This paper optimizes the planar inductor used in Class-E 2 based wireless power transfer (WPT). Different constructions of the planar inductor will change parameters of inductor, such as core loss and copper loss. Four types of connection methods of the planar inductor are proposed and being analyzed to get the loss-minimum one. At the same time, a Litz-wire inductor is set as a control group. In the experiment, the Class-E 2 based on the WPT system with push-pull Class-E rectifier and inverter is set up and compares the performance of it with planar inductors and Litz-wire inductor mentioned before. By using the planar inductor with the proposed interleaved arrangement, the efficiency of the Class-E 2 based WPT system reaches 86.4% at the output of 48V, 203W.
Multivariate matching algorithms "pair" similar study units in an observational study to remove potential bias and confounding effects caused by the absence of randomizations. In one-to-one multivariate matching algorithms, a large number of "pairs" to be matched could mean both the information from a large sample and a large number of tasks, and therefore, to best match the pairs, such a matching algorithm with efficiency and comparatively limited auxiliary matching knowledge provided through a "training" set of paired units by domain experts, is practically intriguing. We proposed a novel one-to-one matching algorithm based on a quadratic score function $S_{\beta}(x_i,x_j)= \beta^T (x_i-x_j)(x_i-x_j)^T \beta$. The weights $\beta$, which can be interpreted as a variable importance measure, are designed to minimize the score difference between paired training units while maximizing the score difference between unpaired training units. Further, in the typical but intricate case where the training set is much smaller than the unpaired set, we propose a \underline{s}emisupervised \underline{c}ompanion \underline{o}ne-\underline{t}o-\underline{o}ne \underline{m}atching \underline{a}lgorithm (SCOTOMA) that makes the best use of the unpaired units. The proposed weight estimator is proved to be consistent when the truth matching criterion is indeed the quadratic score function. When the model assumptions are violated, we demonstrate that the proposed algorithm still outperforms some popular competing matching algorithms through a series of simulations. We applied the proposed algorithm to a real-world study to investigate the effect of in-person schooling on community Covid-19 transmission rate for policy making purpose.
This paper proposes a feature extraction method based on whale optimization algorithm and variational mode decomposition (WOA-VMD) to overcome the low feature extraction accuracy of generator early inter-turn short circuit fault. WOA-VMD process the current signal, and the sample entropy is taken as the fitness function of WOA to optimize the VMD parameter combination of modal components' number K and penalty parament α. Then, the optimized VMD decomposes current signals into K intrinsic mode functions (IMFs). IMFs with higher kurtosis values are selected to extract energy entropy as the feature vectors. Finally, the whale optimization algorithm and support vector machine (WOA-SVM) pattern recognition model is used to classify the feature vectors and diagnose generator inter-turn short circuit degree. The experiments show that the proposed method extracts the weak fault features in the early inter-turn short circuit signal and improves the fault diagnosis accuracy, reaching 97.75%.
Deep learning (DL) networks show a great potential in computed tomography (CT) imaging field. Most of them are supervised DL network greatly based on their capability and the amount of CT training data (i.e., low-dose CT measurements/high-quality ones). However, collection of large-scale CT datasets are time-consuming and expensive. In addition, the training and testing CT datasets used for supervised DL network are highly desired similarities in CT scan protocol (i.e., similar anatomical structure, and same kVp setting). These two issues are particularly critical in spectral CT imaging. In this work, to address the issues, we presents an unsupervised data fidelity enhancement network (USENet) to produce high-quality spectral CT images. Specifically, the presented USENet consists of two parts, i.e., supervised network and unsupervised network. In the supervised network, the spectral CT image pairs at 140 kVp (low-dose CT images/high-dose ones) are used for network training. It should be noted that there is a great difference of CT value between spectral CT images at 140 kVp and 80 kVp, and the supervised network trained with CT images at 140 kVp cannot be directly used for CT image reconstruction at 80 kVp. Then unsupervised network enrolls physical model and the spectral CT measurements at 80 kVp for fine-tuning the supervised network, which is the major contribution of the presented USENet method. Finally, accurate spectral CT reconstructions are achieved for the sparse-view and low-dose cases, which fully demonstrate the effectiveness of the presented USENet method.
Fire detection has long been an important research topic in image processing and pattern recognition, while smoke is a vital indication of fire’s existence. However, current smoke detection algorithms are far from meeting the requirements of practical applications. One major reason is that the existing methods can not distinguish smoke from fog because their colors and shapes are both very similar. This paper proposes a novel texture analysis based algorithm which has the ability to classify smoke and fog more efficiently. First the texture images are decomposed using Contourlet Transform (CT), and then we extract the feature vector from Contourlet coefficients, finally we make use of Support Vector Machine (SVM) to classify the textures. Experiments are performed on the sample images of smoke and fog taking accuracy rate of classification as evaluation criterion, and the accuracy rate of our algorithm is 97%. To illustrate its performance, our method has also been compared with the algorithms using Gray Level Co-occurrence Matrixes (GLCM), Local Binary Pattern (LBP) and Wavelet Transform (WT).
Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.
The intelligent tracking car can realize self-perception, behavioral decision-making, automatic driving, etc. for environmental information, and has a wide application in our lives. In this paper, a new intelligent car is designed based on STM32F407ZGT6 MCU. The track information is collected by th e OV7725 camera. The fast OTSU adaptive threshold algorithm is used to obtain the path guiding center line, which can realize image collection, image analysis and sensor data fusion, blocked path identification and intelligent judgment, automatic tracking function. The test results show that the algorithm is effective and feasible. When there are obstacles or partial loss on the track, it can intelligently identify the effective and obstacles and realize the independent decision making. It has good dynamic and robustness.
Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self-constructed smart obstacle avoidance car, which used a LeTMC-520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.
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