area containing foreignmatter invasion into the YOLOv3, wherein the YOLOv3 outputs the foreign matter type and the foreign matter positioning position information in the corresponding original image according to the trained model weight. According to the method, the false alarm rate caused by the Gaussian mixture algorithm is reduced, the detection effect of the YOLOv3 algorithm on a long-distance target or/and a smalltarget is improved, and railway foreign matter invasion can be effectively monitored.

The invention discloses a method for identifying vehicles from GPS (global positioning system) data. The method includes steps of preliminarily labeling GPS data points; acquiring candidate transfer stations, and in other words, adding potential transfer stations into sets of the candidate transfer stations; acquiring true transfer stations and sectioning tracks; extracting characteristic acceleration change rates ACR; extracting characteristic time slice types TS and dividing user working peak hours into two types T_busy and T_idle; extracting 85% division speed and acceleration characteristics of road sections; extracting characteristics from the various road sections, inputting the characteristics into classification model random forest classifiers and outputting transport modes to obtain prediction results of the transport modes. The transport modes are output results. The method has the advantages that the effective characteristics are extracted according to laws presented by existing traffic, and the vehicle identification rate further can be increased by the aid of post-processing algorithms on the basis of ideology of track integrity.

The invention discloses an optimization method of a parallel pooling layer for optimizing the surface abrasion detection model of a pantograph carbon contact strip. The method comprises the followingsteps: 1) collecting a defect image on the surface of the pantograph carbon contact strip, and carrying out image preprocessing to obtain a data set; wherein the data set comprises training data and test data; the training data comprise two types of data, namely label data and label-free data; 2) building a semi-supervised convolution neural network under a CAFFE framework, and training the modelby utilizing the label-free data; 3) replacing an original pooling layer by a parallel pooling layer on the basis of a random pooling principle, subjecting the label data and the label-free data to differential sampling, and optimizing the surface abrasion detection model of the pantograph carbon slide plate on the basis of the semi-supervised convolution neural network. According to the invention, differential sampling is carried out on different attribute data by adopting the parallel pooling layer. The utilization efficiency of the semi-supervised convolution neural network on the label-free data is improved. The optimization effect of the surface abrasion detection model of the pantograph carbon contact strip is improved.

The invention discloses a semantic edge detection method based on deep learning. The core is a semantic edge detection deep neural network model based on multi-level feature fusion. The method comprises the steps of multi-level feature learning, multi-level feature extraction and multi-level feature fusion. The training comprises three steps of initializing model parameters, preparing a target data set and training an overall model. Compared with the prior art, the method has the following advantages: 1) the low-level features are gradually fused into the high-level features from bottom to top, the semantic discrimination capability of the fused high-level features is enhanced, and the missing underlying detail information is supplemented, so that the semantic edge detection effect is improved (2) training a multi-level feature learning module on the big data set in advance to enable the big data set to learn richer multi-level features on a target data set, and (3) expanding the dataset by adopting a data enhancement technology and preprocessing true value tagse to enhance the learning ability of the whole model.

The invention provides an ultrasonic probe correction method and system. The method comprises steps: the flight time of ultrasonic signals transmitted by all array elements in the ultrasonic probe under a set distance or set time is measured, a reference array element is selected, and a flight time difference/ratio/product between each array element and the reference array element is calculated; and according to the flight time difference/ratio/product, compensation parameters of each array element are calculated, and according to the compensation parameters, transmitting or receiving of the ultrasonic signals of each array element is compensated. According to the method and the system, compensation and correction on process errors of the ultrasonic probe array can be realized, and a basisis provided for compensation and calculation on beam transmitting and receiving by the ultrasonic system.

The invention belongs to the technical field of image recognition and detection, and particularly provides a tire X-ray defect detection method based on twin distance comparison, which comprises the following steps: acquiring a set of pictures of a standard picture, a normal picture and a defect picture of a specific tire pattern; labeling the sample set of the X-ray detection picture according todifferent types of pictures, and dividing samples with labeling information into a training set, a verification set and a test set; preprocessing the X-ray detection picture; establishing a Siamese Network model; training the Siamese Network model based on the training set, iterating the training process for multiple periods, and finally selecting parameters enabling the Siamese Network model tobe highest in accuracy; and after the obtained Siamese Network model is loaded, judging whether a defective part exists in the X-ray detection picture or not. Compared with a traditional method, the detection method has the advantages that the detection effect on unobvious defects can be greatly improved, the detection method is suitable for X-ray films shot by different models, tire patterns andX-ray machines, the burden of tire quality inspection personnel can be reduced, and tire quality control is facilitated.

