On-Line Monitoring and Intelligent Diagnosis Technology of Rail Transit Ventilation System

With the rapid development of economy and urbanization, subway has gradually become the main pillar of urban development. The ventilation system is the key guarantee of air quality in rail transit, and its condition monitoring and intelligent diagnosis are very important. The core problems of the complete set of a ventilation system required by the subway station have not been completely solved. The ventilation system includes the ventilator and additional equipment. The level of informatization and intelligence of the ventilation system and ventilator is not very high, and they have not yet been fully formed into an integrated diagnostic system. In view of the above two core issues, several scientific issues need to be tackled. This chapter studies the online monitoring and intelligent diagnosis mechanism of key equipment in the subway ventilation system. This mainly includes (1) modulation model of acoustic vibration signal; (2) noise reduction technology and feature extraction method; and (3) cases of multi-type typical fault identification fan equipment based on modulation model. Typical fault features were extracted respectively, which verified the effectiveness of the signal demodulation method for the diagnosis of rail transit ventilation systems

ITERL: A Wireless Adaptive System for Efficient Road Lighting

This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.Consejería de Fomento y Vivienda Junta de Andalucía G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI

The concept of energy traceability: Application to EV electricity charging by Res

The energy sustainability, in the era of sources diversification , can be guaranteed by an energy resources utilization most correct, foreseeing no predominance of one source over the others in any area of the world but a proper energy mix, based on locally available resources and needs. In this scenario, manageable with a smart grid system, a virtuous use of RES must be visible, recognizable and quantifiable, in one word traceable. The innovation of the traceability concept consists in the possibility of having information concerning the exact origin of the electricity used for a specific end use, in this case EV charging . The traceability, in a context of increasingly sustainability and smartness city, is an important develop tool because only in this way it is possible to quantify the real emissions produced by EVs and to ensure the real foresight of grid load. This paper wants investigate the real ways to introduce this kind of real energy accounting, through the traceabilit

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification systemComment: 18 page

Starry Messages: Searching for Signatures of Interstellar Archaeology

Searching for signatures of cosmic-scale archaeological artifacts such as Dyson spheres or Kardashev civilizations is an interesting alternative to conventional SETI. Uncovering such an artifact does not require the intentional transmission of a signal on the part of the original civilization. This type of search is called interstellar archaeology or sometimes cosmic archaeology. The detection of intelligence elsewhere in the Universe with interstellar archaeology or SETI would have broad implications for science. For example, the constraints of the anthropic principle would have to be loosened if a different type of intelligence was discovered elsewhere. A variety of interstellar archaeology signatures are discussed including non-natural planetary atmospheric constituents, stellar doping with isotopes of nuclear wastes, Dyson spheres, as well as signatures of stellar and galactic-scale engineering. The concept of a Fermi bubble due to interstellar migration is introduced in the discussion of galactic signatures. These potential interstellar archaeological signatures are classified using the Kardashev scale. A modified Drake equation is used to evaluate the relative challenges of finding various sources. With few exceptions interstellar archaeological signatures are clouded and beyond current technological capabilities. However SETI for so-called cultural transmissions and planetary atmosphere signatures are within reach.Comment: 29 pages including 4 figures and 1 tabl

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig