In the complex and dynamic urban road environment, conflicts in time and space between different traffic participants are inevitable. In this paper, a model to predict the trajectories of oncoming straight vehicles is proposed based on the Gaussian Process Regression (GPR) model with the consideration of analysis of left-turn potential conflict behaviors. Then, a conflict resolution method for left-turn behavior of autonomous vehicles at urban intersections and a driving action selection method considering multiple factors, such as safety, efficiency, comfort and altruism, are proposed. Experiments show that the model can guide the unmanned vehicles to complete the task with a success rate of more than 90%. The research results can provide decision-making support for autonomous vehicles passing through urban intersections safely.
Transimpedance amplifier is an important part of the signal processing circuit in the fluorescent optical fiber temperature sensor to amplify the current signal and convert it into a voltage one. The two-stage CMOS operational amplifier, which can be designed to achieve high gain, high CMRR and low input offset voltage, can be used in transimpedance amplifier circuits. Based on 0.18 $\mu$m CMOS process, a two-stage differential operational amplifier is designed in this paper. The simulation results show that the gain of the reaches 85.5dB, the CMRR and the negative PSRR are both around 90dB. The Miller compensation circuit in the circuit increases the gain bandwidth of the two-stage CMOS operational amplifier and makes the phase margin reach 80°. This paper introduces the circuit principle of CMOS two-stage operational amplifier in detail, and according to the simulation result, this operational amplifier meets the index requirement.
This paper proposes a scheme for multiple source localization by using diffuseness estimation. According to the sparsity of the voice signal, single source time-frequency point (SSTP) always exists in the microphone recorded sound signal. The DOA estimates of these SSTPs have favorable localization statistical significance. Through analysis the characteristics of diffuseness measure, this paper gives an assumption that there is some correlation existed between diffuseness measure and SSTP. Through experiments, we found that the TF point with low diffuseness measure can be classified as SSTP. Therefore, a SSTP detection method based on diffuseness estimation is proposed. Meanwhile, the multiple source localization problem is converted to a single source localization among the detected SSTPs. The objective evaluations revealed that the proposed method achieved higher localization accuracy compared to some existing technique based on single sound source zone detection.
Aiming at the problems of VIENNA rectifier with traditional double closed-loop PI control strategy, such as large output voltage ripple, poor anti-interference ability and large THD content at AC terminal, etc. A double closed-loop sliding mode variable structure control strategy is proposed. The outer loop is based on sliding mode variable structure control, and the calculation of the input reference current of the inner loop is simplified by using the power balance principle; The current inner loop adopts the direct sliding mode variable structure control method, and outputs the command voltage to the SVPWM module to complete the system control. Matlab/Simulink software is used for simulation verification, and the results show that compared with the traditional double closed-loop PI control strategy, the double closed-loop sliding mode variable structure control strategy significantly improves the dynamic performance and robustness of the system.
With the development of new energy industry, it’s beneficial to analyze the characteristics of new energy which stabilize the operation of the power grid and economic dispatch. It’s more and more important to model the uncertainty of new energy. Although there are some probabilistic modeling methods based on real load data for wind power output, they are not accurate and have high computational complexity. In order to solve these problems, this paper proposes a source-charge probability distribution model based on variational autoencoder. The model uses deep learning techniques to perform variational autoencoder and probabilistic modeling for real-time wind power data. This unsupervised method can learn the characteristics of wind power data and generate new data based on the characteristics of the observation without the scene reduction process. It improves the efficiency and quality of scene generation.
The increasing municipal solid waste has become one of the stubborn diseases of modern society. Incineration technology with the advantage of reduction and resource utilization is an important avenue to solve this problem. Toxic gases emitted from incineration not only pollute the environment but also pose a serious threat to human health. However, the complexity of incineration process brings great difficulty to the detection of pollutants concentration in the exhaust gases. In order to address the problem that the concentration of pollutants is difficult to predict, a prediction model of dioxin emission concentration based on stochastic configuration network is developed in this paper. The prediction model takes the online operation data of municipal solid waste incineration power plant as input parameters and realizes the online prediction of dioxin emission concentration. It provides a scientific basis for choosing the appropriate control strategy. When compared with BP neural network, RBF neural network and support vector machine prediction model, it is easily found that the proposed prediction model can effectively improve the prediction accuracy of dioxin emission concentration during the municipal solid waste incineration process.
The technological revolution of the past few decades has brought about some future inventions. Technological breakthroughs are largely driven by information revolution: Internet connects the world, and mobile computing makes this kind of connection become ubiquitous. This revolution makes users’ lives more convenient and smart. Smart products are becoming more and more popular, and smart speaker is considered as the entrance for people to step into smart life. With the support of smart technology, thanks to its special functions being different from that of traditional speaker, it attracts more and more people’s attention, and is especially pursued and loved by technology lovers and young people. The research summarizes 12 related factors affecting consumer’s purchase of smart speaker with the method of literature analysis combining with user survey. It uses decision-making laboratory method to analyze and study 40 pcs of online and offline questionnaire data and draws cause-and-effect diagram. The finally obtained three main factors affecting consumers’ purchase of smart speaker are price factor, brand factor, and function factor; It clarifies the relationship among the relevant factors greatly affecting consumers’ purchase of smart speaker, and further analyzes the problems existing in the product innovation design of smart speaker, thus putting forward relevant countermeasures and suggestions for seeking the breakthrough point in product innovation design.