The invention relates to an adaptive threshold value color image edge detection method based on an FPGA (Field Programmable Gate Array) and Kirsch. The method comprises the following steps that: firstly, converting collected image data into YCbCr, and carrying out Gaussian filtering and median filtering processing on a Y component in the YCbCr; and then, carrying out edge detection, carrying out morphological processing on an image subjected to the edge detection, synthetizing Y'Cb'Cr', and converting the Y'Cb'Cr' into an RGB888 format to be displayed in virtue of a VGA (Video Graphics Array). A Kirsch operator is taken as the reference of the edge detection, the edge detection of the adaptive threshold value color image is realized in virtue of the FPGA platform, and in addition, visual display is carried out through the VGA in the RGB8888 format so as to improve a color image edge detection effect. By use of the method, the defects in the prior art that image processing instantaneity is insufficient can be made up, and the edge detection is more flexible so as to be favorable for improving the accuracy of object edge detection.

The invention provides a method for detecting unexpected hot topics in Chinese microblogs. The method comprises the following steps: acquiring microblog posts of microblog sites through data interfaces of microblog sites, wherein the microblog posts comprise extracted texts and publish time; establishing a reference microblog post set according to the microblog posts; determining the microblog post set to be detected; adopting the binary coverage method for word segmentation; determining unexpected words, and establishing an unexpected word cluster; determining and ordering hot topics. The method can detect unexpected topics in massive Chinese microblog posts, and adopts the unexpected word and unexpected word cluster detection combined manner by utilizing the features of the unexpected topics in the microblog posts to accurately detect the unexpected topics.

The invention relates to a malicious code detection method and system based on a kernel object behavior body, and belongs to the technical field of a computer. The method comprises the following steps of building a malicious family public behavior body; building a suspicious sample individual behavior body; importing the suspicious sample individual behavior body into the malicious family public behavior body to form a malicious code field body; reasoning the individual behavior body; and judging whether the individual behavior body is malicious code or not. The method and the system have the advantages that the method is simple and reliable; a suspicious sample can be detected and classified through building the body; the relationship between two objects and the operation condition of the two objects can be directly seen from a kernel object behavior graph; and the detection and classification effect on the malicious code is good.

The invention discloses a robust sonar target detection method based on a dual-path feature fusion network. According to the traditional image processing method, an image segmentation method is used for distinguishing a background from a target; the method comprises the following steps of: 1, establishing a dual-path feature fusion network; and 2, training the dual-path feature fusion network obtained in the step 1; 3, performing sonar image generation and feature extraction; and 4, classifying and detecting the sonar image target frame by combining the default frame. According to the method,the deep learning technology is fused into target detection, the sonar image generated by the sonar data is input into the network model, feature extraction, target detection and target classificationare completed in the model at a time, and therefore the detection speed is greatly increased. According to the method, more deep features can be extracted, and target classification and regression are optimized fundamentally. According to the invention, multi-scale dense connection is adopted to fuse multi-level characteristics and improve the detection effect of medium and small targets.

The invention relates to a student behavior detection method based on target detection, and the method comprises the following steps: S1, building a data set comprising student behavior information which comprises hand raising, standing and sleeping; S2, establishing a student behavior detection model, wherein the model is an improved Faster R-CNN model based on a residual network ResNet-101; S3,training the student behavior detection model based on an online hard sample mining method; S4, detecting a to-be-detected video by using the trained student behavior detection model to obtain a student behavior result, and visualizing the student behavior result; wherein the residual network ResNet-101 adopts a multi-layer feature fusion strategy and the fifth convolution stage of the residual network ResNet-101 includes multiple branches with different receptive field sizes.. Compared with the prior art, the method has the advantages of high precision and the like.

