[Objective] The electro-thermal floor structure combined with solid wood and aluminum alloy core board is proposed, combined with the embedded manufacturing process of electric heating cable, to build a new type of electro-thermal floor with solid wood and aluminum core, in order to improve the thermal comfort of the electro-thermal floor heating and the heat transfer efficiency, to reduce the security hidden danger, and to provide technical support for the development and production of electro-thermal floor.[Method] The high-grade solid wood plate is used as the surface layer, and the low-grade veneer as the bottom layer. The electrically heating cable and aluminum alloy core plate are combined to form a heating core layer, and then the heating core layer is embedded into the groove of the panel layer of solid wood composite floor. The asbestos net is adhered as a thermal insulation layer under the heating core layer to ensure the thermal efficiency, the surface layer and the bottom plate are glued by a urea-formaldehyde resin glue, the tongue-and-groove floor was made by the high temperature and high pressure made. The finite element analysis software was used to simulate the heating process of composite solid wood floor with aluminum core.[Result] The surface temperature of the electro-thermal floor without aluminum core is 33.3℃ after 1 h heating, the temperature of the heating layer is concentrated, up to 60℃, existing safety hazard. When the electro-thermal floor is installed with a flat aluminum core radiator plate, the surface temperature of floor is about 29.7℃ after 1 h heating; As for the floor with ribbed aluminum core,the surface temperature of the floor is about 34.5℃, though the heat dissipation of ribbed structure is not as uniform as the flat type, but the temperature of the heating wire layer is the lowest and the comprehensive heat transfer efficiency is the best.[Conclusion] The upper surface temperature of the electro-thermal floor with ribbed aluminum core is higher than that without the aluminum core, and the heating speed is fast; the structure of the aluminum core also has an important influence on the heat transfer effect, the electro-thermal floor with ribbed aluminum core has a faster heating rate and a better heat transfer effect.

[Objective] In this paper, a method of detecting heat storage performance on solid wood flooring,which used for ground with heating system,based on closed adiabatic cavity was proposed, and a detecting device of heat storage efficiency has been developed, which could provide reference for the standardization of wood flooring for ground with heating system in China.[Method] In the closed adiabatic cavity, L×M×N small spaces where temperature sensors can be installed are symmetrically divided. A wooden specimen which was heated to T_e is placed to the closed adiabatic cavity whose initial temperature is T₀(T_e > T₀), and the specimens can be considered as a heat source to release. Temperatures of each small space can be detected by sensors when the equilibrium state is reached in space. According to the temperature changes, volume of each small space and volume specific heat capacity of air, and heat release quantity from specimen could be calculated in each small space. By accumulating the heat absorbed in each small space, the total heat quantity released value can be obtained, and the heat storage efficiency of the wooden board can be calculated by the ratio of the total heat and the quantity under these conditions.[Result] Three kinds of wood flooring for ground with heating system,such as Fraxinus mandshurica, Pinus koraiensis and Quercus fabri are tested 10 times respectively under the condition that initial temperature is 20℃ in detecting cavity and sample temperature is 70℃. Then the heat storage efficiency are gotten by averaging detecting result, which are 105.70, 106.27 and 101.99 J·℃^-1,respectively.[Conclusion] In this study, a method for testing heat storage efficiency performance of wood flooring for ground with heating system is proposed.According to the heat storage efficiency test applied on three kinds of wood,the results showed that the all variance is less than 1.00. The detecting device is steady and could accurately evaluate heat storage efficiency to wood flooring. The methods and device proposed in this paper provided analytical method and instrument for the establishment of industry and national standards, which has great significance to standardization of wood flooring for ground with heating system in China.

[Objective] Based on the study method for the inverse heat transfer problem, BP neural network technique is adopted for the inverse calculation of the heat storage of wood flooring with geothermal system. Therefore, provides theoretical and method ological support for the analysis of heat storage performance of wood floor.[Method] Firstly, the numerical model of the testing cavity is established by CFD software. The temperature field data of a single structure sample under different initial temperature range of 50-130℃ are obtained by simulation (different simulation conditions are divided by interval of 5℃). The data are divided into training set and testing set of neural network model. The data of the initial temperature of 50,60,70,80,90,100,110,120,130℃ are used as the training set, while the data of the initial temperature of 55,65,75,85,95,105,115,125℃ are used as the testing set.[Result] After repeated training, a better neural network model is obtained. The average values of the calculation error and the fitting degree of the testing set are:mean relative error (MRE)=0.68%, maximum relative error (MAE)=19.51%, mean square error (MSE)=1.18%, fitting degree (R²)=0.98. Based on this model, Betula platyphylla, Fraxinus mandshurica, Betula alnoides and Quercus mongolica are selected as the four typical solid wood floor samples for the inversion calculation of the heat storage. The results showed that the heat storage performances of the four kinds of solid wood floor are as follows:Quercus mongolica > Betula alnoides > Fraxinus mandshurica > Betula platyphylla.[Conclusion] It can be concluded that the well trained neural network model could effectively predict out the heat storage performance of different wood floor samples, verifying the feasibility of the method based on BP neural network technology to retrieve the thermal storage performance of the wood floor.

