800 publications from this institution
A design project of high-speed laser carving control system based on embedded platform is introduced in this paper. ARM (Advanced RISC Machines) and FPGA (Field Programmable Gate Array) are the kernel processors of the control system which takes Windows CE.NET 5.0 as the software development platform and implements concurrent processing of the functional modules on the basis of the multithread technique. A regulating circuit is designed to realize energy compensation, by which the output power of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> laser can be adjusted. According to the features of high-speed laser graphics carving and cutting, the paper emphatically analyzed an improved S-curve acceleration and deceleration control method. It is proved that the control system can effectively improve the efficiency and the machining quality of high-speed laser carving machine.
This study proposes a new uncertain optimization algorithm to suppress vibration of the crankshaft system. In this new algorithm, the interval expression with random-interval hybrid variables is obtained by the confidence level. In addition, the interval order relation, interval probability, radial basis function neural network technology, and multi-objective genetic algorithm are applied to construct uncertain optimization algorithm with random-interval hybrid variables. Moreover, typical examples are used to demonstrate the effectiveness of the proposed algorithm. To suppress vibration of the crankshaft system, the optimization–Latin hypercube sampling design is used to obtain the experimental scheme and the data sampling is performed by multi-body system simulation of the vibration performance. Then, the radial basis function neural network is built considering the torsional displacement and transient stress of the crankshaft. Finally, the uncertain optimization algorithm is operated on the crankshaft structure design of the high-power reciprocating compressor. The results demonstrate that the robustness of the vibration performance and strength property is improved through the uncertain optimization algorithm, compared with that through deterministic optimization. The uncertain optimization algorithm to suppress vibration of the crankshaft system with random-interval hybrid variables is an efficient and effective approach, which is finally proved by the prototype test.
Simultaneously monitoring changes in both the mean and variance is a fundamental problem in Statistical Process Control, and numerous methods have been developed to address it. However, many existing approaches face notable limitations: some rely on tuning parameters that can significantly affect performance; others are biased toward detecting increases in variance while performing poorly for decreases; and some are computationally burdensome. To address these limitations, we propose a novel adaptive CUSUM chart for jointly monitoring the mean and variance of a Gaussian process. The proposed method is free of tuning parameters, efficient in detecting a broad range of shifts in both mean and variance, and well-suited for real-time monitoring due to its recursive structure. It also has a built-in post-signal diagnostics function that can identify what kind of distributional changes have occurred after an alarm. Simulation results show that, compared to existing methods, the proposed chart achieves the most favorable balance between detection power and computational efficiency, delivering the best overall performance.
Decision support systems (DSSs) are a basic component in the development of business intelligence architecture. They are a specific class of computerized information system that supports business and organizational decision-making activities. The paper first listed its current problems, and presented a cloud solution to provide an effective DSS. It can not only save the capital expenditures, but also provide feasible services, and performance analyses are given to prove its effects.
Condition assessment of existing bridge structures is a valuable tool for bridge owners to make reasonable and optimal maintenance and management decisions. Structural condition assessment based on monitoring data is well-recognized within the civil engineering community as an efficient method to understand structural behavior and performance. One of the significant issues related to structural health monitoring is how to accurately interpret monitoring data in order to provide reliable condition assessment results. In this study, monitoring data obtained from a structural health monitoring system installed on an existing three-span prestressed concrete bridge are analyzed for condition assessment. The central span of the bridge contains eight precast post-tensioned girders with half joints, which are located at the ends of cantilevered lengths of the side girders. The half joints also contain external strengthening. The objective of this study is to conduct purely data-based investigations to explore the feasibility of using several condition indicators to identify any changes occurring to the bridge condition. These indicators include the maximum strain responses in the girders and vertical strengthening rods of half joints, transverse moment-distribution factors, and neutral-axis locations. Measured strain data from a number of events recorded from the structural health monitoring (SHM) system are analyzed, and the distribution as well as statistical characteristics of the afore-mentioned indicators are considered for bridge condition assessment.
Transplanting machine solved the state of a lot of work-consuming and time-consuming through artificial planting.The simplified mechanics model and the force analysis on supporting part consist of the main girder and the wheel reducer boxes of transplanting machine were completed.The stress nephogram and the displacement nephogram of the main girder and the wheel reducer box were achieved through the finite element simulation.The conclusion that the stress and deformation level of the main girder and the wheel reducer box are within safe range was received,and the improvement proposals were presented.Firstly,under the safe strength condition,the wall thickness of the main girder may be appropriately increased to lighten the weight of machine,which causes the machine movement to be more flexibility.Secondly,the reinforcement plate could be increased in the wheel reducer boxes to enhance its strength,which improves the security of the machine.These proposals provided reference for designing and producing the transplanting machines with better performance.
随着中国铁路里程跨越式增长,铁路货车重载化程度不断提高。利用铁路沿线既有通信光缆提出了一种基于光纤中背向瑞利散射信号干涉技术,光纤发生细微振动时,会导致扰动位置的光纤相位及折射率发生变化从而产生背向瑞利散射光。对前后时刻瑞利信号曲线进行差值运算,差分曲线上干涉光强信号发生变化的位置,对应扰动发生的位置,从而实现对铁路车辆的识别和定位,通过采集振动信号的时频特性域信号进行分析,提取信号强度、列车长度和车厢个数等特征,对车型进行精确识别。该技术与传统定位技术相比,可以实现长距离监测,且传感光纤埋藏于铁路两旁的地下,有利于光纤的隐蔽和保护。实验测试结果表明,系统对列车位置的定位误差在±10 m内,可以实现25 km内对列车速度以及位置的监测。
In the Shiyezhou interchange design of Runyang Yangtze R iver Bridge, the arched pier design is adopted in order to solve the problem of supporting the upper bridge when the ramps overlap The key points and ideas of arched pier design are introduced in this paper
由于地球重力场模型存在截断误差,在采用EGM2008模型计算长波高程异常的基础上,将采用DTM2006.0、SRTM模型计算的剩余地形模型(RTM)高程异常和GPS/水准控制点上的残余高程异常作为短波改正项精化似大地水准面模型,比较研究采用不同积分半径组合得到的RTM高程异常模型精度及计算效率,并利用CGGM2015模型和GPS/水准检核点评价似大地水准面精度,验证结果的正确性。