User-friendly mathematical model for the design of sulfate reducing H2/CO2 fed bioreactors

The paper presents three steady-state mathematical models for the design of H2/CO2 fed gas-lift reactors aimed at biological sulfate reduction to remove sulfate from wastewater. Models 1A and 1B are based on heterotrophic sulfate reducing bacteria (HSRB), while Model 2 is based on autotrophic sulfate reducing bacteria (ASRB) as the dominant group of sulfate reducers in the gas-lift reactor. Once the influent wastewater characteristics are known and the desired sulfate removal efficiency is fixed, all models give explicit mathematical relationships to determine the bioreactor volume and the effluent concentrations of substrates and products. The derived explicit relationships make application of the models very easy, fast and no iterative procedures are required. Model simulations show that the size of the H2/CO2 fed gas-lift reactors aimed at biological sulfate removal from wastewater highly depends on the number and type of trophic groups growing in the bioreactor. In particular, if the biological sulfate reduction is performed in a bioreactor where ASRB prevail, the required bioreactor volume is much smaller than that needed with HSRB. This is because ASRB can out-compete methanogenic archarea (MA) for H2 (assuming sulfate concentrations are not limiting), whereas HSRB do not necessarily out-compete MA due to their dependence on homoacetogenic bacteria (HB) for organic carbon. The reactor sizes to reach the same sulfate removal efficiency by HSRB and ASRB are only comparable when methanogenesis is inhibited. Moreover, model results indicate that acetate supply to the reactor influent does not affect the HSRB biomass required in the reactor, but favours the dominance of MA on HB as a consequence of a lower HB requirement for acetate supply

Towards the Modeling of Neuronal Firing by Gaussian Processes

This paper focuses on the outline of some computational methods for the approximate solution of the integral equations for the neuronal firing probability density and an algorithm for the generation of sample-paths in order to construct histograms estimating the firing densities. Our results originate from the study of non-Markov stationary Gaussian neuronal models with the aim to determine the neuron's firing probability density function. A parallel algorithm has been implemented in order to simulate large numbers of sample paths of Gaussian processes characterized by damped oscillatory covariances in the presence of time dependent boundaries. The analysis based on the simulation procedure provides an alternative research tool when closed-form results or analytic evaluation of the neuronal firing densities are not available.Comment: 10 pages, 3 figures, to be published in Scientiae Mathematicae Japonica

Validating DLO models from shape observation

In this paper, the problem of fitting the model of deformable linear objects from the observation of the shape under the effect of known external forces like gravity is taken into account. The model of the deformable linear object is based on dynamic splines, allowing to obtain a reliable prediction of the object behavior while preserving a suitable efficiency and simplicity of the model. The object shape is measured by means of a calibrated vision system, and a fitting between the observed shape and the theoretical model is defined for validation. Experiments are executed in different conditions, showing the reliability of the proposed spline-based model

Fractional queues with catastrophes and their transient behaviour

Starting from the definition of fractional M/M/1 queue given in the reference by Cahoy et al. in 2015 and M/M/1 queue with catastrophes given in the reference by Di Crescenzo et al. in 2003, we define and study a fractional M/M/1 queue with catastrophes. In particular, we focus our attention on the transient behaviour, in which the time-change plays a key role. We first specify the conditions for the global uniqueness of solutions of the corresponding linear fractional differential problem. Then, we provide an alternative expression for the transient distribution of the fractional M/M/1 model, the state probabilities for the fractional queue with catastrophes, the distributions of the busy period for fractional queues without and with catastrophes and, finally, the distribution of the time of the first occurrence of a catastrophe

Proximity sensor for thin wire recognition and manipulation

In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-of-Flight pre-touch sensor, integrated with a previously designed tactile sensor, for an accurate estimation of thin wire pose and shape. The paper presents the design and the characterization of the proposed sensor. Moreover, a specific object scanning and shape detection algorithm is presented. Experimental results support the proposed methodology, showing good performance. Hardware design and software applications are freely accessible to the reader

Tactile sensor data interpretation for estimation of wire features

At present, the tactile perception is essential for robotic applications when performing complex manipulation tasks, e.g., grasping objects of different shapes and sizes, distinguishing between different textures, and avoiding slips by grasping an object with a minimal force. Considering Deformable Linear Object manipulation applications, this paper presents an efficient and straightforward method to allow robots to autonomously work with thin objects, e.g., wires, and to recognize their features, i.e., diameter, by relying on tactile sensors developed by the authors. The method, based on machine learning algorithms, is described in-depth in the paper to make it easily reproducible by the readers. Experimental tests show the effectiveness of the approach that is able to properly recognize the considered object’s features with a recognition rate up to 99.9%. Moreover, a pick and place task, which uses the method to classify and organize a set of wires by diameter, is presented

Vision-Based Robotic Solution for Wire Insertion with an Assigned Label Orientation

This paper tackles the problem of wire insertion in switchgear assembly according to the current regulations. In particular, the wire connections require that the wire label has to be oriented facing up in order to simplify and speed up testing and maintenance of the switchgear. The proposed approach exploits the a priori knowledge of the scenario with a calibrated RGB camera and a robotic arm to estimate both wire end pose and label position. The procedure combines several techniques (gradient base, trained classifier and stereo vision) to elaborate standard images in order to extract some wire features related to its shape and label. Specific frames are fixed according to estimated features and then used to correctly complete the task by using a robotic system. Experiments are reported to verify the effectiveness of the proposed approach

Wire Grasping by Using Proximity and Tactile Sensors

Nowadays robots have to be able to perform increasingly complex tasks. In grasping and manipulation, the knowledge of the environment and the pose of the target object are crucial for the correct execution of the task. Vision systems are widely used for environment and object perception, but they need to be finely calibrated to obtain high accuracy and they are not able to sense small objects like thin wires. Tactile sensors could be used to explore areas close to the target object, but this 'blind' physical interaction is not always feasible. This paper proposes a strategy to use a proximity sensor mounted on the robot's end effector to obtain a pose estimation of the target object that, in this study, is represented by a thin electrical wire