Validation of a context analysis method for microRNA data

We have previously presented a data-oriented approach to the study of microRNA-gene interactions, a hot topic of research in molecular biology, building heavily on methods from the document analysis field. This paper evaluates the performances of the method by means of a cross-validation approach. Latent information can be effectively exploited to suggest directions for laboratory experiments, an important topic in microRNA research, since these experiments are costly in both resources and time

Layered ensemble model for short-term traffic flow forecasting with outlier detection

YesReal time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems

Redundancy in sensors, control and planning of a robotic system for space telerobotics

The analysis and development of a manipulator redundant in structure and sensor devices controlled by a distributed multiprocessor architecture are discussed. The goal has been the realization of a modular structure of the manipulator with evident aspects of flexibility and transportability. The distributed control structure, thanks to his modularity and flexibility could be integrated in the future into an operative structure aimed to space telerobotics. The architecture is applied to the 6 DOF manipulator Gilberto

Tracking time evolving data streams for short-term traffic forecasting

YesData streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems

Graded possibilistic clustering of non-stationary data streams

YesMultidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift

A 2D laser rangefinder scans dataset of standard EUR pallets

open5siopenIhab Mohamed, Alessio Capitanelli, Fulvio Mastrogiovanni, Stefano Rovetta, Renato ZaccariaMohamed, Ihab; Capitanelli, Alessio; Mastrogiovanni, Fulvio; Rovetta, Stefano; Zaccaria, RENATO UGO RAFFAEL

A neural-network-based model predictive control of three-phase inverter with an output LC Filter

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output $LC$ filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy

Dynamic Collection Scheduling Using Remote Asset Monitoring: Case Study in the UK Charity Sector

Remote sensing technology is now coming onto the market in the waste collection sector. This technology allows waste and recycling receptacles to report their fill levels at regular intervals. This reporting enables collection schedules to be optimized dynamically to meet true servicing needs in a better way and so reduce transport costs and ensure that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity that services its textile and book donation banks and its high street stores by using a common fleet of vehicles with various carrying capacities. Use of a common fleet gives rise to a vehicle routing problem in which visits to stores are on fixed days of the week with time window constraints and visits to banks (fitted with remote fill-monitoring technology) are made in a timely fashion so that the banks do not become full before collection. A tabu search algorithm was developed to provide vehicle routes for the next day of operation on the basis of the maximization of profit. A longer look-ahead period was not considered because donation rates to banks are highly variable. The algorithm included parameters that specified the minimum fill level (e.g., 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4%, with the best performance obtained when the donation rates were more variable

A preliminary spectroscopic approach to evaluate the effectiveness of water-and silicone-based cleaning methods on historical varnished brass

Soiling deposition and wrong conservation practices are among the causes of the decay process that can affect the morphological, mechanical, and compositional features of the varnish, the most exposed layer of an artefact. In this perspective, the identification of the best cleaning practices is a priority. During the 18th century, scientific instruments of the highest quality were built, and peculiar varnishes were produced to confer protection and elegance to their metal elements. For this study, based on a historical recipe, we have reproduced a peculiar spirit varnish, enriched with natural resins and colorants, and we have applied on it a synthetic soiling mixture to simulate the aging conditions. We have then performed a non-invasive multi-analytical study to monitor the effectiveness of two water-based and a silicone-based, cleaning methods, namely, water in agarose, Tween 20 (3%) in agarose, and Velvesil Plus. The study includes colorimetry, Fourier transform infrared (FTIR) spectroscopy and X-ray fluorescence (XRF) mapping, coupled with chemometrics. Principal component analysis applied to FTIR spectral data has been demonstrated to be a powerful tool to enhance weak variations in the IR spectra, empowering the interpretation of cleaning effect versus the application time of each cleaning test