Showing papers by "Harbin Engineering University published in 2021"

Two-Dimensional Porous Molybdenum Phosphide/Nitride Heterojunction Nanosheets for pH-Universal Hydrogen Evolution Reaction

Abstract: Herein, we present a new strategy for the synthesis of 2D porous MoP/Mo2 N heterojunction nanosheets based on the pyrolysis of 2D [PMo12 O40 ]3- -melamine (PMo12 -MA) nanosheet precursor from a polyethylene glycol (PEG)-mediated assembly route. The heterostructure nanosheets are ca. 20 nm thick and have plentiful pores ( 55 mA cm-2 in neutral medium and >190 mA cm-2 in alkaline medium.

Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer

Abstract: The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such as convolution neural network (CNN) and recurrent neural network (RNN), which mainly accept formats of images, time series, and text data in the application layer. Therefore, it is of considerable interest to bridge the gap between signal waveforms to DL amenable data formats. In this article, we develop a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waveforms while being represented in an image data format. In this article, we explore several potential application scenarios and present effective CSI-based solutions to address the signal recognition challenges. Our investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.

Two steps synthesis of CeTiOx oxides nanotube catalyst: Enhanced activity, resistance of SO2 and H2O for low temperature NH3-SCR of NOx

Abstract: Cerium and titanium oxides are considered as promising alternative catalysts for selective catalytic reduction with NH3 (NH3-SCR) to remove NOx. However, the poor SO2 or H2O tolerance and stability limit their practical applications. Herein, CeTiOx with nanotube structure (CeTiOx-T) was prepared by hydrothermal method and used for NH3-SCR reaction. CeTiOx-T shows the excellent catalytic activity, SO2 and H2O tolerance and stability, in which more than 98 % NO conversion can be achieved in the range of 180−390 °C with 100 % N2 selectivity. The characterizations verify that CeTiOx-T exhibits amorphous structure due to the strong interaction between Ce and Ti to form short-range ordered Ce-O-Ti species. As results, CeTiOx-T displays the larger BET surface area, more surface Bronsted acid amounts and chemisorbed oxygen, leading to its higher NH3-SCR performance. In situ DRIFTS results suggest the SCR reaction mainly follow L-H and E-R mechanisms at low and high temperature for over CeTiOx-T, respectively.

Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges

Abstract: In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications Being able to learn from experience is an important way to approach this vision Transfer learning (TL) encourages new tasks/domains to learn from experienced tasks/domains for helping new tasks become faster and more efficient TL can help save energy and improve efficiency with the correlation and similarity information between different tasks in many fields of wireless communications Therefore, applying TL to future 6G communications is a very valuable topic TL has achieved some good results in wireless communications In order to improve the development of TL applied in 6G communications, this article performs a comprehensive review of the TL algorithms used in different wireless communication fields, such as base stations/access points switching, indoor wireless localization and intrusion detection in wireless networks, etc Moreover, the future research directions of mutual relationship between TL and 6G communications are discussed in detail Challenges and future issues about integrate TL into 6G are proposed at the end This article is intended to help readers understand the past, present, and future between TL and wireless communications

Manipulating Intratumoral Fenton Chemistry for Enhanced Chemodynamic and Chemodynamic-Synergized Multimodal Therapy.

Abstract: Chemodynamic therapy (CDT) uses the tumor microenvironment-assisted intratumoral Fenton reaction for generating highly toxic hydroxyl free radicals (•OH) to achieve selective tumor treatment. However, the limited intratumoral Fenton reaction efficiency restricts the therapeutic efficacy of CDT. Recent years have witnessed the impressive development of various strategies to increase the efficiency of intratumoral Fenton reaction. The introduction of these reinforcement strategies can dramatically improve the treatment efficiency of CDT and further promote the development of enhanced CDT (ECDT)-based multimodal anticancer treatments. In this review, the authors systematically introduce these reinforcement strategies, from their basic working principles, reinforcement mechanisms to their representative clinical applications. Then, ECDT-based multimodal anticancer therapy is discussed, including how to integrate these emerging Fenton reinforcement strategies for accelerating the development of multimodal anticancer therapy, as well as the synergistic mechanisms of ECDT and other treatment methods. Eventually, future direction and challenges of ECDT and ECDT-based multimodal synergistic therapies are elaborated, highlighting the key scientific problems and unsolved technical bottlenecks to facilitate clinical translation.

Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT

Abstract: The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data. Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air condition, pollution, climate change, etc. However, centralized conventional ML models rely on all clients’ data at a central server, which seriously threatens user privacy. Federated learning (FL) emerges as a promising solution aiming to protect user privacy by enabling model training on a large corpus of decentralized data. The recent studies indicate FL suffers from the heterogeneity issue as it treats all clients’ data equally, that is, FL might sacrifice the performance of the majority of clients to accommodate the performance of the minority of clients with low usability data. In order to overcome this issue, a reinforcement learning (RL)-based intelligent central server with the capability of recognizing heterogeneity is implemented, which can help lead the trend toward better performance for majority of clients. To be specific, an FL central server analyses the benefits of different collaboration by capturing the intricate patterns in heterogeneous clients based on rating feedback and then updates clients’ weights iteratively, until it establishes a coalition of clients with quasioptimal performance. The experimental results on three real data sets under various heterogeneity levels demonstrate the superior performance of the proposed solution.

Toward the development of Mg alloys with simultaneously improved strength and ductility by refining grain size via the deformation process

Abstract: Magnesium (Mg) alloys, as the lightest metal engineering materials, have broad application prospects. However, the strength and ductility of traditional Mg alloys are still relativity low and difficult to improve simultaneously. Refining grain size via the deformation process based on the grain boundary strengthening and the transition of deformation mechanisms is one of the feasible strategies to prepare Mg alloys with high strength and high ductility. In this review, the effects of grain size on the strength and ductility of Mg alloys are summarized, and fine-grained Mg alloys with high strength and high ductility developed by various severe plastic deformation technologies and improved traditional deformation technologies are introduced. Although some achievements have been made, the effects of grain size on various Mg alloys are rarely discussed systematically and some key mechanisms are unclear or lack direct microscopic evidence. This review can be used as a reference for further development of high-performance fine-grained Mg alloys.

2D Piezoelectric Bi 2 MoO 6 Nanoribbons for GSH‐Enhanced Sonodynamic Therapy

Abstract: Reducing the scavenging capacity of reactive oxygen species (ROS) and elevating ROS production are two primary goals of developing novel sonosensitizers for sonodynamic therapy (SDT). Hence, ultrathin 2D Bi2 MoO6 -poly(ethylene glycol) nanoribbons (BMO NRs) are designed as piezoelectric sonosensitizers for glutathione (GSH)-enhanced SDT. In cancer cells, BMO NRs can consume endogenous GSH to disrupt redox homeostasis, and the GSH-activated BMO NRs (GBMO) exhibit an oxygen-deficient structure, which can promote the separation of electron-hole pairs, thereby enhancing the efficiency of ROS production in SDT. The ultrathin GBMO NRs are piezoelectric, in which ultrasonic waves introduce mechanical strain to the nanoribbons, resulting in piezoelectric polarization and band tilting, thus accelerating toxic ROS production. The as-synthesized BMO NRs enable excellent computed tomography imaging of tumors and significant tumor suppression in vitro and in vivo. A piezoelectric Bi2 MoO6 sonosensitizer-mediated two-step enhancement SDT process, which is activated by endogenous GSH and amplified by exogenous ultrasound, is proposed. This process not only provides new options for improving SDT but also broadens the application of 2D piezoelectric materials as sonosensitizers in SDT.

