A hybrid evaluation method for human error probability by using extended DEMATEL with Z-numbers: A case of cargo loading operation

Human error probability (HEP) evaluation and prediction is one of the most significant tasks to enhance human reliability in marine industry. Among various kinds of HEP evaluation techniques, the Human Error Assessment and Reduction Technique (HEART) technique is regarded as an effective and empirical tool that has been widely adopted in various fields. However, current HEART techniques are insufficient to address HEP evaluation problem in which the self-assurance of expert's judgment and inter-dependencies between Error-producing conditions (EPCs) are considered. Therefore, the purpose of this paper is to develop a hybrid HEART framework (H-HEART-F) to address this problem by integrating Z-numbers and the decision-making trial and evaluation laboratory (DEMATEL) method. First, the Z-numbers are introduced to model the uncertainty and self-assurance of evaluation information from various experts. Then, the Z-number power weighted average (Z-PWA) operator is proposed to aggregate the individual evaluation information into a group direct influenced matrix. Next, an extended DEMATEL method based on possibility degree measure is constructed to determine the proportion of effect of each EPC by considering the self-assurance of expert's judgment and inter-dependencies between EPCs. Finally, the HEP estimation for the cargo loading operation in oil or chemical tanker ship is presented to demonstrate the availability and feasibility of the H-HEART-F. After that, the sensitivity analysis and comparison study are conducted to further illustrate the reasonableness and effectiveness of the proposed method.     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece

Achieving high workpiece accuracy is the long-term goal of machine tool designers. There are many causes for workpiece inaccuracy, with thermal errors being the most common. Indirect compensation (using prediction models for thermal errors) is a promising strategy to reduce thermal errors without increasing machine tool costs. The modelling approach uses transfer functions to deal with this issue; it is an established dynamic method with a physical basis, and its modelling and calculation speed are suitable for real-time applications. This research presents compensation for the main internal and external heat sources affecting the 5-axis machine tool structure including spindle rotation, three linear axes movements, rotary C axis and time-varying environmental temperature influence, save for the cutting process. A mathematical model using transfer functions is implemented directly into the control system of a milling centre to compensate for thermal errors in real time using Python programming language. The inputs of the compensation algorithm are indigenous temperature sensors used primarily for diagnostic purposes in the machine. Therefore, no additional temperature sensors are necessary. This achieved a significant reduction in thermal errors in three machine directions X, Y and Z during verification testing lasting over 60 h. Moreover, a thermal test piece was machined to verify the industrial applicability of the introduced approach. The results of the transfer function model compared with the machine tool's multiple linear regression compensation model are discussed.     

液压约束活塞发动机的流量脉动仿真分析

为研究间隙误差对液压约束活塞发动机流量脉动的影响,在ADAMS中建立其含有间隙误差的主运动系统动力学模型,将ADAMS中得到的模型数据导入AMESim中,建立液压系统模型。通过对比不同间隙误差模型的参数输出,分析间隙误差对系统流量脉动的影响。研究结果表明,在一定范围内,间隙误差对流量脉动有明显的影响,并随着间隙误差的增大而增加;而且,连杆大头和曲轴曲柄之间转动副径向间隙误差对系统流量脉动的影响最大。     

A review on spindle thermal error compensation in machine tools

Thermal error caused by the thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. Among all the heat sources which lead to the thermal distortions, the spindle is the main one. This paper presents an overview of the research about the compensation of the spindle thermal error. Thermal error compensation is considered as a more convenient, effective and cost-efficient way to reduce the thermal error compared with other thermal error control and reduction methods. Based on the analytical calculation, numerical analysis and experimental tests of the spindle thermal error, the thermal error models are established and then applied for implementing the thermal error compensation. Different kinds of methods adopted in testing, modeling and compensating are listed and discussed. In addition, because the thermal key points are vital to the temperature testing, thermal error modeling, and even influence the effectiveness of compensation, various approaches of selecting thermal key points are introduced as well. This paper aims to give a basic introduction of the whole process of the spindle thermal error compensation and presents a summary of methods applied on different topics of it.     

Quantitative analysis of semiconductor supply chain contracts with order flexibility under demand uncertainty: A case study

The evidence base for the configuration of rolling horizon flexibility (RHF) contracts (a type of quantity flexibility contract) used in the semiconductor industry to coordinate production and demand remains meagre, more art than science. Informed by the characteristics of actual clauses and demand behaviors drawn from a company’s experience, a discrete-event simulation model is developed to represent the company’s supply chain. It comprises of three parties: a customer, a supplier (semiconductor manufacturer), and a capacity provider. Through analysis of customer forecasted demand the paper characterizes forecast demand as being under, over or unbiased. Models of these forecasted demands drives both long and short term planning. In long term planning, which is given twelve months before an order is delivered, capacity at the capacity provider is booked. Short term planning is also driven by this forecast which, within a binding period, is governed by an RHF contract. Results from the model report inventory levels, and delivery compliance, namely Delivery Performance (DP) and Delivery Reliability (DR), measures widely used in this sector. It is concluded from this work that on the balance of performance measures RHF contracts with asymmetrical flexibility bounds are substantially better than those with symmetrical boundaries, and that this conclusion is robust with regard to both over-planning and under-planning behaviors. This robustness is a critical attribute with respect to the endemic medium-term vacillation between both states experienced in practice in this sector.     

Anti‐Sway Crane Control Considering Wind Disturbance and Container Mass

A method for estimating the sway angle using an observer has already been proposed. The state observer estimates the sway angle accurately and must use the detected sway angle value. However, the estimated sway angle has an error owing to rope length error, friction force, and wind. Moreover, the container mass cannot be determined, and therefore the observer parameter is not suitable. We already proposed robust antisway control for overcoming rope length error without adding a new sensor. Further, we designed a friction disturbance observer to cancel out the influence of the friction force. In this paper, we first propose a container mass estimation method when a crane system performs rolling up control. The observer parameter can be selected using the estimated mass value. Second, in crane parallel shift control, we propose a robust antisway control even when there is a wind disturbance. We design a wind disturbance observer and propose a wind disturbance estimator to separate the friction observer output from the wind disturbance observer output. We confirm through experiments that the proposed method can reduce vibration.     

Precise contour following for biaxial systems via an A-type iterative learning cross-coupled control algorithm

This paper proposes an A-type iterative learning cross-coupled control (CCC) algorithm for biaxial systems. An algebraic equation based contour error model is used as the CCC input. This model has the advantage that it is zero if and only if the real value vanishes. The iterative learning CCC is designed to make its input converge to zero. Hence, it is expected to that the contour error will converge to zero as well. After analyzing the control algorithm convergence condition in the frequency domain, the proposed method is implemented on a motion stage. Experimental results show that the algorithm perfectly follows contours as the cycles approach infinity regardless of whether tracking errors are small or large.     

一种广义不可分支持向量机算法

针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时，对识别错误代价损失差异很大的极端情况表现出的局限性，提出一种通用的 广义支持向量机算法。根据识别错误后所付出的代价，可以把最优分类面向代价损失低的一方进行推移，留给代价损失高的一方更大的空间，提高其识别率，从而减小识别错误后带来的代价损失。该方法进一步提高了标准C SVM的适用性以及样本的正确识别率，将新算法应用到高分辨雷达距离像的识别中，实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。