Showing papers by "Harbin Engineering University published in 2019"

A Survey on Green 6G Network: Architecture and Technologies

Abstract: While 5G is being commercialized worldwide, research institutions around the world have started to look beyond 5G and 6G is expected to evolve into green networks, which deliver high Quality of Service and energy efficiency. To meet the demands of future applications, significant improvements need to be made in mobile network architecture. We envision 6G undergoing unprecedented breakthrough and integrating traditional terrestrial mobile networks with emerging space, aerial and underwater networks to provide anytime anywhere network access. This paper presents a detailed survey on wireless evolution towards 6G networks. In this survey, the prime focus is on the new architectural changes associated with 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack. Along with this, we discuss related potential technologies that are helpful in forming sustainable and socially seamless networks, encompassing terahertz and visible light communication, new communication paradigm, blockchain and symbiotic radio. Our work aims to provide enlightening guidance for subsequent research of green 6G.

Synthesis of novel nanomaterials and their application in efficient removal of radionuclides

Abstract: With the development of nuclear energy, large amounts of radionuclides are inevitably released into the natural environment. It is necessary to eliminate radionuclides from wastewater for the protection of environment. Nanomaterials have been considered as the potential candidates for the effective and selective removal of radionuclides from aqueous solutions under complicated conditions because of their high specific surface area, large amounts of binding sites, abundant functional groups, pore-size controllable and easily surface modification. This review mainly summarized the recent studies for the synthesis, fabrication and surface modification of novel nanomaterials and their applications in the efficient elimination and solidification of radionuclides, and discussed the interaction mechanisms from batch experiments, spectroscopy analysis and theoretical calculations. The sorption capacities with other materials, advantages and disadvantages of different nanomaterials are compared, and at last the perspective of the novel nanomaterials is summarized.

Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

Abstract: Speech emotion recognition is a vital and challenging task that the feature extraction plays a significant role in the SER performance. With the development of deep learning, we put our eyes on the structure of end-to-end and authenticate the algorithm that is extraordinary effective. In this paper, we introduce a novel architecture ADRNN (dilated CNN with residual block and BiLSTM based on the attention mechanism) to apply for the speech emotion recognition which can take advantage of the strengths of diverse networks and overcome the shortcomings of utilizing alone, and are evaluated in the popular IEMOCAP database and Berlin EMODB corpus. Dilated CNN can assist the model to acquire more receptive fields than using the pooling layer. Then, the skip connection can keep more historic info from the shallow layer and BiLSTM layer are adopted to learn long-term dependencies from the learned local features. And we utilize the attention mechanism to enhance further extraction of speech features. Furthermore, we improve the loss function to apply softmax together with the center loss that achieves better classification performance. As emotional dialogues are transformed of the spectrograms, we pick up the values of the 3-D Log-Mel spectrums from raw signals and put them into our proposed algorithm and obtain a notable performance to get the 74.96% unweighted accuracy in the speaker-dependent and the 69.32% unweighted accuracy in the speaker-independent experiment. It is better than the 64.74% from previous state-of-the-art methods in the spontaneous emotional speech of the IEMOCAP database. In addition, we propose the networks that achieve recognition accuracies of 90.78% and 85.39% on Berlin EMODB of speaker-dependent and speaker-independent experiment respectively, which are better than the accuracy of 88.30% and 82.82% obtained by previous work. For validating the robustness and generalization, we also make an experiment for cross-corpus between above databases and get the preferable 63.84% recognition accuracy in final.

$\mathsf{LightChain}$ : A Lightweight Blockchain System for Industrial Internet of Things

Abstract: While the intersection of blockchain and Industrial Internet of Things (IIoT) has received considerable research interest lately, the conflict between the high resource requirements of blockchain and the generally inadequate performance of IIoT devices has not been well tackled. On one hand, due to the introductions of mathematical concepts, including Public Key Infrastructure, Merkle Hash Tree, and Proof of Work (PoW), deploying blockchain demands huge computing power. On the other hand, full nodes should synchronize massive block data and deal with numerous transactions in peer-to-peer network, whose occupation of storage capacity and bandwidth makes IIoT devices difficult to afford. In this paper, we propose a lightweight blockchain system called LightChain , which is resource-efficient and suitable for power-constrained IIoT scenarios. Specifically, we present a green consensus mechanism named Synergistic Multiple Proof for stimulating the cooperation of IIoT devices, and a lightweight data structure called LightBlock to streamline broadcast content. Furthermore, we design a novel Unrelated Block Offloading Filter to avoid the unlimited growth of ledger without affecting blockchain's traceability. The extensive experiments demonstrate that LightChain can reduce the individual computational cost to 39.32% and speed up the block generation by up to 74.06%. In terms of storage and network usage, the reductions are 43.35% and 90.55%, respectively.

MXene-derived TiO2/reduced graphene oxide composite with an enhanced capacitive capacity for Li-ion and K-ion batteries

Abstract: Li-ion and K-ion batteries present their unique advantages of high energy density and low cost, respectively. It is a challenge to explore a universal anode with efficient electrochemical performance. Herein, a two-dimensional TiO2/reduced graphene oxide (RGO) composite was prepared by a facile hydrothermal method. TiO2 nanoparticles are transformed from Ti2C MXene and connect the RGO nanosheets to form a sheet-like structure. Serving as the anode material, the TiO2/RGO presents high capacity, remarkable rate ability and long cycling performance for both Li- and K-ion batteries. The superior electrochemical performance is attributed to the short ion diffusion path due to the small particle size (15–25 nm) and the highway for electron transport provided by RGO. In addition, RGO motivates the capacitive contribution, resulting in enhanced capacity and better rate performance. Meanwhile, the electrochemical kinetics of Li/K-ion storage was investigated by quantitative kinetics analysis. This work demonstrates a possibility to introduce the capacitive capacity to realize rapid ion storage and improve the cycling stability, providing a new strategy to design efficient electrodes for metal-ion batteries.

Artificial Intelligence for Vehicle-to-Everything: A Survey

Abstract: Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

The individual identification method of wireless device based on dimensionality reduction and machine learning

Abstract: The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR $$\ge $$ 10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.

An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks

Abstract: With the popularity and development of network technology and the Internet, intrusion detection systems (IDSs), which can identify attacks, have been developed. Traditional intrusion detection algorithms typically employ mining association rules to identify intrusion behaviors. However, they fail to fully extract the characteristic information of user behaviors and encounter various problems, such as high false alarm rate (FAR), poor generalization capability, and poor timeliness. In this paper, we propose a network intrusion detection model based on a convolutional neural network-IDS (CNN-IDS). Redundant and irrelevant features in the network traffic data are first removed using different dimensionality reduction methods. Features of the dimensionality reduction data are automatically extracted using the CNN, and more effective information for identifying intrusion is extracted by supervised learning. To reduce the computational cost, we convert the original traffic vector format into an image format and use a standard KDD-CUP99 dataset to evaluate the performance of the proposed CNN model. The experimental results indicate that the AC, FAR, and timeliness of the CNN-IDS model are higher than those of traditional algorithms. Therefore, the model we propose has not only research significance but also practical value.

Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach

Abstract: Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.

A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter

Abstract: In this paper, a novel Gaussian–Student's t mixture (GSTM) distribution is proposed to model non-stationary heavy-tailed noises. The proposed GSTM distribution can be formulated as a hierarchical Gaussian form by introducing a Bernoulli random variable, based on which a new hierarchical linear Gaussian state-space model is constructed. A novel robust GSTM distribution based Kalman filter is proposed based on the constructed hierarchical linear Gaussian state-space model using the variational Bayesian approach. The Kalman filter and robust Student's t based Kalman filter (RSTKF) with fixed distribution parameters are two existing special cases of the proposed filter. The novel GSTM distributed Kalman filter has the important advantage over the RSTKF that the adaptation of the mixing parameter is much more straightforward than learning the degrees of freedom parameter. Simulation results illustrate that the proposed filter has better estimation accuracy than those of the Kalman filter and RSTKF for a linear state-space model with non-stationary heavy-tailed noises.

Upconversion-mediated ZnFe2O4 nanoplatform for NIR-enhanced chemodynamic and photodynamic therapy.