The invention provides a multi-target detection method. The multi-target detection method comprises the steps that a preprocessing module carries out convolution and pooling operation on a to-be-detected image; The first dense connection module performs convolution operation on the output of the preprocessing module and then performs series operation with the output of the preprocessing module; The first transition module performs convolution and pooling operation on the output of the first dense connection module; The second dense connection module performs convolution operation on the outputof the first transition module and then performs series operation with the output of the first transition module; The second transition module carries out convolution operation on the output of the second dense connection module; The third transition module performs pooling and convolution operation on the output of the first transition module; The extraction feature layer carries out convolutionoperation and residual module processing on the output of the first transition module and the series connection result of the output of the second transition module and the output of the third transition module; The prediction layer processes the output of the extraction feature layer and decodes the predicted target position; And the non-extreme value suppression module carries out post-processing on the output of the prediction layer.

The invention discloses a high-resolution polarization SAR ship target and interference target identification method, and belongs to the field of SAR image processing. The method is used for removing a false alarm caused by an interference target suspected as a ship in the SAR image ship target detection process. The relation among the polarization information of a ship target, the scattering intensity peak value and the structure is utilized comprehensively, Krogager polarization decomposition is carried out on a target section extracted through a ship target detecting algorithm, then a target complex structure area, namely, a target peak value area, is obtained through the changing curve of the spirochete scattering proportion and the peak value area through calculation, the spirochete scattering proportion of the peak value area is extracted, identification statistical magnitude is formed, judgment is carried out through an experience threshold, and an identification result is obtained. The method has good identification capacity on the ship target in the high-resolution polarization SAR image and the interference target suspected as the ship, the false alarm caused by the interference target in the SAR image ship target detection process can be reduced, and the utilization and popularization value is achieved.

The invention discloses a fire-fighting unmanned aerial vehicle image fusion method based on visible light and infrared thermal imaging. The method comprises the steps: using a visible light camera and an infrared thermal imaging camera of a fire-fighting unmanned aerial vehicle to capture a visible light image and an infrared thermal image respectively; converting the two pictures into an image with the size of 1280 * 720 through a size function in the OpenCV; extracting ROI vertex coordinates in the infrared thermogram and displaying the ROI vertex coordinates in the visible light image by using a red rectangular frame; fusing an image in the edge obtained in the infrared thermogram and an image of a coordinate corresponding to the visible light by using a Laplace pyramid algorithm to obtain fire point information; and after the fire point is detected, calculating the three-dimensional position of the target according to the visual information, and accurately estimating the three-dimensional position information of the fire point under the coordinate system of the fire-fighting unmanned aerial vehicle by calculating the relative height and yaw angle between the unmanned aerial vehicle and the target. The method has the advantages of being simple in structure, convenient to operate and easy to implement.

The invention discloses an intelligent toilet, an excrement monitoring system thereof, and an excrement monitoring method. The excrement monitoring system comprises a gas reaction cavity, a gas detection device, a gas sucking device, a control device, and an analysis device. The gas detection device is arranged in the gas reaction cavity and is used to separate and detect gas in the gas reaction cavity so as to obtain detection data of multiple gases. The gas sucking device is used to suck gas that contains the odor of excrement into the gas reaction cavity. The control device controls the gasdetection device and the gas sucking device to work when detecting a defecation signal. The analysis device carries out health management analysis based on the detection data of multiple gases. The provided excrement monitoring system can identify multiple gas components of excrement gas, provides more abundant and accurate reference data for health management, and better guides the life of users.

The invention discloses downward-looking three-dimension and downward-looking multi-beam integrated underwater panoramic three-dimensional imaging sonar. The sonar comprises downward-looking multi-beam sonar, downward-looking three-dimensional sonar, an underwater control module and a towed body frame, wherein the towed body frame is of an integral flat structure, a downward-looking three-dimensional sonar receiving array is arranged at the bottom of the towed body frame, and a downward-looking multi-beam sonar transmitter and a downward-looking multi-beam sonar receiver are arranged in frontof and at the back of the upper part of the downward-looking three-dimensional sonar receiving array; a downward-looking multi-beam sonar transmitting array and a downward-looking multi-beam sonar receiving array are arranged in the middle of the front of the towed body frame; a downward-looking three-dimensional sonar transmitting array, the underwater control module, a downward-looking three-dimensional sonar transmitter and a downward-looking three-dimensional sonar receiver are arranged at the back of the towed body frame; transmitting and receiving electronic cabins are further arranged in the towed body frame. The downward-looking multi-beam sonar and the down-looking three-dimensional sonar are integrated, buried objects are detected in a panoramic three-dimensional manner, and three-dimensional imaging of the seabed and terrain imaging of the seabed are performed in real time, and equipment exploration capability and detection efficiency are improved.

