Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.

Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’profiles.Then,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious.The IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental evaluations.Normal user access behavior in relation to the database is modelled using the data set.The false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other methods.The experimental results indicate that our proposed method results in very small FN and FP rates.

How can survival processing improve memory encoding?

We investigated the psychological mechanism of survival processing advantage from the perspective of false memory in two experiments.Using a DRM paradigm in combination with analysis based on signal detection theory,we were able to separately examine participants’utilization of verbatim representation and gist representation.Specifically,in Experiment 1,participants rated semantically related words in a survival scenario for a survival condition but rated pleasantness of words in the same DRM lists for a non-survival control condition.The results showed that participants demonstrated more gist processing in the survival condition than in the pleasantness condition;however,the degree of item-specific processing in the two encoding conditions did not significantly differ.In Experiment 2,the control task was changed to a category rating task,in which participants were asked to make category ratings of words in the category lists.We found that the survival condition involved more item-specific processing than did the category condition,but we found no significant difference between the two encoding conditions at the level of gist processing.Overall,our study demonstrates that survival processing can simultaneously promote gist and item-specific representations.When the control tasks only promoted either item-specific representation or gist representation,memory advantages of survival processing occurred.

Bioinspired Adaptive Gel Materials with Synergistic Heterostructures

In nature, many biological soft tissues with synergistic heterostructures, such as sea cucumbers, skeletal muscles and cartilages, exhibit high functionality to adapt to complex environments. In artificial soft materials, hydrogels are similar to biological soft tissues due to the unique integration of ＂soft and wet＂ properties and elastic characteristics. However, currently hydrogel materials lack their necessary adaptability, including narrow working temperature windows and uncontrollable mechanics, thus restrict their engineering application in complex environments. Inspired by abovementionedbiological soft tissues, researchers have increasingly developed heterostructural gel materials as functional soft materials with high adaptability to various mechanical and environmental conditions. This article summarizes our recent work on high-performance adaptive gel materials with synergistic heterostructures, including the critical design criteria and the state-of-the-art fabrication strategies of our gel materials. The functional adaptation properties of these heterostructural gel materials are also presented in details, including temperature, wettability, mechanical and shape adaption.