Well-defined high entropy-metal nanoparticles:Detection of the multi-element particles by deep learning

Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the heterogeneity and large size at the nanoscale,the poorly defined catalyst nanostructure,and thermodynamic immiscibility of the strongly repelling metallic elements.To address these challenges,an ultrasonic-assisted coincident electro-oxidation-reduction-precipitation(U-SEO-P) is presented to fabricate ultra-stable PtRuAgCoCuP NPs,which produces numerous active intermediates and induces strong metal-support interactions.To sort the active high-entropy mNPs,individual NPs are described on the support surface and the role of deep learning in understanding/predicting the features of PtRuAgCoCu@TiO_(x) catalysts is explained.Notably,this deep learning approach required minimal to no human input.The as-prepared PtRuAgCoCu@TiO_(x) catalysts can be used to catalyze various important chemical reactions,such as a high reduction conversion(100% in 30 s),with no loss of catalytic activity even after 20 cycles of nitroarene and ketone/aldehyde,which is several times higher than commercial Pt@TiO_(x) owing to individual PtRuAgCoCuP NPs on TiO_(x) surface.In this study,we present the "Totally Defined Catalysis" concept,which has enormous potential for the advancement of high-activity catalysts in the reduction of organic compounds.

Enhanced Li storage of pure crystalline-C_(60) and TiNb_(2)O_(7)-nanostructure composite for Li-ion battery anodes

We propose a method for producing composite materials(hTNO@C_(60))comprising crystalline C_(60)particles and hollow-structu red TiNb_(2)O_(7)(hTNO)nanofibers via facile liquid-liquid interface precipitation followed by low-temperature annealing.This allows the systematic design of crystalline C_(60)as an active material for Li-ion battery anodes.The hTNO@C_(60)composite demonstrates outstanding cyclic stability,retaining a capacity of 465 mA h g^(-1)after 1,000 cycles at 1 A g^(-1)It maintains a capacity of 98 mA h g^(-1)even after16,000 ultralong cycles at 8 A g^(-1)The enhancement in electrochemical properties is attributed to the successful growth and uniform doping of crystalline C_(60),resulting in improved electrical conductivity.The excellent electrochemical stability and properties of these composites make them promising anode materials.

Analysis of Quenching Resistor Effect to Improve Stability of TIA Circuit for APD-A Secondary Publication

In this paper,since the Avalanche Photo Diode(APD)for Light-to-Voltage LTV conversion uses a high voltage in the operating range unlike other Photo Diodes(PD),the quenching resistor must be connected in series to prevent overcurrent when using the Transimpedance Amplifier(TIA).In such a case,quenching resistance may affect the transfer function of the TIA circuit,resulting in serious stability.Therefore,in this paper,by analyzing the effect of APD quenching resistance on the voltage and current loop transfer function of TIA,we proposed a loop analysis and a method for determining the quenching resistance value to improve stability.A TIA circuit with quenching resistance was designed by the proposed method and its operational stability was verified through simulation and chip fabrication.

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures.Thus,it is crucial to increase the positioning technology's accuracy.The sequential Monte Carlo(SMC)approach is used in various applications such as target tracking and intelligent surveillance,which rely on smartphone‐based inertial data sequences.However,the SMC method has intrinsic flaws,such as sample impoverishment and particle degeneracy.A novel SMC approach is presented,which is built on the weighted differential evolution(WDE)algorithm.Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction,like in a high‐dimensional space,such as a multistory structure.Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set,prevent the usage of an inadequate number of valid samples,and preserve smartphone user position accuracy.The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC,which aids in fast approximating the posterior distributions,to speed up the particle congregation process in the proposed SMC‐based WDE approach.Lastly,the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users.According to simulation findings,the suggested approach provides improved location estimation with reduced localization error and quick convergence.The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92%better in terms of MAPE,15.24%for the case of MAE,and 0.031%when evaluating based on the R2 Score.

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence.It is one of the crucial issues in computer vision and has many real-world applications,mainly focused on predicting future scenarios to avoid undesirable outcomes.However,modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene,such as occlusions,camera movements,delay and illumination.Direct frame synthesis or optical-flow estimation are common approaches used by researchers.However,researchers mainly focused on video prediction using one of the approaches.Both methods have limitations,such as direct frame synthesis,usually face blurry prediction due to complex pixel distributions in the scene,and optical-flow estimation,usually produce artifacts due to large object displacements or obstructions in the clip.In this paper,we constructed a deep neural network Frame Prediction Network(FPNet-OF)with multiplebranch inputs(optical flow and original frame)to predict the future video frame by adaptively fusing the future object-motion with the future frame generator.The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network.Using various real-world datasets,we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Strain-Insensitive Fiber Bragg Grating Composite Structure for Wide-Range Temperature Sensing

This paper reports on the design,fabrication,and temperature strain sensing performance of a fiber Bragg grating composite structure for surface mounted temperature measurements over a wide temperature range,with highly reduced strain cross-sensitivity.The fiber Bragg grating sensor is encapsulated in a polyimide tube filled with epoxy resin,forming an arc-shaped cavity.This assembly is then placed between two layers of glass fiber prepreg with a flexible pad in between and cured into shape.Experimental results,supported by finite element simulations,demonstrate an enhanced temperature sensitivity is 26.3 pm/°C over a wide temperature range of–30°C to 70°C,and high strain transfer isolation of about 99.65%.

