Adaptive and non-adaptive model predictive control of an irrigation channel

The performance achieved with both adaptive and non-adaptive Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the di®erent channel pools to multivariable control using only prox- imal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptive version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and down- stream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown

Adaptive and non-adaptive model predictive control of an irrigation channel

The performance achieved with both adaptive and non-adaptive Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the di®erent channel pools to multivariable control using only prox- imal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptive version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and down- stream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown

Inteligência Artificial em Radiologia: Do Processamento de Imagem ao Diagnóstico

The objective of this article is to present a view on the potential impact of Artificial Intelligence (AI) on processing medical images, in particular in relation to diagnostic. This topic is currently attracting major attention in both the medical and engineering communities, as demonstrated by the number of recent tutorials [1-3] and review articles [4-6] that address it, with large research hospitals, as well as engineering research centers contributing to the area. Furthermore, several large companies like General Electric (GE), IBM/Merge, Siemens, Philips or Agfa, as well as more specialized companies and startups are integrating AI into their medical imaging products. The evolution of GE in this respect is interesting. GE SmartSignal software was developed for industrial applications to identify impending equipment failures well before they happen. As written in the GE prospectus, with this added lead time, one can transform from reactive maintenance to a more proactive maintenance process, allowing the workforce to focus on fixing problems rather than looking for them. With this background experience from the industrial field, GE developed predictive analytics products for clinical imaging, that embodied the Predictive component of P4 medicine (predictive, personalized, preventive, participatory). Another interesting example is the Illumeo software from Philips that embeds adaptive intelligence, i. e. the capacity to improve its automatic reasoning process from its past experience, to automatically pop out related prior exams for radiology in face of a concrete situation. Actually, with its capacity to tackle massive amounts of data of different sorts (imaging data, patient exam reports, pathology reports, patient monitoring signals, data from implantable electrophysiology devices, and data from many other sources) AI is certainly able to yield a decisive contribution to all the components of P4 medicine. For instance, in the presence of a rare disease, AI methods have the capacity to review huge amounts of prior information when confronted to the patient clinical data

Predictive Adaptive Control of water level in canal pools

A case study on the use of a predictive adaptive algorithm to control pool level in a pilot water distribuition canal is described. The algorithm is a modification of the basic MUSMAR controller that includes parallel integral action and, in the case of multiple pools, feedforward action to coordinate the gates. Experimental results in the case of a single pool and simulations for multiple pools are presented. The contributions of the paper stem from the explicitation of rules for tuning the adaptive controller in a practical situation and from the coordination of different pools using reduced complexity controllers and feedforward in a multivariable settin

Lattice dynamics localization in low-angle twisted bilayer graphene

A low twist angle between the two stacked crystal networks in bilayer graphene enables self-organized lattice reconstruction with the formation of a periodic domain. This superlattice modulates the vibrational and electronic structures, imposing new rules for electron-phonon coupling and the eventual observation of strong correlation and superconductivity. Direct optical images of the crystal superlattice in reconstructed twisted bilayer graphene are reported here, generated by the inelastic scattering of light in a nano-Raman spectroscope. The observation of the crystallographic structure with visible light is made possible due to lattice dynamics localization, the images resembling spectral variations caused by the presence of strain solitons and topological points. The results are rationalized by a nearly-free-phonon model and electronic calculations that highlight the relevance of solitons and topological points, particularly pronounced for structures with small twist angles. We anticipate our discovery to play a role in understanding Jahn-Teller effects and electronic Cooper pairing, among many other important phonon-related effects, and it may be useful for characterizing devices in the most prominent platform for the field of twistronics.Comment: 9 pages, 8 figure