In this paper, a fuzzy failure mode and effects analysis (FMEA) method is proposed by combining fault theory and a failure analysis method. The method addresses the problem of board thickness control failure and the problem of thickness deviation defect blanking, which can occur during continuous hot pressing (CHP) process, which is one of the most important processes in the production of medium-density fiberboard (MDF). The method combines the fault analysis with the Hamming code method and using the Hamming code to calculate and represent the cylinder array of the continuous hot-pressed thickness control execution unit to analyze and process the potential fixed thickness failure modes in MDF hot press production, and then summarizes the decision rules for controlling the board thickness and the level of sheet deviation. By combining the fuzzy FMEA method of the Hamming code and the logical OR operation of the experimental analysis, the method of thickness deviation and recognition control fault information for the CHP of MDF, which is proposed in this paper, permits the increase of the number of error levels, which makes optimization for controller more convenient and improves the efficiency to recognize errors.

The three-dimensional reconstruction of forest ecological environment by low-altitude remote sensing photography from Unmanned Aerial Vehicles (UAVs) provides a powerful basis for the fine surveying of forest resources and forest management. A stereo vision system, D-SLAM, is proposed to realize simultaneous localization and dense mapping for UAVs in complex forest ecological environments. The system takes binocular images as input and 3D dense maps as target outputs, while the 3D sparse maps and the camera poses can be obtained. The tracking thread utilizes temporal clue to match sparse map points for zero-drift localization. The relative motion amount and data association between frames are used as constraints for new keyframes selection, and the binocular image spatial clue compensation strategy is proposed to increase the robustness of the algorithm tracking. The dense mapping thread uses Linear Attention Network (LANet) to predict reliable disparity maps in ill-posed regions, which are transformed to depth maps for constructing dense point cloud maps. Evaluations of three datasets, EuRoC, KITTI and Forest, show that the proposed system can run at 30 ordinary frames and 3 keyframes per second with Forest, with a high localization accuracy of several centimeters for Root Mean Squared Absolute Trajectory Error (RMS ATE) on EuRoC and a Relative Root Mean Squared Error (RMSE) with two average values of 0.64 and 0.2 for t_rel and R_rel with KITTI, outperforming most mainstream models in terms of tracking accuracy and robustness. Moreover, the advantage of dense mapping compensates for the shortcomings of sparse mapping in most Smultaneous Localization and Mapping (SLAM) systems and the proposed system meets the requirements of real-time localization and dense mapping in the complex ecological environment of forests.

Aiming at the problems of low classification accuracy and overfitting caused by the limited number of particleboard image samples, a Capsule Network algorithm based on the improved CBAM (Convolutional Block Attention Module) attention model is proposed. The improved algorithm utilizes the GELU equation to improve the CBAM attention model and incorporates it into the convolutional layer of the Capsule Network. In this way, the improved algorithm optimizes the feature maps of surface defects and, meanwhile, improves the training efficiency and stability of the model. The improved algorithm alleviates the overfitting problem by adding a dropout layer, which makes the model more suitable for small sample classification. The effectiveness of the method proposed in this paper is verified by classification experiments on the dataset of particleboard surface defect images.

The automatic identification and three-dimensional reconstruction of house plans has emerged as a significant research direction in intelligent building and smart city applications. Three-dimensional models reconstructed from two-dimensional floor plans provide more intuitive visualization for building safety assessments and spatial suitability evaluations. To address the limitations of existing public datasets—including low quality, inaccurate annotations, and poor alignment with residential architecture characteristics—this study constructs a high-quality vector dataset of raster house plans. We collected and meticulously annotated over 5000 high-quality floor plans representative of urban housing typologies, covering the majority of common residential layouts in the region. For architectural element recognition, we propose a key point-based detection approach for walls, doors, windows, and scale indicators. To improve wall localization accuracy, we introduce CPN-Floor, a method that achieves precise key point detection of house plan primitives. By generating and filtering candidate primitives through axial alignment rules and geometric constraints, followed by post-processing to refine the positions of walls, doors, and windows, our approach achieves over 87% precision and 88% recall, with positional errors within 1% of the floor plan’s dimensions. Scale recognition combines YOLOv8 with Shi–Tomasi corner detection to identify measurement endpoints, while leveraging the pre-trained multimodal OFA-OCR model for digital character recognition. This integrated solution achieves scale calculation accuracy exceeding 95%. We design and implement a house model recognition and 3D reconstruction system based on the WebGL framework and use the front-end MVC design pattern to interact with the data and views of the house model. We also develop a high-performance house model recognition and reconstruction system to support the rendering of reconstructed walls, doors, and windows; user interaction with the reconstructed house model; and the history of the house model operations, such as forward and backward functions.

