Non-fragile state estimation for discrete Markovian jumping neural networks

In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov–Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach

Persistent sulfate formation from London Fog to Chinese haze

Sulfate aerosols exert profound impacts on human and ecosystem health, weather, and climate, but their formation mechanism remains uncertain. Atmospheric models consistently underpredict sulfate levels under diverse environmental conditions. From atmospheric measurements in two Chinese megacities and complementary laboratory experiments, we show that the aqueous oxidation of SO2 by NO2 is key to efficient sulfate formation but is only feasible under two atmospheric conditions: on fine aerosols with high relative humidity and NH3 neutralization or under cloud conditions. Under polluted environments, this SO2 oxidation process leads to large sulfate production rates and promotes formation of nitrate and organic matter on aqueous particles, exacerbating severe haze development. Effective haze mitigation is achievable by intervening in the sulfate formation process with enforced NH3 and NO2 control measures. In addition to explaining the polluted episodes currently occurring in China and during the 1952 London Fog, this sulfate production mechanism is widespread, and our results suggest a way to tackle this growing problem in China and much of the developing world

Minimally invasive surgical techniques for the therapy of far lateral disc herniation in middle-aged and elderly patients

To examine the clinical results of different minimally invasive techniques for the therapy of far lateral disc herniation in middle-aged and elderly patients. Percutaneous endoscopic lumbar discectomy, MIS-TLIF combined with contralateral translaminar screw and MIS-TLIF combined with bilateral pedicle screws were evaluated via a retrospective chart review. Data from 74 consecutive middle-aged and elderly patients with far lateral disc herniation were analyzed. All patients underwent surgery; 19 with PELD, 24 with MIS-TLIF CTS, and 31 with MIS-TLIF BPS. Clinical data included the length of the incision, duration of the operation, blood loss, hospitalization time, operation cost, recurrence rate, and fusion rate. Preoperative and postoperative patient outcomes including the VAS, ODI scores and MacNab criteria were assessed and recorded. The mean follow-up time was 26.4 months (range from 14 to 46 months). Compared with the internal fixation groups, the length of the incision, duration of operation, blood loss, and hospitalization time were obviously lower in the PELD group. The difference in operation cost among the three methods was statistically significant. The postoperative VAS scores for LBP and LP decreased significantly as compared with those recorded preoperatively. The postoperative ODI scores were lower than those recorded preoperatively. MacNab criteria rating excellent, good and fair results were in 27, 37 and 10 patients, respectively. PELD, MIS-TLIF CTS, and MIS-TLIF BPS are all effective minimally invasive techniques for the therapy of single segment far lateral lumbar disc herniation in middle-aged and elderly patients. PELD had a shorter operation time and less surgical trauma, being a less invasive and more economical method; however, there was no recurrence of disc herniation after fixation. Compared with MIS-TLIF BPS, MIS-TLIF CTS obtained a similar fusion rate and certain costs were saved

Chaotic Immune Genetic Hybrid Algorithms and Its Application

To solve the shortage in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, firstly,the immune memory recognition function was introduced, to speed up the searching speed and improve the overall searching capabilities of genetic algorithm. Secondly,the Hénon chaotic map was introduced into the generation of the initial population, made the generated initial population uniformly distributed in the solution space, to reduce data redundancy, increase the diversity of antibody population and the search range of initial population manipulation , prevent the defect of falling into local optimum. Finally, Logistic map was introduced into manipulation of crossover and mutation, meanwhile the map was used to produce the chaotic disturbance strategy on the memory and populations antibodies , to improve the quality of optimal solution and the searching speed of the algorithm, increase efficient of searching. It was proved that the above hybrid algorithm is convergence by mathematics method. The results of function optimization show that the above hybrid algorithm is valid and has better performance than other algorithms. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.206

Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm

Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance

Dependence of the Clogging Possibility of the Submerged Entry Nozzle during Steel Continuous Casting Process on the Liquid Fraction of Non-Metallic Inclusions in the Molten Al-Killed Ca-Treated Steel

In the current study, the nozzle clogging behavior and inclusion composition in Al-killed Ca-treated steels were observed to investigate the relationship between the liquid fraction of non-metallic inclusions and the clogging possibility of the submerged entry nozzle. Clogging materials were mainly MgO-Al2O3 with less than 20% liquid phases, while most of the inclusions were full liquid CaO-Al2O3-MgO in tundish at the casting temperature. Thus, it was proposed that the nozzle clogging can be effectively avoided by modification of solid inclusions to partial liquid ones rather than full liquid ones. There was a critical value of liquid fraction of inclusions causing the nozzle clogging. A critical condition of the inclusion attachment on the nozzle wall was a function of cosθN−S+cosθI−S<0. With the increase of T.Ca content in steel, the evolution route of inclusions was solid MgO-Al2O3→liquid CaO-Al2O3-MgO→solid CaS and CaO. To avoid the clogging of the submerged entry nozzle (SEN) under the current casting condition, the appropriate T.Ca concentration range in Al-killed Ca-treated steels can be enlarged from the 100% liquid inclusion zone of 10–14 ppm to the 20% liquid inclusion zone of 4–38 ppm

T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor

Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme

Accelerated atrazine degradation and altered metabolic pathways in goat manure assisted soil bioremediation

The intensive and long-term use of atrazine in agriculture has resulted in serious environmental pollution and consequently endangered ecosystem and human health. Soil microorganisms play an important role in atrazine degradation. However, their degradation efficiencies are relatively low due to their slow growth and low abundance, and manure amendment as a practice to improve soil nutrients and microbial activities can solve these problems. This study investigated the roles of goat manure in atrazine degradation performance, metabolites and bacterial community structure. Our results showed that atrazine degradation efficiencies in un-amended soils were 26.9–35.7% and increased to 60.9–84.3% in goat manure amended treatments. Hydroxyatrazine pathway was not significantly altered, whereas deethylatrazine and deisopropylatrazine pathways were remarkably enhanced in treatments amended with manure by encouraging the N-dealkylation of atrazine side chains. In addition, goat manure significantly increased soil pH and contents of organic matters and humus, explaining the change of atrazine metabolic pathway. Nocardioides, Sphingomonas and Massilia were positively correlated with atrazine degradation efficiency and three metabolites, suggesting their preference in atrazine contaminated soils and potential roles in atrazine degradation. Our findings suggested that goat manure acts as both bacterial inoculum and nutrients to improve soil microenvironment, and its amendment is a potential practice in accelerating atrazine degradation at contaminated sites, offering an efficient, cheap, and eco-friendly strategy for herbicide polluted soil remediation