Abstract:The automatic generation of test data is a key step in realizing automated testing. Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements. This paper presents an improved Whale Genetic Algorithm for generating test data required for unit testing MC/DC coverage. The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation. At the same time, the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic algorithm. The threshold is dynamically adjusted according to the diversity and evolution stage of current population, which positively guides the evolution of the population. Finally, an improved crossover strategy is proposed to accelerate the convergence of the algorithm. The improved whale genetic algorithm is compared with genetic algorithm, whale algorithm and particle swarm algorithm on two benchmark programs. The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations, and has great advantages in generating test data.

Abstract:Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications. Biometric security, medical rehabilitation, virtual reality, and autonomous driving cars are some of the fields of study that rely on accurate gait recognition. While majority of studies have been focused on achieving very high recognition performance on a specific dataset, different issues arise in the real-world applications of this technology. This research is one of the first to evaluate the effects of changing walking speeds and directions on gait recognition rates under various walking conditions. Dataset was collected using the KINECT sensor. To draw an overall conclusion about the effects of walking speed and direction to the sensor, we define distance features and angle features. Furthermore, we propose two feature fusion methods for person recognition. Results of the study provide insights into how walking speeds and walking directions to the KINECT sensor influence the accuracy of gait recognition.

Abstract:The laser device is the core component of coherent Doppler wind lidar. The peak power and pulse width of laser transmitting pulse have important effects on SNR. Based on coherent Doppler wind pulse lidar, the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis, and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar. But when the peak power is too large, the laser pulse may appear nonlinear phenomenon, which cause the damage of the laser. So, the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy, but it will also make the spatial resolution lower, and the influence of turbulence on SNR will be greater. After a series of simulation analyses, it can be concluded that when the peak power is 650W and the pulse width is 340ns, the SNR of the system can be maximized. In addition, the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification. The experimental results are consistent with the theoretical analysis and simulation, which verifies the correctness of the theoretical analysis and simulation results. It provides theoretical basis and practical experience for the design of laser transmitting pulse in coherent Doppler wind lidar system.

Abstract:A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics, low contrast and complex background texture. Firstly, by analyzing the spectral com-ponent distribution and spatial contour feature of the image, a salient feature model is established in spatial-frequency domain. Then, the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain. Finally, the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target, and the target is segmented by seeded region growing. Experiments have been performed on Berkeley Segmentation Data Set, as well as sample images of online detection, to verify the effectiveness of the algorithm. The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%, which indicates that the proposed algorithm can availably extract the target feature information, suppress the background texture and resist noise interference. Besides, the Hausdorff Distance of the segmentation is less than 10, which infers that the proposed algorithm obtains a high evaluation on the target contour preservation. The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.

Abstract:The country strongly supports the development of new energy industries, with the clean energy wind power industry developing rapidly and the market maturing, the scale of wind farms and installed capacity expanding, and the blade length increasing to 60-70m. The increased blade length and weight increase the probability of damage. the manual inspection method is time-consuming and laborious, with a high economic cost, low inspection efficiency, and high safety risks, and cannot meet the current wind turbine fast and efficient inspection requirements. This paper intro-duces the characteristics of the type of UAV, its working status, and mode, and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field, to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection. It is believed that with the development of UAV technology, UAVs will play a more important role in the field of in-spection.

Abstract:With the development of human–computer interaction technology, brain–computer interface (BCI) has been widely used in medical, entertainment, military, and other fields. Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements. Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination. Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features. This study proposes a classification method of imagined speech Electroencephalogram (EEG) signals with discrete wavelet transform (DWT) and support vector machine (SVM). An open dataset that consists of 15 subjects imagining speaking six different words, namely, up, down, left, right, backward, and forward, is used. The objective is to improve the classification accuracy of imagined speech BCI system. The features of EEG signals are first extracted by DWT, and the imagined words are classified by SVM with the above features. Experimental results show that the proposed method achieves an average accuracy of 61.69%, which is better than those of existing methods for classifying imagined speech tasks.