Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%.

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Clustered Wireless Sensor Network in Precision Agriculture via Graph Theory

Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can produce crops with a minimum amount of water and fertilizer.Even though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability.Towards such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efficientflow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance.Thefindings of blocks 1 and 2 are assessed.Each signal takes just a little amount of energy to communicate,according to the performance.It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs.However,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN.Based on the minimum edgefixed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control stations.The edge-fixed geodetic cluster makes the network fast,efficient and reliable.Moreover,it also solves routing and congestion problems.

Simultaneous Partitioning and Scheduling Algorithm for Clustered Architecture

Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.

Classification of clustered microseismic events in a coal mine using machine learning

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.

Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR):A critical overview on the most promising applications of molecular scissors in oral medicine

The scientific community is continuously working to translate the novel biomedical techniques into effective medical treatments.CRISPR-Cas9 system(Clustered Regularly Interspaced Short Palindromic Repeats-9),commonly known as the“molecular scissor”,represents a recently developed biotechnology able to improve the quality and the efficacy of traditional treatments,related to several human diseases,such as chronic diseases,neurodegenerative pathologies and,interestingly,oral diseases.Of course,dental medicine has notably increased the use of biotechnologies to ensure modern and conservative approaches:in this landscape,the use of CRISPR-Cas9 system may speed and personalize the traditional therapies,ensuring a good predictability of clinical results.The aim of this critical overview is to provide evidence on CRISPR efficacy,taking into specific account its applications in oral medicine.

A clustered routing protocol based on energy and link quality in WSNs

Influenced by the environment and nodes status,the quality of link is not always stable in actual wireless sensor networks( WSNs). Poor links result in retransmissions and more energy consumption. So link quality is an important issue in the design of routing protocol which is not considered in most traditional clustered routing protocols. A based on energy and link quality's routing protocol( EQRP) is proposed to optimize the clustering mechanism which takes into account energy balance and link quality factors. EQRP takes the advantage of high quality links to increase success rate of single communication and reduce the cost of communication. Simulation shows that,compared with traditional clustered protocol,EQRP can perform 40% better,in terms of life cycle of the whole network.

Kangaroo Care (Skin-to-Skin) for Clustered Pain Procedures: Case Study

Background: Pain management for term newborns undergoing clustered painful procedures has not been tested. Kangaroo Care (chest-to-chest, skin-to-skin position of infant on mother) effectively reduces pain of single procedures, but its effect on pain from clustered procedures is not known. Aim: The aim was to test Kangaroo Care’s effect on pain in one term infant who received clustered painful procedures while determining feasibility of the Kangaroo Care intervention. Design, Setting, and Participant: A case study design was used with one healthy term newborn who received two heel sticks and one injection in one session in the mother’s postpartum room. Method: Heart rate and oxygen saturation (recorded from Massimo Pulse Oximeter every 30 seconds), crying time (total seconds of crying on videotape) and behavioral state (using Anderson Behavioral State Scoring system every 30 seconds) were measured before (5 minutes), during (10.5 minutes) and after (30 minutes) the three clustered painful procedures in a newborn who was in Kangaroo Care during all observations. One staff nurse administered the clustered procedures. Results: Heart rate increased sequentially with each heelstick, oxygen saturation remained unchanged, sleep predominated, and crying was minimal throughout the procedures. Conclusion: Kangaroo Care appeared to reduce pain from clustered painful procedures and can be further tested.

Nx-SCFDMA和clustered DFT-S-OFDMA方案研究

随着宽带无线通信的发展,目前的多址技术已经满足不了系统的需求,为了适应更宽的带宽的传输,必须引进增强型的多址接入技术。介绍了4G系统讨论的其中两种热点多址方案,即:Nx-SCFDMA(Nx单载波频分多址接入)、clustered DFT-S-OFDMA(clustered离散傅里叶变换扩展频分多址接入),主要从方案的实现原理、CM(立方量度)性能和FFT(快速傅里叶变换)复杂度方面进行分析和比较。

Clustered Federated Learning with Weighted Model Aggregation for Imbalanced Data

As a promising edge learning framework in future 6G networks,federated learning(FL)faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors.Data imbalance is one of these challenges that can significantly degrade the learning efficiency.To deal with data imbalance issue,this work proposes a new learning framework,called clustered federated learning with weighted model aggregation(weighted CFL).Compared with traditional FL,our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration,and then performs weighted per-cluster model aggregation.Therein,the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients.Moreover,the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate.Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.

The Performance Analysis for Distributed Collaborative Beamforming Transmission of Clustered Mobile Sensor Network

Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.