For a high aspect ratio UAV in low speed, the flows around the probe fairing itself and the airborne vertical gradient magnetic measuring probes on the wing tip were numerically simulated, respectively. The calculation obtained the aerodynamic characteristics influences of the wing-tip vertical gradient magnetic measuring probes installed on the full vehicle. Results showed that the probe fairing itself can not cause flow separation. The lift coefficient, drag coefficient and lateral force coefficient changed little. Obvious flow separation occurred on the fairing near the vertical gradient magnetic measuring probes, and the drag coefficient of the whole aircraft is increased. While the part outside vertical gradient magnetic measuring probes was not obvious, and the increment is mainly the pressure difference drag. The lift and pitching moments were less affected, then the lift-to-drag ratio become small. The full vehicle control and stability is scarcely influenced, and the requirements for safe flight of low-speed and high-aspect-ratio UAVs could be basically met.
the research on the construction of complex electromagnetic environment has always been a hot topic for scholars, but most of them are inseparable from the study of frequency domain, time domain, spatial domain and energy domain. This paper provides a new perspective for the construction of complex electromagnetic environment, studies the construction of complex electromagnetic environment from the perspective of complex network theory, and preliminarily analyzes the feasibility of this construction method.
In this paper, Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection algorithm is proposed. Firstly, the infrared image is constructed as a closed-loop by using the super-pixel segmentation. Then the saliency map of the target area and the corresponding part of the background are extracted respectively by using the graph model-based manifold ranking algorithm and the dense reconstruction error. And the fused result of the two saliency maps is insensitive to the background clutter interference, which can clearly locate the target area. The experimental results show that the proposed algorithm can suppress the background clutter interference and maintain the integrity of the target edge.
When the overall image processing method is used to detect the cracks in the building with different degrees of depth, the shallow cracks could not be detected or the detection is incomplete. In this paper, a shallow crack detection algorithm based on attractor model is proposed. Each pixel on the image is assumed to be a vector and regarded as an attractor. Then according to the degree of attraction between them, the two are matched to obtain multiple attractors. The rectangular region between each set of attractors was determined, and the corresponding rectangular region in the original drawing was processed, the final shallow crack skeleton diagram was obtained by splicing. The results show that this method can detect shallow cracks clearly and accurately.
In this paper, an Attractive-Region-In-Environment (ARIE) based compliant Video Graphics Array (VGA) insertion method is designed which can be realized by low precision robotic system. The ARIE theory is applied to generate assembly strategy, and then the admittance control is used to achieve the force and the orientation constraints required by the assembly strategy. High precision assembly can be achieved with low requirements for the control and sensor accuracy of the robot. Environmental constraints are used to eliminate position errors. And when there are collisions, the admittance control can make the contact force small to ensure safety. The VGA assembly experiment is conducted to illustrate the effectiveness of the designed assembly method. The results show that by using the designed method, the assembly can be achieved with low requirement for location precision, and the contact force is maintained in a safe range when the end effector contacts the VGA.
Squeal noise is normally attributed to the self-excited vibration of the wheel induced by the lateral creepage when the wheel slides laterally on the top of rail. Due to its unique tone characteristics and high sound pressure level, the human ear is very sensitive to it. With the improvement of living standards, people's tolerance for squeal noise is getting lower and lower. In order to mitigate and control squeal noise, it is necessary to analyze the sensitivity of relevant parameters. In this paper, a prediction model is developed to analyze the effect of various parameters on the sound pressure level of squeal noise. The results show that squeal noise can be significantly reduced by decreasing angle of attack and rolling speed. In addition, the sound pressure of squeal noise can be reduced by reasonably controlling the negative slope of creepage. Furthermore, this mathematical model is used to perform a sensitivity analysis. The results show that squeal noise is most sensitive to the parameter of angle of attack, and the reason for this is illustrated. In particular, the analysis shows that modifying the slope of the creepage curve is effective in mitigating squeal noise, which provides a reference for mitigating and controlling squeal noise in practice, such as the application of friction modifiers at wheel/rail interface.
