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In this study, we introduce a distributed algorithm that is specifically designed to address optimization problems featuring a decomposable objective function and equality constraints. To minimize the amount of communication required, we incorporate an event-triggered mechanism that enables information exchange only when variable values exceed predefined thresholds. Importantly, our proposed algorithm possesses a distinctive characteristic where the determination of step size is solely based on the properties of the objective function, regardless of the structure of the communication network. Even in situations where changes occur in the network structure, our algorithm remains valid without necessitating any updates to its step size. Assuming strong convexity and smoothness in local objective functions, along with appropriate event-triggered thresholds, our algorithm achieves a convergence rate that is linear. Several numerical experiments provide evidence supporting the effectiveness and superiority of our proposed approach. [ABSTRACT FROM AUTHOR]

The fault characteristic of rolling bearings under variable speed condition is a typical non-stationary stochastic signal. It is difficult to extract due to the interference of strong background noise makes the applicability of traditional noise reduction methods less. In this paper, an aperiodic stochastic resonance (ASR) method is proposed to study the fault diagnosis of rolling bearings under variable speed conditions. Based on numerical simulation, the effect of noise intensity and damping coefficient on the ASR of the second-order underdamped system is discussed, and an appropriate damping coefficient is found to reach the optimal ASR. The proposed method enhances the fault characteristic information of bearing fault simulation signal. Corresponding to rising-stationary and the stationary-declining running conditions, the method is verified by both simulated and experimental signals. It provides reference for fault diagnosis under variable speed condition. [ABSTRACT FROM AUTHOR]

Stochastic resonance (SR) and self-induced stochastic resonance (SISR) are two kinds of important dynamical phenomena in the nonlinear system. SR occurs at the frequency of the characteristic signal. However, SISR can occur at a frequency that is included in the excitation. In present, there are volumes of literatures focusing on extracting the bearing fault characteristics from the vibration signal by SR method. However, the occurrence of SISR may result in the fault features misjudgment in SR processing. Through experimental verifications, we find that the interference of SISR is illustrated strongly in the fault characteristics identification. More importantly, the transition from SISR to SR corresponds to the evolution process of bearing state from normal to damage. Therefore, this evolutionary process can not only judge the state of bearing, but also describe the severity of bearing failure. The result is verified by processing the signals of bearing fault with different severity in noise background. They are the most important findings in this work. [ABSTRACT FROM AUTHOR]

In previous research works, logical stochastic resonance (LSR) was reported to frequently occur in an asymmetric bistable system, where the bias parameter is the key factor to make the LSR appear. In this work, we investigate the effect of different anharmonic periodic signals on the pitchfork and saddle-node bifurcations in a symmetric bistable system. We focus on the relationship between the static bifurcation and LSR. We use both numerical and circuit simulations to analyze some interesting phenomena. Like the bias parameter, some anharmonic periodic signals also break the symmetry of the symmetric bistable system and lead to the saddle-node bifurcation. The anharmonic periodic signal with a constant term in its expanded Fourier series induces a reliable LSR in the symmetric bistable system. The key factor of LSR is the saddle-node bifurcation which implies the asymmetry of the system. Here, we replace the bias parameter by choosing an anharmonic periodic signal and make the LSR occur in a different way. [ABSTRACT FROM AUTHOR]

Stochastic resonance is significant for signal detection. In this paper, a method to improve the stochastic resonance performance in a bistable system excited by bounded noise is studied. Specifically, we add a high-frequency signal to the system as an auxiliary excitation to induce vibrational resonance and focus on the influence of the auxiliary excitation waveform on the improvement effect. We investigate the stochastic resonance performance improved by a fast excitation in different waveforms through numerical simulations. The results show that, the improvement effect of the stochastic resonance depends on the waveform of the fast excitation closely. The symmetry property and constant component of the fast excitation are two key factors. Further, we accomplish the circuit simulation by constructing a circuit to generate bounded noise and the circuit of the bistable system. [ABSTRACT FROM AUTHOR]

We investigate the effects on the pitchfork bifurcation and the vibrational resonance of an overdamped bistable system subjected to both a slow harmonic excitation and a fast periodic excitation with different waveforms. We use numerical simulations along with theoretical explanations to analyze some interesting phenomena. The bifurcation configuration depends closely on the form of the fast excitation. As a result, we have found that the key factor to influence the bifurcation configuration is the symmetry property of the fast excitation. Further, due to the relationship of the vibrational resonance with the pitchfork bifurcation, the vibrational resonance also depends closely on the form of the fast periodic excitation. Moreover, for anharmonic fast excitations, if it is asymmetric, the vibrational resonance usually depends closely on the initial conditions. [ABSTRACT FROM AUTHOR]

