Analytical design of a generalised predictor-based control scheme for low-order integrating and unstable systems with long time delay

In this study, the problem of controlling integrating and unstable systems with long time delay is analysed in the discrete-time domain for digital implementation. Based on a generalised predictor-based control structure, where the plant time delay can be taken out of the control loop for the nominal plant, an analytical controller design is proposed in terms of the delay-free part of the nominal plant model. Correspondingly, further improved control performance is obtained compared with recently developed predictor-based control methods relying on numerical computation for controller parameterisation. The load disturbance rejection controller is derived by proposing the desired closed-loop transfer function, and another one for set-point tracking is designed in terms of the H-2 optimal control performance specification. Both controllers can be tuned relatively independently in a monotonic manner, with a single adjustable parameter in each controller. By establishing the sufficient and necessary condition for holding robust stability of the closed-loop control system, tuning constraints are derived together with numerical tuning guidelines for the disturbance rejection controller. Illustrative examples taken from the literature along with temperature control tests for a crystallisation reactor are used to demonstrate the effectiveness and merit of the proposed method.This work was supported in part by the National Thousand Talents Program of China, NSF China Grants 61473054, the Fundamental Research Funds for the Central Universities of China, and the Grants TIN2014-56158-C4-4-P and PROMETEOII/2013/004 from the Spanish and Valencian Governments.Chen, Y.; Liu, T.; García Gil, PJ.; Albertos Pérez, P. (2016). Analytical design of a generalised predictor-based control scheme for low-order integrating and unstable systems with long time delay. IET Control Theory and Applications. 10(8):884-893. https://doi.org/10.1049/iet-cta.2015.0670S88489310

Robust tuning of a generalized predictor-based controller for integrating and unstable systems with long time-delay

In this work, a general structure to control long time-delay plants is proposed and an easy methodology to tune the control parameters is outlined. All the sensitivity transfer functions are delay free. The proposed scheme is equivalent to the Smith predictor but able to cope with any kind of systems, including nonminimum phase, unstable and integrating plants. The controllers are designed based on the delay free model. Contrary to other approaches, other than for the digital implementation, no delay approximation is used. A tuning parameter is provided in order to reach an intuitive tradeoff between performance and robust stability. A comparative analysis with respect to recently successful proposals shows a substantial improvement in the performance/robustness tradeoff as well as in the tuning process.This work has been partially granted by the Spanish Ministry of Education research Grants DPI2011-28507-C02-01 and PAID-06-12. The authors are also grateful to the Associate Editor and anonymous reviewers for their valuable feedback and comments.García Gil, PJ.; Albertos Pérez, P. (2013). Robust tuning of a generalized predictor-based controller for integrating and unstable systems with long time-delay. Journal of Process Control. 23(8):1205-1216. https://doi.org/10.1016/j.jprocont.2013.07.008S1205121623

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

Impact of neoadjuvant treatment on total mesorectal excision for ultra-low rectal cancers

<p>Abstract</p> <p>Background</p> <p>This study reviewed the impact of pre-operative chemoradiotherapy or post-operative chemotherapy and/or radiotherapy on total mesorectal excision (TME) for ultralow rectal cancers that required either low anterior resection with peranal coloanal anastomosis or abdomino-perineal resection (APR). We examined surgical complications, local recurrence and survival.</p> <p>Methods</p> <p>Of the 1270 patients who underwent radical resection for rectal cancer from 1994 till 2007, 180 with tumors within 4 cm with either peranal coloanal anastomosis or APR were analyzed. Patients were compared in groups that had surgery only (Group A), pre-operative chemoradiotherapy (Group B), and post-operative therapy (Group C).</p> <p>Results</p> <p>There were 115 males and the mean age was 65.43 years (range 30-89). APR was performed in 134 patients while 46 had a sphincter-preserving resection with peranal coloanal anastomosis. The mean follow-up period was 52.98 months (range: 0.57 to 178.9). There were 69, 58 and 53 patients in Groups A, B, and C, respectively. Nine patients in Group B could go on to have sphincter-saving rectal resection. The overall peri-operative complication rate was 43.4% in Group A vs. 29.3% in Group B vs. 39.6% in Group C, respectively. The local recurrence rate was significantly lower in Group B (8.6.9% vs. 21.7% in Group A vs. 33.9% in Group C) <it>p < 0.05</it>. The 5-year cancer-specific survival rates for Group A was 49.3%, Group B was 69.9% and Group C was 38.8% (<it>p </it>= 0.14).</p> <p>Conclusion</p> <p>Pre-operative chemoradiation in low rectal cancer is not associated with a higher incidence of peri-operative complications and its benefits may include reduction local recurrence.</p

Synthesis and Characterization of LnAg(WO4)(MoO4)

Polycrystalline LnAg(WO4)(MoO4) powders, with Ln = La to Lu and Y, have been obtained by ceramic method. Rietveld refinement for all compounds reveals that they present tetragonal symmetry, space group I41/a (No. 88), where the Ln3+/Ag+ ions are located in the 4a atomic positions, since the W/Mo are randomly distributed into 4b crystal sites. In these compounds, a and b lattice parameters take values between those corresponding to tungstate and molybdate compounds. A progressive decrease in the lattice parameters is observed in going from La to Lu derivatives as a consequence of the well-known lanthanide contraction

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson’s r correlation with human judgments compared to previous metrics

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson's r correlation with human judgments compared to previous metrics.Comment: 9 pages, accepted to NAACL 202

From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne