A homogeneous and mechanically stable artificial diffusion layer using rigid-flexible hybrid polymer for high-performance lithium metal batteries

Artificial solid electrolyte interphase(SEI) is promising to inhibit uncontrollable lithium dendrites and enable long cycling stability for lithium metal batteries. However, the essential mechanical stability is limited since organic layers generally have low modulus whereas intrinsic brittleness for inorganic ones remains a great concern. Polymer-based SEIs with rigid and flexible chains in adequate mechanical properties are supposed to address this issue. Herein, a homogeneous and mechanically stable diffusion layer is achieved by blending rigid chains of polyphenylene sulfone(PPSU) with flexible chains of poly(vinylidene fluoride)(PVDF) in a hybrid membrane, enabling uniform diffusion and stabilizing the lithium metal anode. The Li||Cu cell with the protected electrode exhibits a long lifetime more than 450 cycles(0.5 m A cm^(-2), 1.0 m A h cm^(-2))(fourfold longer than the control group) with higher average Coulombic efficiency of 98.7%. Enhanced performances are also observed at Li||Li and full cell configurations. The improved performances are attributed to the controlled morphology and stable interphase, according to scanning electron microscopy(SEM) and electrochemical impedance. This research advances the idea of uniform lithium plating and provides a new insight on how to create a homogeneous and mechanically stable diffusion layer using rigid-flexible polymers.

THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO INVESTIGATION ON THE THICKNESS OF INTERMETALLIC LAYER UNDER SOLID-LIQUID PRESSURE BONDING OF STEEL AND ALUMINIUM

Artificial neural networks (ANN), being a sophisticated type of information processing system by imitating the neural system of human brain, can be used to investigate the effects of concentration of flux solution, temperature of liquid aluminium, temperture of tools and pressure on thickness of the intermetallic layer at the interface between steel and aluminium under solid-liquid pressure bonding of steel and aluminium perfectly. The optimum thickness has been determined according to the value of the optimum shearing strength.

Artificial Noise Aided Polar Codes for Physical LayerSecurity

The secrecy rates of the existing practical secrecy coding methods are relative low to satisfy the security requirement of 5 G communications.We propose an artificial noise(AN) aided polar coding algorithm to improve the secrecy rate.Firstly,a secrecy coding model based on AN is presented,where the confidential bits of last transmission code block are adopted as AN to inject into the current codeword.In this way,the AN can only be eliminated from the jammed codeword by the legitimate users.Since the AN is shorter than the codeword,we then develop a suboptimal jamming positions selecting algorithm with the goal of maximizing the bit error rate of the eavesdropper.Theoretical and simulation results demonstrate that the proposed algorithm outperforms the random selection method and the method without AN.

Artificial solid electrolyte interface layer based on sodium titanate hollow microspheres assembled by nanotubes to stabilize zinc metal electrodes

Recently,aqueous zinc-ion batteries with intrinsic safety,low cost,and environmental benignity have attracted tremendous research interest.However,zinc dendrites,harmful side reactions,and zinc metal corrosion stand in the way.Herein,we use lepidocrocite-type sodium titanate hollow microspheres assembled by nanotubes to constitute an artificial solid electrolyte interface layer on the zinc metal electrode.Thanks to the hierarchical structure with abundant open voids,negative-charged layered framework,low hydrophilicity,electrically insulting nature,and large ionic conductivity,the sodium titanate coating layer can effectively homogenize the electric field,promote the Zn^(2+)ion transfer,guide the Zn^(2+)ion flux,reduce the desolvation barrier,improve the exchange current density,and accommodate the plated zinc metal.Consequently,this coating layer can effectively suppress zinc dendrites and other unfavorable effects.With this coating layer,the Zn//Zn symmetric cell is able to provide an impressive cumulative zinc plating capacity of 1375 m Ah cm^(-2) at a current density of 5 m A cm^(-2).This coating layer also contributes to significantly improved electrochemical performances of Zn//MnO_(2) battery and zincion hybrid capacitor.This work offers new insights into the modifications of zinc metal electrodes.

Layered learning of soccer robot based on artificial neural network

Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

Experiments and shape prediction of plasma deposit layer using artificial neural network

Plasma surfacing is an important enabling technology in high-performance coating applications. Recently, it is applied to rapid prototyping/tooling to reduce development time and manufacturing cost for the development of new products. However, this technology is in its infancy, it is essential to understand clearly how process variables relate to deposit microstructure and properties for plasma deposition manufacturing process control. In this paper, layer appearance of single surfacing under different parametem such as plasma current, voltage, powder feedrate and travel speed is studied. Back-propagation neural networks are used to associate the depositing process variables with the features of the deposit layer shape. These networks can be effectively implemented to estimate the layer shape. The results Indicate that neural networks can yield fairly accurate results and can be used as a practical tool in plasma deposition manufacturing process.

