Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this paper.Both continuous and discontinuous activations are considered forMNNs.And the mixed delays which are closer to reality are taken into the system.Besides,two kinds of control schemes are proposed,including feedback and adaptive control strategies.Based on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are acquired.Moreover,the upper bound of settling time(ST)which is independent of the initial values is given.Finally,the feasibility of our theory is attested by simulation examples.

Nonlinear Steam Valve Adaptive Controller Design for the Power Systems

Considering generator rotor and valve by external disturbances for turbine regulating system, the nonlinear large disturbance attenuation controller and parameter updating law of turbine speed governor system are designed using backstepping method. The controller not only considers transmission line parameter uncer-tainty, and has attenuated the influences of large external disturbances on system output. The nonlinear con-troller does not have the sensitivity to the influences of external disturbances, but also has strong robustness for system parameters variation, which is because of the transmission line uncertainty being considered in internal disturbances. The simulation results show that the control effect of the large disturbance attenuation controller more advantages by comparing with the control performance of conventional nonlinear robust controller.

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Walking as a unique biometric tool conveys important information for emotion recognition.Individuals in different emotional states exhibit distinct walking patterns.For this purpose,this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram(EEG)and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion.Subjects wear virtual reality head-mounted display(VR-HMD)equipment to immerse in strong emotions during walking.VR environment shows excellent imitation and experience ability,which plays an important role in awakening and changing emotions.In addition,the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform(DWT).These serve as input to the attention-based convolutional neural network(CNN)fusion model.The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features.To effectively improve the performance of the recognition system,the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results.An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal(rbio2.2)wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

WARFARE COMMAND STRATAGEM ANALYSIS FOR WINNING BASED ON LANCHESTER ATTRITION MODELS

Lanchester equations and their extensions are widely used to calculate attrition in warfare models. The current paper addresses the warfare command decision-making problem for winning when the total combats capability of the attacking side is not superior to that of the defending side. For this problem, the corresponding warfare command stratagems, which can transform the battlefield situation, are proposed and analyzed quantitatively by considering the influence of the warfare information factor. The application examples in military conflicts show the feasibility and effectiveness of the proposed model and the warfare command stratagems for winning. The research results may provide a theoretical reference for warfare command decision making.

Composition and emission dynamics of migratory locust volatiles in response to changes in developmental stages and population density

Chemical communication plays an important role in density-dependent phase change in locusts. However, the volatile components and emission patterns of the migratory locust, Locusta migratoria, are largely unknown. In this study, we identified the chemical compositions and emission dynamics of locust volatiles from the body and feces and associated them with developmental stages, sexes and phase changes. The migratory locust shares a number of volatile components with the desert locust （Schistocerca gregaria）, but the emission dynamics of the two locust species are significantly different. The body odors of the gregarious nymphs in the migratory locust consisted of phenylacetonitrile （PAN）, benzaldehyde, guaiacol, phenol, aliphatic acids and 2,3-butanediol, and PAN was the dominant volatile. Volatiles from the fecal pellets of the nymphs primarily consist of guaiacol and phenol. Principal component analysis （PCA） showed significant differences in the volatile profiles between gregarious and solitary locusts. PAN and 4-vinylanisole concentrations were significantly higher in gregarious individuals than in solitary locusts. Gregarious mature males released significantly higher amounts of PAN and 4-vinylanisole during adulthood than mature females and immature adults of both sexes. Furthermore, PAN and 4-vinylanisole were completely lost in gregarious nymphs during the solitarization process, but were obtained by solitary nymphs during gregarization. The amounts of benzaldehyde, guaiacol and phenol only unidirectionally decreased from solitary to crowded treatment. Aliphatic aldehydes （C7 to C10）, which were previously reported as locust volatiles, are now identified as environmental contaminants. Therefore, our results illustrate the precise odor profiles of migratory locusts during developmental stages, sexes and phase change. However, the function and role of PAN and other aromatic compounds during phase transition need further investigation.