We use two traditional indices and propose a new fusion index to quantify the echo chirp signal amplification effectively. They are the cross-correlation coefficient, the spectral amplification factor and the fusion index. Through investigating different numerical examples, we verify the validation of the re-scaled vibrational resonance (VR) method for the echo chirp signal by using the two traditional indices. Besides, taking into account that a single index has its own shortness, we combine the advantages of the two traditional indices mentioned and propose a new fusion index based on the information fusion method. Then we verify the superiority of the fusion index. Furthermore, the effect of noise on VR is also investigated. Finally, with the help of the adaptive inertia weight particle swarm optimization (APSO) algorithm, we propose the adaptive VR in noise background and verify its validity. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has recently been successfully adopted as a regression model in speech enhancement. Nonetheless, training machines to adapt different noise is a challenging task. Because every noise has its own characteristics which can be combined with speech utterance to give huge variation on which the model has to operate on. Thus, a joint framework combining noise classification (NC) and speech enhancement using DNN was proposed. We first determined the noise type of contaminated speech by the voice activity detection (VAD)-DNN and the NC-DNN. Then based on the noise classification results, the corresponding SE-DNN model was applied to enhance the contaminated speech. In addition, in order to make method simpler, the structure of different DNNs was similar and the features were the same. Experimental results show that the proposed method effectively improved the performance of speech enhancement in complex noise environments. Besides, the accuracy of classification had a great influence on speech enhancement. [ABSTRACT FROM AUTHOR]

Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level. [ABSTRACT FROM AUTHOR]

We investigate the vibrational resonance by the numerical simulation and theoretical analysis in an overdamped system with fractional order potential nonlinearities. The nonlinearity is a fractional power function with deflection, in which the response amplitude presents vibrational resonance phenomenon for any value of the fractional exponent. The response amplitude of vibrational resonance at low-frequency is deduced by the method of direct separation of slow and fast motions. The results derived from the theoretical analysis are in good agreement with those of numerical simulation. The response amplitude decreases with the increase of the fractional exponent for weak excitations. The amplitude of the high-frequency excitation can induce the vibrational resonance to achieve the optimal response amplitude. For the overdamped systems, the nonlinearity is the crucial and necessary condition to induce vibrational resonance. The response amplitude in the nonlinear system is usually not larger than that in the corresponding linear system. Hence, the nonlinearity is not a sufficient factor to amplify the response to the low-frequency excitation. Furthermore, the resonance may be also induced by only a single excitation acting on the nonlinear system. The theoretical analysis further proves the correctness of the numerical simulation. The results might be valuable in weak signal processing. [ABSTRACT FROM AUTHOR]

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics. [ABSTRACT FROM AUTHOR]

The theory of general scale transformation (GST) is put forward and used in the second-order underdamped bistable system to extract weak signal features submerged into strong noise. An adaptive stochastic resonance (SR) with GST is proposed and realized by the quantum particle swarm optimization (QPSO) algorithm. The harmonic signal and experimental signal are considered to compare GST with normalized scale transformation (NST) in the second-order system. The results show that detection effect of the adaptive SR with GST is better than the NST SR. In addition, the output signal-to-noise ratio (SNR) is significantly improved in the GST method. Meanwhile, the dependence of the signal extraction efficiency on the noise intensity is researched. The output SNR is decreased with the increase of the noise intensity in two methods. However, the proposed method is always superior to the NST. Moreover, the superiority of the Brown particle oscillation in the single well is discussed. The proposed method has certain reference value in the extraction of the weak signal under the strong noise background. [ABSTRACT FROM AUTHOR]

Several types of middle ear implants (MEIs) have been invented as an alternative to conventional hearing aids for the rehabilitation of sensorineural hearing loss. Temporal bone and clinical studies have shown that the implantation of MEIs' transducers influences middle ear transfer function. But there is little comparative data available about these influences. We conducted comparative studies on the influences of three principal types of MEI transducers in respect to their attachment points on the ossicular chain. To aid the investigation, a human middle ear finite element model was constructed. The model was built based on a complete set of micro-computerized tomography section images of a human ear by reverse engineering technology. The validity of the developed model was verified by comparing the motions obtained by this model with published experimental measurements on human temporal bones. The results show that the eardrum driving transducer (EDT) and the floating mass transducer (FMT) decrease stapes displacement prominently at high frequencies. The greater these transducers' mass, the smaller is the displacement of the stapes footplate. In contrast, the incus body driving transducer (IBDT) decreases stapes displacement severely at low frequencies, and its adverse effect on residual hearing increases with increasing stiffness of the IBDT's driving rod. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We can also adaptively lead the stochastic resonance to be optimized to the greatest extent. The effectiveness of the proposed method is verified by both numerical simulation and lab experimental vibration signals including normal, a chipped tooth and a missing tooth of planetary gearboxes under the loaded condition. Both theoretical and experimental results show that this method can effectively extract weak characteristics in a heavy noise. In the experiment, each weak fault feature is extracted successfully from the fault planetary gear. When compared with the ensemble empirical mode decomposition (EEMD) method, the method proposed in this paper has been found to give remarkable performance. [ABSTRACT FROM AUTHOR]

