Wide frequency phonons manipulation in Si nanowire by introducing nanopillars and nanoparticles

The combination of different nanostructures can hinder phonons transmission in a wide frequency range and further reduce the thermal conductivity(TC).This will benefit the improvement and application of thermoelectric conversion,insulating materials and thermal barrier coatings,etc.In this work,the effects of nanopillars and Ge nanoparticles(GNPs)on the thermal transport of Si nanowire(SN)are investigated by nonequilibrium molecular dynamics(NEMD)simulation.By analyzing phonons transport behaviors,it is confirmed that the introduction of nanopillars leads to the occurrence of lowfrequency phonons resonance,and nanoparticles enhance high-frequency phonons interface scattering and localization.The results show that phonons transport in the whole frequency range can be strongly hindered by the simultaneous introduction of nanopillars and nanoparticles.In addition,the effects of system length,temperature,sizes and numbers of nanoparticles on the TC are investigated.Our work provides useful insights into the effective regulation of the TC of nanomaterials.

DMT:A model detecting multispecies of tea buds in multi-seasons

In China,tea products made from fresh leaves characterized by one leaf with one bud(1L1B)are classified as“Famous Tea”,which has better taste and higher economic value,but suffers from a labor shortage.Aiming at picking automation,existing studies focus on visual detection of 1L1B,but algorithm validation is limited to a specific variety of tea sprouting in a certain harvest season at a certain location,which limits the engineering application of developed tea picking robots working in various natural tea fields.To address this gap,a deep learning model DMT(detecting multispecies of tea)based on YOLOX-S was proposed in this paper.The DMT network takes YOLOX-S as a baseline and adds ECA-Net to the CSP Darknet and FPN of YOLOX-S.The average precision(AP),precision,and recall of DMT are 94.23%,93.39%,and 88.02%,respectively,for detecting 1L1B sprouting in spring;93.92%,93.56%,and 87.88%,respectively,for detecting 1L1Bsprouting in autumn.These experimental results are better than those of the five current object detection models.After fine-tuning the DMT network with another dataset composed of multiple tea varieties,the DMT network can detect 1L1B for different varieties of tea in multiple picking seasons.The results can promote the engineering application of picking automation of fresh tea leaves.

High-efficiency tea shoot detection method via a compressed deep learning model

Achieving high-efficiency and accurate detection of tea shoots in fields are essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algorithm was proposed in this study. First, tea shoot images were collected and data augmentation was performed to increase sample diversity, and then a spatial pyramid pooling module was added to the YOLOv3 model to detect tea shoots. To simplify the tea shoot detection model and improve the detection speed, the channel pruning algorithm and layer pruning algorithm were used to compress the model. Finally, the model was fine-tuned to restore its accuracy, and achieve the fast and accurate detection of tea shoots. The test results demonstrated that the number of parameters, model size, and inference time of the tea shoot detection model after compression reduced by 96.82%, 96.81%, and 59.62%, respectively, whereas the mean average precision of the model was only 0.40% lower than that of the original model. In the field test, the compressed model was deployed on a Jetson Xavier NX to conduct the detection of tea shoots. The experimental results demonstrated that the detection speed of the compressed model was 15.9 fps, which was 3.18 times that of the original model. All the results indicate that the proposed method could be deployed on tea harvesting robots with low computing power to achieve high efficiency and accurate detection.

Automatically Identifying the Sentence Skeleton of Chinese Sentences Based on the Event Model

Many difficulties remain in identifying the sentence skeleton of Chinese sentences. The purpose of this paper is to automatically identify the sentence skeleton of Chinese sentences on the event level. Based on the event model, this paper discusses the relationship between events and concepts, and presents an event conceptualization method. It analyzes the event composition of Chinese sentences, classifies the re- lationships among events, and proposes a recursive description of the event composition of Chinese sen- tences. Based on these analyses, an algorithm for automatically identifying the sentence skeleton of Chi- nese sentences based on the event model is presented. The results of the final experiment show an 89% success rate, which demonstrates that the proposed algorithm is feasible and effective.