Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
An optical fiber liquid level sensor based on two twisted polymer optical fibers twining around a racetrack column is demonstrated\nin this study. The side-coupling power of the passive fiber is modulated by the refractive index (RI) of the environment medium and\ndecreases while the liquid level increases. The variation patterns of the side-coupling power in the bent section and the straight\nsection form a step attenuation, which can improve the measurement range with a superior sensitivity and distinguish liquids\nwith different RIs. Furthermore, the cost-effective sensor also shows good reversibility and low temperature-dependent properties....
Themining industry of the last few decades recognizes that it is more profitable to simulatemodel using historical data and available\nmining process knowledge rather than draw conclusions regarding future mine exploitation based on certain conditions. The\nvariability of the composition of copper leach piles makes it unlikely to obtain high precision simulations using traditional statistical\nmethods; however the same data collection favors the use of softcomputing techniques to enhance the accuracy of copper recovery\nvia leaching by way of prediction models. In this paper, a predictive modeling contrasting is made; a linear model, a quadratic\nmodel, a cubic model, and a model based on the use of an artificial neural network (ANN) are presented. The model entries were\nobtained from operation data and data of piloting in columns. The ANN was constructed with 9 input variables, 6 hidden layers,\nand a neuron in the output layer corresponding to copper leaching prediction.The validation of the models was performed with\nreal information and these results were used by a mining company in northern Chile to improve copper mining processes....
Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion,\nand air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding\nmanagement systems.However, the lack of real-time traffic flow information with a city-wide coverage is amajor limiting factor for\nan optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications,\ndistributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic\nsensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light\nsystem, are used formultilane traffic participant detection and classification. Application of these sensors in time-varying reflective\nenvironments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the\ndissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization\nwith clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and\ndecentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements\nand several European real-world installations....
An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only\nin a short time window, called exposure time.The servers are periodically recovered to their clean state, and any infected servers\nare refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting\ninterest in determining exposure time, short for security and long for performance. In other words, the short exposure time can\nincrease security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo,\nan ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two\ntypes of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then,\nDuo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding\nthe classified traffic toWhite and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of\nservers, Duo can decrease exposure time on average.We have implemented the prototype of Duo and evaluated its performance in\na realistic environment....
In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to\ndiagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions.\nHowever with the great development in automotive industry, it looks feasible today to analyze sensor�s data along with machine\nlearning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of\nvehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in\nfaulty condition (when any failure in specific system has occurred) and in normal condition.The data is transmitted to the server\nwhich analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, ...
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