1209 results about "Direction information" patented technology

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention provides a smart glasses AI voice interaction method, which comprises the following steps: simultaneously acquiring a user gesture image and a voice signal, acquiring a user historical interaction record, extracting key point coordinates and pointing direction information of the gesture image, acquiring hand distance information, generating a gesture motion path, simultaneously extracting frequency spectrum information and an intonation peak value of the voice signal, and generating a gesture motion path; forming a voice beat sequence; extracting a spatial semantic mode of the gesture and voice fusion data, and recognizing a core object of a user pointing instruction according to pointing coordinates of a gesture motion path and an intonation peak value of a voice beat sequence; after the core object pointing to the instruction is recognized, the user intention is determined in combination with the spatial semantic mode and the historical interaction record of the user; and the complete intention analysis result is output to the intelligent glasses display module to execute corresponding operation, and is fed back to the acquisition module to adjust the next capture parameters including the acquisition frequency and the recognition sensitivity, so that the response speed and the accuracy are improved.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention discloses a foam concrete image processing method based on sub-pixel edge reconstruction, and particularly relates to the field of foam concrete image processing.The method comprises the steps that an image of foam concrete is obtained, and a boundary response feature set containing edge direction information, edge gradient change information and local intensity comparison information is extracted; performing edge structure classification on the image region based on boundary response feature set combined threshold judgment; and aiming at the image region which is judged to be the local direction convergence mutation region, identifying a cross mutation point set by fitting a direction tensor convergence trend, and executing directionally-guided corrosion and expansion image processing. By constructing a structure judgment variable based on image boundary response characteristics and guiding a corrosion expansion path and a vein backtracking path to execute sub-pixel-level aperture boundary separation, image structure processing and contour reconstruction of a porous adhesion area are realized.

The invention discloses a full-state wind field simulation system and method for wind resistance detection of an unmanned aerial vehicle. The system comprises a system base, a distribution box, a system control cabinet, a modular wind wall, a protective net and a wind speed and direction sensor. The wind field simulation system is of a cuboid structure, independent fan arrays are embedded in modular wind walls, the three modular wind walls independently generate three single-direction wind fields forming 90-degree included angles, and wind fields of any size and any direction can be generated through space wind field vector superposition. The distribution box provides power support for the ventilation wall to work, and the system control cabinet can independently control the fan arrays. The protective net is fixed on the system base bracket, so that the unmanned aerial vehicle is prevented from causing system damage in the testing process; the wind speed and direction sensor senses wind speed and direction information of a wind field in a test area and quantitatively represents the simulated wind field. According to the invention, a wind field with any size and direction can be generated for an unmanned aerial vehicle wind resistance test, and necessary test platform support is provided for unmanned aerial vehicle wind resistance detection.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention provides an intelligent early warning method and system for phosphorus tailing improved soil heavy metal migration, relates to the technical field of computers, and accurately predicts the main migration direction of heavy metal pollutants by combining factors such as terrain gradient, hydrological flow direction and soil structure heterogeneity. The method comprises the following steps: acquiring soil information of a to-be-detected area and soil potential information of a detection point in the to-be-detected area; determining heavy metal concentration information of the detection point according to the soil potential information; the heavy metal concentration information is used for reflecting heavy metal concentration distribution conditions corresponding to different soil depths of the detection points; determining migration direction information of the heavy metal at the detection point; the migration direction information is used for reflecting the corresponding heavy metal migration direction distribution condition of the heavy metal of the detection point in the three-dimensional space; and predicting a pollution diffusion area based on the migration direction information, the organic decomposition rate and the underground water level fluctuation information, and performing visual display.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of intelligent auxiliary navigation, financial science and technology, medical health and the like, and discloses a navigation instruction generation and path updating method, device, equipment and medium. And fusing the obstacle category information and the environment depth information, generating spatial position information and direction information of the obstacle, generating a navigation instruction containing scene description information, obstacle orientation and distance description information and action suggestion information based on the spatial position information, the direction information and the environment image information, and updating a navigation path. According to the method, the obstacle category information and the environment depth information are fused, the obstacle recognition result with the semantic level is generated in combination with the environment image information, the navigation instruction containing the orientation and the distance is output, and the accuracy of obstacle perception and the practicability of navigation guidance can be improved.