[Objective] In order to provide theoretical bases for the development and application of a new kind phase change energy storage floor used in ground with heating system, the thermal performance of phase change energy storage floor using fatty acid eutectic mixture as phase change material was investigated by a heat transfer mathematic model of ground with heating system in this study.[Method] Differential scanning calorimetry(DSC) and step cooling curves were used to analyze the thermal properties of eutectic mixtures of fatty acids. The chemical properties and thermal cycling stability was tested by DSC and Fourier transform infrared(FTIR). The applicability of fatty acid eutectic mixture to the ground of ground with heating system was confirmed, and the fatty acid eutectic mixture was selected as the phase change material for the phase change energy storage floor. The energy saving performance and heating effect of phase change energy storage floor was analyzed by a heat transfer mathematic model of electric ground with heating system. Firstly, the theoretical model of a typical room was chosen and the thermal dynamic process of ground with heating system was described. The operation of ground with heating system was set to a batch-type. Then, the heat transfer model of the room was established, the heat load was calculated, and the amount of phase change material and the heat transfer relationships between floor surface and indoor were confirmed. The heat transfer model of floor was lastly established, and the heat storage and release process of phase change energy storage floor was analyzed using ANSYS software. The changes of indoor temperature and floor surface temperature were observed, and the energy saving performance and heating effect of the electric floor heating system based on the phase change energy storage floor of fatty acid were analyzed.[Result] The phase change temperature of the fatty acid eutectic mixture was between 20-30℃, which satisfies the temperature requirements of the floor circumstance for floor heating system and thermal comfort of human body. The melting range of fatty acid eutectic mixture was only between 2 and 3℃, which will reduce the temperature change of room during the indoor heating process. The chemical properties of fatty acid eutectic mixtures were stable and after 1 500 cycles of freeze-melting cycle, the changes of melting point and latent heat were only 0.27℃ and 1.7%, respectively. The thermal stability of the fatty acid eutectic mixture was outstanding, which can meet the requirement of long-term application in floor heating system. The phase change energy storage floor used in floor heating system can move the electricity from the peak to the valley and make the temperature of indoor and floor surface within the proper range of human body.[Conclusion] The heating effect and energy saving performance of phase change energy storage floor used in floor heating system were proved to be superior. By changing the type and ratio of the fatty acid eutectic mixture, the phase change temperature could be adjusted flexibly so that the phase change energy storage floor of fatty acid would play a good energy saving and heating effect in different climate areas and different heat load buildings which has great potential for development.

[Objective] In order to study the distribution of temperature field of wood floor after storage of heat, in this paper, wooded heating source was placed on the bottom of the closed cylindrical cavity, and the prediction model of temperature field caused by natural convection is established by using the least squares support vector machine(LS-SVM), which will provide the data base for the subsequent inversion of the heat storage characteristics of wood floor with geothermal system.[Method] Firstly, the boundary conditions of the physical model of a cylindrical cavity and the initial temperature of the wood sample are given. Meanwhile, according to the law of fluid mechanics, the mass, momentum and energy conservation equations of heat transfer in heat field are established and solved by computational fluid dynamics(CFD) software. Secondly, the data obtained by CFD is normalized, and according to the initial temperature of wood samples, the normalized data is divided into two parts:training set and prediction set. There are 5 481 data in the training set, and the forecast set is 8 groups with 609 data in each group. The training set is used to train the LS-SVM model, the optimization algorithm of drosophila is used to optimize the kernel function parameter σ and regularization parameter γ of the model,and the optimal combination of the parameters is(1.0×10¹⁰,0.06).Finally, the optimized model is used to predict the temperature of each prediction set, and the result are compared with the prediction result of the BP-ANN method.[Result] The result showed that when using LS-SVM to predict different sample temperature field, the maximum fitting degree is 0.998 9, the minimum is 0.996 8; the average relative error, the maximum relative error and mean square error of the maximum values are 0.25%, 2.6% and 0.31%, respectively; the minimum values are 0.054%, 0.84% and 0.12%, respectively; the longest modeling time is 12.93 s, and the shortest is 12.72 s. Whereas, when the BP-ANN is used to predict the temperature field, the maximum fitting degree is 0.999 7, and the minimum fit degree is 0.998 3; the average relative error, the maximum relative error and the mean square error maximum value are 0.47%, 5.43% and 0.63%, respectively; the minimum values are 0.21%, 2.08% and 0.33% respectively; the longest modeling time is 107.15 s, and the shortest is 106.23 s. In contrast, it can be seen that the fitting error of LS-SVM to predict different temperature fields is smaller than that of BP-ANN, the fitting degree is similar to that of BP-ANN, the modeling and prediction time is much less than those of BP-ANN.[Conclusion] It is feasible to model the heat storage temperature field of the wood floor with geothermal system by using the LS-SVM method. Because this method is suitable for small sample modeling and has good generalization, it has obvious advantages for modeling and prediction of temperature field in the experimental environment with limited data. It can provide guidance for subsequent work of retrieving heat storage mechanism of wood floor with geothermal system.

[Objective] Closed adiabatic dual cavity structure is used in thermal storage efficiency detector of floor, and the initial set testing condition temperature T₀ which error is less than ±0.25℃, uniformity is less than ±0.20℃ in upper cavity should be realized within 900 s before testing the thermal storage efficiency. Aiming at the special requirements of initial temperature to upper cavity, this paper presents a limiting amplitude fuzzy PID algorithm which could quickly and accuracy get set temperature in closed adiabatic dual cavity.[Method] Firstly, limiting band should be established, set Δe >0 and Δe∈N. When the deviation of measured value between the set value accord with ε ≤|Δe|, the output is given by the PID algorithm. And when ε>Δe or ε<-Δe, the output is given by fuzzy inference. The deviation, deviation rate of temperature and output are divided into different fuzzy values, and then fuzzy rules are established. For any real-time temperature sampling value, controller can automatically determine the deviation of target value, and output is given by fuzzy algorithm, to achieve the purpose of temperature adjustment in upper cavity rapidly.[Result] In this paper, the improved fuzzy PID algorithm and the original control algorithm are tested experimentally. The experimental result show that:1) the upper cavity temperature becomes a steady state at 820 s, 350 s earlier than the original design; 2) accuracy of temperature up to ±0.15℃; 3) uniformity of the upper cavity temperature is ±0.15℃, and all above design meet the design requirements.[Conclusion] This paper presents a limiting amplitude fuzzy PID algorithm for temperature control in closed adiabatic dual cavity, which achieves a preferable control effect that temperature of upper chamber could regulate by adjusting the lower chamber temperature quickly and accuracy on floor thermal storage efficiency detector. That provides reliable guarantee for setting of initial conditions, improves the reliability of whole device and ensuring the accuracy of testing result.

