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To improve the effectiveness and security of image encryption, this article proposes an image encryption scheme based on a Y-index Space Filling Curve (SFC) and variable step Josephus traversal. The scheme adopts a confusion-diffusion structure. First, a novel Y-index SFC is designed. SFC is a method of continuously traversing each pixel of an image to encrypt the image, and images are confused by the Y-index SFC. Second, the random sequences generated by the chaotic map are taken as the starting point and step length of the Josephus traversal, and the pixels of the image are scrambled at a bit-level to change the values of the pixels. By changing the step length, the diversity of the Josephus traversal is increased. Finally, ciphertext feedback and chaotic sequence operations are used to further enhance the confusion and diffusion characteristics of the algorithm. By analyzing the experimental results and comparing them with the results of other image encryption algorithms, we show that this algorithm performs better and achieves higher security than other algorithms.

With the rapid development of new energy such as wind and solar energy, its connection to the grid brings many problems to the safe and stable operation of the power system. So it has great significance to develop seawater variable-speed pumped storage unit for regulation and energy storage. In order to meet the grid harmonic requirement, the common solution is to add the grid side filter for the high-power NPC (neutral point clamping) topology grid-connected converter. But the filter will increase the volume, weight and cost of the equipment, and reduce the system reliability. Therefore, to ensure that the grid current meets the requirement of the standard IEEE 519, this paper adopts a grid-connected optimal modulation algorithm without adding the filter. And the effectiveness of the algorithm is verified by simulation and experiment.

Wavelet denoising and the separability index of state feature class are proposed to evaluate the denoising effect. While introducing the Fisher standard discriminant rate F to measure class separability, the Fisher standard discrimination rate of each characteristic parameter after noise detection by using the rigrsure-sln soft threshold for airflow pulsation and fan noise signal are improved obviously. In this paper, the G-P algorithm is used to calculate the relationship among airflow pulsation, fan noise and vibration signal. It is found that rigrsure-sln soft-threshold method is better than other threshold method for denoising the airflow pulsation and the fan noise signal. The phase diagram of the airflow pulsation and the noise collection signal of the ventilator are disordered due to the influence of noise, and the signal after wavelet denoising shows a clear and regular chaotic attractor.

Based on the Feistel network and dynamic deoxyribonucleic acid (DNA) encoding technology, an image encryption method is proposed using the “permutation-diffusion-scrambling” structure. First, the SHA-3 algorithm is used to calculate the hash value of the plaintext image as the initial value of the hyperchaotic system, and the chaos-generated sequence is used to generate the Hill cipher matrix to replace the image pixel. Second, the DNA sequence operation is used as the F function of the Feistel network. The DNA sequence database is used as the key K of Feistel network, and the image pixel value diffusion is realized by the Feistel network. Finally, further diffusion is carried out through the ciphertext feedback and through the ciphertext confusion and diffusion of three rounds of “chaotic scrambling-DNA encoding-Feistel transformation-DNA decoding,” making the ciphertext more random and resistant to attacks and ensuring that the encrypted ciphertext is more secure. The experimental results show that the proposed method can effectively encrypt the image and has prominent characteristics, such as strong plaintext sensitivity, a large key space, and excellent ciphertext statistical properties.

Model predictive control (MPC) strategy is a typical optimization predictive control method. MPC can achieve satisfying control performance with lower model parameter dependency, and it has been widely applied in process control systems. To improve the system reliability and reduce the hardware cost, extended Kalman filter (EKF) was introduced to achieve sensorless operation. A novel speed sensorless control system of induction motors which coordinates the MPC current controllers and the EKF state estimator is presented in this paper. The proposed scheme has been verified by simulations, and the results show that excellent speed control performance and state estimation performance over wide speed range and load torque range have been achieved either in dynamics or in steady states.