Influence of sulfides on the tribological properties of composites produced by pulse electric current sintering

Self-lubricating A1203-15wt% ZrO2 composites with sulfides, such as molybdenum disulfide （MoS2） and tungsten disulfide （WSz） serving as solid lubricants, were fabricated by using the pulse electric current sintering （PECS） technique. The coefficient of friction （COF） of the A1203-15wt% ZrO2 composite without/with sulfides was in the range of 0.37-0.48 and 0.27-0.49, respectively. As the amoant of sul- fides increased, the COF and the wear rate decreased. The reduction in COF and wear rate of the sulfide-containing composite is caused by a reduction in shear stresses between the specimen and the tribological medium due to the formation of a lubricating film resulting from the lamellar structure of sulfides located on the worn surface.

Comparative assessment of maximum power point tracking procedures for photovoltaic systems

The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.

Refractive Index and Electronic Polarizability of Ternary Chalcopyrite Semiconductors

Simple models are proposed for the calculation of refractive index n and electronic polarizability α of AⅠBⅢC2Ⅵ and AⅡBⅣC2Ⅴ compounds of groups of chalcopyrite semiconductors from their energy gap data. The values family and 12 compounds of AⅡBⅣC2Ⅴ family are calculated for the work. The proposed models are applicable for the whole range of energy gap materials. The calculated values are compared with the available experimental and reported values. A fairly good agreement between them is obtained.

Signal decomposition of HF radar maneuvering targets by using S^2-method with clutter rejection

Multiple maneuvedng targets signal processing in high frequency radar is challenging due to the following difficulties： the interference between signals is severe because of significant spread of the target Doppler spectrum, the low signal to clutter ratio （SCR） environment degrades the performance of signal process- ing algorithms. This paper addresses this challenging problem by using an S2-method and an adaptive clutter rejection scheme. The proposed S2-method improves the S-method by eliminating inter- ference between signals, and thus it enables multi-target signals to be reconstructed individually. The proposed adaptive clutter rejec- tion scheme is based on an adaptive notch filter, which is designed according to the envelop of the clutter spectrum. Experiments with simulated targets added into radar sea clutter echo and real air target data illustrate the effectiveness of the proposed method.

Energy-Efcient Transmission Range Optimization Model for WSN-Based Internet of Thing

With the explosive advancements in wireless communications and digital electronics,some tiny devices,sensors,became a part of our daily life in numerous elds.Wireless sensor networks(WSNs)is composed of tiny sensor devices.WSNs have emerged as a key technology enabling the realization of the Internet of Things(IoT).In particular,the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities,smart homes,smart healthcare,security applications,environmental monitoring,etc.However,the limitations of energy,communication range,and computational resources are bottlenecks to the widespread applications of this technology.In order to tackle these issues,in this paper,we propose an Energy-efcient Transmission Range Optimized Model for IoT(ETROMI),which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication.In particular,we maximize the transmission range by employing linear programming to alleviate the sensor nodes’energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms.Through extensive simulation results,we demonstrate the superiority of the proposed model.ETROMI is expected to be extensively used for various smart city,smart home,and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.

Effect of In Composition on Two-Dimensional Electron Gas in Wurtzite AlGaN/InGaN Heterostructures

The effect of In composition on two-dimensional electron gas in wurtzite AlGaN/InGaN heterostructures is theoretically investigated. The sheet carrier density is shown to increase nearly linearly with In mole fraction x, due to the increase in the polarization charge at the AlGaN/InGaN interface. The electron sheet density is enhanced with the doping in the AlGaN layer. The sheet carrier density is as high as 3.7×1013 cm^-2 at the donor density of 10×1018 cm^-3 for the HEMT structure with x=0.3. The contribution of additional donor density on the electron sheet density is nearly independent of the In mole fraction.

