The influence of the Agulhas Current on the adjacent coastal ocean: possible impacts of climate change

The dynamics of the coastal ocean along the southeastern coast of Africa is dominated by a strong and intense western boundary current, the Agulhas Current. With a near-uniform, narrow continental shelf and a steep shelf slope that stabilizes this current, the trajectory of the Agulhas exhibits great stability. The only substantial perturbation occurs with the irregular passage of a Natal Pulse, a soliton meander. The initiation of this meander at the Natal Bight is due to a barotropic instability when the intensity of the landward border of the current exceeds a certain threshold value. This may come about with natural fluctuations in the current or with the adsorption of deep-sea eddies onto the current. Under a climate change scenario of altered wind stress curl over the South Indian Ocean it is conceivable that the threshold for the triggering of a Natal Pulse will occur more frequently. This will lead to a situation where the current axis on average lies further offshore. The possible consequences of such a situation on the rainfall of the coast, on the ecology of estuaries and the coastal ocean, and on the socio-economics of the region is discussed.     

License plate extraction method for identification of vehicle violations at a railway level crossing

The primary cause of most railroad accidents is vehicle entry into railway level crossings despite warning messages. To identify drivers who violate railway level crossing regulations, vehicle license plate recognition can be applied at railway level crossings. The purpose of this paper is to present an effective method for extracting the license plate region from vehicle images taken at railway level crossings. The method proposed in this paper uses the variation in the gray-level values across the image of a license plate. For license plate region extraction, the character region is first recognized by identifying the character width and the difference between the background region and the character region. The license plate region is then extracted by finding the inter-character distance in the plate region. In addition, the license plate type is identified by the difference in the gray-level value between the background region and the character region. The proposed method is effective in solving the current challenges in extracting the license plate region from the damaged frames of license plates issued for domestic use, including new types of license plates. According to the experimental results, the proposed method yields a high extraction rate of 99.5% for vehicle license plates.     

Torque characteristics analysis for optimal design of a copper-layered eddy current brake system

An enhanced parametric model for a copper-layered eddy current electric machine (retarder) is introduced in this paper. The modeled torque characteristics of the copper-layered electromagnetic retarders are based on the results from a detailed electromagnetic finite element analysis (FEA) of these eddy current machines. The model uses a parameterized double-exponential function to model the steady state speed-torque characteristics of the retarder. The parameters are adjusted for optimal braking performance in conjunction with predicted speed-torque characteristics of a copper-layered retarder. A full vehicle model, along with the proposed retarder speed-torque model has been used to simulate a series braking events. The simulation results show that the peaks of the retarder speed-torque curves must be designed to occur within a specific range of speeds for optimal braking performance.     

Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.     

Conditional modelling of tanker market risk using route specific freight rates

This paper provides statistical evidence in support of the view, widely held in the tanker industry, that there are systematic differences in the degree of risk involved in investing in tankers of different sizes, and in operating tankers in spot and time charter markets. The industry view, broadly supported by the results of this paper, is that larger vessels are 'risker' assets than smaller vessels, and operating vessels in the time-charter market is less risky than employing them on a spot basis. The results are obtained by using a method derived from the financial economics literature, which models both the conditional mean and variance of a variable, known as GARCH modelling. Only one other paper has applied this method to the tanker market, and these results provide confirmatory support of those findings.