The invention discloses a cosmic ray muon scattering imaging method, system and device, and relates to the field of ray imaging, and the method comprises the steps: obtaining the direction information of muons in a to-be-imaged region; calculating the most probable three-dimensional scattering track of the muons in the to-be-imaged region based on Bayesian inference and a particle multiple coulomb scattering distribution function according to the direction information of the muons in the to-be-imaged region; and reconstructing a scattering density image in the to-be-imaged region according to the direction information of the muon in the to-be-imaged region and the most probable three-dimensional scattering track. By calculating the most probable three-dimensional scattering track, the scattering points can be effectively reconstructed, the utilization efficiency of muon events is improved, the scattering points are prevented from being separated from the to-be-imaged area, the most probable three-dimensional scattering track better fits the real scattering track, and then the quality of the scattering density image is improved.

A display method for a skill indicator includes: in response to a trigger operation on a target skill, determining a release position and an effect range of the target skill, the release position being one scene unit in a virtual scene, the release position being located in the effect range, and the effect range including a plurality of scene units; based on relative positions of a plurality of specified scene units in the effect range, determining direction information of a skill indicator of the target skill; and based on an indicator parameter that is preset for the target skill, and using the release position as a reference, generating and displaying the skill indicator in the direction indicated by the direction information.

The invention relates to the technical field of image analysis, in particular to a motorcycle paint surface bubble sagging defect real-time detection system based on image analysis. The method comprises the following steps: firstly, acquiring a plurality of images of a to-be-detected paint surface under different illumination conditions by an image acquisition and processing unit, and preprocessing the images to obtain a preprocessed multi-illumination image; then, a three-dimensional information analysis unit analyzes and calculates a surface normal vector of each pixel point of the paint surface according to brightness information of the same pixel point in the preprocessed multi-illumination image, and generates a surface normal graph for representing three-dimensional direction information of the paint surface; then, a candidate area positioning unit analyzes a gradient field of the surface normal diagram and positions bubble and sagging defect candidate areas respectively; and finally, the feature extraction and classification unit extracts fusion features of the defect candidate region, and outputs a detection result obtained by recognition of the classifier through an output unit. According to the invention, the accuracy of paint bubble sagging defect detection can be improved.

The invention provides a ship traffic flow prediction method and device, electronic equipment and a storage medium, and belongs to the field of marine traffic management. The method comprises the following steps: acquiring ship trajectory data of a to-be-detected port, and performing bidirectional trajectory prediction restoration on the ship trajectory data to obtain restored trajectory data; performing direction identification trajectory clustering on the repaired trajectory data to obtain trajectory clustering data, and performing density adaptive parameter node clustering on the trajectory clustering data to obtain node clustering data; and performing spatial-temporal feature extraction and prediction output on the node clustering data to obtain a ship traffic flow prediction result. According to the method, the integrity of ship trajectory data is ensured through bidirectional trajectory prediction and restoration, trajectory direction information is mined while clustering is performed through direction recognition trajectory clustering, and node comprehensive information is mined while clustering is performed through density adaptive parameter node clustering; dynamic transformation information and a deep relationship can be reserved, data can be effectively integrated, and the accuracy of ship traffic flow prediction is improved.

The invention provides a single-microphone multi-array pickup method and system based on sound attenuation simulation, and the method comprises the steps: obtaining an original audio signal recorded by a single microphone and sound source direction information, and carrying out the sound attenuation simulation based on the sound source direction information and a preset virtual microphone array orientation parameter, calculating the included angle between the sound source direction and each virtual microphone; generating an audio intensity attenuation coefficient of each virtual microphone according to the included angle; and acting the audio intensity attenuation coefficient on the original audio signal to generate multi-channel audio data simulating the multi-microphone array. By adopting the method, the response difference of different microphone positions to the sound source can be simulated, the pickup result of the multi-microphone array is obtained, and high hardware cost and complex installation flow caused by actual deployment of a complex microphone array are avoided; and low-cost and high-diversity training data sources are provided for sound source localization, noise suppression and other models based on deep learning.

