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In this paper, in-band continuous mode transferring (CMT) method is presented for designing broadband Doherty power amplifier (DPA). Specifically, transferable continuous mode, transferring between class-J continuum to class-F-1 continuum, is introduced into DPA at output back-off (OBO) power level for improving bandwidth and efficiency. For validation, a broadband DPA with operation mode transferring from continuous class-J to continuous class-F-1 is designed, fabricated and measured. Experimental results show the drain efficiencies (DEs) of the fabricated DPA are 46.3%-57.7% and 58.4%-69.1% at 6 dB OBO and peaking power levels over 1.7-2.6 GHz. The saturation power of this DPA is 43.1-45.2 dBm with a gain of 9.1-11.2 dB in the interested band. Furthermore, when the fabricated DPA is stimulated by a 20 MHz wideband signal with a peak-to-average power ratio (PAPR) of 7.05 dB at 2.4 GHz, the measured average power is 36.5 dBm with an average DE of 45.7%, and the measured adjacent channel leakage ratios (ACLRs) are -31.9 dBc and -50.4 dBc before and after DPD technique, respectively.

Reasonable planning of EV charging facilities has a great significance to promote the development of EV industry. In order to make the construction of charging facilities more reasonable, the authors propose in this paper a planning method that based on space syntax. Based on the master planning of Tianjin downtown area, this study collects road network data and imports it into the model, and establishes a quantitative analysis model by using spatial syntactic method. After extracting the quantitative characteristic values, the first echelon road set in the downtown area was extracted by summarizing the research. The author collects the existing charging facilities point set and conducts the kernel density analysis. The kernel density distribution map of charging facilities was matched with the distribution results of the first echelon roads to analyze the shortcomings of the current layout. Based on the theory of space syntax, the author proposes a basic optimization method. The optimized kernel density distribution map is well matched with the first echelon roads, which lays a foundation for the optimal layout of charging facilities.

The complex dielectric constant is the intrinsic parameter of microwave material, and the accurate acquisition of its value is critical for its application. Generally, the dielectric properties of microwave materials are obtained under weak electromagnetic field. However, with the continuous development of high power microwave technology, the microwave field intensity around materials gradually increases, and the test results of the dielectric properties of materials under the weak electromagnetic field will not be able to characterize the test results under the high power environment. Therefore, a novel testing technology based on the resonator method is proposed, which provides an effective solution for the measurement and characterization of microwave dielectric properties of materials under strong microwave field. Based on the constructed system, the evolution rules of the microwave characteristics of four typical materials are obtained. In the experiment, the temperature of the samples is precisely monitored, and the evolution rules of the temperature characteristics of the dielectric properties of the samples under strong and weak microwave fields are compared. The comparison results demonstrate that the dielectric properties of materials under strong fields are related not only to the microwave heating effect but also to the mechanism of the non-thermal microwave effect.

While current deep learning frameworks are mainly optimized for dense-accessed models, they show low throughput and poor scalability in training sparse-accessed recommendation models. Our investigation shows that the poor performance is due to the parameter synchronization bottleneck. We therefore propose BiPS, a bi-tier parameter synchronization system that alleviates the parameter update and the sparse-accessed parameters communication bottleneck. BiPS includes a bi-tier parameter server that accelerates the traditional CPU-based parameter update process, a hotness-aware parameter placement and communication policy to balance the workloads between CPU and GPU and optimize the communication of sparse-accessed parameters. BiPS overlaps the worker computation with the synchronization stage to enable parameter updates in advance. We implement BiPS and incorporate it into mainstream DL frameworks including TensorFlow, MXNet, and PyTorch. The experimental results based on various deep learning frameworks show that BiPS greatly speeds up the training of recommenders (5 - 9$\times$) as the model scale increases, without degrading the accuracy.

Aiming at the problem of fuzzy entity recognition and less labeled data in the field of traditional Chinese medicine, a named entity recognition model based on Bert-BiLSTM-CRF is constructed and tested on the corresponding data set. According to the text information, it is divided into five types of entities: symptoms, disease names, time, prescription names, and drug names. The results show that the model has the highest accuracy in identifying drug names. In order to further prove the superiority of this model, three groups of control groups composed of other models are set up. The model far exceeds the control group in terms of accuracy, recall and F value. Applying this model to the named entity recognition of Chinese medicine texts, the best results can be obtained in a small sample of data.

