Design and Development of an Automated Library Management System for Mehran University Library, Jamshoro

This study aims to seek the requirements of the integrated library management system proposed and developed for the Mehran University Library as a step to automate its library services. Study used models to come up with the system. Met most of the goals of the system by enabling library staff follow their clients and resources that they manage. A report generation as easy as all the information has become easier to manipulate because of the nature of electronic storage. Find reading material has been made easy because different criteria can be used to accomplish the task. The user interfaces are friendly and there was a need for re- training other than orientation. The researcher recommends that this system will be built on an ongoing basis to take care of library services other management includes serials and periodicals , and reservations book , e-mail notification automatic reminder , and the use of bar codes , scanners and labels , and the use of RFID ( Radio Frequency Identification) tags to reduce thefts book[1]. It is also recommended that the library system, go online so that access to books and lectures over the Internet by users. Keywords: Library Management, network, Service Deliver

Ship steering control using feedforward neural networks

One significant problem in the design of ship steering control systems is that the dynamics of the vessel change with operating conditions such as the forward speed of the vessel, the depth of the water and loading conditions etc. Approaches considered in the past to overcome these difficulties include the use of self adaptive control systems which adjust the control characteristics on a continuous basis to suit the current operating conditions. Artificial neural networks have been receiving considerable attention in recent years and have been considered for a variety of applications where the characteristics of the controlled system change significantly with operating conditions or with time. Such networks have a configuration which remains fixed once the training phase is complete. The resulting controlled systems thus have more predictable characteristics than those which are found in many forms of traditional self-adaptive control systems. In particular, stability bounds can be investigated through simulation studies as with any other form of controller having fixed characteristics. Feedforward neural networks have enjoyed many successful applications in the field of systems and control. These networks include two major categories: multilayer perceptrons and radial basis function networks. In this thesis, we explore the applicability of both of these artificial neural network architectures for automatic steering of ships in a course changing mode of operation. The approach that has been adopted involves the training of a single artificial neural network to represent a series of conventional controllers for different operating conditions. The resulting network thus captures, in a nonlinear fashion, the essential characteristics of all of the conventional controllers. Most of the artificial neural network controllers developed in this thesis are trained with the data generated through simulation studies. However, experience is also gained of developing a neuro controller on the basis of real data gathered from an actual scale model of a supply ship. Another important aspect of this work is the applicability of local model networks for modelling the dynamics of a ship. Local model networks can be regarded as a generalized form of radial basis function networks and have already proved their worth in a number of applications involving the modelling of systems in which the dynamic characteristics can vary significantly with the system operating conditions. The work presented in this thesis indicates that these networks are highly suitable for modelling the dynamics of a ship