Modified clustered comb pilot-aided fast time-varying channel estimation for OFDM system

As high-speed railway is booming worldwide, the communication system with fast-time varying channel has drawn great attention. The comb pilot based linear minimum mean square error （LMMSE） channel estimator is proved to be an effective method for fast time-varying channel estimation. In this paper, the clustered comb pilot-aided chan- nel estimation for orthogonal frequency-division multiplexing （OFDM） system is discussed, where the time varying channel is approximated by a basis expansion model （BEM）. A modified clustered comb pilot structure is proposed and justified to improve the estimation performance compared with the clustered comb pilot proposed by Tang. Based on the complex-exponential BEM （CE-BEM） model, a suboptimal-pilot structure is proposed. In addition, optimal pilot length is analyzed and simulated with a predefined total number of pilots. The simulation results show that the modi- fied clustered comb pilot can greatly reduce the estimation error especially with high Doppler spread. The suboptimal- pilot structure with guard pilot approximation is proven to be competitive. Optimal nonzero pilot lengths for different Doppler spread are obtained by simulation with a predefined channel order and fixed pilot subcarriers.

Effects of internal noise on the spiking regularity of a clustered Hodgkin–Huxley neuronal network

Spiking regularity in a clustered Hodgkin–Huxley（HH） neuronal network has been studied in this letter. A stochastic HH neuronal model with channel blocks has been applied as local neuronal model. Effects of the internal channel noise on the spiking regularity are discussed by changing the membrane patch size. We find that when there is no channel blocks in potassium channels, there exist some intermediate membrane patch sizes at which the spiking regularity could reach to a higher level. Spiking regularity increases with the membrane patch size when sodium channels are not blocked. Namely, depending on different channel blocking states, internal channel noise tuned by membrane patch size could have different influence on the spiking regularity of neuronal networks.

AN ADAPTIVE-WEIGHTED TWO-DIMENSIONAL DATA AGGREGATION ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORKS

In this paper,an Adaptive-Weighted Time-Dimensional and Space-Dimensional(AWTDSD) data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network.AWTDSD contains three phases:(1) the time-dimensional aggregation phase for eliminating the data redundancy;(2) the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and(3) the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station.AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well.Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.

A NEW DYNAMIC QUOTA-BASED ADMISSION CONTROL WITH SUB-NEGOTIATION FOR SOFTSWITCH-BASED CLUSTERED MEDIA SERVER

Based on the demand of the admission control of softswitch-based clustered media server, this pa- per proposed a new dynamic quota-based admission control algorithm that has a sub-negotiation process. The strongpoint of quota-based algorithm had been inherited in the algorithm and at the same time some new ideas had also been introduced into it. Simulations of the algorithm had been conducted on the Petri net model and the results show that this algorithm has excellent performance. In order to find the optimal resource quota set- ting in real time, the paper proposed two approximation analysis methods. It can be seen from analysis results that these two methods can be used to get sub-optimal quota values quickly and effectively. These two ap- proximation analysis methods will play important roles in implementation of the algorithm in system.

Clustered trajectories anonymity in wireless sensor networks

This paper proposes a clustered trajectories anonymity scheme （CTA） that enhances the kano nymity scheme to provide the intended level of source location privacy in mobile event monitoring when a global attacker is assumed. CTA applies isomorphic property of rotation to create traces of the fake sources distributions which are similar to those of the real sources. Thus anonymity of each trajectory and that of the clustered is achieved. In addition, location kdiversity is achieved by dis tributing fake sources around the base station. To reduce the time delay, tree rooted at the base sta tion is constructed to overlap part of the beacon interval of the nodes in the hierarchy. Both the ana lytical analysis and the simulation results prove that proved energy overhead and time delay. our scheme provides perfect anonymity with improved energy overhead and time delay.

Clustered Pain Procedures in Skin-to-Skin Contact (SSC) Position for Full Term Newborns

As a part of their routine care, full term newborns face many painful procedures immediately after birth and during the first couple days of life. Skin-to-Skin Contact (SSC) has been recommended as a non-pharmacological pain management intervention in newborns. However, the use of SSC in labor and delivery rooms as well as in postnatal units and nurseries is limited due to the discomfort that the nurses and phlebotomists themselves experience during positioning the newborns and themselves to complete these routine procedures. The objective of this paper is to describe a step-by-step procedure that was developed and used in a randomized clinical trial to manage newborns pain during clustered pain procedures. The procedure worked well and no complaints of discomfort were reported by the nurses during the study.