Self-adaptive Ability Research Articles

Trash into Treasure: Nano-coating of Catheter Utilizes Urine to Deprive H2S Against Persister and Rip Biofilm Matrix.

Bacteria-derived hydrogen sulfide (H2S) often contributes to the emergence of antibiotic-recalcitrant bacteria, especially persister (a sub-population of dormant bacteria), thus causing the treatment failure of Catheter-associated urinary tract infection (CAUTI). Here, an H2S harvester nanosystem to prevent the generation of persister bacteria and disrupt the dense biofilm matrix by the self-adaptive ability of shape-morphing is prepared. The nanosystem possesses a core-shell structure that is composed of liquid metal nanoparticle (LM NP), AgNPs, and immobilized urease. The nanosystem decomposes urea contained in urine to generate ammonia for eliminating bacteria-derived H2S. Depending on the oxidative layer of liquid metal, the nanosystem also constitutes a long-lasting reservoir for temporarily storing bacteria-derived H2S, when urease transiently overloads or in the absence of urine in a catheter. Depriving H2S can prevent the emergence of persistent bacteria, enhancing the bacteria-killing efficiency of Ga3+ and Ag+ ions. Even when the biofilm has formed, the urine flow provides heat to trigger shape morphing of the LM NP, tearing the biofilm matrix. Collectively, this strategy can turn trash (urea) into treasure (H2S scavengers and biofilm rippers), and provides a new direction for the antibacterial materials application in the medical field.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

Formation tracking control in open multiagent systems with general linear dynamics

This paper studies the formation tracking problem of the so-called open multiagent systems. In these systems, agents can freely join or leave at any time, i.e. the system sizes are changeable. To achieve formation tracking control in these systems, we propose a general theoretical analysis framework that consists of a formation adjustment strategy and a formation tracking protocol. Therein, the former is used to adjust the predefined task-oriented formation shape to the varying size of the system. This strategy is built on the existing scalable self-healing idea and then modified by a size estimation method. The latter is used to drive the present followers to form the adjusted formation and track a leader. To accommodate undirected and directed graphs, two distributed formation tracking protocols are designed respectively. Due to possible agent migration, the designed tracking protocols are endowed with self-adaptive ability, i.e. without depending on global topology information. Then, their convergence properties are analysed using the Lyapunov theory. Finally, two simulations are presented to verify the effectiveness of the proposed framework.

A novel structure adaptive discrete grey Bernoulli model and its application in renewable energy power generation prediction

Currently, the renewable energy power generation industry has entered a new stage, and accurate renewable energy power generation prediction is of great significance for the strategic planning of energy systems. However, renewable energy power generation data is characterized by nonlinearity and poor information, which brings challenges to accurately predict its development trend. Thus, this paper proposes a novel discrete grey Bernoulli model based on the spiral structure accumulated generating operator to deal with this problem. The spiral structure accumulated generating operator is introduced into the grey model to realize the effective utilization of renewable energy data information. Meanwhile, with the introduction of time delay structure, periodic structure and Bernoulli structure, the novel model can effectively characterize the nonlinearity, volatility, and time lag information between economic growth and energy development of renewable energy data. In addition, using the Differential Evolution optimization (DE) algorithm for nonlinear parameter optimization can effectively improve the stability and accuracy of the model, and also makes the model have the ability of structural self-adaptation. Finally, the new model was used to predict the bioenergy and wind power generation data. Based on comparative experiments and grey correlation analysis, the predictive performance of the novel model is verified, and the prediction results are highly correlated with those of authoritative organization. The experimental results show that the novel model is an effective predictive tool for renewable energy generation, which is an important reference value for energy development decision-making.

Summary of Research on Highway Bridge Vehicle Force Identification

Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential.

Heat transfer enhancement in a triple-layered turbine blade internal cooling channel

An effective measure of enhancing the coupled convection and radiation in the turbine blade internal cooling channel is developed. A triple-layered channel is presented for the first time to further improve the flow organization to suppress the negative effect of secondary flows induced by rotation as well as extend cold surfaces for radiation. Explorations of flow and coupled heat transfer are executed numerically for various channel models. The results manifest that, heat transfer is markedly improved with the triple-layered channel. The highest comprehensive thermal performance is achieved when employing the triple-layered channel with smooth surfaces and ribs (Model 3SW), which can increase the total Nusselt number ratio by 105.78 % and performance evaluation criterion by 18.75 %, comparing with the existing Model 1 N. Besides, the average and maximum wall temperature drop by 54.26 K and 293.02 K, respectively. In addition, the triple-layered channel is demonstrated to have the ability to reduce rotational effects on heat transfer uniformity. The heat transfer difference between the leading and trailing walls induced by the Coriolis force is restricted. It also has the self-adaptive ability to increase the degree of heat transfer enhancement when thermal load increases.