[Objective] With regard to the heat storage characteristics of wooden flooring, a temperature prediction model based on twin support vector machine(TWSVM) and fuzzy algorithm was established for the distribution of temperature field of wooden flooring heat storage, which provides an effective analysis method for further research on heat storage of wooden flooring.[Method] Firstly, the wood samples which are heated to the set temperature are pushed into the detection cavity of the heating floor storage performance testing instrument for free heat dissipation, and the temperature sensor array with multi-layer annular distribution characteristics in the instrument is used to dynamically record the temperature data on the time dimension and spatial dimension, then filtering noise reduction and normalization processing are carried out. Secondly, aiming at the problem that too much modeling data will lead to the rapid expansion of the computational complexity of the twin support vector machine, the test data is evenly partitioned in the paper, and the verification set sample are randomly extracted from each block data, and the remaining ones are training samples. Based on the different training samples, the TWSVM model is trained, and the generalization performance of the model is verified by the verification set in the corresponding samples. The kernel function parameters σ, penalty parameters γ and relaxation factors ξ of the model are optimized by the grid search method. Finally, based on the fuzzy principle, the Gaussian function is constructed for the input space of the test sample.The prediction result of all models are superimposed with the corresponding membership weight values to generate the final training result of the model.[Result] The result show that when TWSVM and fuzzy method are used to predict the temperature values of different samples in time dimension, the maximum fitting degree is 99.59%, the minimum is 98.92%, the longest and shortest modeling time is 186.90 s and 64.39 s, respectively. When the temperature values in spatial dimension are predicted, the maximum fitting degree is 99.23%, the minimum is 98.96%, the longest and shortest modeling time is 274.37 s and 93.30 s, respectively.[Conclusion] Because the TWSVM method involves matrix inversion in the computation process, it is only suitable for processing small data samples. In this experiment, because of the large amount of temperature data needed to study the storage characteristics of wooden flooring, there is a limitation to directly use TWSVM to model the experimental data. After introducing the fuzzy method, the temperature data are divided into several small training samples in time and spatial respectively, then each training sample is modeled and trained by TWSVM, respectively. According to fuzzy rules, the final prediction result is determined by the membership value of each temperature point on the fuzzy function. The above method can improve the adaptation range of TWSVM modeling, and give full play to its fast and generalization advantages.

[Objective] A temperature field prediction algorithm based on ANFIS is proposed to provide data support for the performance analysis of wood floor thermal storage.[Method] In this paper, the airtight cylindrical cavity of the self-developed floor heat storage performance analyzer is taken as the research object. There is a six-layer array of 150 temperature sensors inside the cavity. The inner space of the cavity is divided into 150 subspaces. Firstly, the temperature field model of the adaptive neuro-fuzzy inference system(ANFIS) is constructed by taking the temperature sensor number and time as the input of the system. Meanwhile, the temperature value collected by the sensor array is regard as the output. Then, the selected training data are brought into the model proposed. The corresponding parameters are adjusted to complete the training of the time model of the temperature field in the closed cavity. Finally, the other data not involved in the training are inputted into the trained model. The predicted values are obtained and the corresponding calculation formulas are used to prove that this method is suitable for the prediction and analysis of the temperature field of wood flooring with geothermal system.[Result] The fitting degree of the predicted temperature field obtained from the experimental data is more than 0.988.The fitting error is also controlled at a lower level, in which the mean square error is less than 0.19%, the maximum relative error is below 1.22%, and the average relative error is less than 0.36%.[Conclusion] Temperature field prediction model based on ANFIS can fully display the characteristics of temperature field in the chamber of the test instrument, and has good performance in simplification, generalization and robustness. It can predict the temperature of the sensor at any time point with the trained system.

[Objective] In this paper, the thermal insulation performance of solid wood composite rigid polyurethane thermal insulation board(abbreviation RPIB, the same below) in the floor industry is tested and analyzed, and the influence of different materials on the temperature change of electric-heated floor is measured. Based on the principle of energy saving and consumption reduction, the reasons for energy saving and energy saving of electric heating flooring compared to ordinary solid wood flooring were analyzed. From the perspective of heat transfer and thermodynamics, the heat transfer analysis of RPIB model is carried out. Thermal analysis, and the effect of surface decoration panels of different materials on the thermal conductivity of RPIB were analyzed and verified by experiments.[Method] Two temperature change test models were constructed to measure the change of temperature rise from 0 to 35 min in normal electric floor and RPIB at room temperature of 20℃. Two closed models were established to measure the length of time during which the temperature in the model was lowered from 40℃ to 20℃; a thermodynamic model of a solid wood composite rigid polyurethane thermal insulation board was established through analysis of heat transfer theory. A solid wood composite polyurethane insulation board floor model with oak and poplar wood as the decorative panels was produced, and a temperature rising contrast experiment was performed within 0-30 min.[Result] In the temperature rising experiment, the floor temperature of solid wood composite rigid polyurethane thermal insulation board floor was lower than the temperature of ordinary electric floor per unit time, and the temperature difference between the two floors reached 2.0℃ after 35 min. In the thermal insulation experiment, the ordinary electric floor was reduced from 40℃ to 20℃ for 37 min, and the RPIB was lowered from 40℃ to 20℃ for 55 min.[Conclusion] The performance of the hard polyurethane material with low thermal conductivity is very excellent in retarding the heat transfer, so that the RPIB has better thermal insulation performance than the ordinary electric floor. The high decorative panel's electrically heated floor makes the heat conduction more efficient.

