Search results for " artificial intelligence"
showing 10 items of 1992 documents
Combinatorial Double Auction Radio Resource Allocation Model in Crowd Networks
2018
International audience; Industrial Partners (IPs) with Mobile Network Operators (MNOs) are extending the mobile network infrastructure with Small Cells (SCs) in order to meet the growing mobile traffic demand. Due to the increasing number of telecommunication market competitors and the scarcity of radio resources, static sharing schemes are no more efficient. New dynamic schemes should be considered to meet both user expectations and economic success. In a crowd networking context, we propose in this work a dynamic radio resource scheme based on combinatorial double auctions. The participants in these auctions are the MNOs considered as buyers and the IPs, providers of SCs, considered as se…
Innovation in the Rural Areas and the Linkage with the Quintuple Helix Model
2016
Abstract In this paper we analyze some specific conditions for local development. Our interest is oriented towards a multidimensional aspect of peripheral and rural areas. The rural areas considered as a productive system reflects a strong relationship between the agriculture and the other economic activities, In addition eco-systems must be protected and enhanced to develop innovation models that propose new roles and responsibilities for a new development vision. Following the implementation of the Smart Specialization Strategy and the Quintuple Helix Model this paper underlines the importance of connecting the innovation process with rural territories. We have considered some environment…
Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data
2019
Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based …
Towards Understanding Startup Product Development as Effectual Entrepreneurial Behaviors
2017
With the rapid development of technology and competitiveness of IT sectors, the speed of learning and evolving is vital for success of software startups. However, software startups often face with multiple technical and business challenges, which lengthen the duration of their idea-to-launch process. Little is known about the relation of entrepreneurial characteristics of software startups and their product development. We conducted an empirical study on twenty software startups to understand their challenges that leads long idea-to-launch processes. Six engineering-related challenges were identified and interpreted via a lens of an entrepreneurial behavior theory. Our main finding is that …
An entropy-based machine learning algorithm for combining macroeconomic forecasts
2019
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.
Experimental Validation of a Novel Method for Harmonic Mitigation for a Three-Phase Five-Level Cascaded H-Bridges Inverter
2019
In modern high-power electrical drives, the efficiency of the system is a crucial constraint. Moreover, the efficiency of power converters plays a fundamental role in modern applications requiring also a limited weight, such as the electric vehicles and novel more electric aircraft. The reduction of losses pushes for systems with a dc bus and a high number of dc/ac converters, widespread in the vehicle, not burdened by a too expensive data processing system. The purpose of this article is to concur to reduce losses by proposing an innovative selective harmonic mitigation method based on the identification of the working areas where the reference harmonics present lower amplitudes. In partic…
Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
2018
The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at differ…
Industry 4.0: Advanced digital solutions implemented on a close power loop test bench
2021
Abstract The paradigm of Industry 4.0 allows to increase the efficiency and effectiveness of the production. Companies that will implement advanced solutions in production systems will increase their level of competitiveness and will be able reach high market shares. The present paper is focused on the development of advanced digital solutions to be implemented on a close power loop test bench designed to test high power transmissions for naval unit. In particular, the test configuration consists of a back-to-back connection between two identical mechanical reducers. Since the efficiency of these systems are very high, it is not necessary to use large electric motors, thus managing to conta…
Training Artificial Neural Networks With Improved Particle Swarm Optimization
2020
Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is util…
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
2018
Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, …