Porter's generic strategies offered a dominant paradigm (Campbell-Hunt, 2000) [1] on competition for both business practice and academic study. Nowadays, most research organizations in China have to face competition issues as most businesses do. Can they find out their own generic strategies like Porter's for their survival and development in competitive system? Based on the approach of Porter's generic strategy, this paper discusses the generic strategy model for research organizations. As shown in the model, along with the two dimensions of horizontal difference and vertical difference of intellectual capital, research organizations may handle 3 generic strategies: focus strategy, platform strategy and combination strategy. Organizations stuck in the middle will fall flat and have to look for new approaches to build up their own competitive advantages in the competitive system. Data from 42 public institutes in China indicates that the three generic strategies could be recognized in practice.

The hugely storing information ability, parallel computing ability and lower computing energy cost make DNA computing to be a perfect computing paradigm. Nowadays it has been used to solve various computationally hard problems. In order to improve its reliability and simplify operations, microfluidic chips support an effective way to realize an automatable and universal DNA computer. In this paper we introduce microfluidic logic operators, simple fluidic switches and memory. Furthermore, the use of electronic fluidic control components in microfluidic systems will be demonstrated in such way as to perform dynamic operations and programming. Finally a proposal for an actual fluidic computer will be made which solves the graph vertex coloring problems.

Based on its massive parallel computational power and high density storage capacity, DNA computing has been applied to various computational problems. In order to improve its reliability, reduce time and simplified operations, microfluidic chips support an effective way to realize an automatable and universal DNA computer. In this paper we introduce microfluidic logic operators, simple fluidic switches and memory. Furthermore, the use of electronic fluidic control components in microfluidic systems will be demonstrated in such way as to perform dynamic operations and programming. Finally a proposal for an actual fluidic computer will be made which solves the minimum vertex cover problems.

In this work, a total of twenty nine rice samples, including nineteen Xiang-shui rice samples and ten non-Xiang-shui rice samples, were employed to establish discrimination models used back propagation artificial neural networks (BP-ANN) and least squares support vector machine (LS-SVM) based on the content value of B, Zn, Fe, Cu, Mn, Na, K, Mg and Ca determined by Inductively coupled plasma atomic emission spectrometry (ICP-AES). Although the 100% accuracy of training set achieved by LS-SVM, for test set the accuracy was only 90.91%. So the optimal result, the accuracy of 100% both for Xiangshui rice samples and for non-Xiangshui rice samples in test set, was obtained by BP-ANN models with five nodes in hidden layer. A new discrimination method to discriminate rice from different geography origin was provided, which is significant to detect and prevent fraud and adulteration. And in order to improve expansibility and accuracy of models, a further study through adding the number of samples and comparison of more discrimination methods, was needed.

Invasive weed optimization, which is inspired from the invasive habits of growth of weeds in nature, is a population-based intelligence algorithm. In this paper, we present invasive weed optimization with crossover operation combining the idea of the invasive weed with concepts from evolutionary algorithms. By applying the crossover operation in invasive weed optimization, it not only discourages premature convergence to local optimum but also explores and exploits the promising regions in the search space effectively. This modified algorithm is tested and compared with the standard invasive weed optimization and PSO. The comparative experiments have been conducted on benchmark test functions; invasive weed optimization with crossover operation is able to obtain the result superior to the standard invasive weed optimization and PSO.

Shuffled frog leaping (SFL) is a population based, cooperative search metaphor inspired by natural memetics. Its ability of adapting to dynamic environment makes SFL become one of the most important memetic algorithms. In order to improve the algorithmpsilas stability and the ability to search the global optimum, a novel dasiacognition componentpsila is introduced to enhance the effectiveness of the SFL, namely frog not only adjust its position according to the best individual within the memeplex or the global best of population but also according to thinking of the frog itself. To validate the improved SFL (ISFL) method, numerous simulations were conducted to compare SFL and ISFL using six benchmark problems for continuous and discrete optimization. According to the simulation results, adding the cognitive behavior to SFL significantly enhances the performance of SFL in solving the optimization problems, and the improvements are more evident with the scale of the problem increasing.