Chuan He’s research while affiliated with Jinan University and other places

The paper is aimed at forecasting the locations of next crime. Based on local demographic factors, geographic factors and environment psychological factors, it establishes a Multi-factor Evaluation Model. We then use an actual case (The Yorkshire Ripper case) for model testing, and get good test results.

We build a Geographic Profiling Model to generate the criminal's geographical profile, by combining two complementary strategies: the Spatial Distribution Strategy and the Probability Distance Strategy. In the first strategy, we designate the mean of all the known crime sites as the anchor point, and build a Standard Deviational Ellipse Model, considering the effect of landscape. In the second strategy, we take many factors such as the buffer zone and distance decay theory into consideration and calculate the probability of the offender's residence in a certain area by using the Bayesian Theorem and the Rossmo Algorithm. Then, we combine the result of two strategies and get three search areas suit different conditions of the police to track the serial criminal. Apply the model to the English serial killer Peter Sutcliffe's case, the calculation result shows that the model can effectively be used to track serial criminal.

This paper presents a solution of the Optimal Tolerance Design Problems for the production of power transformers. Based on statistical and probabilistic methods, it establishes a multi-objective and multi-constraint mathematical model. We then present a solution that maximizes the effective utilization rate of sheet material, for producing core columns of power transformers, and the passing rate of quality controls of matching coils and cores. It also minimizes the Tolerance cost of the product. The solution uses Particle Swarm Optimization (PSO), which is very effective as shown by simulation results.

In this paper, we propose a TV-L1 denoising model-based kernel estimation in image deblurring which uses both blurred and noisy images. More details and edges are recovered in the denoised image which is used to replace the true image and do the deconvolution. In the first instance, an initial kernel which might be very noisy can be recovered after primary kernel estimation. Whereafter, the method of hysteresis thresholding by using a mask is used to suppress the noise and finally an accurate estimated kernel can be obtained. Experimental results show that our outcome is significantly evolutional.

In this paper, aim at the characteristics of multi-objective multi-constrained and difficult to solve of the three-dimensional container-packing problem, we propose to solve the problem with improved differential evolution (IDE) algorithm. By overloading the evolutionary operation of differential evolution algorithm that we can deal with discrete optimization problems effectively. Define the adaptive crossover-factor and stagnation variation mechanism to prevent premature stagnation of the algorithm, and improve the population diversity. Simulation results show that the IDE algorithm can excellently solve the three-dimensional container-packing problem.

Given the full consideration of the characteristics of the three-dimensional container-packing problem in this paper, we build a multi-constrained and multi-objective mathematical model, with the constrains of safety and carrying capacity, under the objectives of packing maximum weight of the goods, as well as having the highest stability. Solving the model with Discrete Particle Swarm Optimization (DPSO) algorithm which can deal with discrete optimization problems effectively by redefining the velocity and position formula of basic Particle Swarm Optimization(PSO). The simulation results prove that the DPSO algorithm can excellently solve the three-dimensional container-packing problem.

The ICACTE 2009 was organized to gather members of the international community of Computer Theory and Engineering scientists so that researchers from around the world could present their leading-edge work, expanding our community's knowledge and insight into the significant challenges currently being addressed in that research.