Length-weight relationships of Cuban marine fishes

A total of 140 sets of parameters (a and b) of the length-weight relationships (LWR) of the form W=aL super(b) are presented for fishes caught in Cuban waters. These parameters cover 94 species of fish belonging to 43 families. Most of the parameters were compiled from 107 sets of published and unpublished studies. Twenty-five sets of parameters were from personal communications through colleagues in Cuba, while the remaining eight sets were estimated by the authors from unpublished data

Length-weight relationships of Cuban marine fishes

Length-weight relationships, Marine fish, Cuba,

A multi-MHz wireless power transfer system with mains power factor correction circuitry on the receiver

This paper proposes the implementation of a new system topology for multi-MHz inductive power transfer (IPT) systems, which achieves unity power factor when fed from a mains power supply without traditional active circuitry in the front-end as a mains interface. Experiments were performed using an IPT-link which consists of two 20 cm two-turn air-core printed-circuit-board (pcb) coils separated by an air-gap of 13 cm. At the transmit side, a push-pull load-independent Class EF inverter fed from a rectified 60 Hz power supply with no bulk capacitor was designed to drive the transmit coil at 13.56 MHz. This inverter, which has two choke inductors between the voltage source and the two switches, similar to that of an interleaved boost converter, is suitable to be fed directly from a rectified mains source because it tolerates large changes on the input voltage. The IPT rectifier in the experiments was built using a dual current-driven Class D-based topology which allows for higher output voltage when the induced electromotive force (emf) on the receive coil is low. The final power conversion stage on the receive side is a power factor correction (PFC) boost converter that regulates the output voltage and shapes the current waveform at the input of the system. This stage is the only part of the system with closed-loop control. The end-to-end efficiency was measured at 73.3% with 99.2% power factor, when powering a load of 150 W

Probability-based optimisation for a multi-MHz IPT system with variable coupling

This paper presents the analysis and design of a dynamic inductive power transfer (IPT) system, in which coupling is treated as a stochastic variable and is therefore modelled as a probability distribution. The purpose of this formulation is to optimise the tuning of the inverter and the rectifier to the coupling value that achieves the highest charging energy-efficiency when operating at a broad range of coupling. The analysis is supported by a case study in which two rectifier designs, using the hybrid Class E topology, are tuned at different coupling values in order to verify which version achieves the highest charging efficiency. The load in the experiments is a wirelessly powered drone without a battery hovering randomly over the charging pad, and the range of motion is set by a nylon string tether. The experiments show lower energy consumption when the rectifier is tuned to present the optimal load of the link at the coupling value with the highest probability, as opposed to the first, which was designed to present the optimal load of the link at minimum coupling

Implementation and development of traffic speed and flow prediction through Artificial Neural Networks

In this work we introduce the most recent techniques to predict traffic flow and speed. This work is composed of the following sections: an introduction, a state of the art, and conclusions sections. In the introduction section we see the importance of being able to predict the traffic conditions for speed and flow; we set our hypothesis that is that using the Levenberg-Marquardt training algorithm we’ll be able to find a global minimum for the problem of predicting traffic conditions; we also specify our general objective that is developing efficient algorithms for the traffic prediction for its variables speed and flow; as well as establishing that our scientifical novelty is using the Levenberg-Marquardt as Artificial Neural Network training algorithm. In the state of the art section we present the summarized contents of the most outstanding research papers about traffic prediction. We continue with the presentation of an approach that uses two statistical algorithms for traffic prediction. This information cover spatial impact, and accidents, and construction events. Additionally, it compares the results of outstanding research done with Artificial Neural Networks, and tatistical Methods, to their own statistical method that consists of two statistical methods embedded into only one by using a threshold used to determine which statistical method should be used and when, depending on the road conditions. We also present the results of traffic speed prediction by data mining techniques and a comparative to Artificial Neural Networks. Additionally, we introduce a section that analyze the problem of traffic prediction but only with Artificial Neural Networks done by the company SIEMENS in Germany. We introduce the Los Angeles Department of Transportation (LA DOT) infrastructure used to obtain the speed and flow measurements as well as the set of programs develop by the author of this thesis to retrieve the information from LA DOT, to process this information and calculate the prediction of flow and speed for a specific sensor. We continue with the problem of traffic prediction. In the process of predicting traffic flow, and speed we made our first approach using a Nonlinear Autoregressive Neural Network with External Input in MATLAB and we obtained promising results. However, this Artificial Neural Network does not have the ability to predict multiple outputs, and we transformed it to a Feedforward Neural Network also from MATLAB. The results obtained are impressive because they reduce dramatically the traffic prediction errors. In order to validate our results with the ones in the international research community we use the data from 95 days which is equivalent to 3 months, which is the commonly reported amount of time studied in this kind of problems. We also present an optimization process for the Feedforward Neural Network for both problems speed and flow prediction. Finally, we present a numerical sensibility analysis in order to determine how robust is our Artificial Neural Network. To close this thesis we present our conclusions as a success of using the Levenberg-Marquardt algorithm to train an Artificial Neural Network for the problem of traffic prediction, and we set up the possibilities of exploring the recently found result by using the learning techniques of deep learning.Consejo Nacional de Ciencia y TecnologíaContinental Guadalajara Services S.A. de C.

Dynamic capabilities of multi-MHz inductive power transfer systems demonstrated with batteryless drones

This paper presents the design of a multi-MHz inductive power transfer (IPT) system showcasing lightweight and energy-efficient solutions for non-radiative wireless power transfer. A proof of concept is developed by powering a drone without a battery that can hover freely in proximity to an IPT transmitter. The most challenging aspect, addressed here for the first time, is the complete system level design to provide uninterrupted power-flow efficiently while allowing for variable power demand and highly variable coupling factor. The proposed solution includes the design of lightweight air-core coils that can achieve sufficient coupling without degrading the aerodynamics of the drone, and designing newly-developed resonant power converters at both ends of the system. At the transmittingend, a load-independent Class EF inverter, which can drive a transmitting-coil with constant current amplitude and achieves zero-voltage switching (ZVS) for the entire range of operation, was developed; and at the receiving-end, a hybrid Class E rectifier, which allows tuning for large changes in coupling and power demand, was used. For the demo, the range of motion of the drone was limited by a 7.5 cm nylon string tether, connected between the centre of the transmitting-coil and the bottom of the drone. The design of the IPT system, including all the power conversion stages and the IPT link, is explained in detail. The results on performance and specific practical considerations required for the physical implementation are provided. An average end-to-end efficiency of 60% was achieved for a coupling range of 23% to 5.8%. Relevant simulations concerning human exposure to electromagnetic fields are also included to assure that the demo is safe according to the relevant guidelines. This paper is accompanied by a video featuring the proposed IPT system