Dynamical analysis of the incommensurate fractional-order neural network is a novel topic in the field of chaos research. This article investigates a Hopfield neural network (HNN) system in view of incommensurate fractional orders. Using the Adomian decomposition method (ADM) algorithm, the solution of the incommensurate fractional-order Hopfield neural network (FOHNN) system is solved. The equilibrium point of the system is discussed, and the dissipative characteristics are verified and discussed. By varying the order values of the proposed system, different dynamical behaviors of the incommensurate FOHNN system are explored and discussed via bifurcation diagrams, the Lyapunov exponent spectrum, complexity, etc. Finally, using the DSP platform to implement the system, the results are in good agreement with those of the simulation. The actual results indicate that the system shows many complex and interesting phenomena, such as attractor coexistence and an inversion property, with dynamic changes of the order of q0, q1, and q2. These phenomena provide important insights for simulating complex neural system states in pathological conditions and provide the theoretical basis for the later study of incommensurate fractional-order neural network systems.
The Uninterruptible Power Supply (UPS) is a kind of power supply with electric energy storage, but most UPS systems bring harmonic pollution to the grid, and the power factor is inaccurate in the boost circuit, the output voltage is unstable. Therefore, an active power factor correction circuit (APFC) based on the current and voltage double closed-loop structure is designed in the boost circuit; besides, the fuzzy PID control algorithm is also proposed in the inverter circuit. The effectiveness of the proposed method can be verified by the computer simulation and real experiments, and there are four main results as follows. Firstly, the actual power factor of the UPS system can reach more than 0.996 with APFC correction circuit; then, the UPS system has the strong robustness and the shorter response time; in addition, the voltage regulation rate of the system remains at 0.083% and the load regulation rate is around 0.056%. Finally, the designed UPS system can provide the stable 36V ± 0.2V (50 ± 0.2Hz) AC power.
In order to address the problem that the detailed features of pedestrians are not prominent and the pedestrian pictures are obscured in unique environments in the process of person re-recognition, we propose a person re-recognition method with a multi-grain size generative adversarial network. Firstly, we use the generative adversarial network to recover the occluded pedestrian pictures; secondly, we improve the traditional multi-granularity network by adding an Efficient Channel Attention for Deep Convolutional Neural Networks (ECA-Net) on the coarse-grained branch to focus on the feature information in the pedestrian pictures and use the High-Resolution Net (HRNet) for pose estimation on the fine-grained branch to divide the pedestrian pictures into nine parts, to enhance the network's learning of more detailed features of pedestrians, and thus improve the accuracy of pedestrian re-recognition learning, which in turn improves the accuracy of person re-identification.INDEX TERMS Person re-identification, generative adversarial networks, random occlusion, attention mechanism.
Visible light positioning (VLP) technology is a classic application of visible light communication (VLC), which inherits the advantages of VLC and applies it to the field of positioning. LED (light-emitting diode) is a type of light source. Because of its high brightness, aesthetically pleasing characteristics, and ease of installation, it is used in a variety of indoor lighting applications. However, most of the current VLP technology is still in the laboratory simulation stage and cannot be used in industry or life on a large scale due to various reasons, such as accuracy and cost. Because of the large size of LED flat panel lamps, there are almost no VLP applications with LED flat panel lamps as the emitting light source. Therefore, this paper proposes a VLP technology combining LED flat panel light and a barcode, with a single flat panel light at the transmitting end and a smartphone with a camera at the receiving end, to achieve fuzzy positioning. The paper further uses the angle sensor to assist in designing the “pseudo-two-light positioning” algorithm and selects 16 test points for experiments, and the average positioning error can reach a minimum of 6.5023 cm, achieving centimeter-level positioning accuracy requirements.
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