The three-point sinuosity (TPS) method is proposed to calculate the fractal dimension of surface profile accurately. In this method, a new measure, TPS is defined to present the structural complexity of fractal curves, and has been proved to follow the power law. Thus, the fractal dimension can be calculated through the slope of the fitted line in the log-log plot. The Weierstrass-Mandelbrot (W-M) fractal curves, as well as the real surface profiles obtained by grinding, sand blasting and turning, are used to validate the effectiveness of the proposed method. The calculation values are compared to those obtained from root-mean-square (RMS) method, box-counting (BC) method and variation method. The results show that the TPS method has the widest scaling region, the least fit error and the highest accuracy among the methods examined, which demonstrates that the fractal characteristics of the fractal curves can be well revealed by the proposed method. [ABSTRACT FROM AUTHOR]

Most researchers of time series forecasting devote to design and develop quantitative models for pursuing high accuracy of forecasting on the numerical level. However, in real world, the numerical accuracy is sometimes not necessary for human cognition and decision-making and the numerical results of forecasting based on quantitative model are deficient in interpretability, thus the development of qualitative forecasting model of time series becomes an evident challenge. In this paper, the improved fuzzy cognitive map (IFCM) are proposed first, and then it is applied to develop qualitative model for linguistic forecasting of time series together with fuzzy c-means clustering technology and real-coded genetic algorithm (RCGA). Two real life time series are used to test the developed forecasting model and compare with another method based on FCM, whose results show the developed FCM forecasting model is more simpler and high quality on the linguistic level. [ABSTRACT FROM AUTHOR]

More reaction pathways in the CVD process of preparing boron-carbides with BCl3-C3H6(propene)-H2 precursors were examined in addition to the "lowest" path proposed by Jiang et al. in Theor Chem Acc127:519-538, 2010. The new pathways investigated in this work involve 81 new transition states and 79 new intermediates. Geometries of the species were optimized with the density functional theory at B3PW91/6-311G(d,p) level. Intrinsic reaction coordinates were analyzed to confirm the correct reaction linkages. Energy barriers and reaction energies were calculated with the accurate model chemistry method G3(MP2) after a non-dynamical electronic correlation detection using CASSCF method. Heat capacities and entropies were obtained with the statistical thermodynamics and fitted as a function of temperature. Gibbs free energies at 298.15 K and 1200 K were used to examine the reaction paths. It was found that some steps in the new paths at both 298.15 K and 1200 K are different from the "lowest" path obtained previously and the pathways for the new products CB and C2B were obtained. [ABSTRACT FROM AUTHOR]

Network robot system (NRS) is a new concept that integrates physical autonomous robots, environmental sensors, and human-robot interactions through network-based cooperation. The aim of this paper is to provide a ubiquitous and cooperative service framework for NRS. We first present foundational concepts of semantic map and service definition for the framework. Then, in order to generate feasible service configurations to fulfill tasks, we propose service configuration and reconfiguration algorithms, which dynamically search the appropriate service configurations for different tasks. Additionally, we put forward a service reasoning and enabling process to tackle the service unavailable problems. A cost evaluation function for service configuration is also proposed to facilitate the selection of suitable configurations. We tested and evaluated the framework in both simulation system and physical environment. Specifically, by separately varying the parameter settings, system performance was measured in three aspects: the success rate of tasks, the average waiting time per task, and the average cost per task. The experiment results indicate that the versatile service framework provides self-adaptive capability and utilizes available resources efficiently under a range of different scenarios. [ABSTRACT FROM AUTHOR]

H13 steel samples were implanted with tungsten using a metal vapor vacuum arc (MEVVA) ion source, with an implantation dose of 1×1017cm-2, an extraction acceleration of 30 kV and pulsed ion beam fluxes of between 0.3 mA·cm-2 and 6 mA·cm-2. The surface mechanical properties and microstructure for the W-implanted samples was characterized by the Rutherford backscattering spectroscope (RBS) and a high voltage electron microscope (HVEM). Experimental results of wear and hardness indicated that the hardness and wear of H13 steel increased when the voids were produced by tungsten ion implantation with a high pulsed current density. Forming causes for voids and their influence on the tungsten concentration depth profile in the implanted H13 steel and the surface mechanical properties were discussed in terms of spike theory. [ABSTRACT FROM AUTHOR]

The identification of categories of visitors to a Web-site is very useful towards improved Web designs and improved Web applications. However, the large volume involved in mining access-logs and visitation paths, and the uncertainty to fully identify the visitor demand efficient clustering algorithms that are also resistant to noise and outliers. Also, visitation paths are discrete, and dissimilarity between visitation paths involves sophisticated evaluation and results in attribute-vectors with large dimension. We provide randomized, iterative clustering algorithms for generic dissimilarity in paths. Our algorithms are robust because they use medians rather than means as estimators of location, and the resulting representative of a cluster is actually a path in the data set. We demonstrate mathematically that our algorithms converge and have subquadratic complexity. We also show experimentally that they are resistant to noise by recovering clusters from synthetic data generated by a mixture of distributions of paths in a graph. Our non-crisp method proposed generalizes approaches that allow a data item to have a degree of membership in a cluster. [ABSTRACT FROM AUTHOR]