Applied Mechanics and Materials Vols. 263-266

Abstract: In cryptography, the DES algorithm is known to be advantageous by utilizing the mechanisms of grouping and symmetric private key. The algorithm is critical in the field of encryption. It is adaptive to various applications for encryption in many different systems, especially in the embedded system of Internet of Things (IOT) and the Next-generation Convergence Networks. In this paper, an improved scheme is presented in order to overcome the easily attacked shortcomings. This method is to improve the complexity of encryption key in the process and thus to enhance the ability in resisting attacks, so that we use two keys to crosswise encrypt and the safety of the algorithm is further strengthened. In this paper, the improved algorithm is applied in smartphone-based monitoring of broadcasting transmission systems, so as to realize the transmission of data encryption functions.

Abstract: Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

Abstract: A constraint constant module blind equalization algorithm for medical image based on dimension reduction was proposed. The constant modulus cost function applied to medical image was founded. In order to improve the effect of image restoration, a constraint item was introduced to restrict cost function, and it guarantees that the algorithm converge the optimal solution. Compared to the traditional methods, the novel algorithm improves peak signal to noise ratio and restoration effects. Computer simulations demonstrate the effectiveness of the algorithm.

Abstract: The highly overlapping chromatograms and spectra of the major metabolites of asprin, salicylic acid (SA) and gentisic acid (GA) in the presence of uncalibrated and interfering pyrocatechuic acid (PA) were resolved using HPLC-DAD combined with SWATLD algorithm. The elution time was set from 0.6628 min to 0.9495 min and the detection wavelength was chosen from 268 nm to 332 nm. Both SA and GA were determined simultaneously in aqueous solution with their recoveries were (100.5 ± 6.7) % and (109.2 ± 5.3) %, respectively. The satisfying results indicate this method can be easily performed and applied to solving second-order calibration problems quickly and accurately.

Abstract: The overlapping chromatograms and spectra of sulfonamides, sulfadiazine (SD) and sulfamethoxazole (SM) in both synthetic samples and pharmaceutical tablets in the presence of uncalibrated and interfering sulfanilamide (SN) were resolved by HPLC-DAD combined with SWATLD algorithm. The elution time was set from1.077 min to 1.624 min and the detection wavelength was chosen from 230 nm to 316 nm. The recoveries of SD and SM were (98.1 ± 4.8)% and (100.1 ± 1.6)%, respectively. The satisfying results show this method can be easily performed and applied to solving second-order calibration problems quickly and accurately.

Abstract: As a branch of genetic algorithm (GA), cellular genetic algorithm (CGA) has been used in search optimization of the population in recent years. Compared with traditional genetic algorithm and the algorithm combined with traditional genetic algorithm and BP neural network, energy demand forecast of city by the method of combining cellular genetic algorithm and BP neural network had the characteristic of the minimum training times, the shortest consumption time and the minimum error. Meanwhile, it was better than the other two algorithms from the point of fitting effect.

Abstract: Manifold learning is a new unsupervised learning method. Its main purpose is to find the inherent law of generated data sets. Be used for high dimensional nonlinear fault samples for learning, in order to identify embedded in high dimensional data space in the low dimensional manifold, can be effective data found the essential characteristics of fault identification. In many types of fault, sometimes often failure and normal operation of the equipment of some operation similar to misjudgment, such as oil pipeline transportation process, pipeline regulating pump, adjustable valve, pump switch, normal operation and pipeline leakage fault condition similar spectral characteristics, thus easy for pipeline leakage cause mistakes. This paper uses the manifold learning algorithm for fault pattern clustering recognition, and through experiments on the algorithm is evaluated.

Abstract: To utilize maximum solar energy, maximum power point tracking (MPPT) control is much important for PV system. The paper presents a new MPPT method based on adaptive predictive algorithm which is superior to traditional Perturbation and Observation (P&O) method. PV output power is predicted to improve the tracking speed and deduce the possibility of misjudgment of increasing or decreasing the PV output voltage. Because PV output power can be obtained directly, close loop can be established so as to achieve a precise prediction. Simulations and experiments prove that proposed MPPT control can track the maximum power point rapidly, and the system can operate steadily with this MPPT method.

Abstract: University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timetabling, particle swarm optimization

Abstract: PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope of the particles and variation, and the variation scale and population evolution algebra in proportion.