[Objective] This paper proposes the design idea of solar heating and well water refrigerating wall skirting board, and designs a heat dissipater as the medium for solar water heater or water well and wall skirt for cold and heat conduction, and develops a kind of efficient heat transfer in order to provide a theoretical basis for the development of energy slorage system.[Method] The specific structure of each component of the energy storage system was determined, and the 3D software was used to build the model, to simulate the work flow of solar heating and well water refrigeration wall skirt, to design the control system of energy storage system, and to realize temperature detection, auxiliary heating and automatic water circulation and other functions. Through comparative analysis, insulation materials and thermally conductive metal materials are selected. The heat transfer principle is used to calculate the total heat absorbed by the house and the solar water heater in the heating mode, and compared with the total heat absorbed by the indoor air and the wall skirt plate. The heat flux of the hot water and the heat conductive aluminum plate in the energy storage system and the heat flux of the heat conductive aluminum plate and the air are separately calculated and compared. From the theoretical analysis of the total heat transfer and heat flow, the feasibility of solar heating and the heating mode of the well water refrigerating wall skirt is verified. In the same way, the feasibility of the cooling mode is verified.[Result] After comparative analysis, because the thermal conductivity of the rigid polyurethane composite material is lower and the working temperature is higher, the rigid polyurethane composite material is selected as the thermal insulation material for the energy storage water tank. The copper, which has a high thermal conductivity and a relatively low cost,is selected as a material of thermally conductive metal rod, and is formed into a spiral structure to increase the transmission contact area. In the heating mode, the solar water heater can provide the heat required to raise the temperature inside the house. In the cooling mode, the heat flow rate of the well water and the heat conductive aluminum plate in the energy storage system is 1.1 kW, being greater than that of the heat conductive aluminum plate and the air of 0.8 kW. Through the thermodynamic calculations of the above two modes, the feasibility of solar heating and the cooling mode of the well water refrigerating wall skirt is verified.[Conclusion] The simulation workflow and theoretical analysis prove that the design of solar energy heating and well water refrigeration wall skirt energy storage system has a certain feasibility, which provides a new idea for the research and design of energy storage system in the future. The development and utilization of energy storage systems has a very positive impact.

[Objective] This study focus on a radiant ground with heating system using multi-layer parquet. The indoor temperature and humidity variations, and the concentrations of formaldehyde and volatile organic compounds (VOC) are measured and analyzed in order to provide experimental result for indoor environment quality control of multi-layer parquet for ground with heating system, and as also to provide references of quality standards for multi-layer parquet which is used for ground heating purpose.[Method] A 36 m² room is used to install the radiant ground heating system. The heat carrier fluid is flowing through pipes under the multi-layer parquet floor and the whole system was running under standard conditions. During the entire heating period of the experiment, the outdoor temperature, temperature of supplying water, indoor temperature and humidity, and the formaldehyde and VOC concentrations were measured at certain time intervals. Based on the recorded data, the differences of temperature distribution and the concentration of formaldehyde and VOC released from multi-layer parquet were analysed. The principle and impact factors of pollutants releasing from multi-layer parquet floor were also discussed.[Result] The result showed that, the initial room temperature was 14.6℃ and the background concentrations of formaldehyde and VOC were 0.01 mg·m^-3 and 0.50 mg·m^-3, respectively. The temperature of supplying water was firstly set at 40℃. The room temperature increased to 20.3℃ after 50 h of heating, and the concentrations of formaldehyde and VOC increased to 0.04 mg·m^-3 and 0.70 mg·m^-3, respectively. The peak concentrations, which are 0.05 mg·m^-3 and 0.86 mg·m^-3, appear on the 20^th day of the experiment. When the temperature of supplying water was set at 50℃, the room temperature increased to 24.2℃ after 50 h, and the formaldehyde and VOC peak concentrations are 0.11 mg·m^-3 and 1.01 mg·m^-3 respectively. At the end of the heating experiment, the corresponding reduced to 0.03 mg·m^-3 and 0.72 mg·m^-3 separately.[Conclusion] During the running period of ground with heating system, the release of formaldehyde and VOC from the multi-layer parquet has a positive correlation to the indoor air temperature and humidity, and influenced by the system running mode and outdoor environment. When the room is insulated and the system is running with high water temperature, the indoor VOC concentration might exceed the standard and the windows need to be open. The result of this study could be used to guide the use of multi-layer parquet for ground with heating system and also as a reference of formaldehyde and VOC release limit standards for such kind of system.

[Objective] In this paper a feed forward PID high precision control method is proposed to solve the problem of temperature and humidity oscillation caused by dew point water tank replenishment in the process,which is used to detect formaldehyde and volatile organic compounds(VOCs) released from the furniture in 30 m³ climate chamber.[Method] The feed-forward PID control system and actuator are designed based on the analysis of dew point tank temperature. When dew point tank needs water supply, the system will automatically refill water to the dew point tank, until the dew point tank temperature is reached, minimizing the temperature disturbance of the dew point tank. In this way, the structure of limited open loop and global double closed loop control system is formed, and dynamic and high-precision control can be realized.[Result] The result of this study show that:1) The temperature and dew point temperature control accuracy of 30 m³ climate chamber are within ±0.1℃ and ±1.5% respectively; 2) When the temperature is set at 23.0℃ and the relative humidity is 45.0%, under continuous operating conditions for 3 to 28 days, the deviation under non-large inertia disturbance does not exceed ±0.1℃ and ±2%, and the temperature and humidity oscillation time after each replenishment is less than 1 h; 3) Under winter conditions with a constant temperature of 23.0℃ and a relative humidity of 45.0%, the transition period does not exceed 4 h.[Conclusion] The control method of intelligent feedforward PID can effectively solve the problems of instability and the poor steady state response of the 30 m³ chamber control system. The accuracy and quality of the control system have reached the advanced level in world.

