Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Dynamic process control of twin-column periodic countercurrent chromatography processes

Twin-column periodic countercurrent chromatography has become a promising solution for continuous downstream processes as chromatography equipment for both process development and GMP manufacturing has become available. Twin-column periodic countercurrent processes have been utilized successfully in many applications including purification of biologics, such as monoclonal antibodies (mAbs), bispecific antibodies and fusion proteins, the purification of peptides and also small molecules such as antibiotics and fatty acid ethyl esters. This presentation deals with the online UV-based control of two twin-column periodic countercurrent processes, 2C-PCC and MCSGP, covering applications in chromatographic capture and polishing. 2C-PCC is a capture process significantly improving the process performance (productivity, resin utilization, buffer consumption, product concentration) of affinity capture, e.g. the capture of mAbs, in comparison to traditional single column chromatography. MCSGP is a polishing process to solve difficult ternary separation challenges, allowing purification with high product yield and purity in situations where traditional single column chromatography faces a yield-purity trade-off. For robust operation in view of commercial manufacturing using these two cyclic processes, UV-based dynamic control strategies have been developed and tested. In this presentation a UV-based control strategy for 2C-PCC based on online-determination of breakthrough curve signals is introduced and case studies for its application in protein A chromatography are shown. The control strategy accounts for changes in resin capacity and, in case of continuous upstream, for changes in titer occurring over time, and adjusts the operating parameters such that capacity utilization and yield are kept constant. A second control strategy for MCSGP based on the online evaluation of the elution peak signal is presented based on a case study. The method accounts for shifts of the product peak e.g. due to changes in temperature and buffer preparation (e.g. during buffer refill). An application of the control strategy in protein purification is presented. The presented methods represent important tools for robust manufacturing using twin column processe

Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE Transactions on Signal Processing, Sept. 201

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks

We consider a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by byzantine nodes, i.e. nodes which deliberately alter the result of the local decision to induce an error at the fusion center. When independent states are considered, the optimum fusion rule over a batch of observations has already been derived, however its complexity prevents its use in conjunction with large observation windows. In this paper, we propose a near-optimal algorithm based on message passing that greatly reduces the computational burden of the optimum fusion rule. In addition, the proposed algorithm retains very good performance also in the case of dependent system states. By first focusing on the case of small observation windows, we use numerical simulations to show that the proposed scheme introduces a negligible increase of the decision error probability compared to the optimum fusion rule. We then analyse the performance of the new scheme when the FC make its decision by relying on long observation windows. We do so by considering both the case of independent and Markovian system states and show that the obtained performance are superior to those obtained with prior suboptimal schemes. As an additional result, we confirm the previous finding that, in some cases, it is preferable for the byzantine nodes to minimise the mutual information between the sequence system states and the reports submitted to the FC, rather than always flipping the local decision