Design of online intelligent English teaching platform based on artificial intelligence techniques

Abstract: Artificial intelligence education (AIEd) is defined in the field of education as the utilization of artificial intelligence. There are currently many AIEd‐driven applications in schools and universities. This paper applies an artificial intelligence module combined with the knowledge recommendation to the system and develops an online English teaching system in comparison with the common teaching auxiliary system. The method of English teaching is useful in investigating the potential internal connections between evaluation outcomes and various factors. This article develops deep learning‐assisted online intelligent English teaching system that utilizes to create a modern tool platform to help students improve their English language teaching efficiency in line with their mastery of knowledge and personality. The decision tree algorithm and neural networks have been used and to generate an English teaching assessment implementation model based on decision tree technologies. It provides valuable data from extensive information, summarizes rules and data, and helps teachers to improve their education and the English scores of students. This system reflects the thinking of the artificial intelligence expert system. Test application demonstrates that the system can help students improve their learning efficiency and will make learning content more relevant. Besides, the system provides an example model with similar methods and has a referential definition.

Upconverted Metal-Organic Framework Janus Architecture for Near-Infrared and Ultrasound Co-Enhanced High Performance Tumor Therapy.

Abstract: Strict conditions such as hypoxia, overexpression of glutathione (GSH), and high concentration of hydrogen peroxide (H2O2) in the tumor microenvironment (TME) limit the therapeutic effects of reactive oxygen species (ROS) for photodynamic therapy (PDT), chemodynamic therapy (CDT), and sonodynamic therapy (SDT). Here we fabricated a biocatalytic Janus nanocomposite (denoted as UPFB) for ultrasound (US) driven SDT and 808 nm near-infrared (NIR) light mediated PDT by combining core-shell-shell upconversion nanoparticles (UCNPs, NaYF4:20%Yb,1%Tm@NaYF4:10%Yb@NaNdF4) and a ferric zirconium porphyrin metal organic framework [PCN-224(Fe)]. Our design not only substantially overcomes the inefficient PDT effect arising from the inadequate Forster resonance energy transfer (FRET) process from UCNPs (donor) to MOFs (acceptor) with only NIR laser irradiation, but also promotes the ROS generation via GSH depletion and oxygen supply contributed by Fe3+ ions coordinated in UPFB as a catalase-like nanozyme. Additionally, the converted Fe2+ from the foregoing process can achieve CDT performance under acidic conditions, such as lysosomes. Meanwhile, UPFB linked with biotin exhibits a good targeting ability to rapidly accumulate in the tumor region, verified by fluorescence imaging and T2-weighted magnetic resonance imaging (MRI). In a word, it is believed that the synthesis and antitumor detection of UPFB heterostructures render them suitable for application in cancer therapeutics.

Lightweight, Fire-Retardant, and Anti-Compressed Honeycombed-Like Carbon Aerogels for Thermal Management and High-Efficiency Electromagnetic Absorbing Properties.

Abstract: Ordered porous carbon materials (PCMs) have potential applications in various fields due to their low mass densities and porous features. However, it yet remains extremely challenging to construct PCMs with multifunctionalization for electromagnetic wave absorption. Herein, the honeycombed-like carbon aerogels with embedded Co@C nanoparticles are fabricated by a directionally freeze-casting and carbonization method. The optimized aerogel possesses low density (0.017 g cm-3 ), fire-retardant, robust mechanical performance (compression moduli reach 1411 and 420 kPa in the longitudinal and transverse directions at 80% strain, respectively), and high thermal management (high thermal insulation capability and high-efficiency electrothermal conversion ability). Notably, the optimized aerogel exhibits the excellent electromagnetic wave absorption properties with broad effective absorption bandwidth (13.12-17.14 GHz) and strong absorption (-45.02 dB) at a thickness of only 1.5 mm. Density functional theory calculations and the experimental results demonstrate that the excellent electromagnetic wave absorption properties stem from the synergetic effects among high electrical conductivity, numerous interfaces and dipoles and unique ordered porous structure. Meanwhile, the computer simulation technology (CST) simulation confirms that the multifunctional aerogel can attenuate more electromagnetic energy in a practical environment. This work paves the way for rational design and fabrication of the next-generation electromagnetic wave absorbing materials.

Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks

Abstract: Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50 ${\%}$ on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

Chaos cloud quantum bat hybrid optimization algorithm

Abstract: The bat algorithm (BA) has fast convergence, a simple structure, and strong search ability. However, the standard BA has poor local search ability in the late evolution stage because it references the historical speed; its population diversity also declines rapidly. Moreover, since it lacks a mutation mechanism, it easily falls into local optima. To improve its performance, this paper develops a hybrid approach to improving its evolution mechanism, local search mechanism, mutation mechanism, and other mechanisms. First, the quantum computing mechanism (QCM) is used to update the searching position in the BA to improve its global convergence. Secondly, the X-condition cloud generator is used to help individuals with better fitness values to increase the rate of convergence, with the sorting of individuals after a particular number of iterations; the individuals with poor fitness values are used to implement a 3D cat mapping chaotic disturbance mechanism to increase population diversity and thereby enable the BA to jump out of a local optimum. Thus, a hybrid optimization algorithm—the chaotic cloud quantum bats algorithm (CCQBA)—is proposed. To test the performance of the proposed CCQBA, it is compared with alternative algorithms. The evaluation functions are nine classical comparative functions. The results of the comparison demonstrate that the convergent accuracy and convergent speed of the proposed CCQBA are significantly better than those of the other algorithms. Thus, the proposed CCQBA represents a better method than others for solving complex problems.

Ceramic Superstrate-Based Decoupling Method for Two Closely Packed Antennas With Cross-Polarization Suppression

Abstract: A ceramic superstrate-based decoupling method (CSDM) is proposed to reduce the mutual coupling between two closely packed dipole antennas while maintaining cross-polarization suppression. Compared with other superstrate-based methods, this proposed method can effectively reduce the mutual coupling between the antennas without using any periodic structures on the superstrate. The ceramic superstrate is a 2 mm thin slab with a relative dielectric constant of 20.5 and is suspended over the antennas coupled in H-plane with the spacing of only 0.28 wavelength at 3.5 GHz. It is demonstrated by both simulation and measurement that the isolation between two dipole antennas can be improved from 10 to more than 25 dB within the operation band while their reflection coefficients remain to be below −10 dB after the ceramic superstrate is introduced. The radiation patterns of the antenna maintain stable at different operation frequencies within the band of interest and the peak gain has increased by around 1.5 dB. Meanwhile, the total efficiency is enhanced by about 15% and the envelope correlation coefficient (ECC) between the two antennas is reduced from 0.7 to 0.4.

Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle.

Abstract: An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input-output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor-critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost. Both simulations and virtual-reality experiments demonstrate the remarkable effectiveness and superiority of the proposed DPRLC scheme.

Observer-Based Event-Triggered Approach for Stochastic Networked Control Systems Under Denial of Service Attacks

Abstract: In this article, we investigate the stability analysis and controller synthesis problems for a class of stochastic networked control systems under aperiodic denial-of-service (DoS) jamming attacks. First, an observer is constructed to estimate the unmeasurable states, and then a new adaptive event-triggered mechanism on the basis of the observer is proposed to eliminate the adverse effects of DoS attacks and schedule the transmission instants so as to realize a reduction of transmission burden in the network. Under the proposed event-driven communication scheme, an observer-based controller is designed, and a new switched system with time-varying delays is introduced. Conditions for the underlying systems to be mean-square exponentially stable with a weighted $L_2$ -gain are established. Also, conditions to co-design the observer, the controller, and the event-triggered scheme are developed. A mass-spring-damper mechanical system is used to demonstrate the effectiveness and advantages of the new design techniques.

Porous geopolymer composites: A review

Abstract: Porous geopolymers have emerged as one of the most promising inorganic porous materials over the last decade, due to their inexpensive and easy fabrication process, suitable properties, thermal and chemical stability, and extensive applications. To further improve or optimize the properties of porous geopolymers or to endow them with new functionalities, a significant effort has been devoted to the exploitation and application of porous geopolymer composite materials. This review article highlights the current state-of-the-art in the field of porous geopolymer composites manufacturing methods (direct foaming, embedding lightweight (porous) fillers, additive manufacturing, etc.), properties (mechanical properties, thermal properties, adsorption properties, and others), and applications. With the summary and analysis of previous research literature, this review aims to foster further investigations into developing innovative routes for the fabrication of porous geopolymer components with improved properties and to encourage the widespread technological application of these materials.

Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery

Abstract: The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the l2,1-norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.