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The core of generator internal cooling water treatment is to control the pH of the internal cooling water within the range of 8.0-9.0, to realize the control of copper wires corrosion through a slightly alkaline environment. It is quite important to monitor the pH of the internal cooling water in generators. However, due to the low conductivity of the internal cooling water and the influence of factors such as liquid contact potential, flow potential, and pure water temperature compensation, it is difficult to accurately determine its pH using traditional pH electrodes, and it is very easy to dissolve CO2 in the internal cooling water. Therefore, conventional computational pH instruments are not suitable. A mathematical model was established for the relationship between pH, electrical conductivity, and hydrogen conductivity in internal cooling water of generator with dissolved CO2 and NH3 as alkalization agent, based on Kohlrausch’s law of ion independent movement. The accuracy of the model was verified through dynamic simulation experiments. The maximum error of pH calculated using the model was 0.03, indicating the high accuracy of the model. This model was applied to guide the on-site NH3 dosing in on-site industrial experiments. Compared with using traditional pH electrodes to control the dosage of alkalizing agents, the copper concentration in the internal cooling water was reduced from above 60.0 μg/L to below 5.0 μg/L, indicating that the corrosion of copper wires was suppressed with good application results.

The application of FPGA is becoming wider because of its advantages of reconfigurability, high performance, low power consumption, and strong real-time performance. However, its shortcomings such as high development threshold, low efficiency, and long development cycle become more prominent as the increase of system size and complexity. This paper uses model-based development methods to study the correlation algorithm of FPGA, including Simulink model building, test validation, code generation and simulation. The results of comparison with traditional manual coding show that the FPGA software development cycle using model-based design methods is only one fifth of the original method and the error in calculation results of the correlation coefficient is also under 0.3%. Although the length of generated code far exceeds that of handwritten code, the resource utilization of logic units, register, storage units, and multiplier units, etc. at runtime are less than those of handwritten code after code synthesis. It can be seen that model-based development methods not only meet the accuracy requirements (not exceeding ±5‰), but also greatly shorten the development cycle and lower the coding threshold.

Fault diagnosis methods based on deep learning have high requirements for the quality of the dataset, requiring a large amount of data for good model training to achieve accurate fault diagnosis. However, the fault signals that can be collected in practical applications are usually limited. A method for diagnosing roller faults based on small sample sound signals is proposed to address the problem of limited performance of intelligent fault diagnosis methods due to the difficulty in obtaining sound signals for roller faults and the small sample size. The feature transformation method is used to convert one-dimensional sound signals into two-dimensional time-frequency images, incorporating features from the frequency domain to improve the dataset's capability to express fault features. A dataset expansion method combining multiple types of time-frequency maps has been proposed. The method combines time-frequency maps drawn by three time-frequency analysis methods: short time fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert Huang transform (HHT) to expand the dataset and increase data styles. The concept of deep transfer learning is introduced, using bearing datasets to pre-train the model, and then using roller data to fine-tune the pre-trained model to further improve the recognition accuracy of the model. The experimental results show that the dataset expansion method combining multiple types of time-frequency maps can effectively solve the problem of overfitting when training models with small sample data. After using transfer learning, the testing accuracy of the model reaches 98.81%, an improvement of 7% compared to not using transfer learning. There was no overfitting phenomenon, indicating that the model is well-trained. Compared to the method of generating adversarial networks to expand the STFT time-frequency map dataset and transfer learning, the method of dataset expansion by combining multiple types of time frequency maps and transfer learning has an accuracy improvement of 4%. It is easier to implement, and has stronger interpretability.

[Objective] In the process of URT (urban rail transit) asset formation, there are challenges in the smooth transmission of asset information, leading to issues such as ′inconsistency between accounts and reality,′ difficulties in equipment maintenance and replacement due to untraceable information during the operation-maintenance phase. To address these issues, it is essential to strengthen the management of asset information throughout the entire lifecycle and construct an asset information system for URT, thereby enhancing the asset management efficiency in URT enterprises. [Method] By reviewing existing research on asset management value, summarizing the value of URT asset management, and using this as a guide, the deficiencies in the existing URT asset information system are identified. Based on this analysis and utilizing axiomatization design theory, asset management value is integrated into the dynamic management process of the whole life cycle of assets. Demand research for asset information users is conducted, and the findings are incorporated into the top-level design of the information system. [Result & Conclusion] The value of URT asset management is reflected in reliability, functionality, effectiveness, and sustainability. Taking Nanjing Metro as the research object, a top-level information system for URT assets is constructed, consisting of an asset information classification system, a coding system and information templates.

