Three-dimensional atrous inception module for crowd behavior classification.

Recent advances in deep learning have led to a surge in computer vision research, including the recognition and classification of human behavior in video data. However, most studies have focused on recognizing individual behaviors, whereas recognizing crowd behavior remains a complex problem because of the large number of interactions and similar behaviors among individuals or crowds in video surveillance systems. To solve this problem, we propose a three-dimensional atrous inception module (3D-AIM) network, which is a crowd behavior classification model that uses atrous convolution to explore interactions between individuals or crowds. The 3D-AIM network is a 3D convolutional neural network that can use receptive fields of various sizes to effectively identify specific features that determine crowd behavior. To further improve the accuracy of the 3D-AIM network, we introduced a new loss function called the separation loss function. This loss function focuses the 3D-AIM network more on the features that distinguish one type of crowd behavior from another, thereby enabling a more precise classification. Finally, we demonstrate that the proposed model outperforms existing human behavior classification models in terms of accurately classifying crowd behaviors. These results suggest that the 3D-AIM network with a separation loss function can be valuable for understanding complex crowd behavior in video surveillance systems.

Charge order landscape and competition with superconductivity in kagome metals.

In the kagome metals AV3Sb5 (A = K, Rb, Cs), three-dimensional charge order is the primary instability that sets the stage for other collective orders to emerge, including unidirectional stripe order, orbital flux order, electronic nematicity and superconductivity. Here, we use high-resolution angle-resolved photoemission spectroscopy to determine the microscopic structure of three-dimensional charge order in AV3Sb5 and its interplay with superconductivity. Our approach is based on identifying an unusual splitting of kagome bands induced by three-dimensional charge order, which provides a sensitive way to refine the spatial charge patterns in neighbouring kagome planes. We found a marked dependence of the three-dimensional charge order structure on composition and doping. The observed difference between CsV3Sb5 and the other compounds potentially underpins the double-dome superconductivity in CsV3(Sb,Sn)5 and the suppression of Tc in KV3Sb5 and RbV3Sb5. Our results provide fresh insights into the rich phase diagram of AV3Sb5.

Facile and scalable synthesis of silicon nanowires from waste rice husk silica by the molten salt process.

Designing nanostructured silicon, such as in the form of nanoparticles, wires, and porous structures, for high-performance Li-ion electrodes, has progressed significantly. These approaches have largely overcome the capacity fading of silicon electrodes from volume expansion during lithiation/de-lithiation. However, they involve high costs, complex processes, and hazardous precursors. Herein, we propose an electrochemical fabrication of silicon nanowires from waste rice husks via a molten salt process based on electrodeoxidation. The addition of NiO as an electric conductor improved the production efficiency and created pores in the nanowires after washing. The electrically produced high-purity silicon yielded high capacity, and the nanowires provided sufficient free volume to accommodate silicon electrode expansion, resulting in improved cycle life. The converted silicon nanowires from the molten salt process will help develop sustainable energy storage materials.

Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme.

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

Least Squares Neural Network-Based Wireless E-Nose System Using an SnO2 Sensor Array.

Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH4) and carbon monoxide (CO), using an array of SnO2 gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH4 and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.