The invention discloses a double-parameter CFAR (Constant False Alarm Rate) ship detection method based on Fourier transformation. The method comprises the steps of acquiring SAR (Synthetic Aperture Radar) radar echo data, and imaging the SAR radar echo data to obtain an SAR radar imagingdata matrix I, and further obtain a binary image matrix Ibw subjected to threshold processing, wherein the Ibw is a binary image containing a ship target and a false target; setting an all 1 matrix as a background window; sequentially calculating a clutter number statistical matrix N in the background window and a clutter number statistical matrix in the background window after reciprocal taking operation, and further calculating a clutter mean value statistical matrix M in the background window; then calculating a clutter variance statistical matrix V in the background window, and calculating a ship target determination matrix F; finally detecting to obtain a plurality of ship targets according to I and F, wherein each detected ship target is an interesting ship target, so that the separation of the interesting ship targets and false targets is accomplished, and the detection of the interesting ship targets is further accomplished.

The invention relates to a fluorescence immunochromatography detection method and device based on a sparse self-coding neural network. The device comprises a photoelectric signal detection unit, a mechanical scanning unit, an STM32 microprocessor control unit, and a computer in which a fluorescence detection sparse self-coding neural network module and a fluorescence immunochromatography detection database are established, wherein the mechanical scanning unit controls the movement of a fluorescence test strip; the photoelectric signal detection unit detects the fluorescence intensity change of the moving fluorescence test strip and converts the fluorescence intensity change into electrical signal data; then the electrical signal data is transmitted to a computer through the STM32 microprocessor control unit, so as to be subjected to filtering processing, and background interference of a base line is eliminated; data analysis is performed through the fluorescence detection sparse self-coding neural network module and the fluorescence immunochromatography detection database is performed, so that a fluorescence immunochromatography detection result is obtained. According to the method, characteristic value extraction of detection data is avoided, data can be directly detected, character representation of data is learnt in a layered mode, and the fluorescence immunochromatography detection effect is improved.

The invention discloses an inspection device used for production of a high voltage vacuum circuit breaker, comprising a shell, wherein a plurality of insulated sleeves are sequentially arranged at thetop of the shell from left to right in sequence, a wiring terminal is arranged at the top end of each insulated sleeve, a breaking-closing operation handle is movably connected to the right side of the front part of the shell, an insulated handle is fixedly connected to the top end of the breaking-closing operation handle, an insulated handle sleeve is sleeved on the left side of the insulated handle, and an insulated pressure lever is fixedly connected to the center of the bottom of the insulated handle sleeve. According to the invention, by the match of the insulated handle sleeve, the insulated pressure lever, a fixed rack, a sliding sleeve, a fixed transverse plate, a bearing seat, a movable contact, a fixed seat, a static contact, a pressure sensor, a junction box, a wire and a control panel, pressure values in high voltage vacuum circuit breaker breaking and closing can be accurately measured, and therefore the detection effect of the detection device is improved, the problem ofpoor detection effect of conventional detection devices is solved, and the percent of pass of the high voltage vacuum circuit breaker can be increased.

The invention relates to a self-adaptive detection method for synchronism of a lamplight/motor and sound. The self-adaptive detection method comprises the following steps: detecting a phonetic acoustic signal by an ADC (analog to digital converter) controlled by AGC (automatic gain control); outputting a narrow dynamic range phonetic signal with AGC information; restoring the narrow dynamic range phonetic signal with the AGC information into a wide dynamic range phonetic signal by an energy normalization algorithm; carrying out phonetic real-time energy calculation and threshold calculation by the restored phonetic signal; and through comparison of the real-time energy and the threshold, calculating a motor starting and stopping state or a lamplight turning on and off state corresponding to the current phonetic frame. Through the introduction of the AGC information, high dynamic range obtained on the ADC with low sampling depth is detected by the motor opening and closing state or the lamplight turning on and off state information; and with the adoption of the ADC with 12 bit low sampling depth, the motor opening and closing state or lamplight turn on and off state when users speak can be detected at the distance of 5-300cm, so that the overall cost of the system is greatly reduced.