The invention provides an arbitrary point turn-back method under a train control system route architecture based on communication, which comprises the following steps of: when an arbitrary point turn-back command for a train is received and the train is determined to be in a stable stop state, generating a reverse route containing turnout opening information. And according to the reverse direction head code, generating a starting end signal machine list of which the route needs to be manually solved and sending the starting end signal machine list to an automatic train protection system. And an unlocking request command of the first signal machine in the starting end signal machine list is sent to the area controller, and the access in the starting end signal machine list is unlocked through interactive verification with a train automatic protection system. After it is confirmed that the route corresponding to the first signal machine is successfully unlocked, a turn-back instruction is sent to the automatic train protection system, and meanwhile the automatic train protection system is controlled to execute end changing operation. After the end changing operation is completed, new movement authorization is established to control the train to run, route unlocking in the original direction and passenger prompting are triggered when preset conditions are met, meanwhile, a turn-back mark of any point is updated and displayed on a human-computer interface, and turn-back of any point is achieved.

The invention discloses an infrared small sample target detection method based on domain self-adaption, and solves the problems that an infrared image generally lacks rich visual details and semantic information, so that effective features are more difficult to extract in a deep learning model, and an existing cross-domain algorithm is lower in accuracy in infrared target detection. And realizing style migration from the visible light image to the infrared image through a cross-domain visual optimization model. A YOLOv8 target detection network is improved, a DCNv4C2f module is used for replacing a specific C2f layer in a backbone network, and deformable convolution is introduced, so that the features of objects with complex shapes and appearances can be captured more effectively. A new multi-pooling mixed attention mechanism is designed and introduced into a target detection network, cross-channel information can be effectively captured, direction information and position information can also be obtained, the detection capability of the multi-pooling mixed attention mechanism on a fuzzy target and a dense target in an infrared image is improved, and the problem of missing detection is effectively avoided.

The present application relates to the field of federated learning and privacy protection, and particularly relates to a direction-aware local differential privacy federated learning method. The present application effectively solves the problem of serious decline in model performance caused by the destruction of gradient direction information by traditional differential privacy methods by adopting a direction-aware noise selection strategy based on an exponential mechanism locally on the client side. Specifically, the client first performs L2 norm clipping on the gradient to control the sensitivity, then generates K candidate noise vectors from a Gaussian distribution, evaluates the direction consistency of each candidate noise and the true gradient through cosine similarity, and selects the noise closest to the gradient direction in a probabilistic manner using the exponential mechanism, and finally adds the selected noise to the gradient to complete the perturbation. The method provides a strict epsilon-local differential privacy mathematical proof for the entire noise selection process, reduces the calculation time by about 80% compared to existing direction-aware methods, and achieves a triple balance of privacy protection, model utility and computational efficiency.

The invention discloses a medical image weak supervision variation segmentation method based on single-point labeling, and relates to the technical field of tumor segmentation in medical images. The method comprises the following steps: firstly, randomly taking a point in a target tumor area, and constructing a new anisotropic Riemannian metric according to texture direction information of a whole CT image, local direction information of gray scale at a boundary and heterogeneity of the gray scale; secondly, calculating two distances from all points in the whole CT image to the points taken in the tumor area: geodesic distance and Euclidean distance based on new Riemannian metric; then, the two kinds of point-by-point distance information are embedded into a Chan-Vese model; and finally, proving the existence and uniqueness of a model solution, and solving the model solution by adopting a Douglas-Rachford algorithm to obtain a final tumor segmentation result. According to the method, a more accurate, more effective and more robust segmentation result can be obtained, the method can be used for tumor detection, positioning and segmentation, the workload of manual labeling can be greatly reduced, and the method has high clinical application value.

The invention discloses a two-dimensional construction drawing exporting method and system based on a three-dimensional BIM model, and the method comprises the steps: analyzing the geometric information and attribute information of a component from obtained three-dimensional BIM model data, and the geometric information comprises pipeline geometric information, equipment geometric information and structure geometric information; on the basis of the analyzed geometric information, automatically identifying a labeling object; carrying out labeling processing on the identified labeling object, calculating an optimal angle and a character avoidance position of a labeling lead in a labeling process, and generating optimized labeling information; on the basis of the analyzed attribute information, automatic layer mapping is conducted on the component, and standardized layer information is generated; on the basis of the analyzed pipeline geometric information, performing axonometric projection transformation on topological relation data obtained through pipeline topological relation analysis, and generating axonometric map information including a pipeline space connection relation and flow direction information; and integrating the labeling information, the layer information and the axonometric drawing information, and outputting a standardized two-dimensional construction drawing.