[Objective] The phenolic resin was co-modified with boric acid and bio-oil to prepare a heat-resistant and toughening phenol formaldehyde resin, which can chronically satisfy the special requirements of the wood flooring used in a relatively high temperature and humid environment.[Method] The optimum technology of phenolic resin co-modified by boric acid and bio-oil(BBPF resin) was put forward based on examining index on the solid content, carbon residue rate, tensile strength and bond strength as well as examine factors on the substitute rate of bio-oil to phenol, the addition of boric acid and NaOH/P.[Result] 1) Range analysis of the orthogonal experiment showed that the substitute rate of bio-oil to phenol has the greatest influence on the solid content, tensile strength and bond strength of the BBPF resin, followed by the addition of boric acid to phenol and the molar ratio of NaOH/P among the main factors. While the substitute rate of bio-oil to phenol had the most pronounced influence on the residual carbon rate of the BBPF resin, followed by the molar ratio of NaOH/P and the addition of boric acid among the main factors. 2) With the increase of the substitute of bio-oil to phenol, the solid content and residual carbon rate of the BBPF resin both showed a downward trend, so as the bond strength of the plywood, but the tensile strength exhibited an increasing trend. Meanwhile, the solid content, residual carbon rate, tensile strength and bond strength increased first and then decreased along with the addition of boric acid. In addition, the solid content, residual carbon rate and bond strength increased initially and then decreased while the tensile strength increased due to the increase of NaOH/P molar ratio. 3) The optimum synthesis process of the BBPF resin was as follows:the substitute rate of bio-oil was 20%, the addition of boric acid to phenol was 4% and the molar ratio of NaOH/P was 0.5. Resin prepared under the optimum condition had a residual carbon rate of 58.10% at 800℃, and a tensile strength of 3.15 MPa, an elongation at break of 15.7%, the bond strength was 1.12 MPa. 4) The results of thermogravimetric analysis showed that the mass loss of the resins was divided into four stages in the range of 0-800℃. The first stage was 30-350℃. In this stage, the residual moisture in the resin evaporated, along with the post-curing of the resin. The hydroxymethyl group which was not involved in curing was oxidized and then removed. Meanwhile, the ether bond was split into methylene bonds which result ing the release of formaldehyde. In addition, the extremum of the weight loss rate of the BBPF resin moved to the high temperature zone. The methylene bond in the resin broke and decomposed and the mass loss was less in the second stage ranging from 350℃ to 450℃. The third stage of 450-600℃, the phenolic hydroxyl group underwent dehydration and cyclization and the resin was decomposed obviously. In this stage, the BBPF resin had a higher temperature on the fastest degradation which was higher than that of PF resin and phenolic resin modified by bio-oil(BOPF) but lower than phenolic resin modified by boric acid(BPF). In the fourth stage, the residual carbon rate of BBPF at 800℃ was higher than that of PF resin and BOPF resin, and lower than that of BPF resin. The infrared spectrum of the BBPF resin showed an absorption peak at 1 384 cm^-1 of B-O bond. Simultaneously, the intensity of the CH₂ characteristic peak at 2 924 cm^-1 was enhanced as well as peak at 876 cm^-1 which indicated the replacement of the phenolic active site.[Conclusion] The high-bonding B-O bonds wereintroduced into the BBPF resin owing to the reaction between boric acid and phenolic hydroxyl group. In addition, the flexible groups of bio-oil were also introduced to the BBPF resin. These improved the heat resistance and toughness of the BBPF resin.

[Objective] In order to scan the wooden column of ancient buildings by X-ray, a climbing robot is developed.[Method] Firstly, The structure of the encircling-rotating mechanism of the climbing robot is presented, and the process of the robot climbing along the wooden column is introduced. At the same time, the structure of the control system is completed, which adopts wireless remote and has the hardware structure based on the bus. Then, the coupling PD algorithm for the horizontal translational joints and the coupling sub-PI synchronization control algorithm for vertical translational joints are researched respectively, and the speed control of the rotating joint is also realized. Finally, the function test and control effect analysis are carried out through the CT scanning climbing robot.[Result] In the process of climbing along the wooden column, the contact detection force of the horizontal translational joints from the initial position to the contact state is set to 50 N. Setting value of clamping force to 5 000 N, the precision of the actual clamping force is up to 96.2%, and the load capacity of the robot is more than 60 kg. The average displacement error of the vertical translational joints is less than 0.5 mm through synchronous control. When angular velocity is set at 3(°)·s^-1, the maximum angular velocity of the rotating joint is 0.116(°)·s^-1 and the average error is 0.003(°)·s^-1, which all satisfy the requirements of CT scanning.[Conclusion] The climbing robot can climb up or down along the wooden column, and the CT scanner revolves round the wooden column 360° through the rotating body. Then, the image of the cross section is reconstructed to evaluate the internal characteristics and the health status accurately.

[Objective] In order to achieve precise control of clamping force, a fuzzy PID control algorithm is proposed for horizontal translational joints of the climbing robot.[Method] Firstly, a Butterworth two-order low pass filter is designed to filter the output of the force sensor for weakening the influence of the interference on the force sensor. Then, the error and the variation rate of force are input to the system, and fuzzification is done by choosing the trigonometric function as the membership function. Followly, the fuzzy inference rules of PID are established according to the PID rules, and defuzzication of output is achieved using coefficient weighted average method. Finally, the effect of the force control test of the fuzzy PID algorithm and the PID coupling algorithm is analyzed comparatively.[Result] In the contact, the fluctuation of force is weakened obviously by the filter. In the clamping, comparing to the coupling PD algorithm, the maximum precision is increased by 51.8%, the maximum force error between the relative joints is improved by 99.8%, and the time of dynamic adjustment is also optimized in different degrees. In addition, the overshoot of the clamping force is eliminated as well.[Conclusion] The filter can effectively filter the interference in the signals of the force sensors, and improve the reliability of the contact detection. The effectiveness of fuzzy PID clamping force control algorithm is also verified better than that of the coupling PD clamping force control algorithm, which can not only improve the reliability of the climbing robot, but also guarantee the safety of the wooden column.