The embodiment of the invention discloses an infrared touch screen lamp tube receiving and transmitting method which is used for eliminating an inclined blind eliminating area of an infrared touch screen and improving the detection effect of the infrared touch screen. The infrared touch screen lamp tube receiving and transmitting method comprises the steps that at the two ends of a transmitting lamp tube and receiving lamp tube queue, a transmitting lamp tube transmits light rays, and multiple corresponding receiving lamp tubes receive the light rays and feed back the light rays to a processor; or one receiving lamp tube receives the light rays transmitted by multiple corresponding transmitting lamp tubes, and feeds back the light rays to the processor for processing. The embodiment of the invention further provides the infrared touch screen. According to the infrared touch screen lamp tube receiving and transmitting method and the infrared touch screen, the inclined blind eliminating area of the infrared touch screen can be eliminated, and the detection effect of the infrared touch screen can be improved.

The invention provides a formaldehyde-removing building material inspection device, and relates to the field of building material inspection. The formaldehyde-removing building material inspection device comprises an inspection box, a crushing mortar and a crushed pestle; the interior of the inspection box is hollow, and a buckle fixing frame is arranged on the outer wall of the inspection box; asealing plate is slidably connected into the top of the inspection box, and a sealing cover plate is connected into the top of the sealing plate; a containing vessel is located in the inspection box;the crushing mortar is connected into a buckle fixing frame on the outer side of the inspection box in a buckled mode; tension springs are arranged at two positions, the left end and the right end ofthe crushing pestle are connected with the left end and the right end of the crushing mortar through the tension springs respectively; according to the building material inspection device, blocky building materials in the crushing mortar are repeatedly smashed through the crushing pestle, the blocky building materials in the crushing mortar are forced to be further crushed and refined, the crushedbuilding materials are detected, the material materials contained in the building materials can be fully exposed in the air, and the building material detection effect is improved.

The invention discloses a phased array self-aggregation probe, an arc chipset and a chip for detecting a solid carriage axle. A plurality of chips with curvature of 100 mm-300 mm form the arc chipset, the chips are arranged in a mode that a XY plane-coordinate system is established, the chips are bended along X-axis, and arranged along Y-axis in a line, wherein the curvature of the chips is 100 mm-300 mm. when used, the arc chipset is arranged on the phased array self-aggregation probe. The phased array self-aggregation probe comprises a probe body, and further comprises the arc chipset, and the frequency of the probe body is 1 MHz-17 MHz. The phased array self-aggregation probe is an arc probe, and the best bending curvature of the chip is provided according to the range (the journal diameter is in a range of 100 mm to 300 mm ) of the carriage axle in China, namely the curvature is 100 mm-300 mm. The phased array self-aggregation probe, the arc chipset and the chip can be compatible with all types of carriage axles, and the detection effect is improved.

The invention discloses an anti-terrorist unmanned aerial vehicle control system. The system can monitor parameters such as temperature, humidity, combustible gas concentration, smoke concentration and illuminance, controls various devices through a central controller, has the functions of environment monitoring, person monitoring, alarm prompting, weapon monitoring and automatic adjustment, accordingly improves the investigation and hitting capability of an anti-terrorist unmanned aerial vehicle, also has a remote control function and is stable, reliable and convenient to maintain and manage.

The invention relates to a method for clustering image areas through LLC based on an adaptive codebook. Color, texture, Gabor, center of mass and other features are extracted from the segmented super-pixels and then the neighbor areas are clustered through the fused features, the identifiable degree between the multiple neighbor areas is enhanced by the form of a similarity structure diagram, the extremely valuable clues are provided for reducing and even eliminating the noise near the boundary of the foreground in the generated saliency map and the boundary of the foreground is enabled to be clearer; multiple features are used as the basis for calculating the saliency map, and when the color feature cannot be used for effectively extracting the saliency target under the complex scene, multi-feature information is used as the beneficial supplement to effectively enhance the detection result; and as the extended LLC coding scheme, the method that multiple feature descriptors in the original LLC are coded and then fused in the objective function is extended into the method that the multiple feature descriptors are fused firstly and then coded in one step so that the coding process can be simplified and the integrity of the multiple features can be emphasized.