Chain-type wireless sensor network node scheduling strategy

In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

From Single-type to Overall Networked Type： Gradual Progress Trend of the BPM Technology

Since the middle and later period of the 1990s, business process management has become the major thought and method of modem business administration. After tracking back of the management origin and probing into the relations between business process and management theory development, this paper finds after the proposition of Taylor＇s scientific management, the business process management began to sprout and was divided into four stages： technological stage of single procedure, interior networked stage, the stage of electronic network and overall networked stage. This paper also proposes the view that the evolvement of business progress management enlightens us a lot in the improvement of today＇s business administration.

BP neural network classification on passenger vehicle type based on GA of feature selection

This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA)of feature selection.We have obtained an optimal feature subset,including length,ratio of width and length,and ratio of height and length.And then we apply this optimal feature subset as well as another feature set,containing length,width and height,to the network input.Back-propagation(BP)neural network and support vector machine(SVM)are applied to classify the passenger vehicle type.There are four passenger vehicle types.This paper selects 400 samples of passenger vehicles,among which 320 samples are used as training set(each class has 80 samples)and the other 80 samples as testing set,taking the feature of the samples as network input and taking four passenger vehicle types as output.For the test,we have applied BP neural network to choose the optimal feature subset as network input,and the results show that the total classification accuracy rate can reach 96%,and the classification accuracy rate of first type can reach 100%.In this condition,we obtain a conclusion that this algorithm is better than the traditional ones[9].

From non-obese diabetic to Network for the Pancreatic Organ Donor with Diabetes: New heights in type 1 diabetes research

Since the discovery of therapeutic insulin in 1922 and the development of the non-obese diabetic spontaneous mouse model in 1980,the establishment of Network for Pancreatic Organ Donor with Diabetes(n POD) in 2007 is arguably the most important milestone step in advancing type 1 diabetes(T1D) research. In this perspective,we briefly describe how n POD is transforming T1 D research via procuring and coordinating analysis of disease pathogenesis directly in human organs donated by deceased diabetic and control subjects. The successful precedent set up by n POD is likely to spread far beyond the confines of research in T1 D to revolutionize biomedical research of other disease using high quality procured human cells and tissues.

Global asymptotic stability for Hopfield-type neural networks with diffusion effects

The existence, uniqueness and global asymptotic stability for the equilibrium of Hopfield-type neural networks with diffusion effects are studied. When the activation functions are monotonously nondecreasing, differentiable, and the interconnected matrix is related to the Lyapunov diagonal stable matrix, the sufficient conditions guaranteeing the existence of the equilibrium of the system are obtained by applying the topological degree theory. By means of constructing the suitable average Lyapunov functions, the global asymptotic stability of the equilibrium of the system is also investigated. It is shown that the equilibrium （if it exists） is globally asymptotically stable and this implies that the equilibrium of the system is unique.

Existence of Periodic Solutions for Neutral-Type Neural Networks with Delays on Time Scales

In this paper, we employ a fixed point theorem due to Krasnosel’skii to attain the existence of periodic solutions for neutral-type neural networks with delays on a periodic time scale. Some new sufficient conditions are established to show that there exists a unique periodic solution by the contraction mapping principle.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

Eddy current quantitative evaluation of high-speed railway contact wire cracks based on neural network

Purpose–The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway.Design/methodology/approach–Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China,a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation.A crack detection system based on eddy current detection was built,and eddy current detection voltage data was obtained for cracks of different depths and widths.By analyzing the variation law of eddy current signals,characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation(BP)neural network.Findings–Numerical simulation and experimental detection of eddy current signal change rule is basically consistent,based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability.BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%,respectively,and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification.Originality/value–This study establishes a new type of high-speed railway contact wire crack detection and identification method,which provides a new technical means for high-speed railway contact wire injury detection.The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance,and it has certain guiding significance for the detection technology of contact line in high-speed railway.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Periodic Solution for Neutral Type Neural Networks

The principle aim of this paper is to explore the existence of periodic solution of neural networks model with neutral delay. Sufficient and realistic conditions are obtained by means of an abstract continuous theorem of k-set contractive operator and some analysis technique.