Abstract: ZnFe2O4, a semiconductor catalyst with high photocatalytic activity, is ultrasensitive to ultraviolet (UV) light and tumor H2O2 for producing reactive oxygen species (ROS). Thereby, ZnFe2O4 can be used for photodynamic therapy (PDT) from direct electron transfer and the newly defined chemodynamic therapy (CDT) from the Fenton reaction. However, UV light has confined applicability because of its high phototoxicity, low penetration, and speedy attenuation in the biotissue. Herein, an upconversion-mediated nanoplatform with a mesoporous ZnFe2O4 shell was developed for near-infrared (NIR) light enhanced CDT and PDT. The nanoplatform (denoted as Y-UCSZ) was comprised of upconversion nanoparticles (UCNPs), silica shell, and mesoporous ZnFe2O4 shell and was synthesized through a facile hydrothermal method. The UCNPs can efficiently transfer penetrable NIR photons to UV light, which can activate ZnFe2O4 for producing singlet oxygen thus promoting the Fenton reaction for ROS generation. Besides, Y-UCSZ possesses enormous internal space, which is highly beneficial for housing DOX (doxorubicin, a chemotherapeutic agent) to realize chemotherapy. Moreover, the T2-weighted magnetic resonance imaging (MRI) effect from Fe3+ and Gd3+ ions in combination with the inherent upconversion luminescence (UCL) imaging and computed tomography (CT) from the UCNPs makes an all-in-one diagnosis and treatment system. Importantly, in vitro and in vivo assays authenticated excellent biocompatibility of the PEGylated Y-UCSZ (PEG/Y-UCSZ) and high anticancer effectiveness of the DOX loaded PEG/Y-UCSZ (PEG/Y-UCSZ&DOX), indicating its potential application in the cancer treatment field.

Nonlinear and Adaptive Intelligent Control Techniques for Quadrotor UAV – A Survey

Abstract: Parametric uncertainties and coupled nonlinear dynamics are inherent in quadrotor configuration and infer adaptive nonlinear approaches to be used for flight control system. Numerous adapt...

The construction of self-supported thorny leaf-like nickel-cobalt bimetal phosphides as efficient bifunctional electrocatalysts for urea electrolysis

Abstract: Urea electrolysis offers the prospect of cost-effective and energy-saving hydrogen production together with mitigating urea-rich wastewater pollution instead of overall water splitting. Hence, here, high-efficiency bifunctional electrocatalysts were developed for both the urea oxidation reaction (UOR) and hydrogen evolution reaction (HER) via the in situ vertical growth of thorny leaf-like (2D nanosheets supporting 1D nanowires) NiCoP on a carbon cloth (NiCoP/CC). After integrating the advantages of the synergistic effect between Ni and Co as well as the unique hierarchical structure combined with 1D nanowires, 2D nanosheets and a 3D conductive carbon cloth substrate, the electrode exhibited excellent electrocatalytic activity toward HER and UOR. The electrolytic cell assembled using NiCoP/CC as the anode and the cathode could provide current density of 10 mA cm−2 at a cell voltage of 1.42 V (160 mV less than that for its urea-free counterpart) as well as remarkable durability over 30 h. Thus, the cost-effectiveness and high activity of the NiCoP/CC electrode pave the way to explore transition metal-based electrocatalysts for urea electrolysis.

Dual-Band Eight-Antenna Array Design for MIMO Applications in 5G Mobile Terminals

Abstract: This paper proposes a dual-band eight-antenna array for multiple-input and multiple-output (MIMO) applications in 5G mobile terminals. The designed MIMO antenna array comprises eight L-shaped slot antennas based on stepped impedance resonators (SIRs). The required dual-resonance can be obtained by adjusting the impedance ratio of the SIR, and good impedance matching can be ensured for each antenna element by tuning the position of the microstrip feed line. The experimental results show that a measured return loss of higher than 10 dB and a measured inter-element isolation of greater than 11.2 dB have been obtained for each antenna element with a simulated total efficiency of larger than 51% across the long term evolution (LTE) band 42 (3400-3600 MHz) and LTE band 46 (5150-5925 MHz). In addition, the measured envelope correlation coefficient (ECC) is lower than 0.1 between arbitrary two antenna elements, and the proposed MIMO antenna array realizes a simulated channel capacity of higher than 36.9 bps/Hz within both operation bands. Furthermore, the MIMO antenna array can maintain acceptable radiation and MIMO performance in the presence of specific anthropomorphic mannequin (SAM) head and human hands.

Robust Kalman Filters Based on Gaussian Scale Mixture Distributions With Application to Target Tracking

Abstract: In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.

