Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Aimed at the abominable influences to blind equaliza-tion algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization （DFE） algorithm adapting to different under-water acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in dif-ferent unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

Micro-dynamic Behavior and Self-adjusting Water Transmit Mechanism of Water-transferring Composite

Constructional and micro-dynamic process of the water-transferring composite was analyzed. This composite can transmit water to soil with a self-adjustable speed to ensure the survival of seedlings in arid and semi-arid regions when it is embedded in soil around the roots of the seedlings. It is obtained from natural plant fiber coated with a colloid made by mixing a certain proportion of polyacrylamide and montmorillonite. The rules of water being transmitted to soil by the coating under different condition were tested by M-30 quick moisture measure instrument. The process of water-desorption of the coating material was investigated by a Perkin Elmer Diamond S Ⅱ thermal multi-analyzer. Moreover, the micro-dynamic behavior was detected by a FEIQuanta 2000 environment scanning electron microscope. The results demonstrate that montmorillonite has lower water-desorption energy barrier than polyacrylamide and can lose water more easily. montmorillonite particles bridge up to be the main water-transmit material at low water potential （when the soil relatively dry or when the temperature is high）, and they break bridge at high water potential while the polyacrylamide acts as the main water-transmit material.

Traffic prediction using a self-adjusted evolutionary neural network

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.

基于Self-adjust网络模型的人脸图像情感分析方法

人脸表情具有丰富的情感内涵,是情感分析的一个重要研究方向。模糊的面部表情及标注者的主观性所带来的不确定性,给情感分析研究带来了挑战。鉴于此,提出了一种基于Self-adjust网络模型的人脸图像情感分析方法。首先用人脸对齐方法进行图像预处理,然后利用注意力机制来处理Focal损失加权,再对其进行秩正则化排序,最后通过重新分类对有误标签进行矫正,并用实验验证了该方法的有效性与优越性。该方法在准确率这个评价指标上有所提高,能够有效抑制人脸图像情感分析的不确定性,防止深层网络对不确定的人脸图像进行过拟合。

Spectral analysis and self-adjusting mechanism for oscillation phenomenon in hydrogen-oxygen continuously rotating detonation engine

Abstract The continuously rotating detonation engine （CRDE） is a new concept of engines for air- craft and spacecraft. Quasi-stable continuously rotating detonation （CRD） can be observed in an annular combustion chamber, but the sustaining, stabilizing and adjusting mechanisms are not yet clear. To learn more deeply into the CRDE, experimental studies have been carried out to inves- tigate hydrogen-oxygen CRDE. Pressure histories are obtained during each shot, which show that stable CRD waves are generated in the combustor, when feeding pressures are higher than 0.5 MPa for fuel and oxidizer, respectively. Each shot can keep running as long as fresh gas feeding main- tains. Close-up of the pressure history shows the repeatability of pressure peaks and indicates the detonation velocity in hydrogen-oxygen CRD, which proves the success of forming a stable CRD in the annular chamber. Spectrum of the pressure history matches the close-up analysis and confirms the CRD. It also shows multi-wave phenomenon and affirms the fact that in this case a single detonation wave is rotating in the annulus. Moreover, oscillation phenomenon is found in pressure peaks and a self-adjusting mechanism is proposed to explain the phenomenon.

Fuzzy Shape Control Based on El man Dynamic Recursion Network Prediction Model

In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

Research on Alternatively Moving Vacuum Absorbing Wall-climbing Robot

In conection with the complex working-surroundings of the wall-climbing Robot, this paper researched akind of alternatively moving mechanism with good obstacle-surmounting ability and high moving speed, making use ofthe thought of bionics. This paper designed a kind of self-adjusting multi-vacuum sucker. Furthermore, it employedthe theory of vacuum system to establish the work mathematics madel of control switch to are sucking disc and presented the design parameter of the control switch. In addition, this paper made use of the thought of bionics to design aobstacle-surmounting mechanism used in wall-climbing robot. Also it employed the theory Of robotics to analyze the kinematics and the dynamics movement of die robot.

Improved Algorithm of Variable Bandwidth Kernel Particle Filter

Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm,a self-adjusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution,the bandwidth is dynamically adjusted,and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile,the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.

A Blockchain-Based Certificate System with Credit Self-Adjustment

Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates issued by educational institutions,we propose a novel blockchain-based system with credit self-adjustment(BC-CS).In BC-CS,employers can provide feedback according to the performances of their employees(i.e.,students)holding different certificates.Based on the feedback,BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm.To verify the feasibility of our proposed system,a decentralized application prototype has been developed on an Ethereum network.Experimental results demonstrate that the proposed system can fully support multistep accreditation and automatic adjustment for certificate credit.

