Every day, people all around the world strive to make their lives more comfortable through technological advancements. Nobody anticipates wasting their time, effort, and money by waiting in line at a counter especially during their hospital visits. Henceforth, this paper proposed web development as a solution for the patients to schedule hospital appointments online, with doctors approving them based on their availability. The developed system aims to regulate the knowledge of patients based on doctor availability, hospital and specialist schedules, and patient appointment. The proposed system has been designed within the ASP to automate the day-to-day activities in a hospital like room activities, admission of latest patient, doctor visits. The proposed distributed resource allocation algorithm aims to search the availability of nearby hospitals to make an appointment. Similarly, once the user has registered for an appointment and if the appointment has been accepted by the appropriate doctor, the user will receive a confirmation email. The proposed model has used Visual Studio.NET 2008 environment, ASP.NET for frontend processing, and MS-SQL SERVER 2008 for backend processing.

Online Appointment Management System in Hospitals Using Distributed Resource Allocation Algorithm

Multi-project management is very complex task, which often involves the integration of varieties of resources and schedules. In recent years, many researchers have proposed many techniques for project scheduling problems. However, due to its complexity, it is difficult to find out the best scheduling scheme using heuristics or non-traditional techniques. In this paper, the aim is to find out the high-performance algorithms to solve combination optimisation problems. Related comparative analysis of swarm intelligence and heuristic priority rules for scheduling a multi-project environment with an objective to minimise the makespan of the projects is explored. Firstly, the multi-mode resource constrained multi-project scheduling problem MRCMPSP and its conceptual model are proposed. Then, swarm intelligence and heuristic priority rules to the MRCMPSP problem are discussed. Thirdly, the simulation experiments are given in order to test the different searching abilities for various scale scheduling problems using these two methods. Finally, some interesting insights are also illustrated through numerical examples.

Comparative analysis of swarm intelligence and heuristic priority rules for solving multi-project scheduling problem

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: 1 researching the image transformation technology from greyscale pattern to entropy pattern; 2 studying the anisotropic smoothing and local constraint criterion of entropy flow; and 3 introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background.

Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

The colour harmony design can be regarded as a combinatorial optimisation problem. As a novel population-based stochastic algorithm inspired by artificial physics, APO is applied to colour harmony design. Firstly, the paper constructs the mathematical model of colour harmony theory according to Munsell's theories of colour harmony. Then, the colour harmony aesthetics measure is regarded as the object function to be optimised, and the colours do not fall into the Moon and Spencer ambiguity region and are satisfied with the balance of colour patch, which are the constrained conditions. When APO is used to solve the problem, each feasible colour is regarded as an individual. The higher the aesthetics measure, the bigger the mass of the individual, which has a bigger force to other individuals with lower aesthetics measure. Driven by the attractive-repulsive rule, the population can search the better region of the higher aesthetics measure value. Finally, the APO algorithm used in the colour harmony design of machine tool shows the effectiveness of the proposed method.

Computer-aided colour harmony design based on artificial physics optimisation

Compressed sensing is a new signal sampling theory put forward in recent years. It can obtain a signal's discrete sample in the condition that the sampling rate of signal is far smaller than the Nyquist sampling rate, and then make the original signal perfectly reconstructed with nonlinear reconstruction algorithm. In this paper, three typical greedy reconstruction algorithms, i.e., orthogonal matching pursuit, regularised orthogonal matching pursuit, and subspace tracking algorithm are compared in terms of reconstruction accuracy, error rate and time of reconstruction. Furthermore, a new transpose measurement matrix reconstruction algorithm is proposed and tested through simulation. The experiments on both simulation and calculation showed that this new algorithm can effectively improve the optimisation of the signal transmission.

An adaptive transpose measurement matrix algorithm for signal reconstruction in compressed sensing

This paper describes a successful adaptation of a discrete particle swarm optimisation DPSO to optimal design of bellows. EJMA standard and engineering practices-based mathematical model of unreinforced U-shaped bellows are established. An n-DPSO and scheme of constraint levels with huge value penalty are proposed to reproduce new particles and keep the best particles. Through benchmark problems, the obtained optimal results demonstrate that the approach clearly outperforms those from literatures. The bellows with diameters of 100 mm to 1,500 mm at 0.6 MPa to 1.6 MPa are designed by the approach and established mathematical model, the optimal solutions show that they are much better than those of bellows in-service and highly close to theoretical solutions. It is concluded that the established mathematical models are reasonable and the approach can be adopted in bellow design as it yields rational, reliable, time-saving and economically practical designs.

Jingui Lu

Papers

The optimal design of bellows using a novel discrete particle swarm optimisation algorithm