During takeoff, landing and flight, the airborne radar has to endure a variety of vibration and shock. Due to the complicated environmental conditions, the structural components of radar need to be well designed. Radar cabinet is a typical part of avionics. Its main function is to connect, tighten and support airborne electronic equipment. It is the guarantee of normal operation of airborne electronic equipment. Because the airborne equipment may be subject to worse mechanical vibration, and the weight reduction requirements, the cabinet is required to be designed in lighter weight and higher reliability. In this paper, a carbon fiber integrated forming cabinet is introduced. The structural design of a complex carbon fiber cabinet is described. Through mechanical simulation analysis, the mechanical properties of the carbon fiber cabinet under airborne vibration and impact conditions are studied. The high reliability of the carbon fiber cabinet is verified by strain test and other relate experiments. The research of this paper has an important reference for the lightweight design of airborne radar cabinet.
The relationship between sonority sequencing principle and syllable structures has been stated in phonology, and sonority hierarchy has been classified according to the type of articulation. Based on the above theory of acoustic phonetics, this experiment was designed to extract the sound intensity of each segment as the reference for sonority grade to analyze the combination mechanism of consonant clusters and their properties in syllable fabric in Kazakh. The results indicate that the combination mechanism of Kazakh consonant clusters is in the pattern of “strong +weak /high +low”. This pattern determines that Kazakh consonant cluster can only appear at the end of syllables rather than at the beginning of syllables. In all consonant clusters, the frequency of the first segment is inversely proportional to the sound intensity of the segment, which ensures the smooth transfer of sonority from the strong to the weak between the syllable nuclear and the first segment of consonant cluster. At the same time, the Kazakh syllable division rules determine that the combination of Kazakh consonant clusters is not stable. Consonant clusters could be destroyed and divided into two different syllables, so consonant clusters are more likely to be located at the end of a word.
The determination of flow directions is an essential step for drainage network extraction, and flat surfaces are common features in flow direction determination. With the challenge of a massive volume of digital elevation models (DEMs), to reduce the running time and memory usage, there is a growing need to develop parallel algorithms to calculate flow directions over flat surfaces. We propose an efficient parallel algorithm for flow directions over flat surfaces based on the existing serial algorithm and three-step parallel framework. The proposed algorithm assigns pre-divided tiles to consumer processes to build local graphs. Then the producer process builds global graphs based on all the local graphs. Finally, consumer processes update the local graphs based on the global graphs and determine flow directions over flat surfaces. For all tested DEMs, the speed-up ratios are greater than 5 with 11 consumer processes. The strong scaling efficiencies are greater than 40% with 11 consumer processes. The proposed algorithm can run generally faster, use less memory, and process massive DEMs that cannot be successfully processed using the existing serial algorithm. This study shows that the proposed algorithm is an ideal parallel algorithm for determining flow direction over flat surfaces in massive DEMs.
How to combine the big data technologies and the corporate financial analysis methodologies to collect, store and analyze various data and finally dig out useful and valuable business information is an interesting research question. This paper summary the application principles and the realization path of the big data technologies in corporate financial analysis. Then, this research analyses the theoretical and practical values of the combination of the big data technologies and corporate financial analysis methodologies. Last but not least, this paper constructed a process framework model for corporate financial analysis in the big data environment. The proposed process framework model is beneficial for the process improvement of corporate financial analysis. The proposed process framework model is also useful to improve the accuracy of corporate financial analysis.
The underground monitoring environment is more complicated, and the rock mass rupture signal is often mixed with various noises. How to identify and extract the rock failure signal are the basis of application research on microseismic monitoring technology. In order to solve this problem, a combined analysis method based on qualitative and quantitative analysis was proposed in the paper. Firstly, based on field tests and empirical judgment, significant regularity signals, such as electromagnetic pulse, artificial rock percussion, tramcar transportation, and multi-millisecond blasting, are filtered based on different characteristics in amplitude, phase and frequency; Secondly, the energy characteristics informations of signals which are difficult to distinguish using qualitative method, can be mapped to different frequency bands. These can be significantly showed under different resolutions, which were not obvious originally. Such as the energy distribution difference between underground secondary blasting signals and rock fracture signals: the energy of underground secondary blasting signals is concentrated in 250~500 Hz, accounting for about 69.24% of the total energy; when rock fracture signals are more obviously concentrated in 0~125 Hz, accounting for about 77.22% of the total energy. The research may offer new methods and ideas for the identification and analysis of microseismic signals.
In the binocular vision system, the parallax image is different when the real face of the three-dimensional structure is viewed from different viewing angles, while the face in photos and videos is flat or the planar structure is the same when viewed from different angles, so it is proposed A new method that uses a manual face binocular disparity map made by a synchronized binocular visible light camera combined with a convolutional neural network (convolutional neural network, CNN) to achieve two classifications. First, the Harr cascade classifier is used to extract faces in binocular images at the same time, and the extracted partial faces are made into a data set; then, the disparity map form that meets the experimental hypothesis is proposed and analyzed; finally, the ResNet18 network is used to compare different methods Binocular live body detection solution for human face. The accuracy rate of 99.12% is achieved on the self-made data set in the laboratory, and it has a good resistance to facial attacks on photos and videos. The results show that the method has a small amount of data, low hardware cost and good robustness to human faces due to changes in light and angle.