Dynamic coal quantity detection for conveyor belt is the foundation and key to achieving energy consumption optimization measures for multi-stage belt conveyors such as coal flow starting and automatic speed regulation. The existing coal quantity detection methods based on ultrasonic have low precision. Multiple ultrasonic sensors are susceptible to interference. In order to solve the above problems, a dynamic coal quantity detection system for conveyor belts based on ultrasonic array is designed. Using the principle of ultrasonic ranging, the coal material height corresponding to the detection points of each ultrasonic sensor array element is detected in real-time through an ultrasonic array. The cross-section slicing method is used to calculate the total volume of coal material passing through the conveyor belt per unit time. The real-time coal flow and total coal quantity of the conveyor belt are calculated based on the coal material stacking density. In order to reduce the crosstalk of the same frequency acoustic wave and the error caused by the attenuation of ultrasonic waves in harsh underground environments, 10 ultrasonic sensor arrays with different center frequencies are selected and arranged in a 2×5 linear array form. The collected coal height data is compensated through multiple rows of ultrasonic sensors to improve the accuracy of coal height data detection. The analysis results of real-time performance indicate that the ultrasonic array detection speed theoretically meets the coal quantity detection requirements of a belt conveyor with a belt speed of 5 m/s. The experimental results show that the average relative errors of regular material volume detection are 4.99% and 5.16% at belt speeds of 0.125 m/s and 0.170 m/s, respectively. Under simulated actual operating conditions, the average relative error of coal quantity detection is 5.56%. In the low belt speed state, the system has a measurement accuracy of over 94% for regular materials and coal. It basically achieves real-time and accurate detection of the dynamic coal quantity of the conveyor belt, meeting the coal quantity detection requirements of the belt conveyor.

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice， medicine， archaeology， etc. As a non-invasive evaluation method of the epiphyseal development stage， MRI is widely used in living age estimation. In recent years， the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation， which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI， introduces the current research trends， and future application trend.

ObjectiveTo reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis （PCA） and partial least squares （PLS） methods. To establish a support vector machine （SVM） classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.MethodsEighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting， and the remaining 20% was used as testing set to verify the accuracy of the test， respectively. Through CT image input， preprocessing， feature extraction， feature information dimension reduction， feature selection， parameter selection， model establishment and model comparison， a discriminative model of pelvic injury was established.ResultsThe PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier （NBC） model. The accuracy of the modeling set， leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%， 100% and 93.33%， respectively.ConclusionIn the evaluation of pelvic injury， the pelvic injury data mining model based on CT images reaches high accuracy， which lays a foundation for automatic and rapid identification of pelvic injuries.

In the early stage of offshore oilfield development, due to the lack of drilling, limited data admission, and lack of dynamic data, there is great uncertainty in the understanding of oilfield reserves. With increased degree of development, production is usually inconsistent with reserves, and reserve re-evaluation is an important way to resolve this problem. In order to improve the development efficiency and reduce costs, offshore oilfields are dominated by horizontal wells, resulted in limited data acquisition. Therefore, new horizontal well data has become the key to obtainnew geological reservoir parameters and improve the accuracy of reserve estimation. In order to further obtain reliable reserve evaluation results and resolve the dynamic and static contradictions of the oilfield, based on analyzing the main controlling parameters of reserve change in the Pearl River Mouth Basin, this study fully excavates the role of horizontal development wells in improving the accuracy of primary controlling parameters.Our results show that the oil-bearing area and effective thickness are the most uncertain in the reserve assessment, and cause the change of reserves. Horizontal development well is critical in improving the production capacity of the oilfield, determining the lower limit of effective thickness, investigating microstructure, and identifying lithological boundary and fluid interface, etc. In the case of limited data access, the utilizationof horizontal development wells can effectively improve the accuracy of major controlling parameters to obtain relatively reasonable geological reserves, and solve the dynamic contradictions of oilfield production. It also points out the direction for late exploration and development of oilfields. Application examples from multiple oil fields in the eastern South China Sea also confirm this understanding.

The goaf water accumulation of abandoned mine will damage the strength of boundary coal pillar, cause coal pillar failure, and pose a threat to the safety of adjacent production mine. The research on the safety limit of goaf water level after mining damage of coal pillar at mine boundary is not comprehensive. In order to solve the problem, taking East Shaft of Shuoshi Mining Industry of Huaibei Mining Group(Shuoshi East Shaft) and Huaibei Shuanglong Mining Co., Ltd.(Shuanglong Company) as the research objects, this paper analyzes the impact of goaf water in abandoned mine of Shuoshi East Shaft on Shuanglong Company, and puts forward corresponding water disaster prevention measures. The mining damage of coal pillar at mine boundary is studied by theoretical calculation and numerical simulation, and the safety limit of goaf water level is calculated according to the damage results. Based on the Bernoulli equation and the Darcy-Weisbach pipeline flow theory, the drainage capacity of the existing drainage boreholes is calculated and the safety is evaluated. The research results show that under the impact of mining, the damaged width of coal pillar at the mine boundary is about 19 m, the effective width is only 21 m, and the maximum water level difference that can be borne is 33 m. When the water level in the abandoned mine of Shuoshi East Shaft rises to −398 m, the coal pillar may be unstable. The drainage capacity of the existing drainage boreholes in the mine is about 89 m3/h, which is less than the actual abandoned mine water inflow of 160 m3/h. After reaching the limit water level, the goaf water level may continue to rise, and there is a threat of water disaster. The water disaster prevention and control measures such as enlarging the borehole diameter and adding drainage boreholes are proposed. The research results can provide reference for the water disaster prevention and control of abandoned mine under similar conditions.