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

无阻塞Clos-Type网上的多源点多播

在并行和分布式环境中 ,提供无阻塞的多对多通信是至关重要的 .Clos- Type网络能很好地满足这些要求 ,因此得到广泛的应用 .但是目前对于这类网中的多源点多播 ,通常的办法是通过对输入端的请求逐个进行一到多播送的方式来实现 ,这样的方式算法效率较低 ,在 N× N的网络中时间复杂度达到Θ (N3/2 ) ,其中 N为网络输入端的总数 .文章主要研究的是 Clos- Type网上进行多源点多播的充分条件 ,并且通过引入分组、竞争互斥等机制 ,在中间级开关数目数量级不变的情况下使路由算法的时间复杂度降低至 :Θ N log Nloglog N2 log N ,从而在

Cohen-Grossberg-type BAM神经网络吸引集和不变集(英文)

利用矩阵理论和不等式分析技巧,讨论了一类具分布时滞的Cohen-Grossberg型BAM神经网络的不变集和吸引集,获得了一些最新结果,并给出一个实例说明我们结果的可行性.

TypeSampler:一种基于gossip的类型采样方法

在很多P2P应用中,节点可以根据其兴趣或资源划分为不同的类型,而以特定类型节点为目标的基于覆盖网的路由也就成为实现数据分发及查询的关键.非结构化覆盖网具有维护开销低、鲁棒性高的优点,却也因此难以保证路由效率.提出了一种基于gossip的类型采样方法——TypeSampler,它以等概率采样不同类型的节点(称为类型采样),以此保证在任意节点发现特定类型邻居节点的概率下界,进而保证非结构化覆盖网中的路由效率.为了实现类型采样,TypeSampler首先通过基于gossip的节点采样及反熵聚集估计各类型节点的比例,然后,TypeSampler再根据这些比例估计值周期性地维护每个节点的类型采样表.理论分析与实验结果表明,TypeSampler能够实现精确的类型比例估计以及近似均匀随机的类型采样,并能适应动态的网络环境.而且相对于已有的方法,TypeSampler能够支持更高效的路由,且具有更好的可扩展性.

DPP-4抑制剂治疗的2型糖尿病患者中焦虑和抑郁发生风险的Network meta分析

目的使用Network meta分析系统评价DPP-4抑制剂类降糖药致2型糖尿病(type 2 diabetes mellitus,T2DM)患者发生焦虑、抑郁的风险。方法系统检索Medline、Embase、Cochrane Library和Clinical Trials.gov网站数据库(截至2016年4月28日)中比较DPP-4抑制剂与其他降糖药发生焦虑、抑郁风险的随机对照试验(randomized controlled trial,RCT),采用传统meta分析和Network meta分析方法对纳入的RCT研究结果进行合并。结果共纳入44项研究,63 731例T2DM患者,包含9种干预措施:5种DPP-4抑制剂(西格列汀、维格列汀、沙格列汀、利格列汀和阿格列汀)、胰高糖素样肽1受体激动剂、磺脲类、噻唑烷二酮类和安慰剂。传统meta分析结果显示,分别与安慰剂、磺脲类、噻唑烷二酮类相比,DPP-4抑制剂导致焦虑、抑郁发生的风险均较低,但差异无显著性(P>0.05)。Network meta分析发现,与噻唑烷二酮类相比,维格列汀致T2DM患者发生焦虑、抑郁的风险较高,差异有显著性(P<0.05,OR=2.64,95%CI 1.03~6.79),与西格列汀相比,维格列汀致T2DM患者发生焦虑、抑郁的风险较高,差异有显著性(P<0.05,OR=2.42,95%CI 1.00~5.85)。与安慰剂或磺脲类比较,DPP-4抑制剂致T2DM患者发生焦虑、抑郁的风险相当,差异无显著性(P>0.05)。采用基于贝叶斯理论的Network meta分析对9种干预措施进行排序,结果显示西格列汀的风险最低。结论 DPP-4抑制剂不会加重T2DM患者罹患焦虑、抑郁等情绪问题的风险,但不同的DPP-4抑制剂导致的风险程度可能存在差异,建议今后开展的大型前瞻性研究应重视焦虑、抑郁等情绪问题的发生情况,以期为获得更加明确的结论提供证据支持。

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.