In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved $A{P}_{0.5}$ of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.

Based on 58 million J/ψ events collected by the BES II detector at the BEPC, J/ψ → Λ π⁺π⁻ is observed for the first time. The branching fraction is measured to be Br(J/ψ → Λ π⁺π⁻) = (4.30±0.13±0.99)×10⁻³, excluding the decays to intermediate states, namely J/ψ → Ξ⁻⁺, J/ψ → Σ(1385)⁻(1385)⁺, and J/ψ → Σ(1385)⁺(1385)⁻. The branching fractions for these intermediate resonance channels are measured to be: Br(J/ψ → Ξ⁻⁺) = (0.90±0.03±0.18)×10⁻³, Br(J/ψ → Σ(1385)⁻(1385)⁺) = (1.23±0.07±0.30)×10⁻³, and Br(J/ψ → Σ(1385)⁺(1385)⁻) = (1.50±0.08±0.38)×10⁻³, respectively. The angular distribution is of the form ∝ (1 + αcos²θ) with α = (0.35±0.29±0.06) for J/ψ → Ξ⁻⁺, α = (−0.54±0.22±0.10) for J/ψ → Σ(1385)⁻(1385)⁺, and α = (−0.35±0.25±0.06) for J/ψ → Σ(1385)⁺(1385)⁻.

In the process of recycled aluminum smelting, timely measurement of the temperature of the smelting furnace is very important for the aluminum yield and quality. However, it is sometimes difficult or costly to measure the temperature in a timely manner due to the high temperature and pressure environment in the furnace. To tackle this problem, a soft sensor modeling framework which combines an operating condition classification and a prediction model based on locally sample-weighted long short-term memory (LSTM) neural network is proposed. In the operating condition classification, a hybrid of dynamic time warping (DTW) based fuzzy c-means and convolutional neural network is used to cluster the training samples and to classify the query samples. In the prediction model, the dynamic time warping and locally sample-weighted technique are introduced to LSTM to solve time-varying and strong nonlinear problems of the process. By adopting the method of classifying the operating conditions of the query samples before temperature prediction, the prediction time can be effectively reduced and the prediction accuracy can be maintained. The results of the experiment show that the proposed method can meet the prediction accuracy and time efficiency requirements of the regenerative aluminum smelting furnace.