The invention discloses a brain tumor MRI image semantic segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. According to the invention, the problem of low segmentation precision obtained based on the existing brain tumor MRI image semantic segmentation technology is solved. The invention provides a semantic segmentation model which combines a U-Net framework with an LWD module, an MFDF module and various filters, the LWD module can keep information as much as possible in a down-sampling process, the MFDF module extracts operators by constructing new directional gradient features, and combines the operators in different directions by using dual-channel filtering, so that the semantic segmentation of the U-Net framework is realized. And the constructed multi-directional filter can respectively extract low-frequency and high-frequency characteristic direction information. The MFDF module transmits detail features from the coding module to the corresponding decoding module, so that spatial information including feature boundaries and textures is recovered, the precision of model segmentation is improved, and the method has good adaptability to the randomness of brain tumor shapes, sizes and boundaries. The method can be applied to brain tumor MRI image segmentation.

The invention discloses a visual sag detection method, system and device. The method comprises the following steps: S1, pre-calibrating external parameters of a camera and a laser radar, and obtaining internal parameters and relative external parameters of the camera and the laser radar; s2, synchronously acquiring image data and three-dimensional point cloud data of the to-be-detected power transmission conductor respectively; s3, performing fitting by using the wire local three-dimensional point cloud to extract a wire part point cloud, and determining a vertical plane where the wire is located by combining gravity direction information and external parameters; s4, segmenting image pixels of the leads in the image acquired by the camera, and determining pixel coordinates of the leads in a camera coordinate system; and S5, projecting the segmented conductor image pixels to a vertical plane, expanding the conductor image pixels into a three-dimensional space point cloud under a camera coordinate system, fitting a catenary of the power transmission conductor line based on the three-dimensional space point cloud, and calculating a sag value of the conductor. According to the invention, the sensing precision of the conductor state in the sag detection technology can be improved, and high-precision sag detection can be continuously carried out after a single system is installed.

The invention discloses an agricultural machinery field and road track classification method fusing spatial-temporal clustering and semantic segmentation. The requirements for farmland track and road track classification precision and operation efficiency in an actual agricultural machinery operation scene are met at the same time. The method mainly comprises three stages: firstly, track preprocessing; and removing abnormal track points by using four preprocessing operations of null value filling, attribute filtering, speed cleaning and linear interpolation, and complementing missing tracks. And 2, track clustering. A space-time neighbor track segment clustering method based on track position, time sequence and direction information is adopted to form a track group on the basis of removing a long-distance road driving track. And 3, track segmentation. And combining a channel and space mixed attention mechanism and a focus loss function to construct a trajectory diagram segmentation model based on U-Net so as to realize segmentation of a road driving trajectory, a drifting trajectory and a farmland trajectory close to a farmland plot in each trajectory group. Experiments show that the method provided by the invention has the highest classification precision in a real agricultural machine operation scene data set, and the average reasoning time is superior to that of a method with the highest precision in latest classification methods.

The invention discloses an unmanned aerial vehicle autonomous positioning navigation method and device based on a cross-view azimuth regression network. The method comprises the steps of 1, constructing a cross-view-angle data set which is oriented to a cross-view-angle scene and contains azimuth information, 2, constructing an unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene, and 3, training and verifying the unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene by using a training set and a verification set. And 4, carrying out regression performance test on the unmanned aerial vehicle position heading regression model facing the cross-view scene by using the test set. The method only depends on a visual image, can reduce the dependence degree of unmanned aerial vehicle positioning navigation on a sensor, improves the survivability in an extreme environment, uses a cross-visual-angle remote sensing map to construct simulation training data and generate the absolute orientation regression capability, can eliminate the drift problem, and also has natural advantages in the aspect of migration from simulation to a real scene. The model is light in weight and is more suitable for unmanned aerial vehicle continuous positioning and directional navigation scenes with limited computing power resources.

