An information-theoretic and dissipative systems approach to the study of knowledge diffusion and emerging complexity in innovation systems

The paper applies information theory and the theory of dissipative systems to discuss the emergence of complexity in an innovation system, as a result of its adaptation to an uneven distribution of the cognitive distance between its members. By modelling, on one hand, cognitive distance as noise, and, on the other hand, the inefficiencies linked to a bad flow of information as costs, we propose a model of the dynamics by which a horizontal network evolves into a hierarchical network, with some members emerging as intermediaries in the transfer of knowledge between seekers and problem-solvers. Our theoretical model contributes to the understanding of the evolution of an innovation system by explaining how the increased complexity of the system can be thermodynamically justified by purely internal factors. Complementing previous studies, we demonstrate mathematically that the complexity of an innovation system can increase not only to address the complexity of the problems that the system has to solve, but also to improve the performance of the system in transferring the knowledge needed to find a solution

On the Inclusion of Temperature in the Friction Model of Industrial Robots

This paper deals with a modelling technique that takes into account the effects of the temperature in the joint friction of industrial robot manipulators. In particular, it is shown that a general friction model can be suitably modified by explicitly considering the temperature as a parameter. This allows to estimate the friction term accurately in different operating conditions without the direct measurement of the joint internal temperature, which makes the overall technique suitable to apply in practical cases. Experimental results show the effectiveness of the methodology

Modelling the temperature in joint friction of industrial manipulators

In this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed

Modelling the temperature in joint friction of industrial manipulators

In this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed

A Fast Autotuning Method for Velocity Control of Mechatronic Systems

In this paper a fast automatic tuning methodology for velocity controllers of mechatronic systems is proposed. In order to be applicable in general, the method takes into account the position, velocity and torque constraints of the motion control system and it requires a minimum intervention of the operator. Further, it can be implemented also with small computational capabilities which makes it suitable for industrial drives. Simulation results show the effectiveness of the technique

Future Urban Scenes Generation Through Vehicles Synthesis

In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset

CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components

Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images. Despite their efficiency, NeRF models can pose challenges in certain scenarios such as vehicle inspection, where the lack of sufficient data or the presence of challenging elements (e.g. reflections) strongly impact the accuracy of the reconstruction. To this aim, we introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques. The dataset is publicly released at https://aimagelab.ing.unimore.it/go/carpatch and can be used as an evaluation guide and as a baseline for future work on this challenging topic.Comment: Accepted at ICIAP202

A novel bioluminescent NanoLuc yeast-estrogen screen biosensor (nanoYES) with a compact wireless camera for effect-based detection of endocrine-disrupting chemicals

The presence of chemicals with estrogenic activity in surface, groundwater, and drinking water poses serious concerns for potential threats to human health and aquatic life. At present, no sensitive portable devices are available for the rapid monitoring of such contamination. Here, we propose a cell-based mobile platform that exploits a newly developed bioluminescent yeast-estrogen screen (nanoYES) and a low-cost compact camera as light detector. Saccharomyces cerevisiae cells were genetically engineered with a yeast codon-optimized variant of NanoLuc luciferase (yNLucP) under the regulation of human estrogen receptor α activation. Ready-to-use 3D-printed cartridges with immobilized cells were prepared by optimizing a new procedure that enables to produce alginate slices with good reproducibility. A portable device was obtained exploiting a compact camera and wireless connectivity enabling a rapid and quantitative evaluation (1-h incubation at room temperature) of total estrogenic activity in small sample volumes (50 μL) with a LOD of 0.08 nM for 17β-estradiol. The developed portable analytical platform was applied for the evaluation of water samples spiked with different chemicals known to have estrogen-like activity. Thanks to the high sensitivity of the newly developed yeast biosensor and the possibility to wireless connect the camera with any smartphone model, the developed configuration is more versatile than previously reported smartphone-based devices, and could find application for on-site analysis of endocrine disruptors. [Figure not available: see fulltext.]

On the use of a temperature based friction model for a virtual force sensor in industrial robot manipulators

In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted

Extracting Accurate Long-Term Behavior Changes from a Large Pig Dataset

Visual observation of uncontrolled real-world behavior leads to noisy observations, complicated by occlusions, ambiguity, variable motion rates, detection and tracking errors, slow transitions between behaviors, etc. We show in this paper that reliable estimates of long-term trends can be extracted given enough data, even though estimates from individual frames may be noisy. We validate this concept using a new public dataset of approximately 20+ million daytime pig observations over 6 weeks of their main growth stage, and we provide annotations for various tasks including 5 individual behaviors. Our pipeline chains detection, tracking and behavior classification combining deep and shallow computer vision techniques. While individual detections may be noisy, we show that long-term behavior changes can still be extracted reliably, and we validate these results qualitatively on the full dataset. Eventually, starting from raw RGB video data we are able to both tell what pigs main daily activities are, and how these change through time