Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism

Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

A membrane-inspired algorithm with a memory mechanism for knapsack problems

Membrane algorithms are a class of distributed and parallel algorithms inspired by the structure and behavior of living cells. Many attractive features of living cells have already been abstracted as operators to improve the performance of algorithms. In this work, inspired by the function of biological neuron cells storing information, we consider a memory mechanism by introducing memory modules into a membrane algorithm. The framework of the algorithm consists of two kinds of modules (computation modules and memory modules), both of which are arranged in a ring neighborhood topology. They can store and process information, and exchange information with each other. We test our method on a knapsack problem to demonstrate its feasibility and effectiveness. During the process of approaching the optimum solution, feasible solutions are evolved by rewriting rules in each module, and the information transfers according to directions defined by communication rules. Simulation results showed that the performance of membrane algorithms with memory cells is superior to that of algorithms without memory cells for solving a knapsack problem. Furthermore, the memory mechanism can prevent premature convergence and increase the possibility of finding a global solution.

Formation Mechanism of Curved Martensite Structures in Cu-based Shape Memory Alloys

The curved martensite structures have been observed in CuZnAI-based shape memory alloys by both transmission electron microscope and optical microscope. It was found that the curved martensite structures observed in as-solution treated, as-aged and as-trained alloys usually occurred around dislocation tangles or precipitate, at the plate boundary or grain boundary, and when the growing plates collided with each other or alternate mutually.

Role of OX40 in mechanisms of memory T cells in islet transplant tolerance

Objective To investigate the role of OX40 in the mechanisms of memory T cells in islet transplant tolerance. Methods The expression of OX40 on native, like memory and memory CD8 + T cells was detected by RT - PCR. Splenic T ceels from B6 mice were injected into Rag - / - mice via the tail vein,and the Rag mice were divided into three groups ( n = 8 each) :

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.

Review on structural fatigue of NiTi shape memory alloys:Pure mechanical and thermo-mechanical ones

Structural fatigue of NiTi shape memory alloys is a key issue that should be solved in order to promote their engineering applications and utilize their unique shape memory effect and super-elasticity more sufficiently. In this paper, the latest progresses made in experimental and theoretical analyses for the structural fatigue features of NiTi shape memory alloys are reviewed. First, macroscopic experimental observations to the pure mechanical and thermo-mechanical fatigue features of the alloys are summarized; then the state-of-arts in the mechanism analysis of fatigue rupture are addressed; further, advances in the construction of fatigue failure models are provided; finally, summary and future topics are outlined.

Diffusion tensor tractography studies on mechanisms of recovery of injured fornix

The fornix,which connects the medial temporal lobe and the medial diencephalon,is involved in episodic memory as an important part of the Papez circuit.The mechanisms of recovery of an injured fornix revealed by diffusion tensor tractography in the five studies are summarized as follows：1） recovery through the nerve tract from an injured fornical crus to the medial temporal lobe via the normal pathway of the fornical crus;2）recovery through the nerve tract originating from an ipsi-lesional fornical body connected to the ipsi-lesional medial temporal lobe via the splenium of the corpus callosum;3） recovery through the nerve tract from the ipsi-lesional fornical body extending to the contra-lesional medial temporal lobe via the splenium of the corpus callosum;4） recovery through the nerve tract originating from the ipsi-lesional fornical column connected to the ipsi-lesional medial temporal lobe;and 5） recovery through the nerve tract originating from the contra-lesional fornical column connected to the ipsi-lesional medial temporal lobe via the contra-lesional medial temporal lobe and the splenium of the corpus callosum.These diffusion tensor tractography studies on mechanisms of recovery of injured fornical crus appeared to provide useful information for clinicians caring for patients with brain injury,however,studies on this topic are still in the beginning stages.

Economic“Activity-Silent”Synaptic Mechanisms of Working Memory

Working memory（WM）allows humans to hold necessary information in temporary storage and manipulate such information online for higher-order cognitive functions,such as language understanding,decision making,and problem solving.Since its first appearance in the science of psychology in the 1960s,many theories have sought to elucidate the nature of WM.The most accepted model is

Towards the universal defense for query-based audio adversarial attacks on speech recognition system

Recently,studies show that deep learning-based automatic speech recognition(ASR)systems are vulnerable to adversarial examples(AEs),which add a small amount of noise to the original audio examples.These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices.The existing defense methods are either limited in application or only defend on results,but not on process.In this work,we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.The insight of this method is based on the observation:many existing audio AE attacks utilize query-based methods,which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process.Inspired by this observation,We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query.Thus,we can identify when a sequence of queries appears to be suspectable to generate audio AEs.Through extensive evaluation on four state-of-the-art audio AE attacks,we demonstrate that on average our defense identify the adversary’s intent with over 90%accuracy.With careful regard for robustness evaluations,we also analyze our proposed defense and its strength to withstand two adaptive attacks.Finally,our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

World’s first spaceflight on-orbit demonstration of a flexible solar array system based on shape memory polymer composites

With a 10%reversible compressive strain in more than 10 deformation cycles,the shape memory polymer composites(SMPCs)could be used for deployable structure and releasing mechanism.In this paper,without traditional electro-explosive devices or motors/controllers,the deployable SMPC flexible solar array system(SMPC-FSAS)is studied,developed,ground-based tested,and finally on-orbit validated.The epoxy-based SMPC is used for the rolling-out variable-stiffness beams as a structural frame as well as an actuator for the flexible blanket solar array.The releasing mechanism is primarily made of the cyanate-based SMPC,which has a high locking stiffness to withstand 50 g gravitational acceleration and a large unlocking displacement of 10 mm.The systematical mechanical and thermal qualification tests of the SMPC-FSAS flight hardware were performed,including sinusoidal sweeping vibration,shocking,acceleration,thermal equilibrium,thermal vacuum cycling,and thermal cycling test.The locking function of the SMPC releasing mechanisms was in normal when launching aboard the SJ20 Geostationary Satellite on 27 Dec.,2019.The SMPC-FSAS flight hardware successfully unlocked and deployed on 5 Jan.,2020 on geostationary orbit.The triggering signal of limit switches returned to ground at the 139 s upon heating,which indicated the successful unlocking function of SMPC releasing mechanisms.A pair of epoxy-based SMPC rolled variable-stiffness tubes,which clapped the flexible blanket solar array,slowly deployed and finally approached an approximate 100%shape recovery ratio within 60 s upon heating.The study and on-orbit successful validation of the SMPC-FSAS flight hardware could accelerate the related study and associated productions to be used for the next-generation releasing mechanisms as well as space deployable structures,such as new releasing mechanisms with low-shocking,testability and reusability,and ultra-large space deployable solar arrays.

On-chip mechanical computing:status,challenges,and opportunities

With increasing challenges towards continued scaling and improve-ment in performance faced by electronic computing,mechanical com-puting has started to attract growing interests.Taking advantage of the mechanical degree of freedom in solid state devices,micro/nano-electromechanical systems(MEMS/NEMS)could provide alternative solutions for future computing and memory systems with ultralow power consumption,compatibility with harsh environments,and high reconfigurability.In this review,MEMS/NEMS-enabled memories and logic processors were surveyed,and the prospects and challenges for future on-chip mechanical computing were also analyzed.

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.