The invention relates to the field of wind power generation, and discloses a wind power generation control system and method. The data acquisition module is used for acquiring front three-dimensional wind field information detected by the laser radar, near-field wind speed and direction information measured by the ultrasonic sensor, regional wind regime data issued by the meteorological satellite and operation parameters of the wind generating set. According to the invention, through multi-source information fusion, wind regime accurate perception is realized, power generation stability is improved from the source, through multi-source fusion of the laser radar, the ultrasonic sensor and meteorological satellite data, a three-dimensional wind regime perception system is constructed, wind speed and wind direction changes can be predicted in advance, wind regime measurement errors are reduced, and power generation efficiency is improved. The problem of power generation fluctuation caused by wind energy intermittency is solved from the source, and accurate data support is provided for dynamic adjustment of the pitch angle.

The invention discloses a quick door opening method and system based on a pan-tilt door lock, and the method comprises the steps: generating a broadcast message according to a family member face image of an intelligent lock A, so that other intelligent locks receive and update a local door lock list, and obtaining a shared door lock list; according to the face image recognized by the intelligent lock B, performing similarity comparison with the shared door lock list, generating a notification message that the member is about to arrive, and obtaining direction information of the member; according to a member arrival notification message received by the intelligent lock A, the preset position direction of the intelligent lock B in the door lock list is inquired, and the adjusted direction of the pan-tilt camera is obtained; according to a plurality of member arrival notification messages received by the intelligent lock A, obtaining an optimized pan-tilt camera direction; and obtaining a door opening result according to the adjusted direction of the holder camera. By utilizing the embodiment of the invention, an efficient, convenient and safe door opening solution can be realized, so that the dual requirements of modern families on quick door opening experience and safety can be met.

The invention discloses a multi-stage text-to-three-dimensional model generation method, and particularly relates to the technical field of artificial intelligence, comprising three stages of initialization, optimization and clone segmentation; the method comprises the following steps: firstly, generating an image and depth through a multi-view diffusion model, and guiding to construct an initial three-dimensional Gaussian point cloud; secondly, multi-step diffusion inversion is executed under micro rendering, and the point cloud is optimized in combination with consistency loss and structure regularization; and finally, according to a significance score and a detail hierarchy mechanism, in combination with curvature and principal direction information, executing local clone splitting, and improving structural detail expression and density regulation and control capabilities. The method has the advantages of high semantic fidelity, high structural consistency and the like, and is suitable for scenes of text-driven three-dimensional modeling, virtual reality, point cloud reconstruction and the like.

The invention relates to the field of obstacle detection, and particularly discloses a dynamic obstacle detection warning system for a reach stacker, comprising: an obstacle detection module for capturing environmental data in a working area of the reach stacker in real time by using a sensor technology, the environmental data comprising position, speed, acceleration and direction information, potential obstacles are identified and analyzed, and data acquisition and primary processing are carried out; by capturing and analyzing environmental data in a working area in real time, the system can accurately identify potential obstacles and predict future motion trails of the obstacles, and once a collision risk is predicted, the system immediately triggers a warning signal and provides detailed obstacle information through a user interface, so that an operator can quickly make a response; the control interface and the reach stacker control system are seamlessly integrated, so that the obstacle avoidance operation can be automatically executed according to the emergency degree of the warning signal, the occurrence of collision accidents is effectively avoided, and the operation safety is remarkably improved.

The invention relates to a vehicle projection warning method, device, equipment and system, a vehicle, a storage medium and a program product. The method comprises the steps of obtaining video data of an environment where a current vehicle is located; performing target detection processing on the video data to obtain target movement information; wherein the target movement information comprises detected movement trend information and arrival time of each moving target; determining a risk level and motion direction information of the moving target according to the motion trend information and the arrival time of the moving target; and performing projection warning processing outside the vehicle according to the risk level and the movement direction information of the moving target. According to the invention, traffic participants around the vehicle can be reminded to improve attention, driving is controlled, traffic accidents can be reduced, and traffic safety is improved.

Provided are an apparatus and method for operation of an apparatus in a wireless communication system. The apparatus may be a UE, a chipset, or a base station. The UE receives an FD configuration message notifying a full duplex symbol from among a plurality of symbols, receives a cell-specific common TDD configuration message, and receives a UE-specific dedicated TDD configuration message. Here, only for the full duplex symbol from among the plurality of symbols, second direction information notified by the dedicated TDD configuration message overrides first direction information notified by the common TDD configuration message.