Many current analyses on protein-protein interaction (PPI) networks concentrate on developing the algorithms for identifying the functional modules within the PPI networks. However, understanding the internal structure in the functional modules is needed to define their roles within the entire network. We propose the use of a two-club meso-scale structure that possesses refined inner topological structural properties, useful for deciphering the aforementioned obscure structural supporting evidences. In this paper, we: 1) illustrate the feasibility and advantages of modeling functional modules as two-clubs in PPI networks by taking statistics on the diameter distribution of benchmark functional modules within several golden standard sets; 2) categorize the two-clubs into six subcategories through the use of well-defined internal graph-theoretic characterizations; and 3) analyze these six subcategories based on factors, such as their structure, and various metrics, such as topological centralities, the numbers of involved transcription factors and essential genes, and the numbers of matched protein complexes or GO terms. Our structure-driven analysis allows us to predict the roles of identified functional modules from their network structure alone. Our subcategories of coteries and social circles serve as a classification scheme for determining which functional modules are central, regional, and satellite and which are coordinating. Experimental results show that in order to precisely control and examine the PPI networks for further research, a clear understanding of how the internal topology of modules affects their function is essential.

In this paper, we propose a quadrotor control algorithm which use the historical strengthened partial state observations as input information to control the quadrotor drone using reinforcement learning algorithm. Reinforcement learning method could enable the agent to learn a policy which could map the observations to control commands, which, in our work, is the actuator command of the quadrotor. Besides, we conduct our method in the control task via simulation, the results of which show excellent performance.

With the continuous development of computer technology, virtual reality technology has been applied more and more in traditional teaching. In order to improve the students' interest in learning Chinese acupuncture, this paper studies the key technologies in the development of virtual acupuncture teaching software based on virtual reality technology. Model the human body is modeled and pays attention to the contour drawing of the key parts of the human body. The software combines with the current most popular virtual reality device HTC VIVE and uses the Unity3d software to create virtual scenes. Finally, acupoint information models are represented and stored through JavaScript Object Notation (JSON) language, which completes the design of human-computer interaction mode. The implementation of virtual reality technology provides a new method and reference solution for assisting Chinese acupuncture teaching.

In this paper, the microwave network cascade method is proposed for the measurement of dispersion of slow-wave system. Different from the traditional resonance methods, the new method mainly includes three networks, which are the coaxial line to slow-wave system network A, the slow-wave system network B, and the large reflection terminal network C. Theoretical analysis shows that it is easier to measure the dispersion characteristic using the method given in this paper than the traditional resonance method. Moreover, the cold attenuation of slow-wave system can be measured at the same time. The test system is also built in this paper.

Many machine learning systems are built to solve the toughest planning problems. Such systems usually adopt a one-size-fits-all approach to different planning problems. This can lead to a waste of precious computing resources in simple planning problems, while not investing enough in complex problems. This requires a new model framework that does not require the learning of a single, fixed strategy, but rather introduces a series of decision controllers to resolve various planning tasks through learning to build, predict, and evaluate plans. Therefore, we propose a forward-looking imaginative planning framework combined with Prioritized-Replay Double DQN, which is a model-based continuous decision controller that determines the number of iterations of the decision-making process to be run and the model to be negotiated in each iteration. Before any single unit action, it can imagine and select actions based on the current state, including advance imagination with limited steps, and evaluate it with its model-based imagination. All imagined actions or outcomes will be iteratively integrated into a “plan environment”, which can test alternative imagined actions and be able to flexibly use a learned policy in the previously imagined state. Basis on these, the prioritized replay mode is adopted to improve the sampling weight and the training efficiency, which will make the metadata obtain lower overall cost than the traditional fixed strategy method, including task loss and calculation cost.

Frequency dependence of beam waist radius is discussed based on the dispersion property of Gaussian beam, and cutoff frequency of Gaussian beam is deduced by an analogy with the metallic waveguide. For the free-space test system that using focusing antennas with a Gaussian beam illumination, the effect of this dispersion property on material electromagnetic parameters test is analyzed. Correction factor that introducing beam waist radius is proposed to improve the test precision of free-space method.

In this paper, a microwave cavity for investigating the effect of external microwave fields on the dielectric behavior of semiconductor material is proposed. The method is based on a dual-mode rectangular cavity where the motivating and testing signals are performed by two different swept frequency microwave sources. By adjusting the power level of the motivating signal, the intensive of microwave field in the cavity is changed. Measurement results show that the dielectric properties of indium phosphate have a manifest nonlinear behavior under the electronic field intensity of 105 V/m. According to the effective experimental method and theoretical analysis, we conclude that the nonlinear behavior is caused by the materials inherent characteristics.

The purpose of this paper is to solve the finite-time consensus problem for heterogeneous dynamic agents networks with unavailable velocity information. The considered system is composed of first-order agents and second- order integrator agents in which the velocity information is unavailable over fixed topologies. Two classes of new consensus protocols are presented by feat of auxiliary systems. By constructing new Lyapunov functionals and applying homogeneity theory and Lasalle's invariance principle, the proposed protocols are proved to make all states of the systems tend to be consistent. Some simulation examples are given to verify the efficiency of the obtained results.