The effective and comprehensive utilization of forest resources has become the theme of the global “dual-carbon strategy”. Forestry restructured wood is a kind of wood-based panel made of wood-based fiber composite material by high-temperature and high-pressure restructuring–molding, and has become an important material in the field of construction, furniture manufacturing, as well as derivative processing for its excellent physical and mechanical properties, decorative properties, and processing performance. Taking Medium Density Fiberboard (MDF) as the recombinant material as the research object, an event-triggered synergetic control mechanism based on interventional three-way decision making is proposed for the viscoelastic multi-field coupling-distributed agile control of the “fixed thickness section” in the MDF continuous flat-pressing process, where some typical quality control problems of complex plate shape deviations including thickness, slope, depression, and bump tend to occur. Firstly, the idea of constructing the industrial event information of continuous hot pressing based on information granulation is proposed, and the information granulation model of the viscoelastic plate shape process mechanism is established by combining the multi-field coupling effect. Secondly, an FMEA-based cyber granular method for diagnosing and controlling the plate thickness diagnosis and control failure information expression of continuous flat pressing is proposed for the problems of plate thickness control failure and plate thickness deviation defect elimination that are prone to occur in the continuous flat-pressing process. The precise control of the plate thickness in the production process is realized based on event-triggered control to achieve the intelligent identification and processing of the various types of faults. The application test is conducted in the international mainstream production line of a certain type of continuous hot-pressing equipment for the production of 18 mm plate thickness; the synergistic effect is basically synchronized after 3 s, the control accuracy reaches 30%, and the average value of the internal bond strength is 1.40, which ensures the integrity of the slab. Practical tests show that the method in the actual production is feasible and effective, with detection and control accuracy of up to ±0.05 mm, indicating that in the production of E0- and E1-level products, the rate of superior products can reach more than 95%.

With the ultimate goal of intelligent sensing for flocking control of MDF continuous flat-pressing system, the deviation type control granular construction and effective expression of state information are applied to IoT human-computer interaction in the real production site to satisfy the multi-objective optimization requirements of flexible process and agile production of MDF in intelligent manufacturing. Accordingly, an innovative research work on the distributed control mode and deviation control synergy method of MDF continuous hot pressing is carried out, and interventional three-way decision cooperative control method is proposed for the problem of distributed agile control of viscoelastic multi-field coupling in the "constant thickness section" of MDF continuous flat pressing process. According to the MDF continuous flat pressing process mechanism and control standards, the distributed cyber granular structure model of MDF continuous press cylinder array is defined; in order to meet the distributed position and pressure servo requirements required for the design of multi-stage hot pressing process, the process control sequence planning model of single and multi-group cylinder array is constructed by the cyber granular computing; oriented to the typical deviation of 4 types of plate production, namely, thickness, slope, depression and bump, the intervening three-way decision-making cooperative controller of MDF continuous flat pressing is designed. Through the control of cyber granular-enabled intelligent sensing technology, the MDF continuous flat pressing system position and pressure sensor data acquisition system for real-time monitoring and analysis of the various indicators of the plate; the designed fault type decision-making controller can quickly and accurately identify different types of plate faults, and take the corresponding control strategy for correction and adjustment. The software has excellent viability and practicability, as demonstrated by the actual production test, and accurate quality control can raise the productivity and quality of MDF panels production.