Modelling the consensus and differences between assessors inspecting the colour quality of apples by ‘tree‐based modelling’

This research investigated the consensus and differences between different quality inspectors assessing the colour quality classification of a two‐coloured apple variety (Malus domestica Borkh cultivar ‘Jonagold’). An image analysis system measured the colour characteristics objectively. These objective measures formed the basic reference to compare the grading behaviour. Different quality inspectors were asked to assess the colour quality of different sets of apples applying the commercial quality standards. Agreement and association measures of the intra‐inspector contingency tables indicated a moderate ability of the inspectors to reconstruct their own quality classification. If the intra‐agreement and intra‐association were lower than a threshold, the repeatability of the inspector's assessments was considered as too poor and his classification results were omitted from the model development that simulated the apple colour classification behaviour. The statistical method ‘tree‐based modelling’ was applied to connect the individual quality assignments with the objective apple colour measurements. These models indicated that the blush colour was more important than the blush area for the quality assessment.The individual grading decision models were compared by correspondence analysis. Four different grading archetypes were detected and simulated. The predictive power of the four archetypal models was much higher than the predictive power of a consensus model including all inspectors. These results underlined the fact that poorly defined commercial standards lead to different quality interpretation. © 2000 Society of Chemical Industry     

基于 WebGL 的多AGV物流调度仿真系统的研究与应用

针对K公司汽车总装生产线中多AGV(Automated Guided Vehicle)电量分配不均、路径冲突、AGV利用率低等物流调度问题，为了提高K公司生产线中多AGV物流调度的效率，设计了一种多AGV物流调度仿真系统；利用WebGL(Web Graphics Library) 技术建立了整个生产线场景的三维模型；系统能够自动给AGV分配任务，在AGV执行任务时，将AGV的电池电量和AGV路径规划问题考虑到物流调度系统中；该系统实现了对物流调度系统中AGV运输任务的合理分配，提高了物流调度的效率；经实际应用满足了K公司汽车总装生产线多AGV物流调度工程上的应用。     

Connection of nonlinear model predictive controllers for smooth task switching in autonomous driving

Motion planning, decision making, and control are vital functions in autonomous driving for accomplishing the desired driving task while considering passenger comfort, road infrastructure, and surrounding traffic participants. Model predictive control (MPC) is a promising method for simultaneously realizing these functions. However, formulating a single MPC that can run through all driving scenarios is difficult, and previous research has often been conducted to design an MPC for a specific driving task. To extend the availability of MPC for all driving tasks, smooth switching between different MPCs designed for each driving task must be addressed. One of the difficulties in switching between MPCs is guiding the state to a feasible set of optimization problems after switching. In this paper, we present a new framework to realize the smooth connection of MPCs, that is, to reduce the optimization infeasibility at the time of MPC switching. In our proposed method, two general nonlinear MPCs with different state spaces, cost functions, constraints, and formulations can be systematically switched via automatically generated intermediate-MPCs without requiring any particular alterations. This can help reduce the system complexity of the hybrid MPC system.     

MGA-Net+: Acceleration-based packaging work recognition using motif-guided attention networks

This study presents a new method for recognizing complex human activities within the logistics domain, such as packaging operations, using acceleration data from a body-worn sensor. The recognition of packaging tasks using standard supervised machine learning is complex because the observed data vary considerably depending on the number of items to be packed, the size of the items, and other parameters. In this study, we focused on the characteristics and necessary key actions (motions) that occur during a specific operation. For instance, when the packaging tape is stretched while assembling the shipping boxes. To focus on these characteristic actions when recognizing data, we propose the use of an attention-based neural network. With our method, the attention-based neural network’s training is guided such that its focus is on the motifs. In addition, this method was designed to accurately recognize short operations by leveraging data augmentation techniques. We tested our method on two logistics datasets and achieved a 3.9% improvement over the previous MGA-Net approach.