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.

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.

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

Combustible gases, such as CH 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.

Preparation of Highly Porous PAN-LATP Membranes as Separators for Lithium Ion Batteries.

Separators are a vital component to ensure the safety of lithium-ion batteries. However, the commercial separators employed in lithium ion batteries are inefficient due to their low porosity. In the present study, a simple electrospinning technique is adopted to prepare highly porous polyacrylonitrile (PAN)-based membranes with a higher concentration of lithium aluminum titanium phosphate (LATP) ceramic particles, as a viable alternative to the commercialized separators used in lithium ion batteries.

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.