MRC Laboratory of Molecular Biology
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Simulation, Learning and Control Methods to Improve Robotic Vegetable Harvesting
No full textAgricultural robots are subject to a much harsher environment than those in the factory or lab and control strategies need to take this into account while maintaining a low cycle time. Three control strategies were tested on Vegebot, a lettuce-picking robot, in both simulation and on the real robot. Between a fast open loop that was vulnerable to environmental noise and a slow but robust visual servoing technique, a Learned Open Loop strategy was tested where the robot learned from successful picks to pick at an intermediate speed. This reduced the projected cycle time from 31 s to 17.2 s, a 45% reduction
A comprehensive framework from real-time prognostics to maintenance decisions
No full textStudying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory-based prognostics module employing the Weibull time-to-event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade-off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time
Influence of riblet shapes on the occurrence of Kelvin-Helmholtz rollers
No full textWe investigate turbulent flow over streamwise-Aligned riblets (grooves) of various shapes and sizes. Small riblets with spacings of typically less than viscous units are known to reduce skin-friction drag compared to a smooth wall, but larger riblets allow inertial-flow mechanisms to appear and cause drag reduction to break down. One of these mechanisms is a Kelvin-Helmholtz instability that García-Mayoral and Jimenez (J. Fluid Mech., vol. 678, 2011, pp. 317-347) identified in turbulent flow over blade riblets. In order to evaluate its dependence on riblet shape and thus gain a broader understanding of the underlying physics, we generate an extensive data set comprising 21 cases using direct numerical simulations of fully developed minimal-span channel flow. The data set contains six riblet shapes of varying sizes between maximum drag reduction and significant drag increase. Comparing the flow fields over riblets to that over a smooth wall, we find that in this data set only large sharp-Triangular and blade riblets have a drag penalty associated with the Kelvin-Helmholtz instability and that the mechanism appears to be absent for blunt-Triangular and trapezoidal riblets of any size. We therefore investigate two indicators for the occurrence of Kelvin-Helmholtz rollers in turbulent flow over riblets. First, we confirm for all six riblet shapes that the groove cross-sectional area in viscous units serves as a proxy for the wall-normal permeability that is necessary for the development of Kelvin-Helmholtz rollers. Additionally, we find that the occurrence of the instability correlates with a high momentum absorption at the riblet tips. The momentum absorption can be qualitatively predicted using Stokes flow
A Single-Letter Upper Bound to the Mismatch Capacity
No full textWe derive a single-letter upper bound to the mismatched-decoding capacity for discrete memoryless channels. The bound is expressed as the mutual information of a transformation of the channel, such that a maximum-likelihood decoding error on the translated channel implies a mismatched-decoding error in the original channel. In particular, it is shown that if the rate exceeds the upper-bound, the probability of error tends to one exponentially when the block-length tends to infinity. We also show that the underlying optimization problem is a convex-concave problem and that an efficient iterative algorithm converges to the optimal solution. In addition, we show that, unlike achievable rates in the literature, the multiletter version of the bound cannot not improve. A number of examples are discussed throughout the paper
Ionic and molecular transport in aqueous solution through 2D and layered nanoporous membranes
No full textTwo-dimensional (2D) materials provide an intriguing means to not only study physical phenomena but also serve as disruptive membranes for ionic selectivity and sensing based applications. Atomic thinness of these materials affords a unique environment in an all-surface material to unlock challenges towards improving desalination, energy harvesting and DNA sensing. This review provides an overview on some common 2D materials used in membrane applications for solving these challenges along with opportunities where 2D materials could add value to existing solutions. Following this, different types of 2D materials and structures are discussed with their relative advantages and disadvantages highlighted. Fabrication and methods of creating pores within 2D membranes are then presented with a focus on altering surface characteristics. Selected works within the field are highlighted and placed into a wider context, comparing their merits and shortfalls. A discussion of state-of-the-art performance for ionic transport, molecular sensing and power generation is then presented. This review concludes with an outlook on emerging methods and discussing exciting future directions
An instrumented cochlea model for the evaluation of cochlear implant electrical stimulus spread
No full textCochlear implants use electrical stimulation of the auditory nerve to restore the sensation of hearing to deaf people. Unfortunately, the stimulation current spreads extensively within the cochlea, resulting in 'blurring' of the signal, and hearing that is far from normal. Current spread can be indirectly measured using the implant electrodes for both stimulating and sensing, but this provides incomplete information near the stimulating electrode due to electrode-electrolyte interface effects. Here, we present a 3D-printed 'unwrapped' physical cochlea model with integrated sensing wires. We integrate resistors into the walls of the model to simulate current spread through the cochlear bony wall, and 'tune' these resistances by calibration with an in-vivo electrical measurement from a cochlear implant patient. We then use this model to compare electrical current spread under different stimulation modes including monopolar, bipolar and tripolar configurations. Importantly, a trade-off is observed between stimulation amplitude and current focusing among different stimulation modes. By combining different stimulation modes and changing intracochlear current sinking configurations in the model, we explore this trade-off between stimulation amplitude and focusing further. These results will inform clinical strategies for use in delivering speech signals to cochlear implant patients