Boosting the supercapacitor performance of activated carbon by constructing overall conductive networks using graphene quantum dots

Abstract: Although high-surface-area activated carbons have been widely used for supercapacitors, they usually have limited capacitive and rate performances primarily because of the low conductivity and sluggish electrochemical kinetics caused by their amorphous microporous structure. Here, we report a simple strategy to boost the electrochemical performance of activated carbons by embedding highly crystallized graphene quantum dots. Benefiting from the formation of the overall conductive networks, the charge-transfer and ion migration kinetics of the activated carbon are significantly improved, facilitating electrolyte ion transport and storage in deep and branched micropores. As a result, the graphene quantum dot embedded activated carbon, possessing a microporous structure with a specific surface area of 2829 m2 g−1, achieves a remarkably high electric double-layer capacitance of 388 F g−1 at 1 A g−1 as well as excellent rate performance with 60% capacitance retention at 100 A g−1 in a two-electrode system. The capacitive and rate performances are much higher than not only those of the activated carbon without graphene quantum dots, but also those of most porous carbons reported in the literatures. This strategy provides a new route for designing advanced porous carbon materials for high performance energy storage.

Vibration analysis of functionally graded porous cylindrical shell with arbitrary boundary restraints by using a semi analytical method

Abstract: The main purpose of this paper is to provide a new semi analytical method to analyze the free vibration of functionally graded porous (FGP) cylindrical shell with arbitrary boundary restraints. According to the distributions of porous along thickness direction of the structure, two typical types of symmetric and non-symmetric porosity distributions are performed in this paper. The formulations are established on the basis of energy method and first-order shear deformation theory (FSDT). The displacement functions are expressed by unified Jacobi polynomials and Fourier series. The arbitrary boundary restraints are realized by penalty method. The final solutions of FGP cylindrical shell structure are obtained by Rayleigh–Ritz method. To sufficient illustrate the effectiveness of proposed method, some numerical examples about spring stiffness, Jacobi parameters etc. are carried out. In addition, to verify the accuracy of this method, the results are compared with those obtained by FEM, experiment and published literature. The results show that the proposed method has ability to solve the free vibration behaviors of FGP cylindrical shell.

Vanadium dioxide-assisted broadband tunable terahertz metamaterial absorber.

Abstract: Tunable terahertz (THz) functional devices have exhibited superior performances due to the use of active materials, such as liquid crystals, graphene, and semiconductors. However, the tunable range of constitutive parameters of materials is still limited, which leads to the low modulation depth of THz devices. Here, we demonstrate a broadband tunable THz absorber based on hybrid vanadium dioxide (VO2) metamaterials. Unlike other phase change materials, VO2 exhibits an insulator-to-metal transition characteristic and the conductivity can be increased by 4–5 orders of magnitude under external stimulus including electric fields, optical, and thermal pumps. Based on the unique transition character of VO2, the maximum tunable range of the proposed absorber can be realized from 5% to 100% by an external thermal excitation. Meanwhile, an absorption greater than 80% in a continuous range with a bandwidth about 2.0 THz can be obtained when VO2 is in its metal phase at high temperature. Furthermore, the absorber is insensitive to the incident angle up to 50° and such a broadband THz absorber can be used in applications including imaging, modulating, cloaking, and so on.

Nitrogen and Phosphorus Dual-Doped Multilayer Graphene as Universal Anode for Full Carbon-Based Lithium and Potassium Ion Capacitors

Abstract: Lithium/potassium ion capacitors (LICs/PICs) have been proposed to bridge the performance gap between high-energy batteries and high-power capacitors. However, their development is hindered by the choice, electrochemical performance, and preparation technique of the battery-type anode materials. Herein, a nitrogen and phosphorus dual-doped multilayer graphene (NPG) material is designed and synthesized through an arc discharge process, using low-cost graphite and solid nitrogen and phosphorus sources. When employed as the anode material, NPG exhibits high capacity, remarkable rate capability, and stable cycling performance in both lithium and potassium ion batteries. This excellent electrochemical performance is ascribed to the synergistic effect of nitrogen and phosphorus doping, which enhances the electrochemical conductivity, provides a higher number of ion storage sites, and leads to increased interlayer spacing. Full carbon-based NPG‖LiPF6‖active carbon (AC) LICs and NPG‖KPF6‖AC PICs are assembled and show excellent electrochemical performance, with competitive energy and power densities. This work provides a route for the large-scale production of dual-doped graphene as a universal anode material for high-performance alkali ion batteries and capacitors.