Tool Wear Classification Using Fuzzy Logic for Machining of Al/SiC Composite Material

Tool wear state classification has good potential to play a critical role in ensuring the dimensional accuracy of the work piece and prevention of damage to cutting tool in machining process. During machining process, tool wear is an important factor which contributes to the variation of spindle motor current, speed, feed and depth of cut. In the present work, online tool wear state detecting method with spindle motor current in turning operation for Al/SiC composite material is presented. By analyzing the effects of tool wear as well as the cutting parameters on the current signal, the models on the relationship between the current signals and the cutting parameters are established with partial design taken from experimental data and regression analysis. The fuzzy classification method is used to classify the tool wear states so as to facilitate defective tool replacement at the proper time.

Parameter Deviation Effect Study of the Power Generation Unit on a Doubly-Fed Induction Machine-based Shipboard Propulsion System

To lower the difficulty of fault protection,a doubly-fed induction machine based shipboard propulsion system(DFIM-SPS)that is partially power decoupled is presented.In such an intrinsically safe SPS architecture,a synchronous generator(SG)is employed for power generation,and the accuracy of the parameters of power generation unit(PGU)plays an important role in SPS stable operation.In this paper,the PGU parameter deviations are studied to evaluate the effects on system performance.The models of salient-pole SG,type DC1A excitation system(EXS)and DFIM are illustrated first.Besides,the corresponding control scheme is explained.For the 16 important parameters of PGU,up to 40%of parameter deviations are applied to implement parameter sensitivity analysis.Then,simulation studies are carried out to evaluate the parameter deviation effects on system performance in detail.By defining three parameter deviation effect indicators(PDEIs),the effects on the PGU output variables,which are the terminal voltage and output active power,are studied.Moreover,the increasing rates of PDEIs with different degrees of parameter deviations for the key parameters are analyzed.Furthermore,the overall system performance is investigated for the two most influential PGU parameters.This paper provides some vital clues on SG and EXS parameter identification for DFIM-SPS.

Parseval Theory of Complex Wavelet Transform for Wavelet Family Including Rotational Parameters

A rotational parameter Rθ has been introduced to complex wavelet transform （CWT）. The rotational CWT （RCWT） corresponds to a matrix element 〈φ｜U2（θ;μ;κ）[F〉 in the context of quantum mechanics, where U2（θ;μ;κ） is a two-mode rotational displacing-squeezing operator in the 〈η｜ representation. Based on this, the Parseval theorem and the inversion formula of RCWT have been proved. The concise proof not only manifestly shows the merit of Dirac＇s representation theory but also leads to a new orthogonal property of complex mother wavelets in parameter space.

Preserving Privacy of User Identity Based on Pseudonym Variable in 5G

The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication industry.This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home network.Through this mechanism,the user identity privacy would be secured and hidden.Moreover,it improves the synchronization between mobile users and home networks.Additionally,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous generations.It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message size.Moreover,the addition of any hardware to the AKA carries minor adjustments on the network parties.In this paper,the ProVerif is used to verify the proposed scheme.

Implementation and Evaluation of Dynamically Weighted Low Complexity Fair Queuing(DWLC-FQ) Algorithm for Packet Scheduling in WiMAX Networks

Services provided by internet need guaranteed network performance. Efficient packet queuing and scheduling schemes play key role in achieving this. Internet engineering task force(IETF) has proposed Differentiated Services(Diff Serv) architecture for IP network which is based on classifying packets in to different service classes and scheduling them. Scheduling schemes of today's wireless broadband networks work on service differentiation. In this paper, we present a novel packet queue scheduling algorithm called dynamically weighted low complexity fair queuing(DWLC-FQ) which is an improvement over weighted fair queuing(WFQ) and worstcase fair weighted fair queuing+(WF2Q+). The proposed algorithm incorporates dynamic weight adjustment mechanism to cope with dynamics of data traffic such as burst and overload. It also reduces complexity associated with virtual time update and hence makes it suitable for high speed networks. Simulation results of proposed packet scheduling scheme demonstrate improvement in delay and drop rate performance for constant bit rate and video applications with very little or negligible impact on fairness.

Thorough Research on Application of Embedded Web server in Video Surveillance System

Embedded web technique is one of the elements most used in Video Surveillance design. According to actual requests in Surveillance System, such as throughput and dependability, combined with new progresses in correlative researches, this thesis discusses application skills of Web Server in Surveillance System thoroughly, and implements as well. This thesis can supply special and effective instructive effects in all Surveillance Systems based on Embedded web technique.

On Application of Metal Additive Manufacturing

Metal additive manufacturing is an important branch of AM, which provides an effective method for the innovative manufacturing of metal parts. Here, flow chart and main techniques of metal additive manufacturing are firstly described according to the used material types. Many application examples of metal additive manufacturing are then listed based on application value. The summary is finally given to point development direction of metal additive manufacturing in the future. Additive manufacturing, which is an effective supplement to traditional methods, will play an important role in intelligent and digital manufacturing.