Assessment of Noise Exposure of Sawmill Workers in Southwest,Nigeria

Economic wood processing employs the use of industrial machines for cutting, shaping, milling, and sawing timber, thereby leading to the generation of high levels of noise. Published data from empirical studies have categorized noise as an environmental hazard of global significance. Furthermore, noise exposure limits for different industries and all the industrial machines available has not been formally established as it presently exists in developed nations around the world. Therefore, this study assessed the daily exposure of sawmills workers to noise in Southwestern Nigeria. Reconnaissance surveys were first carried out in Osun, Oyo, Ondo, Ekiti, Lagos, and Ogun States to select sawmills that were fully operational and fit for the study. Two fully functional sawmills in two cities of each State were eventually selected for data collection, making a total of 24 sawmills, while the Circular Machines (CM), Planer Machines (PM), and Band-saw Machines (BM) were the machines in each sawmill considered. Two machines each of CM, PM, and BM were considered in each sawmill, making a total of forty-eight (48) machines each of CM, PM, and BM. Sound data were collected between 7 am and 7 pm each day for six days (between Monday and Saturday) using Extech 407732 sound level meter and all stabilized measurements were taken three times at different intervals. The data collected were in three different periods: Machine No-work Period (NP_m), Machine Idle Period (IP_m), and Machine Working Period (WP_m). A two–way Analysis of Variance (ANOVA) was carried out at P < 0.05 to determine whether there is a significant difference in the sound level average before and after the break, for both the idle and working periods of the three machines considered. This was also done to determine whether there is a significant difference between the sound level average of the results collected during idle and working periods of the three machines. Noise Pollution Levels (L_np) ranged from 83.20 dB (PM) to 107.65 (BM) and 93.42 (CM and PM) – 116.00 (BM) respectively, while IP_m also gave the least noise pollution level of 95.79 dB and WP_m gave the highest level of 102.88 dB. The results revealed that all the machines’ L_np values in the working period are more than the 90 dB acceptable limit the recommendation value of 90 dB while 89.6% of CMs, 75% of PMs, and 89.6% of BM had their L_np above 90 dB in the idle period respectively. The minimum and the maximum noise dose levels for IP_m, WP_m and overall are 0.09 (BM) and 2.37 (CM), 0.50 (CM), and 4.77 (PM) and 0.69 (BM) and 6.64 (PM) respectively. The study found out that the fundamental contributing factors to the high noise levels in sawmills are poor machine maintenance, use of old and obsolete machines, poor housekeeping strategy, limited space, workers’ negligence, lack of PPE, and lack of occupational safety training. The study recommends that proper workplace practices such as use of personal protective equipment, new and modern machines, training, and occupational safety programmes be implemented in the considered sawmills.     

电机驱动微装配在双光子制备微结构中的应用

对微结构的制作、微装配系统进行了研究. 采用飞秒激光双光子聚合微加工技术制作有底座、精细的三维立体“拱形”微结构, 其高250μm、长300μm、厚50μm. 将此微结构与实验室自主搭建的二维微装配平台相结合, 利用自主编程的人机交互界面驱动步进电机, 远程操控微装配设备; 将荧光闪烁陶瓷粉末装配到微结构中, 对装配后的微结构进行荧光光谱表征发现, 纯荧光粉末和微结构中的荧光粉末的发射光谱在测量误差范围内基本一致, 表明荧光粉末的光学性质未发生改变. 利用该装置可以将各类微纳米级材料和微结构进行装配, 形成含有不同材料的微结构系统.     

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.     

基于模糊神经元PID算法的注塑机注射速度控制算法研究

基于一款市场较为畅销的注塑机, 设计出一种能精确控制注射速度的模糊神经元PID控制器. 首先, 设计出具有自学能力的神经元PID控制器, 利用模糊算法对其进行优化; 其次, 在原有注射速度线性数学模型的基础上, 构建注塑机注射速度的非线性模型; 最后, 利用MATLAB在所建数学模型的基础上对模糊神经元PID控制器进行仿真实验. 实验结果表明, 所设计控制器具有响应迅速、无超调量、控制精度高、控制稳定等优点.     

基于不确定控制理论的最优策略研究

本文在不确定理论的框架下,研究一类带背景状态变量的最优控制模型.在乐观值准则下,利用不确定动态规划的方法,证明了不确定最优性原则,得到最优性方程.作为应用,求解一个固定缴费(DC)型养老金的最优投资策略问题,在乐观值准则下,以工资变量为背景状态变量,建立养老金模型.通过求解不确定最优性方程得到最优投资策略和最优支付率.     

基于贝叶斯参数优化的无模型自适应硅单晶直径控制

直拉硅单晶的生长过程涉及多场多相耦合与复杂的物理化学变化,其中工艺参数的波动是导致晶体直径不均匀的重要原因,如何实现工艺参数的控制以获得理想的、均匀的晶体直径具有重要的研究意义。本文分析现有控制方法存在不稳定以及控制效果不佳的问题后,提出基于贝叶斯参数优化的无模型自适应控制模型来控制硅单晶生长过程中的晶体直径。首先以坩埚上升速度与加热器的功率作为控制输入参数,晶体直径作为输出,搭建无模型自适应控制模型,并分析算法的稳定性。其次将控制模型进行仿真实验,发现硅单晶直径控制模型中不同的超参数设定会影响控制过程的迭代次数以及控制效果。最后,利用贝叶斯优化超参数的取值范围,并进行最终的仿真实验,结果表明,经贝叶斯参数优化后的控制模型计算快、迭代次数少,输出的晶体直径稳定,同时将生长工艺参数控制在实际生产要求范围内。因此,基于贝叶斯参数优化的无模型自适应控制实现了硅单晶直径均匀稳定的有效控制,具有结合工程背景的实际应用前景。