[Objective] A method for extracting defect regions on image of particleboards surfaces using gray level co-occurrence matrix and hierarchical clustering was proposed in this paper,which separated the targets from images according to the texture differences between defects and normal parts,in order to solve the problem of inaccurate defect segmentation caused by particle board surface texture in board surface defect detection system.[Method] The surface image was divided into several small windows and the texture of each window was characterized by the statistical textural feature parameters of the gray level co-occurrence matrix. Then, a hierarchical clustering method was used to distinguish the defect windows and the normal windows using the texture feature parameters. Firstly,the values of the four structural factors were chosen to build the gray level co-occurrence matrixes for each window,including the window size,gray level,direction and step. Secondly, the classification ability and correlation of the 14 statistical parametersof textural features for gray level co-occurrence matrix were evaluated using fisher criteria and linear correlation. Bydoing this,the features which have better classification ability and low correlation were chosen. The feature vector of each window was composed of the selected features values and all of the feature vectors constituted a sample set. The sample set was clustered by the BIRCH hierarchical clustering algorithm. In order to obtain an accurate clustering result,an optimization strategy was proposed in this paper. The histogram of sum average was drawn and the number of wave peaks was counted as the target category quantity. When the number of classes generated by the initial clustering was larger than the target category quantity,the clusters having closer distance were merged, which can avoid the over segmentation caused by high clustering precision. Finally,according to the clustering result,the windows in the original image were all marked and the defect areas were extracted.[Result] In the experiment,particleboards with five types of defects including sundries,oil stains,glue spots,big wood shavings and loose regions were used,and the size of the surface images was 512 pixels×512 pixels. Defect regions were extracted by the method proposed in this paper. The result showed that the method can extract the defect area with an accuracy of 92.2% and a recall rate of 91.8%.[Conclusion] The surface defect extraction method based on gray level co-occurrence matrix and hierarchical clustering can be used to solve the problem of inaccurate defect extraction caused by the texture of the particleboard surface,and it provides a good support for the measurement and identification of the defectsinmachine vision board defect detection system.

[Objective] In order to satisfy the surface defect recognition quickly with high accuracy, a classification model based on random forest(RF) algorithm is proposed in this study, which can be used for identifying the big shavings, glue spots, debris, oil pollution quickly and accurately on the surface of fibreboard.[Method] Obtaining 100 surface defect images of 4 800 mm×2 400 mm fibreboard, using the Otsu algorithm to realize image segmentation, features including area(S), length(L), the length-width tatio(OR), compactness(J), rectanglarity(P), circularity(O), mean value(u), standard deviation(σ_D), smoothness(σ_P),skewness(σ_S), kurtosis(σ_K) and root mean square value(σ_R) of defect area were extracted, these were used as the experimental data. The experimental data were used for constructing RF classifier, choosing 2/3 data and eight features randomly by bootstrap method to construct k decision trees, the RF classifier were consist of these trees. The number of k is determined by calculating the outside data of the bag(OOB) error rate. 100 fibreboards with the wood shavings, glue spots, debris, and oil pollution were used to test by RF classifier.[Result] While k=600, the lowest average OOB error rate of the RF classifier is 0.004, the recognition accuracy of test is 99%, and the recognition time of one fibreboard is 525 ms. Therefore, the RF classifier is superior to NN and SVM on recognition time and accuracy.[Conclusion] The study proves the classifier based on RF algorithm is more feasibility and superiority on defect identification of fibreboard surface, it can achieve surface defect recognition quickly with high accuracy, satisfy the needs of on-line defect detection system of fibreboard.

[Objective] In order to obtain the internal structure form of wood and improve the identification rate of internal defects in wood,according to the computed tomography image,a method of identification and classification of wood interior defects based on convolution neural network algorithm is proposed,which realizes the automatic classification of wood efficiently.[Method] First of all, the internal cross-sectional CT images of wood samples on independent design were obtained, and the computed tomography system equipment was developed. Then,700 original sample images are randomly select after processed these sample images,from which 20 000 sample images of single defect region can be intercepted. The data set is expanded to 70 000 images by image enhancement algorithm. The size of normalized image is 28×28 pixel,and it can be divided into four parts:normal,cracked,insect hole and knot,from which 60 000 pictures of them are taken as training set,and the remaining 10 000 pictures are taken as test set. The remaining 100 images are used to implement doing experimental test.[Result] 60 000 sample images are used to train the network model,and the 10 000 sample images are classified. The classification accuracy is up to 99.3%. The accuracy of the average classification is 95.87% by verifying the remaining 100 original sample data.[Conclusion] The classification method that based on convolution neural network algorithm overcomes the problem of complicated image preprocessing,complex training method,numerous training parameters and amounts of time consuming and so on. It has the advantages of high precision,low complexity and good robustness. The identification accuracy and time are more accurate and shorter than those of the current conventional algorithms. It is a non-destructive,efficient and accurate classification method.

[Objective] An adaptive fast thresholding image segmentation algorithm was proposed in this paper, which could quickly and accurately separate the defects from the surface images of wood-based panels,and provide support for on-line detection of wood-based panel surface defects.[Method] Firstly,the algorithm divided the whole image into several sub-regions. Secondly,the defect areas were located by calculating the variance of each sub-region. And then,the image segment was only done in defect areas for solving the problem of accurate segmentation of small targets.The one-dimension gray scale histogram of extracted defect area was processedusing histogram smoothing to remove the non-significant peaks.According to the main wave peaks reserved in the histogram after the processing,the number of the thresholds and segmentation interval for each threshold were determined adaptively. At each interval,the threshold was searched using an improved fast Otsu segmentation algorithm.Through the analysis of the Otsu algorithm,the threshold was found by a conditional search instead of the exhaustive search and the search direction was specified. In each segmentation interval, the improved fast Otsu segmentation algorithm was used to search the threshold,which improved the search speed.[Result] The segmentations of surface images of wood-based panels with five types of defects such as oil stains,big wood shavings,glue spots,sundries and loose regions were done using the adaptive fast algorithm proposed in this paper. Although the number and type of the defects were not fixed,this algorithm still could determine the number of the thresholds automatically. All kinds of defects were separated from the surface images in 15 ms with a above 97% segmentation accuracy rate.[Conclusion] The adaptive fast threshold segmentation algorithm presented in this paper can quickly and accurately separate the defects from the surfaces of the panels,and the execution speed and the segmentation effect meets the requirements of the on-line defect detection system. It provides a new approach for automatic on-line detection of surface defects on wood-based panels.

