A Parametric Design Method for Optimal Quick Diagnostic Software.

Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time of a fault diagnostic software to the fault. Second, none of these methods take into account how the objective to improve the response time may affect the accuracy of the designed fault diagnostic software. In this work, a measure of the response time is provided, which was formulated using the time complexity of the algorithm and the signal acquisition time. Model optimization was built into the designed method. Its objective was to minimize the response time. The constraint of the method is to guarantee diagnostic accuracy to no less than the required accuracy. An improved feature selection method was used to solve the optimization modeling. After that, the design parameter of the optimal quick diagnostic software was obtained. Finally, the parametric design method was evaluated with two sets of experiments based on real-world bearing vibration data. The results demonstrated that optimal quick diagnostic software with a pre-defined accuracy could be obtained through the parametric design method.

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study.

In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster-Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.

Comparison of the Abundance and Community Structure of N-Cycling Bacteria in Paddy Rhizosphere Soil under Different Rice Cultivation Patterns.

Eco-agricultural systems aim to reduce the use of chemical fertilizers in order to improve sustainable production and maintain a healthy ecosystem. The aim of this study was to explore the effects of rice-frog farming on the bacterial community and N-cycling microbes in paddy rhizosphere soil. This experiment involved three rice cultivation patterns: Conventionally cultivated rice (CR), green rice-frog farming (GR), and organic rice-frog farming (OR). The rice yield, paddy soil enzyme activities, physicochemical variables and bacterial and N-cycling bacterial abundances were quantitatively analyzed. Rice-frog cultivations significantly increased soil protease, nitrate and reductase activity. Additionally, the nirS gene copy number and the relative abundance of denitrifying bacteria also increased, however urease activity and the relative abundance of nitrifying bacteria significantly decreased. The bacterial community richness and diversity of OR soil was significantly higher than that of the GR or CR soil. Nitrogen use efficiency (NUE) of GR was highest. The N-cycling bacterial community was positively correlated with the total carbon (TC), total nitrogren (TN) and carbon to nitrogen (C:N) ratio. The present work strengthens our current understanding of the soil bacterial community structure and its functions under rice-frog farming. The present work also provides certain theoretical support for the selection of rational rice cultivation patterns.

Distributed State Estimation Over Wireless Sensor Networks With Energy Harvesting Sensors.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

Recursive Filtering Under Probabilistic Encoding-Decoding Schemes: Handling Randomly Occurring Measurement Outliers.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

Quantized recursive filtering for networked systems with stochastic transmission delays.

This paper investigates the recursive filtering problem for a class of networked systems subject to the uniform quantization effects and stochastic transmission delays. The system output is quantized according to a uniform quantization mechanism, and then sent to the remote filter via a communication network undergoing stochastic transmission delays (which are modeled by a sequence of independent and identically distributed variables). To deal with the stochastic transmission delays, an indicator function is delicately designed to ensure that the filtering process is implemented based on the quantized measurement with the newest timestamp available for the filter. With the aid of the indicator function, a free-delay system is obtained by using the augmented system method. The aim of this paper is to design a Kalman-type filter for the augmented system such that an upper bound of the filtering error covariance is guaranteed and minimized. With the aid of the stochastic analysis method, the desired upper bound of the filtering error covariance is derived by recursively solving two Riccati-like difference equations. Then, the upper bound is minimized by properly selecting the filter parameters. Finally, a numerical example is provided to illustrate the validity of the developed filtering scheme.

Dynamic event-based finite-horizon H∞ secure consensus control of a class of nonlinear multi-agent systems.

In this paper, we investigate the H∞ consensus control issue for nonlinear multi-agent systems (MASs) subject to multiple attacks over a finite time interval. A novel and comprehensive model to characterize the multiple attacks is presented that includes denial-of-service (DoS) attacks, scaling attacks and replay attacks. With the hope of easing the communication burdens, we implement a dynamic event-triggered scheme to schedule the process of data sharing among the individual subsystems, which helps judge if the collected data should be shared to neighboring agents for control input update. The aim of the proposed problem is to develop an output feedback strategy to meet the desired H∞ consensus performance despite the existence of multiple attacks. Some conditions are presented for the solvability of the investigated problem, and the feedback gains are obtained via certain convex optimization algorithms. The proposed theoretical result is finally demonstrated by virtue of two illustrative simulation examples.

Pyrolysis behaviors of anaerobic digestion residues in a fixed-bed reactor with rapid infrared heating.

Fast pyrolysis via rapid infrared heating may significantly enhance the heat transfer and suppress the secondary reaction of the volatiles. The effects of various pyrolysis temperatures on pyrolysis behaviors of anaerobic digestion residues (ADR) were studied in this research utilizing a fixed-bed reactor equipped with rapid infrared heating (IH), as well as to compare the pyrolysis products produced by rapid infrared heating (IH) to those produced by conventional electric heating (EH). Thermogravimetric (TG) analysis revealed that pyrolysis of ADR occurred in three decomposition stages. The results of pyrolysis experiments showed that increasing temperature first raised the bio-oil yield for IH and EH, peaking at 500-600 °C, but thereafter decreased the yield. In contrast to the findings achieved with EH, infrared heating (IH) presented a greater overall bio-oil yield but a lower gas yield. The bio-oil produced by IH increased from 8.35 wt.% at 400 °C to 12.56 wt.% at 500 °C before dropping to 11.22 wt.% at 700 °C. Gaseous products produced by IH have a higher heating value than those generated by EH. Nitrogenous compounds, ketones, and phenols make up the majority of the bio-oil. In the IH bio-oil, nitrogen compounds rose with increasing temperature, while those varied slightly in the EH bio-oil. The phenols content in IH bio-oil was much more than that of EH, exhibiting values of 8.63% and 2.95%, respectively. The findings of the FTIR spectra of biochar indicated that as the temperature increased, the chains of aliphatic side professedly reduced and the structure of biochar became considerably ordered for both heating techniques. The Raman spectra of IH biochar showed that the ratio of AG/AD rose progressively from 0.17 to 0.20 as pyrolysis temperature rose from 500 to 700 °C.