Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Compact Microstrip Dual-/Tri-/Quad-Band Bandpass Filter Using Open Stubs Loaded Shorted Stepped-Impedance Resonator

Abstract: This paper presents a new class of dual-, tri- and quad-band BPF by using proposed open stub-loaded shorted stepped-impedance resonator (OSLSSIR). The OSLSSIR consists of a two-end-shorted three-section stepped-impedance resistor (SIR) with two identical open stubs loaded at its impedance junctions. Two 50- Ω tapped lines are directly connected to two shorted sections of the SIR to serve as I/O ports. As the electrical lengths of two identical open stubs increase, many more transmission poles (TPs) and transmission zeros (TZs) can be shifted or excited within the interested frequency range. The TZs introduced by open stubs divide the TPs into multiple groups, which can be applied to design a multiple-band bandpass filter (BPF). In order to increase many more design freedoms for tuning filter performance, a high-impedance open stub and the narrow/broad side coupling are introduced as perturbations in all filters design, which can tune the even- and odd-mode TPs separately. In addition, two branches of I/O coupling and open stub-loaded shorted microstrip line are employed in tri- and quad-band BPF design. As examples, two dual-wideband BPFs, one tri-band BPF, and one quad-band BPF have been successfully developed. The fabricated four BPFs have merits of compact sizes, low insertion losses, and high band-to-band isolations. The measured results are in good agreement with the full-wave simulated results.

High performance polyester reverse osmosis desalination membrane with chlorine resistance

Abstract: Chlorination is a common practice to prevent biofouling in municipal water supplies, wastewater reuse and seawater desalination. However, polyamide thin-film composite reverse osmosis membranes—the premier technology for desalination and clean-water production—structurally deteriorate when continually exposed to chlorine species. Here, we use layer-by-layer interfacial polymerization of 3,5-dihydroxybenzoic acid with trimesoyl chloride to fabricate a polyester thin-film composite reverse osmosis membrane that is chlorine-resistant in neutral and acidic conditions. Strong steric hindrance and an electron-withdrawing group effectively prevent direct aromatic chlorination, and residual OH groups capped with isophthaloyl dichloride preclude reaction with active chlorine. The poly(isophthalester) membrane exhibits high salt rejection (99.1 ± 0.2%) and water permeability (2.97 ± 0.13 l m−2 h−1 bar−1), even after demonstrating biofouling prevention with chlorine (50 mg l−1 of NaOCl for 15 min). We anticipate that our chlorine-resistant membrane will greatly advance reverse osmosis desalination as a sustainable technology to meet the global challenge of water supply. Reverse osmosis membranes are the primary technology used for desalination and wastewater recycling, but they are prone to biofouling and subsequent performance deterioration due to poor tolerance to disinfecting agents such as chlorine. Here a chlorine-resistant polyester reverse osmosis membrane is developed to prevent biofouling and increase the sustainability of desalination and wastewater reuse.

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

Abstract: In this work, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamic for single-person action recognition due to its ability to capture the temporal motion information in a range. However, most existing LSTM-based methods focus only on capturing the dynamics of human interaction by simply combining all dynamics of individuals or modeling them as a whole. Such methods neglect the inter-related dynamics of how human interactions change over time. To this end, we propose a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among a group of persons for recognizing human interactions. Specifically, we first feed each person's static features into a Single-Person LSTM to model the single-person dynamic. Subsequently, at one time step, the outputs of all Single-Person LSTM units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly consists of multiple sub-memory units, a new cell gate, and a new co-memory cell. In the Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively. Extensive experiments on several public datasets validate the effectiveness of the proposed H-LSTCM by comparing against baseline and state-of-the-art methods.

Fault detection of networked dynamical systems: a survey of trends and techniques

Abstract: Fault detection of networked dynamical systems (NDSs) has attracted ever-increasing attention since it can maintain high-quality products as well as operational safety. Considering the utilisation ...