Research on None-Line-of-Sight/Line-of-Sight Identification Method Based on Convolutional Neural Network-Channel Attention Module.

None-Line-of-Sight (NLOS) propagation of Ultra-Wideband (UWB) signals leads to a decrease in the reliability of positioning accuracy. Therefore, it is essential to identify the channel environment prior to localization to preserve the high-accuracy Line-of-Sight (LOS) ranging results and correct or reject the NLOS ranging results with positive bias. Aiming at the problem of the low accuracy and poor generalization ability of NLOS/LOS identification methods based on Channel Impulse Response (CIR) at present, the multilayer Convolutional Neural Networks (CNN) combined with Channel Attention Module (CAM) for NLOS/LOS identification method is proposed. Firstly, the CAM is embedded in the multilayer CNN to extract the time-domain data features of the original CIR. Then, the global average pooling layer is used to replace the fully connected layer for feature integration and classification output. In addition, the public dataset from the European Horizon 2020 Programme project eWINE is used to perform comparative experiments with different structural models and different identification methods. The results show that the proposed CNN-CAM model has a LOS recall of 92.29%, NLOS recall of 87.71%, accuracy of 90.00%, and F1-score of 90.22%. Compared with the current relatively advanced technology, it has better performance advantages.

Texture and volatile profiles of beef tallow substitute produced by a pilot-scale continuous enzymatic interesterification.

Edible beef tallow (BT) has been widely used in Sichuan hotpot due to its unique flavor and texture. However, BT should not be consumed in excess caused by its trans-fatty acids and cholesterol issues. In this study, a BT substitute was prepared after enzymatic interesterification in a pilot-scale packed-bed reactor using soybean oil and fully hydrogenated palm oil (4:3, w/w) as feedstock. The products were characterized against BT in terms of fatty acid/triacylglycerol compositions, solid fat content, polymorphism, and melting/crystallization behaviors to select the most promising BT substitute. The optimal flow rate was 120 mL/min. Changes in volatile compounds during stir-frying and simmering were also investigated for Sichuan hotpots made with these two oils. The volatile compounds of BT substitute were similar to that of natural BT. The findings will contribute to expanding the base oil categories of Sichuan hotpot oils.

Optimization of solar-driven systems for off-grid water nanofiltration and electrification.

The work described is motivated by an inability to extend central infrastructure for power and water to low-population-density areas of the Navajo Nation and elsewhere. It is estimated that 35% of the Navajo population haul water for household use, frequently from unregulated sources of poor initial quality. The proposed household-scale, solar-driven nanofiltration (NF) system designs are economically optimized to satisfy point-of-use water purification objectives. The systems also provide electrical energy for a degree of nighttime household illumination. Results support rational design of multiple-component purification systems consisting of solar panels, a high-pressure pump, NF membranes, battery storage and an electrical control unit subject to constraints on daily water treatment and excess energy generation. The results presented are conditional (based on initial water quality, membrane characteristics and geography) but can be adapted to satisfy alternative treatment objectives in alternate geographic, etc. settings. The unit costs of water and energy from an optimized system that provides 100 gpd (1 gallon is 3.78 L) and 2 kWh/day of excess electrical energy are estimated at $0.16 per 100 gallons of water treated and $0.26 per kWh of nighttime electrical energy delivered. Methods can be used to inform dispersed infrastructure design subject to alternate constraint sets in similarly remote areas.

An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite.

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.