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

Predicting the collapse process and tipping points for mutualistic and competitive networks with k-core method

Abstract Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances, which is always called resilience. However, once the external disturbances exceed the tipping points of the system resilience, the consequences would be catastrophic, and eventually lead the ecosystem to complete collapse. We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method, and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core. Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem. Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions, and provides theoretical insights for further avoiding the occurrence of ecosystem collapse.

Scalable self-adaptive radiative cooling film through VO2-based switchable core–shell particles

The development of highly efficient passive cooling technology has garnered significant attention. However, there is a mismatch between the cooling power of radiative cooling materials and actual cooling demand due to temperature variations. Therefore, there is a necessity for radiative cooling materials that can adjust to diverse weather conditions. In this study, a scalable self-adaptive radiative cooling film was proposed. By incorporating VO2-based core-shell nanoparticles into PE matrix, the radiative cooling film with self-adaptive ability has been achieved. The radiative properties of the film were predicted through the Mie theory and the Monte Carlo (MC) method. Two shell materials, BaF2 and ZnS, were considered. The effects of shell material, particle diameter D, inner/outer diameter ratio r/R, doping concentration fv, and film thickness t on the radiative properties of the film were evaluated. An optimal design of the film was carried out. The change in cooling power was calculated to evaluate the self-adaptive performance. The films achieved a maximum change in cooling power of 61.7 W/m2 (BaF2) and 60.7 W/m2 (ZnS). The energy-saving performance of the film was simulated by TRNSYS. This study provides a promising solution for scalable radiative cooling film with high self-adaptive cooling performance and durability.

Traffic image dehazing based on sky region segmentation and transmittance optimization

Traffic sign recognition is of great significance to promote traffic sustainability and maintain traffic safety. GPS monitoring systems and advanced autonomous vehicles are often heavily reliant on camera imagery. Algorithms based on dark channel prior are susceptible to color distortion when processing traffic images containing bright sky or high-brightness areas, which can negatively impact the identification of traffic signals and signage located in elevated positions. To address this issue, this paper proposes a dehazing algorithm (SRSTO) that combines sky region segmentation and transmittance optimization. Firstly, the gradient, brightness and saturation information are calculated, followed by the construction of a threshold function used in area segmentation. This approach is utilized to partition the image into areas not containing sky highlights and the area that contains them. Subsequently, the dark channel images of the sky and the non-sky regions are acquired, morphological operations are further performed in layers and blocks, and then the atmospheric scattered light value is calculated. Secondly, the functional relationship between the transmittance of the sky region and the brightness of the image is constructed, the transmittance of the sky and the non-sky region are optimized, and the transmittance map is further improved by using guided filtering. A simulated annealing algorithm is employed to intelligently optimize parameters such as sky segmentation threshold and sky brightness area transmittance, followed by improving the adaptability of the algorithm. Finally, combined with Gaussian filtering and Sobel edge enhancement, the image brightness is further adjusted. Using Information Entropy and NIQE as objective evaluation indexes, combined with subjective evaluation, it is concluded that the proposed method has good convergence and self-adaptive ability, and the objective indexes and subjective effects are better, especially for the hazed images containing air traffic signs.

Non-fibril amyloid aggregation at the air/water interface: self-adaptive pathway resulting in a 2D Janus nanofilm.

The amyloid states of proteins are implicated in several neurodegenerative diseases and bioadhesion processes. However, the classical amyloid fibrillization mechanism fails to adequately explain the formation of polymorphic aggregates and their adhesion to various surfaces. Herein, we report a non-fibril amyloid aggregation pathway, with disulfide-bond-reduced lysozyme (R-Lyz) as a model protein under quasi-physiological conditions. Very different from classical fibrillization, this pathway begins with the air-water interface (AWI) accelerated oligomerization of unfolded full-length protein, resulting in unique plate-like oligomers with self-adaptive ability, which can adjust their conformations to match various interfaces such as the asymmetric AWI and amyloid-protein film surface. The pathway enables a stepwise packing of the plate-like oligomers into a 2D Janus nanofilm, exhibiting a divergent distribution of hydrophilic/hydrophobic residues on opposite sides of the nanofilm. The resulting Janus nanofilm possesses a top-level Young's modulus (8.3 ± 0.6 GPa) among amyloid-based materials and exhibits adhesive strength two times higher (145 ± 81 kPa) than that of barnacle cement. Furthermore, we found that such an interface-directed pathway exists in several amyloidogenic proteins with a similar self-adaptive 2D-aggregation process, including bovine serum albumin, insulin, fibrinogen, hemoglobin, lactoferrin, and ovalbumin. Thus, our findings on the non-fibril self-adaptive mechanism for amyloid aggregation may shed light on polymorphic amyloid assembly and their adhesions through an alternative pathway.

A Tailored Interface Design for Anode-Free Solid-State Batteries.