[Objective] In order to optimize the best filter in wood tomography image reconstruction and provide scientific methods for the study of wood tomography, the reconstructed image effects of R-L, S-L, Cosine, Hamming and Hamming filters commonly used in X-ray computed tomography scanning reconstruction algorithm were compared in this study, and the reconstructed image quality of filter function were also analyzed.[Method] According to the CT imaging principle, the image filtering process was analyzed, and the working characteristics of different filters and the design steps of the filters were discussed. Firstly, the projection data is stored in a binary two-dimensional array. The projection value of each point is determined by the probe point number and the rotation angle. The filter length is determined according to the length of the projection data sequence, and the projection data at different angles are zero-padded before and after, so that it constitutes a series of new projection data that is three times the ones of the original length. Then, a one-dimensional fast Fourier transform is performed, and the frequency domain result is multiplied by the filter frequency domain discrete form, and the projection data is filtered line by line. Finally, the filtered result of the product is inversely transformed by Fourier to obtain the projection data in the time domain. The imaging result of different filters are compared, and different filters are evaluated and analyzed.[Result] After comparing with the back-projected reconstruction image without filtering, the boundary of the growth ring of the five filtered images is clearer, the artifacts are less, and the edges are smoother. The image artifacts are directly reconstructed by the back projection without filtering with an extremely poor reconstruction effect, with the structural characteristics of the growth wheel and crack are not clear. After reconstruction with R-L, S-L and Cosine filters, respectively, the edges of the tomographic image are smooth and clear, and the non-directional periphery is diffused. After the Hamming and Hamming filters are reconstructed, respectively, the image artifacts are slight, and there are obvious corrugated artifacts. From the difference of each filtered image and the detection model, the image quality is the highest after reconstruced by the R-L filter, and the error of the Hamming filter is the largest after reconstruction.[Conclusion] The reconstructed image obtained by R-L filter has the smallest error, the reconstructed image is the clearest, the spatial resolution is high, and the artifact can be effectively removed. The filtered wood tomographic image can accurately determine the shape, position and size of the defect.

[Objective] A new solar energy-storage flooring heating system was presented and some reference and theoretical basis for the design and research of the new type domestic heating floor system were provided in this study.[Method] Firstly, the overall structure and composition of the solar energy-storage flooring were introduced. Based on three-dimensional software, the three-dimensional entity of the solar energy-storage flooring system was drawn. Then, based on the overall structure of the solar energy-storage flooring, the working principle and heat transfer process of the solar energy-storage flooring were analyzed. And the working process of the indoor flooring heating system was analyzed. Finally, based on the heat transfer principle and working process of the green energy-storage flooring, the heat transfer mathematical model was established for the aluminum core-floor-insulation layer system in the green energy-storage flooring system according to the heat balance relationship. The discrete mathematical relationship model was established by each control unit, and the mathematical model was simulated by using MATLAB software programming, to obtain temperature distribution at cloud diagram of aluminum core and floor, while the aluminum core-floor-insulation layer system was simulated in combination with ANSYS software to obtain the temperature distribution cloud diagram of the system.[Result] The aluminum core temperature cloud map and the floor temperature cloud map were obtained by MATLAB software, according to the aluminum core temperature distribution cloud diagram, it can be seen that the temperature transferring rate of the aluminum core in the width direction is different, and the temperature in the length direction shows the trend of high temperature at mid and low temperature at both ends. It can be seen from the figure that large proportion of the overall temperature is higher than 30℃, and temperature distribution about 40℃ is also large, which shows that the aluminum core has a good heat transfer effect. The curve of the extracted data was fitted by Origin 9.0 software and the temperature distribution quadratic curve of the aluminum core plate at 2 m was obtained. The correlation coefficient(R²) was 0.989 5, which showed that the curve fitting effect was fine. From the temperature distribution cloud map of the flooring, it is indicated see that the heat transfer rate of the flooring is slow, which is related to its own thermal conductivity. After the simulation and solution of ANSYS, the temperature distribution of the solar heating flooring system was obtained. The temperature distribution on the surface of the floor basically shows the trend of higher temperature distribution at both ends and lower temperature in the middle, and finally the temperature is stable at about 24℃. Furthermore, the experimental analysis shows that with the increase of time, the temperature of the floor surface is continuously increasing. When the temperature elevate for 50 min, the lowest temperature of the floor surface is 20.5℃, and the fitting degree is 85.42%. This shows that the model accuracy is good.[Conclusion] The energy-storage flooring has a high degree of fitting between the theoretical model and the actual situation through model establishment, temperature distribution simulation and experimental verification. Thus, this study provides certain ideas for the design and research of solar energy-storage flooring and is conducive to the development and utilization of solar energy-storage flooring in the new era.

[Objective] Based on the temperature data of the finite points obtained from the test, BP neural network is used to predict and analyze the temperature field in the closed cavity in the time and space dimensions, so as to provide the theoretical and data support for the thermal storage performance analysis of wood floor.[Method] Based on the equipment for testing the thermal released field by the wood floor which was developed by the author's research group, the temperature data in the closed cavity is acquired and divided into training and testing sets of neural network. In the time dimension, the coordinates and the temperature values of the first three time nodes are taken as inputs and the temperature value of the fourth time node is defined as the output. In this process, the previous 80 sets of temperature data are defined as training set while the next 28 sets of temperature data are defined as testing set. In the space dimension, temperature data of 4/5 of the total sensors are chosen as training set while the remaining 1/5 of the total sensors are defined as testing set. The BP networks of time and space are constructed based on the training set, respectively. Furthermore, the constructed models could be validated according to the testing set.[Result] In the time dimension, the computed errors and R² are as following: mean relative error(MRE)=0.269 2%,maximum relative error(MAE)=5.916 0%,mean square error(MSE)=0.422 4%,fitting degree(R²)=0.998 7. In the space dimension, MRE=0.364 2%,MAE=4.781 8%,MSE=0.521 9%,R²=0.998 5.[Conclusion] The prediction result derived by BP neural network are adequately reliable, demonstrating that this method can effectively obtain the continuous and complete temperature field inside the measurement cavity of the wood floor, thus a new theoretical support is provided for the analysis and calculation of the thermal storage performance of the wood floor.

