Artificial Neural Network Application to the Friction Stir Welding of Al 6061 Alloy to Stainless Steel 304

The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.

Applications of Access Networks in India

1 IntroductionIndia has a population of over 1 billion, thesecond largest in the world, with a territo-ry area of 3.3 million square kilometresand 28 states; it is the largest country inSouth Asia. Since independence in 1947, In-dia has made great progress on its national e-conomy and infrastructure. By December

ZTE's Softswitch Networking and Application Cases

The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are analyzed,and NGN's features in aspects of network architecture,service provision and network management are summarized.

Vehicular Communications,Networks,and Applications

Avehicular ad hoc network （VANET） is a packet-switched network, consisting of mobile communication nodes mounted on vehicles, with very limited or no infrastructure support [1]. It supports communications among nearby vehicles,

APPLICATIONS OF FAST SIMULATED ANNEALING IN NEURAL NETWORKS

Fast simulated annealing is implemented into the learning process of neural network to replace the traditional back-propagation algorithm. The new procedure exhibits performance fast in learning and accurate in prediction compared to the traditional neural networks. Two numerical data sets were used to illustrate its use in chemistry.

Call for papers Journal of Control Theory and Applications Special issue on Wireless sensor networks:from theory to practices and applications

Wireless Sensor Network （WSN） is characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. The main advantages of WSN include its low cost, rapid deployment, self-organization, and fault tolerance. WSN has received tremendous interests of various research communities,

Wireless Network Technologies Supporting M2M Applications

When computers and communication devices are available everywhere in the future, the categories of communication will expand to cover not only the man-man and the man-machine, but also the machine-machine (M2M) communication. Someday, the traffic generated by machines will greatly exceed those of man-machine and man-man applications. Large numbers of M2M applications will need various wireless networks to support them. This paper introduces the characteristics, advantages and disadvantages of the currently available various wireless network technologies, including WiFi, Bluetooth, ZigBee, passive RFID and the 802.15 standard networks.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

STUDY ON THE OPTIMIZATION OF TRANSPORT CONTROL POLICY IN COMMUNICATION NETWORK

In communication networks with policy-based Transport Control on-Demand (TCoD) function,the transport control policies play a great impact on the network effectiveness. To evaluate and optimize the transport policies in communication network,a policy-based TCoD network model is given and a comprehensive evaluation index system of the network effectiveness is put forward from both network application and handling mechanism perspectives. A TCoD network prototype system based on Asynchronous Transfer Mode/Multi-Protocol Label Switching (ATM/MPLS) is introduced and some experiments are performed on it. The prototype system is evaluated and analyzed with the comprehensive evaluation index system. The results show that the index system can be used to judge whether the communication network can meet the application requirements or not,and can provide references for the optimization of the transport policies so as to improve the communication network effectiveness.

Advancing 5G Network Applications Lifecycle Security:An ML-Driven Approach

As 5th Generation(5G)and Beyond 5G(B5G)networks become increasingly prevalent,ensuring not only networksecurity but also the security and reliability of the applications,the so-called network applications,becomesof paramount importance.This paper introduces a novel integrated model architecture,combining a networkapplication validation framework with an AI-driven reactive system to enhance security in real-time.The proposedmodel leverages machine learning(ML)and artificial intelligence(AI)to dynamically monitor and respond tosecurity threats,effectively mitigating potential risks before they impact the network infrastructure.This dualapproach not only validates the functionality and performance of network applications before their real deploymentbut also enhances the network’s ability to adapt and respond to threats as they arise.The implementation ofthis model,in the shape of an architecture deployed in two distinct sites,demonstrates its practical viability andeffectiveness.Integrating application validation with proactive threat detection and response,the proposed modeladdresses critical security challenges unique to 5G infrastructures.This paper details the model,architecture’sdesign,implementation,and evaluation of this solution,illustrating its potential to improve network securitymanagement in 5G environments significantly.Our findings highlight the architecture’s capability to ensure boththe operational integrity of network applications and the security of the underlying infrastructure,presenting asignificant advancement in network security.

Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

HIV incidence among men who have sex with men using geosocial networking smartphone application in Beijing, China: an open cohort study

Background:Sexual transmission among men who have sex with men(MSM)is the dominant route of HIV transmission in China.Extensive use of geosocial networking(GSN)smartphone application(app)has dramatically changed the pattern of sexual behaviors and HIV risk among MSM;but data on HIV incidence and the changing risk behaviors of GSN app-using MSM are limited.We aims to assess the HIV incidence and its correlates among gay GSN app-using MSM in China.Methods:We constructed an open cohort which was initiated and maintained using a GSN app to assess the HIV incidence among app-using MSM,recruited from June 2017 to December 2018.MSM completed an online questionnaire on their sociodemographic characteristics,sexual behaviors,recreational drug use and sexually transmitted infections status.Then each man had an HIV test,and those tested negatives were enrolled into the cohort.Participants completed follow-ups with additional HIV tests though the app during the study period,and were censored at HIV seroconversion or study end date.HIV incidence was calculated by dividing the sum of observed HIV seroconversions by the observed person-time.Univariate(Chi-square test and Fisher's exact test)and multivariate(proportional hazards regression)analyses were used to examine correlates of HIV incidence.Results:A total of 6957 HIV negative MSM were enrolled in the open cohort,37 seroconversions occurred among 1937 men contributing 1065 observed person-years:HIV incidence was 3.47 per 100 person-years[95%confidence interval(Cl):2.37-4.57].More than five sexual partners[hazard ratio(HR)=2.65,95%Cl:1.04-6.67],and sex with HIV positive partners(HR=3.82,95%Cl:1.16-12.64)in the preceding six months were positively associated with HIV seroconversion.Consistent condom use for anal sex(HR=0.27,95%Cl:0.07-0.96),and reporting insertive anal sex only(HR=0.23,95%Cl:0.08-0.62)in the preceding six months were protective factors for HIV seroconversion.Conclusions:Tailored interventions targeting app-using MSM are urgently needed given their high risk of HIV.As a new tool for accessing MSM at higher HIV risk,GSN smartphone app could play an important role in HIV research among MSM.

Application of neural networks and ultraviolet spectroscopy to simultaneous determination of multiple vitamins

Multivariate analysis and filtering techniques are widely applied to simultaneous and/or selective determination of multicomponent systems. Many methods among them are based on the principle of linear addition, while this principle does not always hold due to various physical and chemical factors. Using quite a different way, neural network (NN) based on a given learning rule, such as back propagation (BP) model, needs neither knowing nor using any form of input/output relationship. Particularly, NN can resolve various problems such as those with casual relation, those with fuzzy backgrounds, and those with uncertain inferential processes. NN was used by us to investigate quantitative struc-

Application of a multilayer feedforward network to voiced-unvoiced-silence classifications of speech

A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtained for each segment . The feature vector served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classification was made. The network had a 6-12-3 node architecture and was trained using the generalized delta rule for back propagation of error . The performance of the network was evaluated using speech samples from 3 male and 3 female speakers . A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% were obtained. It is concluded that the voiced, unvoiced , and silence classification of speech can be effectively accomplished using a multilayer feedforward network.

Trends of Communication Processors

Processors have been playing important roles in both communication infrastructure systems and terminals.In this paper,both application specific and general purpose processors for communications are discussed including the roles,the history,the current situations,and the trends.One trend is that ASIPs(Application Specific Instruction-set Processors) are taking over ASICs(Application Specific Integrated Circuits) because of the increasing needs both on performance and compatibility of multi-modes.The trend opened opportunities for researchers crossing the boundary between communications and computer architecture.Another trend is the serverlization,i.e.,more infrastructure equipments are replaced by servers.The trend opened opportunities for researchers working towards high performance computing for communication,such as research on communication algorithm kernels and real time programming methods on servers.

Application of Automatic Chromatic Dispersion Compensation for Realization of All-Optical Network

We have studied the algorithm for the automatic chromatic dispersion compensation using bit error rate (BER) and Q-factor optimization for realization of dynamically reconfigurable all-optical .network. We have made sure good performance using the compensation system by laboratory experiments.

A Proposal for a Benchmark Generator of Weakly Connected Directed Graphs

The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. However, there are also other problems whose modeling entails obtaining a weakly connected directed graph such as the student access to the university, the public transport networks, or trophic chains. Those cases deserve particularized study with an analysis and the resolution adjusted to them. Additionally, this is a challenge, since the existing algorithms in most of the cases were originally designed for non-directed graphs or symmetrical and regular graphs. Our proposal is a Benchmark Generator of Weakly Connected Directed Graphs whose properties can be defined by the end-users according to their necessities. The source code of the generators described in this article is available in GitHub under the GNU license.