Comparison of perfectly matched layer and multi-transmitting formula artificial boundary condition based on hybrid finite element formulation

The theory of perfectly matched layer （PML） artificial boundary condition （ABC）, which is characterized by absorption any wave motions with arbitrary frequency and arbitrarily incident angle, is introduced. The construction process of PML boundary based on elastodynamic partial differential equation （PDE） system is developed. Combining with velocity-stress hybrid finite element formulation, the applicability of PML boundary is investigated and the numerical reflection of PML boundary is estimated. The reflectivity of PML and multi-transmitting formula （MTF） boundary is then compared based on body wave and surface wave simulations. The results show that although PML boundary yields some reflection, its absorption performance is superior to MTF boundary in the numerical simulations of near-fault wave propagation, especially in comer and large angle grazing incidence situations. The PML boundary does not arise any unstable phenomenon and the stability of PML boundary is better than MTF boundary in hybrid finite element method. For a specified problem and analysis tolerance, the computational efficiency of PML boundary is only a little lower than MTF boundary.

Stable Boundary Layer Height Parameterization: Learning from Artificial Neural Networks

Artificial neural networks (ANN) are employed using different combinations among the surface friction velocity u*, surface buoyancy flux Bs, free-flow stability N, Coriolis parameter f, and surface roughness length z0 from large-eddy simulation data as inputs to investigate which variables are essential in determining the stable boundary layer(SBL) height h. In addition, the performances of several conventional linear SBL height parameterizations are evaluated. ANN results indicate that the surface friction velocity u* is the most predominant variable in the estimation of SBL height h. When u* is absent, the secondly important variable is the surface buoyancy flux Bs. The relevance of N, f, and z0 to h is also discussed;f affects more than N does, and z0 shows to be the most insensitive variable to h.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

The Full-Duplex Artificial Noise Scheme for Security of a Cellular System

This paper focuses on the problem of secure transmission in a cellular system. A full-duplex base station using artificial noise is adopted to improve both the uplink and downlink secrecy rate via pairing terminals which reverses the downlink and uplink of each other. We give the designs of artificial noise and the user's desired signal, and derive the pairing prin-ciple between terminals. Moreover, the influence of self-interference cancellation on secrecy rate is ex-plored. Simulation results show that the secrecy rate can get much better performance by adopting full-duplex artificial noise scheme and proposed pair-ing method. The downlink secrecy rate decreases with the distance between terminals. Besides the uplink secrecy rate is sensitive to the ability of self-interference cancellation.

Integration of metal-organic layers with quantum dots for artificial photosynthesis

Metal-organic layers(MOLs), a type of new-emerging two-dimensional ultrathin metal-organic framework materials with large surface areas and highly exposed active sites, have shown promising applications in photocatalytic CO_(2) reduction. However, due to a lack of photosensitivity and photooxidation capability, photosensitizers and sacrificial reductants are usually necessary for MOLs-based photocatalytic CO_(2) reduction systems. In this article, by integration of MOLs and quantum dots(QDs), we constructed MOLs-based catalysts with multi-functions of photosensitivity, photoreduction and photooxidation, which thus can serve as photocatalysts for CO_(2) reduction with H_(2)O as an electron donor. Specifically, by an electrostatic self-assembly approach,nickel(Ⅱ)-based MOLs(Ni-MOLs) and CsPbBr_(3)QDs have been assembled, constructing valid Ⅱ-Scheme Ni-MOLs/CsPbBr_(3) heterojunctions with close Ni-MOLs/CsPbBr_(3)heterointerface. Such a close heterointerface shortens the charge transfer distance,thus effectively boosting the charge separation and transfer. As a result, upon illumination by visible light(λ ≥ 400 nm,100 m W cm^(-2)), the optimized photocatalyst shows high efficiency and stability in photochemical CO_(2) reduction in the absence of any photosensitizers and sacrificial reductants. The CO yield reaches as high as 124 μmol g^(-1)in 4 h, over 6 times higher than that achieved by CsPbBr_(3). Additionally, the selectivity reaches 100%. This work provides a new way to construct MOL-based catalysts for artificial photosynthesis.

A gelatin-based artificial SEI for lithium deposition regulation and polysulfide shuttle suppression in lithium-sulfur batteries

Lithium-sulfur(Li-S) battery is one of the best candidates for the next-generation energy storage system due to its high theoretical capacity(1675 mA h-1),low cost and environment friendliness.However,lithium(Li) dendrites formation and polysulfide shuttle effect are two major challenges that limit the commercialization of Li-S batteries.Here we design a facile bifunctional interlayer of gelatin-based fibers(GFs),aiming to protect the Li anode surface from the dendrites growth and also hinder the polysulfide shuttle effect.We reveal that the 3D structural network of GFs layer with abundant polar sites helps to homogenize Li-ion flux,leading to uniform Li-ion deposition.Meanwhile,the polar moieties also immobilize the lithium polysulfides and protect the Li metal from the side-reaction.As a result,the anodeprotected batteries have shown significantly enhanced performance.A high coulombic efficiency of 96% after 160 cycles has been achieved in the Li-Cu half cells.The Li-Li symmetric cells exhibit a prolonged lifespan for 800 h with voltage hysteresis(10 mV).With the as-prepared GFs layer,the Li-S battery shows approximately 14% higher capacity retention than the pristine battery at 0.5 C after 100 cycles.Our work presents that this gelatin-based bi-functional interlayer provides a viable strategy for the manufacturing of advanced Li-S batteries.

