Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.

Performance comparison of the optimal and the zero-forcing beamforming algorithms under practical conditions

The optimal and the zero-forcing beamformers are two commonly used algorithms in the subspace-based blind beamforming technology. The optimal beamformer is regarded as the algorithm with the best output SINR. The zero-forcing algorithm emphasizes the co-channel interference cancellation. This paper compares the performance of these two algorithms under some practical conditions: the effect of the finite data length and the existence of the angle estimation error. The investigation reveals that the zero-forcing algorithm can be more robust in the practical environment than the optimal algorithm.

Second meal effect: modified sham feeding does not provoke the release of stored triacylglycerol from a previous high-fat meal

The present study was carried out to determine whether cephalic stimulation, associated with eating a meal, was sufficient stimulus to provoke the release of stored triacylglycerol (TAG) from a previous high-fat meal. Ten subjects were studied on three separate occasions. Following a 12 h overnight fast, subjects were given a standard mixed test meal which contained 56 g fat. Blood samples were taken before the meal and for 5 h after the meal when the subjects were randomly allocated to receive either water (control) or were modified sham fed a low-fat (6 g fat) or moderate-fat (38 g fat) meal. Blood samples were collected for a further 3 h. Compared with the control, modified sham feeding a low- or moderate-fat meal did not provoke an early entry of TAG, analysed in either plasma or TAG-rich lipoprotein (TRL) fraction (density ,1´006 kg/l). The TRL-retinyl ester data showed similar findings. A cephalic phase secretion of pancreatic polypeptide, without a significant increase in cholecystokinin levels, was observed on modified sham feeding. Although these data indicate that modified sham feeding was carried out successfully, analysis of the fat content of the expectorant showed that our subjects may have accidentally ingested a small amount of fat (0´7 g for the low-fat meal and 2´4 g for the moderate-fat meal). Nevertheless, an early TAG peak following modified sham feeding was not demonstrated in the present study, suggesting that significant ingestion of food, and not just orosensory stimulation, is necessary to provoke the release of any TAG stored from a previous meal.

Prolonged effects of modified sham feeding on energy substrate mobilization

Background: Vagal stimulation in response to nutrients is reported to elicit an array of digestive and endocrine responses, including an alteration in postprandial lipid metabolism. Objective: The objective of this study was to assess whether neural stimulation could alter hormone and substrate metabolism during the late postprandial phase, with implications for body fat mobilization. Design: Vagal stimulation was achieved by using the modified sham feeding (MSF) technique, in which nutrients are chewed and tasted but not swallowed. Ten healthy subjects were studied on 3 separate occasions, 4 wk apart. Five hours after a high-fat breakfast (56 g fat), the subjects were given 1 of 3 test meals allocated in random order: water, a lunch containing a modest amount of fat (38 g), or MSF (38 g fat). Blood was collected for 3 h poststimulus for hormone and metabolite analyses. Results: Plasma insulin and pancreatic polypeptide concentrations peaked at 250% and 209% of baseline concentrations within 15 min of MSF. The plasma glucose concentration increased significantly (P = 0.038) in parallel with the changes observed in the plasma insulin concentration. The nonesterified fatty acid concentration was significantly suppressed (P = 0.006); maximum suppression occurred at a mean time of 114 min after MSF. This fall in nonesterified fatty acid was accompanied by a fall in the plasma glucagon concentration from 122 to 85 pmol/L (P = 0.018) at a mean time of 113 min after MSF. Conclusions: Effects on substrate metabolism after MSF in the postprandial state differ from those usually reported in the postabsorptive state. The effects of MSF were prolonged beyond the period of the cephalic response and these may be relevant for longer-term metabolic regulation.

Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

Genetic algorithms for optimal control of beer fermentation

This paper uses genetic algorithms to optimise the mathematical model of a beer fermentation process that operates in batch mode. The optimisation is based in adjusting the temperature profile of the mixture during a fixed period of time in order to reach the required ethanol levels but considering certain operational and quality restrictions.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.

Evaluation of two "integrated" polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band

Two so-called “integrated” polarimetric rate estimation techniques, ZPHI (Testud et al., 2000) and ZZDR (Illingworth and Thompson, 2005), are evaluated using 12 episodes of the year 2005 observed by the French C-band operational Trappes radar, located near Paris. The term “integrated” means that the concentration parameter of the drop size distribution is assumed to be constant over some area and the algorithms retrieve it using the polarimetric variables in that area. The evaluation is carried out in ideal conditions (no partial beam blocking, no ground-clutter contamination, no bright band contamination, a posteriori calibration of the radar variables ZH and ZDR) using hourly rain gauges located at distances less than 60 km from the radar. Also included in the comparison, for the sake of benchmarking, is a conventional Z = 282R1.66 estimator, with and without attenuation correction and with and without adjustment by rain gauges as currently done operationally at Météo France. Under those ideal conditions, the two polarimetric algorithms, which rely solely on radar data, appear to perform as well if not better, pending on the measurements conditions (attenuation, rain rates, …), than the conventional algorithms, even when the latter take into account rain gauges through the adjustment scheme. ZZDR with attenuation correction is the best estimator for hourly rain gauge accumulations lower than 5 mm h−1 and ZPHI is the best one above that threshold. A perturbation analysis has been conducted to assess the sensitivity of the various estimators with respect to biases on ZH and ZDR, taking into account the typical accuracy and stability that can be reasonably achieved with modern operational radars these days (1 dB on ZH and 0.2 dB on ZDR). A +1 dB positive bias on ZH (radar too hot) results in a +14% overestimation of the rain rate with the conventional estimator used in this study (Z = 282R^1.66), a -19% underestimation with ZPHI and a +23% overestimation with ZZDR. Additionally, a +0.2 dB positive bias on ZDR results in a typical rain rate under- estimation of 15% by ZZDR.

Modelling niches of arbitrary shape in genetic algorithms using Niche Linkage in the Dynamic Niche Clustering framework

We present some additions to a fuzzy variable radius niche technique called Dynamic Niche Clustering (DNC) (Gan and Warwick, 1999; 2000; 2001) that enable the identification and creation of niches of arbitrary shape through a mechanism called Niche Linkage. We show that by using this mechanism it is possible to attain better feature extraction from the underlying population.