Applied Mechanics and Materials Vols. 284-287

Abstract: This paper presents the use of fuzzy-based Taguchi method to optimize the design of the ultrathin centrifugal fan with multiple performance characteristics. An orthogonal array, the S/N ratio, MPCI, ANOVA, and CFD were used to study the multiple-objectives in the ultrathin centrifugal fan design. The design parameters, outlet dimensions, inlet dimensions, blade angle, and impeller diameter were optimized with considerations of the performance characteristics, including volume flow rate, static pressure, and noise. The results demonstrate that operation section of the new fan P-Q curve design was larger than that of the original design. This study also identified the optimized design parameters that affect the cooling performance of the centrifugal fan.

Abstract: Conventional 2D face recognition methods often struggle when a subject's head is turned even slightly to the side. In this study, a face recognition system based on 3D head modeling that is able to tolerate facial rotation angles was constructed by leveraging the Open source graphic library (OpenGL) framework. To minimize the extensive angle searching time that often occurs in conventional 3D modeling, Particle Swarm Optimization (PSO) was used to determine the correct facial angle in 3D. This reduced the angle computation time to 6 seconds, which is significantly faster than other methods. Experimental results showed that successful ID recognition can be achieved with a high recognition rate of 90%.

Abstract: A new 3D image encryption method using integral imaging technology and maximum length cellular automata (MLCA) is proposed in this paper. First, an elemental image (EI) is generated by the integral imaging pickup process. The Wolfram rule is then selected and the state transition matrix, T, is created by MLCA. A random number matrix (RNM) is generated according to the MLCA rule. The generated EI and RNM are compared and pixel values are transitioned. Finally, a basis image is generated by another MLCA rule. The basis image is applied by performing a logic bit exclusive-OR (XOR) operation on the final encrypted image. This method changes the basic image information. Using pixel values to visualize image data gives better encryption results than the previous method. The robustness of the encryption method for lost data, including added noise or cropping attacks, was analyzed and the results of encryption method safety test experiments are presented using histograms.

Abstract: Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. Although watermarks possess advantageous secrecy and robustness, environmental interference in the image propagation through the Internet is inevitable and, certainly, human-based image modification can also destroy the watermark. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh-Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First, we used a low-pass filter and median filter to remove noise interferences. Although these filters can suppress noises, watermarked images remain unidentifiable when the noise interferences strongly. Finally, we used a back-propagation neural network algorithm to filter noises, obtaining results that exceeded our expectations. We removed nearly all noise and recovered the originally embedded watermarks of Walsh-Hadmard codes.

Abstract: A continuous nearest neighbor (CNN) query retrieves the nearest neighbor of every point on a line segment and indicates its valid segments. Zheng et al. have proposed a Hilbert-curve index for the CNN query. This method contains two phases, searching candidates in the approximate search range, and filtering the candidates to get the final answer. However, it may determine a wide search range in the first phase based on this method, resulting in the decrease of the accuracy and the increase of the processing time. Therefore, in this paper, to avoid this disadvantage, we propose a forward moving method to efficiently support the CNN queries. The proposed method locally expands the search range along the query line segment to find the neighbors. Experimental results show that our method outperforms Zheng et al.’s method in terms of the accuracy and the processing time.

Abstract: Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

Abstract: It is difficult to objectively and quantitatively judge image quality by a single criterion, such as contrast. In general, excessive contrast enhancement easily leads to a loss of image quality. Thus, it easily gives a wrong evaluation to rank image quality according to contrast values. In order to achieve a consistent result with human vision perception, balancing multi-criteria will be a feasible approach. Therefore, we propose a multi-criteria image quality evaluation scheme for ranking seven existing contrast enhancement methods. The scheme applies four criteria to a newly proposed way of computing a grey relational grade (GRGd), called the consistent grey relational grade (CGRGd). Experimental results show that our proposed CGRGd do provides a very effective mechanism to choose the best method for a specific purpose.

Abstract: A new algorithm based orthogonal arrays was developed to determine the optimal solution amid all the optional settings for a new initiated device. The proposed algorithm begins with several successive orthogonal arrays and ends with a full factorial array. After each orthogonal array is accomplished, the algorithm employs variance analyses to screen the dominating variable from among all the current variables and then determines the best level of the dominating variable. Here, after successive orthogonal arrays were completed, the number of unfixed variables was gradually reduced to a number that was small enough to feasibly conduct a full factorial array, resulting in the final selection of the problem’s optimum setting. We verified the proposed algorithm by first applying it to the Rosenbrock function.To distinguish the superior performance of the proposed algorithm in comparison with other design of experiments approaches, we experimented with three different problems with non-interactive or interactive variables. The results show that the proposed method not only provides a final solution that is identical to the problem’s exact solution but also that the computation time in comparison with that required by a full factorial array drops drastically as the number of variables increases.

Abstract: The course of socio-cultural transition is not a phenomenon of aimless, arbitrary development, but towards a clear goal. Such process of moving towards higher-level soul experience and mental state is a common goal of social species' evolution. In this paper, the model of cultural development is imitated to be the system thinking frame for developing an evolution algorithm, namely cultural evolution algorithm. It consists of several search methods with similar thinking and then proposes four strategies of the cultural evolution algorithm. Seven benchmark functions are utilized to validate the search performance of the proposed algorithm. The results show that all of the four strategies of cultural evolution algorithm have better performance when compared with relevant literatures.

Abstract: Three dimensional (3-D) images encryption schemes can provide feasible and secure for images encryption due to the 3-D properties of images. In this paper, we present a novel 3-D images encryption algorithm by combining use of integral imaging (II) and maximum-length cellular automata (MLCA) as the secret key ciphering for 3D image encryption technique. In this proposed algorithm, a lenslet array first decomposes the 3-D object into 2-D elemental images (EIs) via the pick-up process of II. We encrypt the 2-D EIs with an encryption method based on linear and complemented MLCA. Decryption process is the opposite of operation encryption process: The 2-D EIs is recovered by the MLCA key, 3-D object is reconstructed by the recovered EIs via computational integral imaging (CII) reconstruction. To verify the usefulness of the proposed algorithm, we carry out the computational experiments and present the experimental results for various attacks. Experimental results show that the proposed algorithm can improve the performance of encryption against various attacks due to large key space in MLCA and 3-D characteristic of data redundancy.