Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.

Force measurement capability for robotic assisted minimally invasive surgery systems

An automated laparoscopic instrument capable of non-invasive measurement of tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems_ is introduced in this paper. It has the capability to measure normal grasping forces as well as lateral interaction forces without any sensor mounted on the tip jaws. Further to non-invasive actuation of the tip, the proposed instrument is also able to change the grasping direction during surgical operation. Modular design of the instrument allows conversion between surgical modalities (e.g., grasping, cutting, and dissecting). The main focus of this paper is on evaluation of the grasping force capability of the proposed instrument. The mathematical formulation of fenestrated insert is presented and its non-linear behaviour is studied. In order to measure the stiffness of soft tissues, a device was developed that is also described in this paper. Tissue characterisation experiments were conducted and results are presented and analysed here. The experimental results verify the capability of the proposed instrument in accurately measuring grasping forces and in characterising artificial tissue samples of varying stiffness.

Underwater swarm robotics consensus control

The control of a swarm of underwater robots requires more than just a control algorithm, it requires a communications system. Underwater communications is difficult at the best of times and so large time delays and minimal information is a concern. The control system must be able to work on minimal and out of date information. The control system must also be able to control a large number of robots without a master control, a decentralized control approach. This paper describes one such control method.

Design of an angular radial robotic stingray

The propeller is the primary propulsion method for underwater vehicles. It is relatively simple to implement and generally uses rotational motion from the drive through to the propeller. However, it is difficult to seal a high speed propeller shaft from water ingress. As an alternative we can look at nature's own underwater inhabitants and study their locomotive methods.

A progress report on a prospective randomised trial of open and robotic prostatectomy

A randomised trial of robotic and open prostatectomy commenced in October 2010 and is progressing well. Clinical and quality of life outcomes together with economic costs to individuals and the health service are being examined critically to compare outcomes.

An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm

The Levenberg Marquardt (LM) algorithm is one of the most effective algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt algorithm by training the ANNs with meta-heuristic nature inspired algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) algorithm and other hybrid variants. Based on the experimental result, the proposed algorithms APSO_LM successfully demonstrated better performance as compared to other existing algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence.

Effects of realistic force feedback in a robotic assisted minimally invasive surgery system

Robotic assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic procedures but also restore the surgeon's hand-eye coordination and improve the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Several research efforts have already attempted to restore this feature and study the effects of force feedback in robotic systems. The proposed methods and studies have some shortcomings. The main focus of this research is to overcome some of these limitations and to study the effects of force feedback in palpation in a more realistic fashion.

Neural network training by hybrid accelerated cuckoo particle swarm optimization algorithm

Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird migrates by traversing more search space. It can successfully search better solutions by performing levy flight with APSO. In the proposed Hybrid Accelerated Cuckoo Particle Swarm Optimization (HACPSO) algorithm, the communication ability for the cuckoo birds have been provided by APSO, thus making cuckoo bird capable of searching for the best nest with better solution. Experimental results are carried-out on benchmarked datasets, and the performance of the proposed hybrid algorithm is compared with Artificial Bee Colony (ABC) and similar hybrid variants. The results show that the proposed HACPSO algorithm performs better than other algorithms in terms of convergence and accuracy.

Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

 Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) array’s efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection. VC 2014 Author(s).

Robotic grasping and manipulation controller framework: architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.

Soft tissue characterisation using a force feedback-enabled instrument for robotic assisted minimally invasive surgery systems

An automated laparoscopic instrument capable of non-invasivemeasurement of tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems is introduced in this chapter. It has the capability to measure normal grasping forces as well as lateral interaction forces without any sensor mounted on the tip jaws. Further to non-invasive actuation of the tip, the proposed instrument is also able to change the grasping direction during surgical operation. Modular design of the instrument allows conversion between surgical modalities (e.g., grasping, cutting, and dissecting). The main focus of thispaper is on evaluation of the grasping force capability of the proposed instrument. The mathematical formulation of fenestrated insert is presented and its non-linear behaviour is studied. In order to measure the stiffness of soft tissues, a device was developed that is also described in this chapter. Tissue characterisation experiments were conducted and results are presented and analysed here. The experimental results verify the capability of the proposed instrument in accurately measuring grasping forces and in characterising artificial tissue samples of varying stiffness.