During the pigging operation in an 80° slope section of Myanmar-China Natural Gas Pipeline, huge impact can be brought easily to the elbows at bottom of the pipeline, which could result in catastrophic consequences such as natural gas leakage. Herein, the process parameters such as the operating pressure and the operating speed of the pigs in the slope section, as well as the liquid holdup thereof, were obtained with OLGA software. On this basis, the stress and displacement model during pigging was established with the finite element software ANSYS and CAESAR II, the finite element analysis was also performed, and thereby recommendations on safe pigging were put forward. The results show that no excessive stress will be applied and the displacement is within a controllable range if a 4 000 kg pig is used under the current operating conditions. The water and soil conservation structure can fix and protect the pipelines effectively. In addition, when the weight of the pig is increased from 1 200 kg to 4 000 kg, the stress and displacement of the pipeline are increased by 12.3% and 1.3% respectively. Therefore, the research results are of great significance for improving the pigging efficiency and safety of the steep slope section, and could provide technical reference for the safe pigging of the steep slope sections.

The tooth root bending fatigue durability is the main content of the structural strength specification in developing the slat gear-rack mechanism. The design is difficult and the development cycle is long considering the motion coordination and later test validation. Based on the analysis of aerodynamic loads, the virtual bending fatigue durability model of slat gear-rack mechanism is established by using ANSYS and nCode. The virtual bending fatigue life data of slat gear-rack mechanism under different surface roughness factors are obtained through the analysis. The results show that the virtual bending fatigue life of slat gear-rack mechanism can be predicted and the economic surface roughness parameters can be determined by using the virtual fatigue durability analysis method. Meanwhile, the design result can be revised when the structural strength specification is not satisfied before testing. The best design result is obtained, and the development cycle is shortened.

In order to verify the components strength of slat sector gear transmission mechanism,moreover improve its service life,transmission stability and transmission accuracy,the loading and control method of slat sector gear transmission mechanism testing is analyzed according to the development and production demand of a certain type aircraft slat sector gear transmission mechanism. The test equipment of slat sector gear transmission mechanism is developed by using the principle of motion similarity and dynamic similarity,and the equipment adopted closed loop control and hydraulic loading. The static strength test and delivery running-in of the slat sector gear transmission mechanism are carried out by this equipment,and the results of these works are good.The requirements of running-in and relevant test is satisfied.

Ultrasonic assisted method is an effective method to prepare nanomaterials, which can improve their magnetic and optical properties. Described in this paper, GdPO4:Ce, Tb ternary system fluorescent powder was synthesized under different pH conditions by ultrasonic-assisted. Optical and structural properties of the fluorescent powder were determined by X-ray Powder Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Photoluminesce (PL).The following conclusions are drawn:(1) When pH=1, synthetic samples are 15 nm×104 nm hexagonalnanobars like orthophosphate, green phosphor. Cystals prefer growing at the face of (102) and (200). When pH < 1, no samples can be synthesized, whereas granular orthophosphate green phosphors were synthesized when pH>1. (2) A possible formation mechanism has been put forward, which provided further evidence for the fluorescent efficiency enhancement. Because the 5d energy states of Ce3+ ions are high, they can be transferred to the Gd3+ ions efficiently. However, the emission transition peak of Gd3+ ions and the absorption peak of Tb3+ ions have large spectral overlap in the Gd, Ce, Tb ternary system. Therefore, the sequence of energy transmission is the Ce3+→Gd3+→Tb3+. A few Ce3+ ions doping to GdPO4 matrix can enhance fluorescence intensity of the luminescent materials. The result provides theoretical basis for the exploit of rare earth materials.

A thermodynamic analysis was performed on possible reactions of Fe3O4 in sediment material, intermediate product FeO, Fe, and other matters. In combination with the predominant area phase diagram of the Fe-Si system under different carbon mon-oxide partial pressures and excess carbon and silicon dioxide as well as the thermodynamic parameter state diagram of the Fe-O-N system, it is found that Fe finally exists in the form of Fe3Si, which provides a thermodynamic theoretical basis for using sediment to synthesize O'-Sialon-SiC-Fe3Si (Fe-Sialon) composites. These Fe-Sialon matrix composites made from sediment were prepared by carbothermal reduction-nitridation on the basis of thermodynamic analysis. X-ray diffraction and scanning electron microscopy resuhs in-dicate that plentiful O'-Sialon phase and a small quantity of SiC and Fe3Si phases appear in samples, and the crystalline grains are fi-ber-, cotton-, or short column-shaped, which is consistent with thermodynamic analysis results.