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In this paper, a non-intrusive load identification algorithm for residents based on prior knowledge and statistical learning model is proposed to solve the problem of insufficient electric heating subdivision capability in traditional identification technology. In this paper, the electric heating subdivision research is carried out for the auxiliary heating equipment of washing machine, electric kettle, electric rice cooker, electric water heater. The subdivision of auxiliary heating equipment is realized through the equipment operation association algorithm, and the model training of non-auxiliary heating equipment classification is realized based on the limited feedback information of users and expert annotation. The experimental results show that the technical framework proposed in this paper realizes the subdivision of electric heating equipment on the basis of the event detection load identification algorithm and F1 socre above 0.9 is achieved in the decomposition of operation state.

For the effective, convenient and unified management of materials about military logistics, this paper studies and designs a monitoring system for monitoring the presence of items. The system uses the motherboard of Raspberry Pi to collect the level signal of the infrared sensor about the presence of the items and the images taken by camera of the items. Then it converts the data into the general data frame, and sends the frame to the data processing module through the source-specific multicast. Finally, the image recognition based on the convolutional neural network is used to judge the correctness of the item, and display its status in real time through the monitoring interface. It has been verified that the system can ensure the real time of data acquisition and the accuracy of identifying items. It has strong practicability.

In the manned space flight mission, the ground commanders and medical experts shall timely grasp the physiological conditions of astronauts in the cabin to ensure the safety of them. The heart rate and respiratory rate of astronauts are important physiological indexes to evaluate the health status of astronauts. How to calculate the heart rate and respiratory rate of astronauts quickly and accurately is one of the difficult problems faced by engineering tasks. Comprehensively considering the computing power under the condition of domestic autonomous controllable software and hardware, this paper presents a heart rate calculation R-wave detection algorithm based on self-learning threshold, and the zero-detection algorithm of respiratory rate calculation, and also designs a set of visual display software of heart rate and respiratory rate. It improves the real-time and accuracy of astronauts′ physiological data processing, and provides intuitive and reliable decision-making basis for the commanders and experts. The calculation and analysis of data shows that the algorithm has high accuracy and low computational requirements. It can meet the needs of medical support and medical supervision for astronauts in the waiting and ascending stages.

Under the background of dual carbon strategy and coal shortage,the existing demand response mechanism of evaluation and incentive is relatively extensive,causing challenges such as excessive redundancy of invitation and difficulty in mining high-quality users,so the accurate evaluation and dynamic incentive decision method of aggregators' demand response considering contribution degree is proposed. Firstly,the accurate evaluation system of demand response considering static and dynamic process is developed and the static evaluation follows the existing demand response evaluation system at the grid side,while the dynamic evaluation focuses on the consistency between the actual load and the target load in each period. Then,the user contribution index is described from the economic and load dimensions,and a two-part incentive settlement scheme with static and dynamic evaluation is designed. Based on that,the dynamic incentive decision model of aggregator's demand response with the contribution degree is got. Finally,the extensive numerical study confirms that the proposed method can achieve the directional incentive of high-quality users,thus improving the response quality and reducing the invitation redundancy without affecting the existing demand response system.

Natural bone is a complex material that has been carefully designed. To prepare a successful bone substitute, two challenging conditions need to be met: biocompatible and bioactive materials for cell proliferation and differentiation, and appropriate mechanical stability after implantation. Therefore, a hybrid Poly ε-caprolactone/Poly(lactic-co-glycolide)/β-tricalcium phosphate (PCL/PLGA/β-TCP) scaffold has been introduced as a suitable composition that satisfies the above two conditions. The blended PCL and PLGA can improve the scaffold's mechanical properties and biocompatibility compared to single PCL or PLGA scaffolds. In addition, the incorporated β-TCP increases the mechanical strength and osteogenic potential of PCL/PLGA scaffolds, while the polymer improves the mechanical stability of ceramic scaffolds. The PCL/PLGA/β-TCP scaffold is designed using spiral structures to provide a much better transport system through the gaps between spiral walls than conventional cylindrical scaffolds. Human fetal osteoblasts (hFOBs) were cultured on spiral PCL/PLGA/β-TCP (PPBS), cylindrical PCL/PLGA/β-TCP (PPBC), and cylindrical PCL scaffolds for a total of 28 days. The cell proliferation, viability, and osteogenic differentiation capabilities were analyzed. Compared with PCL and PPBC scaffolds, the PPBS scaffold exhibits great biocompatibility and potential to stimulate cell proliferation and differentiation and, therefore, can serve as a bone substitute for bone tissue regeneration. [ABSTRACT FROM AUTHOR]

An arc fault is the leading cause of electrical fire. Aiming at the problems of difficulty in manually extracting features, poor generalization ability of models and low prediction accuracy in traditional arc fault detection algorithms, this paper proposes a fault arc detection method based on the fusion of channel attention mechanism and residual network model. This method is based on the channel attention mechanism to perform global average pooling of information from each channel of the feature map assigned by the residual block while ignoring the local spatial data to enhance the detection and recognition rate of the fault arc. This paper introduces a one-dimensional depth separable convolution (1D-DS) module to reduce the network model parameters and shorten the time of single prediction samples. The experimental results show that the F1 score of the network model for arc fault detection under mixed load conditions is 98.07%, and the parameter amount is reduced by 46.06%. The method proposed in this paper dramatically reduces the parameter quantity, floating-point number and time complexity of the network structure while ensuring a high recognition rate, which improves the real-time response ability to detect arc fault. It has a guiding significance for applying arc fault on the edge side. [ABSTRACT FROM AUTHOR]