Mixed Learning- and Model-Based Mass Estimation of Heavy Vehicles

This research utilized long short-term memory (LSTM) to oversee an RLS-based mass estimator based on longitudinal vehicle dynamics for heavy-duty vehicles (HDVs) instead of using the predefined rules. A multilayer LSTM network that analyzed parameters such as vehicle speed, longitudinal acceleration, engine torque, engine speed, and estimated mass from the RLS mass estimator was employed as the supervision method. The supervisory LSTM network was trained offline to recognize when the vehicle was operated so that the RLS estimator gave an estimate with the desired accuracy and the network was used as a reliability flag. High-fidelity simulation software was employed to collect data used to train and test the network. A threshold on the error percentage of the RLS mass estimator was used by the network to check the reliability of the algorithm. The preliminary findings indicate that the reliability of the RLS mass estimator could be predicted by using the LSTM network

Smart plugs: A low cost solution for programmable control of domestic loads

Balancing energy demand and production is becoming a more and more challenging task for energy utilities. This is due to a number of different reasons among which the larger penetration of renewable energies which are more difficult to predict and the meagre availability of financial resources to upgrade the existing power grid. While the traditional solution is to dynamically adapt energy production to follow the time-varying demand, a new trend is to drive the demand itself by means of Direct Load Control (DLC). In this paper we consider a scenario where DLC functionalities are deployed at a large set of small deferrable energy loads, like appliances of residential users. The required additional intelligence and communication capabilities may be introduced through smart plugs, without the need to replace older 'dumb' appliances. Smart plugs are inserted between the appliances plugs and the power sockets and directly connected to the Internet. An open software architecture allows to abstract the hardware sensors and actuators integrated in the plug and to easily program different load control applications

An Indoor and Outdoor Navigation System for Visually Impaired People

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented

Optimal Resource Allocation in Multi-Hop Networks: Contention vs. Scheduling

CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) is actually the most used method in ad-hoc networks for transmitting on a contending medium, even if it shows poor performance in presence of hidden nodes. To increase performance, we propose an algorithm that combines CSMA and TDMA (Time Division Multiple Access) approaches. The adopted solution consists of grouping contending nodes in non-interfering subsets and granting a different numbers of time slots to different groups, while using the CSMA to manage medium access among nodes belonging to the same subset. An optimization procedure to assign the time slots to each subset of nodes and to find an equilibrium between contention and scheduling is presente

Application of model quality evaluation to systems biology

Application of model quality evaluation to the quasispecies models is presented. These models are useful for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. An estimate of the parameters together with their interval of variability is computed and the quality evaluation is tested on the basis of the model prediction error capability

Learning research methods via experience sharing

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