Comparing the fit of N95, KN95, surgical, and cloth face masks and assessing the accuracy of fit checking
No full textIntroduction The COVID-19 pandemic has made well-fitting face masks a critical piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks has been acknowledged, there remains a lack of understanding about the role of good fit in rendering protective equipment useful. In addition, supply chain constraints have caused some organizations to abandon traditional quantitative or/and qualitative fit testing, and instead, have implemented subjective fit checking. Our study seeks to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit checks by comparing fit check results to quantitative fit testing results. Methods Seven participants first evaluated N95 and KN95 respirators by performing a fit check. Participants then underwent quantitative fit testing wearing five N95 respirators, a KN95 respirator, a surgical mask, and fabric masks. Results N95 respirators offered higher degrees of protection than the other categories of masks tested; however, it should be noted that most N95 respirators failed to fit the participants adequately. Fit check responses had poor correlation with quantitative fit factor scores. KN95, surgical, and fabric masks achieved low fit factor scores, with little protective difference recorded between respiratory protection options. In addition, small facial differences were observed to have a significant impact on quantitative fit. Conclusion Fit is critical to the level of protection offered by respirators. For an N95 respirator to provide the promised protection, it must fit the participant. Performing a fit check via NHS selfassessment guidelines was an unreliable way of determining fit
Frozen algorithms: how the brain's wiring facilitates learning
No full textSynapses and neural connectivity are plastic and shaped by experience. But to what extent does connectivity itself influence the ability of a neural circuit to learn? Insights from optimization theory and AI shed light on how learning can be implemented in neural circuits. Though abstract in their nature, learning algorithms provide a principled set of hypotheses on the necessary ingredients for learning in neural circuits. These include the kinds of signals and circuit motifs that enable learning from experience, as well as an appreciation of the constraints that make learning challenging in a biological setting. Remarkably, some simple connectivity patterns can boost the efficiency of relatively crude learning rules, showing how the brain can use anatomy to compensate for the biological constraints of known synaptic plasticity mechanisms. Modern connectomics provides rich data for exploring this principle, and may reveal how brain connectivity is constrained by the requirement to learn efficiently
Room Temperature Wafer-Scale Synthesis of Highly Transparent, Conductive CuS Nanosheet Films via a Simple Sulfur Adsorption-Corrosion Method
No full textThe development of highly conductive electrodes with robust mechanical durability and clear transmittance in the visible to IR spectral range is of great importance for future wearable/flexible electronic applications. In particular, low resistivity, robust flexibility, and wide spectral transparency have a significant impact on optoelectronic performance. Herein, we introduce a new class of covellite copper monosulfide (CuS) nanosheet films as a promising candidate for soft transparent conductive electrodes (TCEs). An atmospheric sulfur adsorption-corrosion phenomenon represents a key approach in our work for the achievement of wafer-scale CuS nanosheet films through systematic control of the neat Cu layer thickness ranging from 2 to 10 nm multilayers at room temperature. These nanosheet films provide outstanding conductivity (∼25 ω sq-1) and high transparency (> 80%) in the visible to infrared region as well as distinct flexibility and long stability under air exposure, yielding a high figure-of-merit (∼60) that is comparable to that of conventional rigid metal oxide material-based TCEs. Our unique room temperature synthesis process delivers high quality CuS nanosheets on any arbitrary substrates in a short time (< 1 min) scale, thus guaranteeing the widespread use of highly producible and scalable device fabrication
Applications of symmetry in point-line-plane frameworks for CAD
No full textComputer-Aided design (CAD) typically deals with geometries (points, lines, and planes) subject to constraints on distances and angles. Simple counting of freedoms and constraints, as used in the analysis of engineering structures, also provides a useful condition on the residual freedoms of a CAD drawing. Here, we derive general symmetry-extended counting equations to account more fully for the balance of freedoms and constraints in 2D point-line and 3D point-line-plane frameworks. General forms are given for symmetries of the freedoms of points, lines, and planes, and constraints based on distances and angles. The resulting toolkit can be used to give stronger conditions on dimensioning of CAD drawings. This importation to CAD of a physical point of view, in which residual freedoms correspond to the mechanisms and redundant constraints to the states of self-stress of a structure composed of bodies and joints, can often reveal hidden freedoms and redundancies in CAD systems. Point-group symmetry is not a panacea: Mechanisms that depend on specific geometries may escape detection by symmetry alone. One systematic limitation of this type is proved for polyhedra with planar faces and prescribed edge lengths