[Objective] A computed tomography(CT) imaging system for wood based on equidistant fan-shaped X-Ray beam was developed and a method of correcting the rotation center based on the sinogram was presented in this paper. Applied the CT imaging system in wood,the reconstructed images of wood could be used to facilitate universities and scientific research institution, and to provide technical support for the structural morphology research.[Method] Firstly, the image reconstruction principle of the CT system was analyzed to verify that there was a functional relationships between the projection address of any point in the scanning fault and the rotation angle. The trajectory of projection addresses was a sinusoidal curve associated with the rotation angles during scanning process.Secondly, the difference between the center of symmetry of the projection address and the midpoint of the detector was calculated. In the process of image reconstruction, the offset of rotation center was compensated to obtain the correct projection position of any point.Thirdly, a Chinese fir with the diameter of 205 mm and the height of 400 mm was selected as the sample, and the offset of rotation center was corrected by 8, 9, 10, 11 and 12 detecting pointsrespectively. Five reconstructed images were obtained by scanning the fault 1 of the sample, and these images were analyzed quantitatively. Finally, the fault 2 of the sample was scanned to reconstruct two images before and after the correction of the rotation center, and the images were evaluated and analyzed.[Result] The 5 reconstructed images of fault 1 could all reflect the internal structural characteristics of wood. As the corrected values getting closer to the offset of rotation center, the PSNR and SSIM values increased, and the RE values were decreased. It showed that the reconstructed images were more similar to the original image. In order to obtain the best reconstructed image, the corrected value of the rotation center must be equal to the offset. The two reconstructed images of fault 2 were quite different. For example, quite a lot of ring artifacts of the reconstructed image before correction brought many difficulties in distinguishing the growth rings. The edge marks diffused to the periphery, and the false similar crack structures appeared in the reconstructed image. In the corrected image, there weren't ring artifacts or peripheral edge marks. Growth rings in the heartwood could be seen clearly, and the reconstructed image was perfect.The experimental result show that the image at the corrected rotation center was much better than the pre-correction image. The method of correcting the rotation center through the sinogram has a good application effect in the wood CT tomography system.[Conclusion] The method for correcting rotation center was proposed in this paper. It effectively solved the problem on random migration of the rotation center, suppressed the ring artifacts, and improved the quality of reconstructed images. After the rotation center is corrected, the reconstructed images can show internal structural characteristics suchascracks, growth rings, heartwood and sapwood much more clearly.The CT imaging system meets the requirements of universities and scientific research institutions for detecting and analyzing the internal structural characteristics of wood.

[Objective] A method to collect the high quality image of the moving wood-based panel is proposed to eliminate the motion blur caused by the relative movement between camera and boards, which guarantees the reliable surface defects identification on the production line.[Method] A high quality image acquisition method was proposed, in which the camera was driven mechanically to track the board in real time in order to eradicate the motion blur. To do this, a cam was used as the driving mechanism and the spring was used for camera restoration. As a result, the camera would move synchronously with the boards based on the appropriate cam contour design, leading to the elimination of the relative movements. The kinematics simulation was carried out using Adams to study the influences of structure parameters on cam movement behavior, and optimize the cam design. The experiment was also conducted to testify the operation efficiency of the proposed method, where the PSF function of each image was computed to evaluate the improvement of the image quality.[Result] According to the simulation result, it was shown that the increases in base radius and pressure angle of constant velocity section would reduce the rotate speed, increase the displacement of the follower, decrease the stress inside the mechanism and improve the movement stability. The roller radius had no impact on the follower movement, but the mismatching between the actual contour and roller radius would lead to the additional speed fluctuation. The cycloidal law made the acceleration of the follower varying consecutively, leading to no flexible impact. At last, the base radius of 80 mm, pressure angle of constant velocity section of 20°, the roller radius of 13.5 mm and cycloidal law for acceleration were selected as the optimized structure parameters of the cam. The optimized cam was machined and was used to collect the board images. It was indicated that the blurring lengths of the images collected by camera moving synchronously with boards were significantly reduced compared with those collected when camera was keeping motionless, and they would maintain within ±2 pixels in the speed range of 0.05-1.2 m·s^-1, leading to the images clear enough to be used for surface testing.[Conclusion] The feasibility of synchronous tracking of camera for moving boards using cam mechanism was verified by both simulation and experiment. The high quality image acquisition method suggested in this study successfully eliminated the motion blur, which significantly improved the image quality and might reduce the time used in conventional image processing. This new method might create possibility of online measurement of the wood-based panel. It also provided the new idea for the wood-based panel testing equipment design.

[Objective] In order to provide a theoretical and practical foundation for the detection of wood floor heat storage performance,a real-time parallel acquisition method for temperature field of wood floor heat storage performance is proposed in this study.[Method] A heat-insulated confined space of the vacuum interlayer is constructed, and a push port is opened at the edge of the confined space, and sealed by a wedge-shaped door. The sample to be tested is placed in the middle of the insulated space, and 75 sensors are installed in the space above and below the test sample to form two sensor arrays. Each sensor in the sensor array group is connected to the controller I/O port by a single bus, and each I/O port is connected to the three micro sensors in a single bus manner. The upper and lower parts of the test space use 25 I/O ports, which are controlled by two controllers. The controller sends the matching and conversion commands simultaneously and collects the converted temperature values. When the measured sample with a certain temperature is pushed into the adiabatic confined space with a certain initial temperature, the sensor array group records the change of the temperature field in the confined space in real time and stores it in the real-time database.[Result] The data acquisition time of 150 temperature sensors is 2.4 s, and the temperature acquisition accuracy is ±0.1℃, which can meet the data needs of the detection for heat storage performance of wood floor.[Conclusion] The data acquisition system designed in this paper is able to collect data in real time during the test process, providing the precondition for data acquisition and analysis of the equipment, and ensuring the reliability of the test result of the whole detection device.