Identification and Prediction of Internet Traffic Using Artificial Neural Networks

This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.

Forward prediction for tunnel geology and classification of surrounding rock based on seismic wave velocity layered tomography

Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in front of the tunnel face.In this work,a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography.In particular,for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel,a layered inversion based on regularization is proposed.By reducing the inversion area of each iteration step and applying straight-line interface assumption,the convergence and accuracy of wave velocity inversion are effectively improved.Furthermore,a surrounding rock classification network based on autoencoder is constructed.The mapping relationship between wave velocity and classification of surrounding rock is established with density,Poisson’s ratio and elastic modulus as links.Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer(CL)is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell(PEMFC).Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL.In this study,a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC.Moreover,a research framework combining the response surface method(RSM)and artificial neural network(ANN)model is proposed to conduct a quantitative analysis,and further a rapid and accurate prediction.With the help of this research framework,the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated.The results show that the mass of platinum,the mass of carbon,and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance.At the selected points,the sensitivity of the decision variables is ranked:volume fraction of dry ionomer>mass of platinum>mass of carbon>agglomerate radius.In particular,the sensitivity of the volume fraction of dry ionomer is over 50%at these points.Besides,the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set.This in-depth study is beneficial to provide feasible guidance for high-performance CL design.

Biological Inspiration—Theoretical Framework Mitosis Artificial Neural Networks Unsupervised Algorithm

The modified approach to conventional Artificial Neural Networks (ANN) described in this paper represents an essential departure from the conventional techniques of structural analysis. It has four main distinguishing features: 1) it introduces a new simulation algorithm based on the biology;2) it performs relatively simple arithmetic as massively parallel, during analysis of a structure;3) it shows that it is possible to use the application of the modified approach to conventional ANN to solve problems of any complexity in the field of structural analysis;4) the Neural Topologies for Structural Analysis (NTSA) system are recurrent networks and its outputs are connected to its inputs [1] and [2]. In NTSA system the DNA of the neuron mother and daughters would be defined by: 1) the same entry, from the corresponding neuron in the previous layer;2) the same trend vector;3) the same transfer function (purelin). The mother’s neuron and her daughter’s neuron differ only in the connection weight and its output signal.

Artificial Noise Based Security Algorithm for Multi-User MIMO System

The existing physical layer security algorithm, which is based on artificial noise, could affect legitimate receivers negatively when the number of users is no less than sending antennas in multi-user MIMO system. In order to improve security of multi-user MIMO system under this scenario, we propose a new multi-user MIMO system physical layer security algorithm based on joint channel state matrix. Firstly, multiple users are processed together, thus a multi-user joint channel state matrix is established. After achieving Singular Value Decomposition (SVD) of the joint channel state matrix, the minimum singular value is obtained, which can be utilized for precoding to eliminate the interference of artificial noise to legitimate receivers. Further, we also present an approach to optimize the power allocation. Simulation results show that the proposed algorithm can increase secrecy capacity by 0.1 bit/s/HZ averagely.

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It’s about: 31.77% ± 1.05% for the limit of liquidity;18.71% ± 0.76% for the plastic limit;13.06% ± 0.79% for the plasticity index;83.00% ± 3.33% for passing of 2 mm sieve;76.22% ± 3.2% for passing of 400 μm sieve;89.07% ± 2.99% for passing of 4.75 mm sieve;70.62% ± 2.39% passing of 80 μm sieve;1.66 ± 0.61 for the consistency index;−0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.

时间反转OFDM系统中增强安全性能的功率分配与人工噪声设计

时间反转技术特有的时空聚焦特性使其具有抗窃听能力.本文对提高时间反转正交频分复用系统安全性能的优化方案进行研究.首先,通过应用时间反转预滤波的时空聚焦特性来提高合法接收端相对于窃听端的信号强度;然后,以最大化保密速率为目标对子载波的功率分配进行优化.为进一步提升系统保密传输能力,利用循环前缀提供的自由度实现零空间人工噪声,在对窃听端形成有效干扰的同时,不对合法接收端造成影响.通过对子载波功率分配和人工噪声协方差阵进行联合优化,最大化系统的保密传输速率.仿真结果表明,所提优化方案能显著提高系统的保密速率.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.