Advanced Materials Research Vols. 403-408

Abstract: Correct skidding time prediction and having precise information about the efficiency of skidding system rescues in a high quality of harvesting in a variable conditions of woodlands. This paper represents one of the Artificial intelligence methods, that is called Artificial Neural Network (ANN), for creating the predicting time model of wheeled skidder Timberjack 450C. The study of components of project has been done by continuous time study method. Effective factors on time of skidding were: skidding distance, skidding slope, winching distance, number of logs, and volume of load. In the present study, the time of 105 skidding cycles were investigated and used as the training data for neural network. The determination coefficient and the root mean square error for the best trained network were, 71% and 0.1778, respectively. The results showed that ANN provides a good accuracy for estimating the time of one cycle skidding time.

Abstract: Data Mining has been applied to the world of industrial process. Through this paper, modeling of such a process, a boiler, is discussed focusing on the two methods of Partial Least Square (PLS) Regression and Neural Networks. In modeling the system behavior, the former has the capability of reducing the database dimension and taking to account the latent relations between data, while the later handles the nonlinearity of the process in order to predict the system response through the database of observed boiler operation data.

Abstract: Decision making can be defined as the selection process of the best cases from the alternatives in order to achieve a goal. Process planning engineers often use their intuition and experience in decision making. Fuzzy set theory has been used since 1970 in decision making process by using human experiences. This paper presents a new approach in the selection method for a Suitable flow meter based on fuzzy set theory for the industrial and automation process. The technical parameters such as turn down ration, power loss, accuracy, installation considerations and etc. with economic aspects, environmental effects, and safety considerations are established with field and laboratory tests together with the determination of other qualitative variables too. Classical method for flow meter selections generally produce a complex situation and take a long period of time. But new proposed method for flow meter selection has enabled users and suppliers to decide more quickly, easily and sensitively.

Abstract: This article contains explanations on how to develop an intelligent agent in war simulation environment by RoboGenius team. The main Focus of the team is on artificial intelligence application in war simulation server [1]. For this purpose we used a combined base code part of which is related to Robotoos team and its general parts are related to UVA2003 in 2D soccer simulation [2]. In this article we analyze the environment from an intelligent agent view point rather than exploring software engineering issues. And, considering the kind of simulation environment we study exploring issues in this environment. Simulation environment has been considered as a semi-visible dynamic environment. Robot decision making problems and priority of its decisions have been explored and implemented by using the decision tree. For the problem of environment exploration in war simulation we have presented a new approach. Also, we have explored the possibility of using artificial intelligence in more developing war simulation agent.

Abstract: Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.

Abstract: Cargo ports operational performance was specified typically through revenue earned, quantum of cargo handled and number of ships serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over pre-berth waiting time of vessels waiting and berthing time of ships at a port. An Indian port’s ship movement and port operational characteristics had been studied for five years (2005-2009). Ship’s service time was the crucial parameter used to quantify the port performance. This paper focused on building an artificial neural network technique based model to illustrate the relationship between service time and port operational characteristics. Validations of ANN model, comparing multiple linear regression model outputs were reported.

Abstract: Due to the advancement of technology, war or civil ships are carrying more and more antennas and sensors, both for entertainment (TV antennas) as well as for everyday tasks (navigation radar antennas, positioning, arms and communication). This article describes a proposal for a digital control system aiming the positioning of a digital TV antenna mounted on a ship. In this case, beside the electrical signal of usual sensors, the data provided by a GPS (Global Positioning System) receiver are utilized to design the control system. Simulation results confirm the feasibility of this approach.

Abstract: In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

Abstract: This paper presents the modeling and simulation of 14-bus system using TCSC and UPFC. Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) are included in 14-bus system to improve the power quality of the power system. The voltage sag is created by adding an extra load at the receiving end. This sag is compensated by using FACTS devices like TCSC and UPFC. Improvement in the voltage and power are presented using simulation studies.

Abstract: This paper presents 250W, 20 KHz LCL resonant inverter having Efficiencies greater than 95% were obtained down to resistive loads of 50%. Efficiencies greater than 80% were obtained at significantly reduced loads (11%). Operation above resonance was utilized to increase the efficiency and maintain zero voltage switching (ZVS) for varied loads. Total harmonic distortion (THD) of less than 8% was achieved for all resistive loads. The above results were obtained from evaluation version of PSIM also used to model the LCL topology for varied loads and LCL configurations. A LCL Resonant Inverter is proposed for applications in high frequency distributed AC power systems.