This article proposes an efficient thermoelectric temperature control system based on an improved proportional integral differential (PID) algorithm in which energy feedback technology is used to enhance thermoelectric cooling. In the proposed power management system, two groups of batteries are efficiently and alternatingly charged and discharged such that the information of the circuit can be monitored in real time. The PID algorithm is improved by using the idea of a state machine to control the thermoelectric coolers (TEC) through an H-bridge circuit with pulsewidth modulation. Finally, the energy feedback circuit combined with improved synchronous switching technology is designed to recycle the energy to drive the sensor. By inputting current of 3.1 A, a wide range of temperature control from 1.437 to 60.187 °C was implemented. While targeting a temperature of 10 °C at an ambient temperature of 22 °C, the proposed temperature control system had a control time of 30.5 s, compared with 287 s when using the conventional method, with an accuracy of 0.1 °C, and an error of only ±0.35 °C. The results confirm that electric energy at a peak voltage of 1.2 V and current of 24 μA can be recovered. The proposed energy feedback system can thus improve the efficiency of energy utilization of TEC from a peripheral circuit.

Thermoelectric coolers (TECs) are widely used in state-of-the-art thermal management systems. Recently, there is a big trend to power TECs using thermoelectric generators (TEGs). Mainstream research efforts focus on attaining a higher figure of merit (ZT) of thermoelectric material, which now faces a great challenge. Alternatively, this article proposes a different approach to improve the performance of TEC, that is, integration of a TEG with a TEC. The TEG converts the collected heat energy into electric current, which reduces the power consumption and enhances the cooling capacity of the TEC. Using different methods of connecting the TEC and TEG, two thermoelectric collaborative cooling systems are proposed. Accurate SPICE models of the two cooling systems are established. The experimental results demonstrate that the discrepancy between the currents flowing through the TEC in the experiments and in the SPICE models is less than 4.8% on average. Based on the verified SPICE models, the proposed TEC-TEG collaborative cooling systems are assessed in terms of power consumption, cooling capacity, coefficient of performance, and cooling efficiency. Compared with a typical Peltier cooling system, the two collaborative cooling systems achieve significant performance improvements.

Uniform magnetic fields are widely used in atomic magnetometers, MRI and other fields. The traditional method of solving closed-form equations is usually used to design highly uniform magnetic field coils. In this paper, a new intelligent algorithm, wolf pack algorithm (WPA), is proposed to design the coil structure of a highly uniform magnetic field. This method can replace the traditional method to obtain the coil parameters. We additionally use a discrete optimization method to solve the rounding error. According to the investigation, compared with particle swarm optimization (PSO) algorithm, WPA has stronger global optimization ability, faster convergence speed and multiple optimization strategies. We set appropriate coil constraints (structural and process constraints) and simplify the multi-dimensional solutions to one-dimensional solutions to reduce the difficulty of operation. The distribution of magnetic field intensity of three axes of coils is analyzed by the finite element analysis method, and the 1%, 0.1%, and 0.01% uniform regions are studied. Under the same conditions, the uniform magnetic field regions of the coils designed by the PSO and WPA, as well as a single pair of axial and radial coils are compared. The performance results of multiple pairs of coils are superior to those of a single pair of coils. In general, the uniformity of the axial and radial magnetic field coils designed by the WPA is better than that designed by the PSO. Finally, the experimental value of the magnetic field uniformity is nearly consistent with the theoretical value.

Graphene has been widely utilized in optoelectronic applications due to its high carrier mobility, and extremely fast optical response. Microcavity-integrated graphene waveguide structure is one basic module of integrated photonic devices which can greatly improve the light-matter interaction strength. The enhanced optical absorption in the undoped graphene layer results from the light trapping and the corresponding long light-graphene interaction length. Tuning the Fermi energy level of the graphene layer enables the electro-optical modulation. We report the realization of reconfigurable electro-optical attenuator and switch with unity-order modulation depth in light reflection and transmission at near-infrared frequency. The transformation from a lossy absorber to a quasi-perfect transparent condition of the monolayer graphene by tuning the Fermi level leads to the unity-order tunability of the electro-optical attenuator and switch. We investigate theoretically and numerically the absorption properties of the designed microcavity-integrated graphene with respect to different graphene Fermi levels. Electro-optical attenuator with attenuating coefficient from 10% to 98.29% is fulfilled. On-off electro-optical switching with a switching contrast larger than 21 dB is demonstrated. Our approach provides the possibilities of graphene photonics applied in communications, and sensing.

In recent years, research on emotion recognition has become more and more popular, but there are few studies on emotion recognition based on cerebral blood oxygen signals. Since the electroencephalogram (EEG) is easily disturbed by eye movement and the portability is not high, this study uses a more comfortable and convenient functional near-infrared spectroscopy (fNIRS) system to record brain signals from participants while watching three different types of video clips. During the experiment, the changes in cerebral blood oxygen concentration in the 8 channels of the prefrontal cortex of the brain were collected and analyzed. We processed and divided the collected cerebral blood oxygen data, and used multiple classifiers to realize the identification of the three emotional states of joy, neutrality, and sadness. Since the classification accuracy of the convolutional neural network (CNN) in this research is not significantly superior to that of the XGBoost algorithm, this paper proposes a CNN-Transformer network based on the characteristics of time series data to improve the classification accuracy of ternary emotions. The network first uses convolution operations to extract channel features from multi-channel time series, then the features and the output information of the fully connected layer are input to the Transformer netork structure, and its multi-head attention mechanism is used to focus on different channel domain information, which has better spatiality. The experimental results show that the CNN-Transformer network can achieve 86.7% classification accuracy for ternary emotions, which is about 5% higher than the accuracy of CNN, and this provides some help for other research in the field of emotion recognition based on time series data such as fNIRS.

A photovoltaic-thermoelectric (PV-TE) hybrid system can be used for efficient thermal energy utilization from the generated waste heat in PV devices. In this article, an efficient PV-TE hybrid system with intelligent power-track switching technique and thermal management based on energy conversion is proposed. To make the output power of PV-TE system stable and normalized, an incorporated stable voltage circuit is designed based on energy conversion. In addition, a control-and-monitoring strategy is launched in the system to realize the normal collecting for the output power of PV-TE system. Finally, a battery protection circuit is performed to ensure that the energy converted by the entire system is effectively stored. The experimental results show that more electrical energy about 84 034 J was obtained with our energy harvesting system than that of a single photovoltaic (PV) cell. Besides, the thermal gradient of PV cells is indirectly reduced the operation of the whole system, which is automatically monitored due to the proposed intelligent power-track switching technique.