At present, most of the researches on friction and wear of bearing are focused on rubber bearing and Babbitt bearing. Study on characteristics of bearing that is made of Polytetrafluoroethylene (PTFE) composite material is less reported. This paper mainly carried out the calculation and experimental research on the rule of wearing between PTFE composited bearing and steel shaft. A correct model was built in Pro/E at first, and then it is imported to ANSYS Module. After dividing grid and setting proper boundary conditions to derive and analyze, the friction stress distribution diagram of the bearing was obtained. Then the wear volume of the bearing was calculated according to the stress distribution, and the simulation and calculation were repeated after modifying the bearing model to a worn one. The results demonstrate that the stress of the bearing under lower half of bearing shell is larger than that of the upper one, and the stress of each bearing at the side that is close to armature is larger than the one at the side that is far away from armature. At the same time, the atomic emission spectrometer (AES) was used to analyze the lubricating oil. In order to get the differences between used oil and new oil, the AES data are compared to the result of bearing wear simulation to verify the correctness of model and method. And the correlation between them are build up to predict the wearing of bearing by the online monitoring data of bearing lubricant oil.

In order to realize the matching design of screw propeller and diesel engine conveniently and accurately, and feedback measurement data to calculation model on shore to guide the ship propulsion system design, a web based remote interactive platform for screw propeller and diesel engine matching calculation was designed as an interface, which combined .NET platform with MATLAB platform. And this platform is built up on B/S architecture. The open water characteristics of ship were chosen as the fundamental data for whole process of matching calculation. The preliminary matching, ultimate matching and cavitation checking can be done in this platform. Through the comparison between calculated matching data and the same matching data from document, the experiment result shows that this calculation platform can quickly and accurately realize the matching design, and it is adequate to meet the requirement of different level of matching. This research work has some important engineering application value.

It is critical for the well running of electricity factory to monitor and predict the statues of turbine oil used by turbine generator. However, there are a lot of monitoring parameters for turbine oil and the statues trend is hard to predict. So the principle component analysis method was used to research 7 groups and monitor data for 10 months. The two principle components obtained from analysis are able to reflect the trend for the synthetic statues variation of the turbine oil within this period. The fact is that the actual data is less. In order to use more data to predict, the three times linear interpolation method is used to increase the data volume so that the data obtained from the curve is smoother. Then we forecast data analysis with dynamic neural network. The analysis and discussion to the experiment result shows that the PCA method can get a useful synthetic information from a couple of groups of monitoring data for the turbine oil, so that the trend of oil getting bad and the interval time or period of oil changing can be predicted according to the information.

Detecting outliers in massive trajectory data is helpful to discover anomalous events and understand human mobility. Traditional trajectory outlier detection methods are inadequate in dealing with massive data. Researchers have studied several ways to introduce distributed solutions to outlier detecting, trying to enhance massive data process efficiency. But most of these methods employ MapReduce model. Spark, on the other hand, is proven to be better in terms of time. So, in this paper, Spark platform is utilized to present a trajectory outlier detection method. The key technologies focus on trajectory data management, preprocessing, and the parallel strategies of trajectory outlier detection in a global perspective of the procedure. An experiment is further conducted to test the proposed approach. The result indicates that the suggested method is valid to detect trajectory outlier in terms of the accuracy and efficiency.

A novel micro-inverter with power decoupling circuit on DC side for photovoltaic power generation is proposed in this paper. The micro-inverter contains the basic boost circuit which is parallel with the current source inverter circuit of high-frequency, and a decoupling capacitor is added in parallel with two circuits. Peak current control method is used to increase the voltage across the capacitor and the voltage ripple, reduce the capacitance requirements and prolong the lifetime of inverter. Five working states of the micro-inverter circuit are investigated. The corresponding circuit parameters and peak current control strategy are designed. Simulations of the proposed circuit are performed with PSIM and the results show that the proposed topology and control strategy can reduce the decoupling capacitance requirements.

An experimental investigation on the effect of the contact resistance of passive intermodulation distortion (PIM) is presented. The effect happens when the value of contact resistance changes, which leading to a variation of PIM in micostrip lines. In the measurement of the effect, the testing segment of the microstrip lines is working at the power of about 40dBm.While changing the contact resistance of the microstrip lines, there are unreasonable amplitude altered of IMD 3 , which draw my attention. The way of changing the contact resistance of the testing microstrip lines is presented in this paper. The result of this study demonstrates that the contact resistance is one of the aspects, which should be considered when measuring the PIM. And it provides an important new consideration for improving the precision of measuring the PIM.