Self-adjusted elastic action and its CBM pool-forming effect of the high rank coal reservoir

The coal reservoir is a kind of geologic body with considerable elasticity, and its response to stress is more sensitive than the routine ones. In the pool-forming process of the coalbed methane, (CBM) the action of many external geodynamic factors in the coal reservoir is represented by the ability of CBM’s diffusion and migration in the coal reservoir. Therefore, the occurrence of the natural fissures in the coal reservoir becomes a key that determines whether the CBM pool can be formed or not. Based on the principle, the authors have designed a comprehensive method of physical modeling to study the elasticity of the coal matrix block with different coal ranks in the light of the solid to fluid coupling, have established the model of the volume deformation (self-adjusted elastic effect) of the coal reservoir under the condition of the effective stress-adsorption/desorption, and have obtained a correct understanding of the law of the elastic deformation. Afterwards, a new viewpoint, named as the self-closing elastic effect for forming the CBM pool in the high rank reservoir, is put forward, providing an initial point and a scientific basis for a further investigation into its dynamic factors, possible mechanism and role in the CBM energy-balancing system.

Self-adjustment of a nonlinear lasing mode to a pumped area in a two-dimensional microcavity [Invited]

We numerically performed wave dynamical simulations based on the Maxwell–Bloch(MB) model for a quadrupole-deformed microcavity laser with spatially selective pumping. We demonstrate the appearance of an asymmetric lasing mode whose spatial pattern violates both the x-and y-axes mirror symmetries of the cavity.Dynamical simulations revealed that a lasing mode consisting of a clockwise or counterclockwise rotating-wave component is a stable stationary solution of the MB model. From the results of a passive-cavity mode analysis, we interpret these asymmetric rotating-wave lasing modes by the locking of four nearly degenerate passive-cavity modes. For comparison, we carried out simulations for a uniform pumping case and found a different locking rule for the nearly degenerate modes. Our results demonstrate a nonlinear dynamical mechanism for theformation of a lasing mode that adjusts its pattern to a pumped area.

A low-power current self-adjusted VCO using a bottom PMOS current source

This paper presents the design and implementation of a current self-adjusted VCO with low power consumption. In the proposed VCO, a bottom PMOS current source instead of a top one is adopted to decrease the tail noise. A current self-adjusted technique without additional external control signals is taken to ensure the VCO starts up in the whole band while keeping the power consumption relatively low. Meanwhile, the phase noise of the VCO at the low frequency （high Cvar） can be reduced by the technique. The circuit is implemented in 0.18 μm CMOS technology. The proposed VCO exhibits low power consumption of 〈1.6 mW at a 1.5 V supply voltage and a tuning range from 11.79 to 12.53 GHz. The measured phase noise at 1 MHz offset from the frequency 11.79 GHz is-104.7 dBc/Hz, and the corresponding FOM is -184.2 dBc/Hz.

Use of self-organizing suppression and q-Gaussian mutation in artificial immune systems

Purpose-The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems(AISs).Design/methodology/approach-The proposed operators are applied in substitution to the suppression and mutation operators used in AISs.The proposed mechanisms were tested in the opt-aiNet,a continuous optimization algorithm inspired in the theories of immunology.The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population.This procedure is computationally expensive,as the Euclidean distances between every possible pair of candidate solutions must be computed.This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality(SOC)phenomenon,which is less dependent on parameter selection.This work also proposes the use of the q-Gaussian mutation,which allows controlling the form of the mutation distribution during the optimization process.The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem:the rigid docking of proteins.Findings-The proposed suppression operator presented some limitations in unimodal functions,but some interesting results were found in some highly multimodal functions.The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark,and also in the docking problem.Originality/value-First,the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet.Second,the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and,as a consequence,a better performance when compared to the traditional Gaussian mutation.

An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems

Purpose–The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier(SONeFMUC)to classify the operating faults of a hydraulic system.The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.Design/methodology/approach–In the proposed methodology,first,the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms,i.e.a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy(CMA-ES)and a swarm-based explorer with adaptive fuzzified parameters(SBEAFP).Thereafter,a revised version of the group method data handling(GMDH)policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.Findings–Based on comparative numerical experiments,the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments.It is proved that the method outperforms some well-known classifiers such as support vector machine(SVM)and particle swarm optimization-based SVM(PSOSVM).Besides,it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier.For the case,it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities,and consequently optimize the structure of SONeFMUC.Originality/value–The originality of the paper can be considered from both numerical and practical points of view.The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults,i.e.cylinder fault,pump fault,valve leakage fault and rupture of the piping system.Besides,to elaborate on the authenticity and efficacy of the proposed method,its performance is compared with well-known rival techniques.

The Mode of Urban Renewal Base on the Smart City Theory under the Background of New Urbanization

The Smart City, coordinated development of economy, ecology and society, is the future of cities. Urban renewal is an important way to build the Smart City. Based on the Smart City theory, the authors propose the "12345"model of urban renewal, which means developing a tailored proposal of the city and citizen, and building"one city management system, two supporting safeguard measures, three information infrastructure platforms, four urban management modes, and five application service systems." The city will gain the ability of self-adjustment and self-improvement. Smart urban renewal model not only solves the problems of the city at this stage, but also promotes the all-round development of the city, and access to sustainable development.