Abstract:This paper presents a two-degree-of-freedom (2DOF) hybrid piezoelectric-electromagnetic energy harvester (P-EMEH). Such a 2DOF system is designed to achieve two close resonant frequencies. The combined piezoelectricelectromagnetic conversion mechanism is exploited to further improve the total power output of the system in comparison to a stand-alone piezoelectric or electromagnetic conversion mechanism. First, a mathematical model for the 2DOF hybrid P-EMEH is established. Subsequently, the maximal power output of the 2DOF hybrid P-EMEH is compared both experimentally and theoretically with those from the 1DOF piezoelectric energy harvester (PEH), 1DOF electromagnetic energy harvester (EMEH), 2DOF PEH, and 2DOF EMEH. Based on the validated mathematical model, the effect of the effective electromechanical coupling coefficients (EMCC) on the maximal power outputs from various harvester configurations is analyzed. The results indicate that for the 2DOF hybrid P-EMEH, although the increase of the power output from one electromechanical transducer will lead to the decrease of the power output from the other, the overall performance of the system is improved in weak and medium coupling regimes by increasing electromechanical coupling. In weak and medium coupling scenarios, the hybrid energy harvester configuration is advantageous over conventional 1DOF or 2DOF harvester configurations with a stand-alone conversion mechanism.

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This paper proposes a color image encryption algorithm based on a cloud model Fibonacci chaotic system, as well as a matrix convolution operation that can protect image content effectively and safely. The algorithm combines the cloud model with the generalized Fibonacci, creating a new complex chaotic system that realizes the dynamic random variation of chaotic sequences. The chaotic sequence is used to scramble the pixel coordinates of the mosaic images of the R, G, and B components of the color image. Then, the chaotic sequence value is used as a matrix convolution cloud algorithm that alternately updates the input value of the matrix convolution operation and the pixel value to obtain the permutation transformation of the original pixel value. Finally, the pixel values of the replacement and cloud model Fibonacci chaotic sequence and the pixel values of the front (rear) adjacent pixel points are subjected to a two-way exclusive XOR operation. Realizing the change of the arbitrary pixel value causes a chain transformation of the pixel values of all of the pixel points, and sequentially generates an encrypted image. Experiments show that the histogram of the encrypted image is smoother and adjacent pixels of the image have low correlation. In addition, this algorithm can resist attack experiments such as differential attack, select plaintext attack and noise attack and provides high encryption security, high anti-interference, and strong robustness. The dynamic chaotic system is used to realize the color image encryption of the dynamic key, and the encryption algorithm has higher security and the validity of the algorithm.

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Driver's emotion is a psychological reaction to environmental stimulus. Driver intention is an internal state of mind, which directs the actions in the next moment during driving. Emotions usually have a strong influence on behavioral intentions. Therefore, emotion is an important factor that should be considered, to accurately identify driver's intention. This study used the support vector machine (SVM) theory to develop a driver intention recognition model, with the involvement of driver's emotions. Various materials including visual materials, auditory materials, and olfactory materials, were used to induce driver's emotions. Real driving, virtual driving and computer simulation experiments were conducted to collect human-vehicleenvironment dynamic data in two-lane roads. The results present that the proposed model can achieve high accuracy and reliability in recognizing driver's intentions. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.