压铸件全自动生产系统的设计与控制

为了满足国际客户对压铸件批量、品质及压铸工艺统计过程控制的要求,使用工业机器人替代程控压铸机取件、喷淋、吹干机械手;增设剪口模用液压机;机器人控制压铸机、液压机及压铸循环,实现压铸件全自动生产。概述了压铸件全自动生产系统的设计和控制方法,并指出了自动压铸工艺的局限性。     

Advanced experimental and numerical methods for the analysis of the dynamic forming press behavior

Numerical simulations are used to estimate the dynamic behavior of new concepts of forming presses as well as to improve the dynamic properties of existing machines. These simulations are to be parametrized with appropriate values in order to yield most accurate results. In many cases neither the way of building up the simulation model for the specific problem nor an appropriate method for gathering the needed date are given in advance. Regarding these facts this paper intends both to characterize the way of building up complex simulation models by using basic simulation modules and to describe methods for the identification of the needed parameters that work even under conditions that hitherto made parameter identifications impossible or at least very time-consuming. In this context recent developments in simulating press dynamics will also be characterized as new modal analysis methods that are not yet established in the field of forming technique. The research activities were financed by the European Research Association for Sheet Metal Working.     

Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency

We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by different users or user groups in large parallel systems, where both user-level fairness and system-wide efficiency are of important concerns. To achieve fairness, we use the well-known equi-partitioning algorithm to distribute the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler that periodically adjusts the processor allocations within each set by consciously exploiting the jobs’ execution history. We show that our algorithm achieves asymptotically competitive performance with respect to the set response time, which incorporates two widely used performance metrics, namely, total response time and makespan, as special cases. Both theoretical analysis and simulation results demonstrate that our algorithm improves upon an existing scheduler that provides only fairness but lacks efficiency. Furthermore, we provide a generalized framework for analyzing a family of scheduling algorithms based on feedback-driven policies with provable efficiency. Finally, we consider an extended multi-level hierarchical scheduling model and present a fair and efficient solution that effectively reduces the problem to the two-level model.     

Segmentation of abdominal organs from CT using a multi-level,hierarchical neural network strategy

Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature–classifier combinations show that hierarchical systems outperform composite feature–single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature–classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges.     

Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX

This study evaluated newly proposed Human–Machine Interface (HMI) design concepts for improving the ergonomics of hydraulic excavators. The design concepts were based on an augmented interaction technique which involved the use of heads-up display (HUD) and coordinated control as HMI elements. Two alternative HMI designs were elaborated in order to separately evaluate the ergonomic impacts of the head-up display and the coordinated control by comparing them to the standard HMI design. The effectiveness of these three HMI designs in terms of the reduction of the operators' mental and physical workload were assessed by conducting experiments utilizing human subjects, ages 23–35 years. The National Aeronautics and Space Administration's Task Load Index (NASA TLX) method was used for collecting subjective workload scores based on a weighted average of ratings of six factors: Mental Demand, Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration Level. The results showed that the type of HMI design affects different aspects of the operator's workload. Indeed, it showed how the proposed augmented interaction is an effective solution for reducing the ergonomic gaps in terms of mental workload, and to a lesser extent the physical workload, subjected by the standard HMI design.     

A multi-echelon production–inventory system with supply disruption

The article investigates an integrated multi-layer supply chain model consisting of supplier, manufacturer and retailer while supply disruption, machine breakdown, safety stock, maintenance breakdown occur simultaneously. At beginning of the production, manufacturer keeps some raw materials in stock received from second supplier at high price, as safety stock due to supply disruption of first supplier. Corrective maintenance is done immediately to restore its normal stage when machine breakdown occurs. Stock out situations at manufacturer and retailer are considered due to disruption of production for machine breakdown. The integrated expected costs of the chain in centralized (collaborating) and decentralized (Stakelberg approach) system are compared. A numerical example and its sensitivity analysis are provided to test feasibility of the model.     

Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach

In recent years, autonomous robots have increasingly been deployed in unknown environments and required to manipulate or categorize unknown objects. In order to cope with these unfamiliar situations, improvements must be made both in sensing technologies and in the capability to autonomously train perception models. In this paper, we explore this problem in the context of tactile surface identification and categorization. Using a highly-discriminant tactile probe based upon large bandwidth, triple axis accelerometer that is sensitive to surface texture and material properties, we demonstrate that unsupervised learning for surface identification with this tactile probe is feasible. To this end, we derived a Bayesian nonparametric approach based on Pitman–Yor processes to model power-law distributions, an extension of our previous work using Dirichlet processes Dallaire et al. (2011). When tested against a large collection of surfaces and without providing the actual number of surfaces, the tactile probe combined with our proposed approach demonstrated near-perfect recognition in many cases and achieved perfect recognition given the right conditions. We consider that our combined improvements demonstrate the feasibility of effective autonomous tactile perception systems.     

Applying semi-supervised learning in hierarchical multi-label classification

In classification problems with hierarchical structures of labels, the target function must assign labels that are hierarchically organized and it can be used either for single-label (one label per instance) or multi-label classification problems (more than one label per instance). In parallel to these developments, the idea of semi-supervised learning has emerged as a solution to the problems found in a standard supervised learning procedure (used in most classification algorithms). It combines labelled and unlabelled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label hierarchical classification. Therefore, this paper proposes a new method for supervised hierarchical multi-label classification, called HMC-RAkEL. Additionally, we propose the use of semi-supervised learning, self-training, in hierarchical multi-label classification, leading to three new methods, called HMC-SSBR, HMC-SSLP and HMC-SSRAkEL. In order to validate the feasibility of these methods, an empirical analysis will be conducted, comparing the proposed methods with their corresponding supervised versions. The main aim of this analysis is to observe whether the semi-supervised methods proposed in this paper have similar performance of the corresponding supervised versions.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

基于增加数据宽度的方法提高访存速度

低效率的访存操作是限制微处理器性能提高的一个关键因素。因此提高访存速度可以有效改善微处理器的性能。提出了一种基于增加数据宽度的方式来提高访存速度的方法。通过使用多字宽存储器来增加数据带宽,降低失效开销的时钟周期,从而达到提高访存效率的目的。