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In this paper, we introduce the principle of identifying state transition based on the Van der Pol-Duffing oscillator in weak signal detection, which demonstrate the importance to indicate external perturbation existence and detect weak signal accurately. Starting from analyzing the insufficiency of employing classical identification methods occurring in the detecting process, we put forward a novel identification method named region analyzation method to improve the deficiency. Then, an establishment and feasibility analysis of this proposed method are described exhaustively. Numerical experiments on merits of this proposed method that is compared with the classical methods are carried out. The results indicate that this proposed method has better identifying capability than the classical methods and provides extendibility to engineering application.

We propose a joint exponential function and Woods–Saxon stochastic resonance (EWSSR) model. Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function, it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter. In the novel system, the influence of different parameters on the shape of the potential function has its own emphasis, making it easier for us to adjust the shape of the potential function. The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters, so that the system can match different types of input signals adaptively. By adjusting the system parameters, the potential function model can be transformed between the bistable model and the monostable model. The potential function of EWSSR has richer shapes and geometric characteristics. The effects of parameters, such as the height of the barrier and the width of the potential well, on SNR are studied, and a set of relatively optimal parameters are determined. Moreover, the EWSSR model is compared with other classical stochastic resonance models. Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models. Simultaneously, the EWSSR model is applied to the detection of actual bearing fault signals, and the detection effect is also superior to other models.

In this paper, we originally present a memristor-based chaotic oscillator which comes from the classical Chua circuit and is devoted to weak signal detection application. On this basis, a non-autonomous memristor-based chaotic oscillator is proposed to estimate the amplitude of weak character signals at diverse frequency, enhance signal-to-noise ratio (SNR) and especially implement square wave signals detection. The detection properties utilizing the proposed oscillator and those of the traditional chaotic oscillators have been compared to demonstrate that this novel oscillator has superior detection performance on strong anti-noise characteristics, detection sensitivity and so on. As a result, the proposed memristor-based chaotic oscillator for weak signal detection has a wide range of engineering applications.

In this paper, an attribute reduction method is proposed based on the model of three-way decisions for decision tables (3WD-D). First, the discernibility relations among regions are analyzed based on the component of trisecting in 3WD-D model, and the discernibility relations among the granules in the same regions are discussed based on the other component of acting. On the basis, three discernibility matrices are presented to describe these discernibility relations and calculate the significances of attributes. Next, a heuristic attribute reduction algorithm based on discernibility matrix is proposed for finding a single reduct. Besides, the differences and relationships between 3WD-D and rough set are analyzed in terms of discernibility matrices, reducts and types of rule set. These theoretical analysis and experimental results show the effectiveness and advantages of the proposed attribute reduction method.

In order to resolve the degradation phenomenon of the classical three-way decisions model based on rough set three-regions, the notion of decision trap is proposed to make a novel three-way decisions model for decision tables (3WD-D). 3WD-D adopts the trisection-acting frame, and redefines three types of strategies in acting component as accept right decisions, reject decision traps and non-commitment, respectively. Based on the new strategies, the universe is divided into three new regions. They are acceptance region (AR), rejection region (RR), and delay region (DR), respectively. The related theory analysis and experiments show that 3WD-D resolve the degradation phenomenon and is effective in decision tables.

In blast furnace, the accurate prediction of silicon contents in hot metal plays an important role in comprehensively judging the furnace states and making correct decisions. However, traditional prediction models are easily affected by multi-collinearity among the independent variables and imbalance sample distribution in hot metal datasets. The prediction values of silicon contents would be difficult to track the real values accurately when the furnace conditions fluctuate dramatically. In this paper, a classification and compensation model for silicon contents is proposed by combining the ridge regression and random forest classification algorithm. Firstly, the ridge regression algorithm is applied to fit and optimize the prediction values outputted by a front-end prediction model. Then, the random forest technology is expected to classify and identify the furnace states. Finally, the predicted values are compensated according to classification results. In this paper, the random forest regression and BP neural network algorithm were used to predict silicon content as front-end models. The related applications show that the proposed classification and compensation model improves the accuracy of predicted values effectively.

Due to the poor anti-jamming performance, less gas species, low detection accuracy, short life, medium leaking or carbon monoxide poisoning and other defects that current sensors for gases dissolved in transformer oil have, a SOFC-based multi-component gas sensor has been designed. The physical, chemical and electrochemical mechanisms of gases in the SOFC have been analyzed, and the mathematical model which contains the static, dynamic and load effects has been established. And the model parameters have been optimized in this paper. The results showed that: (1) temperature is the decisive factor for the static characteristic of the sensor, and the accuracy of temperature should be controlled within ± 0.1 °C to achieve the 0.1 μL/L detection limit of C2H2. (2) The polarization resistance is inversely proportional to the input gas concentrations, while the open-circuit voltage is proportional to them. (3) The relationship between the short-circuit current and the square of input gas concentration is direct proportion, and the polarization resistance is the determinant of sensor dynamic characteristics. Finally, a gas experimental system has been built to verify the above conclusions. This gas sensor can realize the high-precision detection for multi-component gases dissolved in transformer oil effectively.

In view of photoacoustic spectroscopy theory, the relationship between weak photoacoustic signal and gas concentration is described. The studies, on the principle of Duffing oscillator for identifying state transition as well as determining the threshold value, have proven the feasibility of applying the Duffing oscillator in weak signal detection. An improved differential Duffing oscillator is proposed to identify weak signals with any frequency and ameliorate the signal-to-noise ratio. The analytical methods and numerical experiments of the novel model are introduced in detail to confirm its superiority. Then the signal detection system of weak photoacoustic based on differential Duffing oscillator is constructed, it is the first time that the weak signal detection method with differential Duffing oscillator is applied triumphantly in photoacoustic spectroscopy gas monitoring technology.

The expressions of the second harmonic (2f) signal are derived on the basis of absorption spectral and lock-in theories. A parametric study indicates that the phase shift between the intensity and wavelength modulation makes a great contribution to the 2f signal. A self-calibration wavelength modulation spectroscopy (WMS) method based on tunable diode laser absorption spectroscopy (TDLAS) is applied, combining the advantages of ambient pressure, temperature suppression, and phase-shift influences elimination. Species concentration is retrieved simultaneously from selected 2f signal pairs of measured and reference WMS-2f spectra. The absorption line of acetylene (C2H2) at 1530.36 nm near-infrared is selected to detect C2H2 concentrations in the range of 0–400 ppmv. System sensitivity, detection precision and limit are markedly improved, demonstrating that the self-calibration method has better detecting performance than the conventional WMS.

The principle of the Van der Pol Duffing oscillator for state transition and for determining critical value is described, which has been studied to indicate that the application of the Van der Pol Duffing oscillator in weak signal detection is feasible. On the basis of this principle, an improved two-coupled differential Van der Pol Duffing oscillator is proposed which can identify signals under any frequency and ameliorate signal-to-noise ratio (SNR). The analytical methods of the proposed model and the construction of the proposed oscillator are introduced in detail. Numerical experiments on the properties of the proposed oscillator compared with those of the Van der Pol Duffing oscillator are carried out. Our numerical simulations have confirmed the analytical treatment. The results demonstrate that this novel oscillator has better detection performance than the Van der Pol Duffing oscillator.

Using the time-domain scheme, the chaos and bifurcations behaviors in the fractional-order unified system are investigated numerically. Complex dynamics with interesting characteristics are presented by means of bifurcation diagrams. Chaos does exist in this system for a wide range of fractional orders, and some typical bifurcations are observed, such as pitchfork bifurcation, period-doubling bifurcation, an attractor-merging crisis bifurcation, and transient chaos. The results show that the lowest order we found for this fractional-order system to yield chaos is 2.65.

The orthogonal frequency division multiplexing (OFDM) modulation technique has been used widely in visible light communications (VLC) systems to combat intersymbol interference. At the same time, the inherent drawback of OFDM with a high peak-to-average power ratio (PAPR) is brought into OFDM visible light communications (VLC-OFDM). Furthermore, considering the limited dynamic range characteristics of light-emitting diodes, the performance degradation caused by a high PAPR is more serious in VLC-OFDM. In this paper, we propose a partial transmit sequence (PTS) technique based on the combination of a genetic algorithm (GA) and a hill-climbing algorithm (GH-PTS) to solve the problem of high PAPR. GH-PTS is a modified PTS technique based on GA-PTS. Essentially, GH-PTS is a local optimization of GA-PTS. Simulation results show that the optimized technique is able to reduce PAPR more effectively without any loss of bit error rate performance than the GA-PTS technique in VLC-OFDM system.