Anode-free solid-state batteries (AFSSBs) are considered to be one of the most promising high-safety and high-energy storage systems. However, low Coulombic efficiency stemming from severe deterioration on solid electrolyte/current collector (Cu foil) interface and undesirable Li dendrite growth impede their practical application, especially when rigid garnet electrolyte is used. Here, an interfacial engineering strategy between garnet electrolyte and Cu foil is introduced for stable and highly efficient AFSSBs. By utilizing the high Li ion conductivity of LiC6 layer, interfacial self-adaption ability arising from ductile lithiated polyacrylic acid polymer layer and regulated Li deposition via Li-Ag alloying reaction, the garnet-based AFSSB delivers a stable long-term operation. Additionally, when combined with a commercial LiCoO2 cathode (3.1mAhcm-2 ), the cell also exhibits an outstanding capacity retention due to the tailored interface design. The strategies for novel AFSSBs architecture thus offer an alternative route to design next-generation batteries with high safety and high density.

Heat transfer enhancement by inserting a radiation-turbulence component in a wedge channel

This study proposed the concept of coupled convective and radiative heat transfer enhancement to improve the internal cooling near turbine blade's trailing edge. An original method, which employed a radiation-turbulence component inside a wedge-shaped channel was developed. Flow and heat transfer for four channel configurations were studied numerically. The results indicated that, in condition of Re=10,000 and q = 10,000 W·m−2, compared with the existing channel's configuration merely with pin-fins and without considering radiation (Model NN), the total Nusselt number ratio of the channel using the radiation-turbulence component (Model YY) increased by 48.10 %. The maximum temperature dropped by 354.73 K and the average temperature dropped by 38.59 K. This was attributed to extension of cold surfaces for enhancement of radiative heat transfer and change of flow disturbance distribution for improvement of convective heat transfer uniformity, both of which were induced by using the radiation-turbulence component. Furthermore, the influences of the Reynolds number as well as the wall heat flux were explored. This method was proved to have an excellent self-adaptive ability, which showed that the increase of the thermal load resulted in the growth of the total Nusselt number.

Self-Tuning Process Noise in Variational Bayesian Adaptive Kalman Filter for Target Tracking

Many practical systems, such as target tracking, navigation systems, autonomous vehicles, and other applications, are usually applied in dynamic conditions. Thus, the actual noise statistics characteristics of these systems are generally time varying and unknown, which will deteriorate the state estimation accuracy of the Kalman filter (KF) and even cause filter diverging. To address this issue, this paper proposes an adaptive process noise covariance (Qk)-based variational Bayesian adaptive Kalman filter (AQ-VBAKF) algorithm. Firstly, the adaptive factor is introduced to self-tune the process noise covariance; the adaptive factor is obtained based on the innovation sequences, which can adapt to the input measurement values. Then, the VB solution is applied to approximate the time variant and unknown measurement noise covariance. Therefore, this proposed algorithm can adjust the process noise covariance and the measurement noise covariance simultaneously based on the variable input signals, which can improve the self-adaptive ability of the state estimation filter in dynamic conditions. According to the dynamic target tracking test results, the proposed AQ-VBAKF outperforms several other existing filtering methods in estimation accuracy, robustness, and computational efficiency.

Hierarchical thermal management for PEM fuel cell with machine learning approach

Thermal management is crucial for the mass transport and water balance of proton exchange membrane fuel cell (PEMFC). Inspired by this, a hierarchical thermal management strategy (TMS) is proposed for fuel cell hybrid electric vehicle (FCHEV). In particular, the transient TMS demands are determined by a well-designed energy management strategy (EMS) taking health and thermal safety into consideration. Furthermore, along with the high-efficiency heat dissipation, a hydrogen consumption minimization strategy (HCMS) is proposed via optimal temperature tracking, which investigates the desirable trace offline. These parallel strategies are incorporated through the deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) algorithm. With the help of its self-adaptive ability, DDPG deals with the complicated TRS problem in multidimensional coupled cooling system, through a mutually updated actor-critic framework. Results suggest the superiority and reliability of proposed TMS with respect to the stack efficiency, fuel economy and tracking performance.

Ultrathin SrTiO3-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing

Abstract Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here, we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO3/Nb: SrTiO3 heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity (STP/LTP), transition from STP to LTP, learning–forgetting–relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give rise to the coexistence of volatile- and nonvolatile-type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally suitable for dealing with event streams due to its temporal information processing capability and event-driven nature. However, existing works SNN neglect the fact that the input event streams are spatially sparse and temporally non-uniform, and just treat these variant inputs equally. This situation interferes with the effectiveness and efficiency of existing SNNs. In this paper, we propose the feature Refine-and-Mask SNN (RM-SNN), which has the ability of self-adaption to regulate the spiking response in a data-dependent way. We use the Refine-and-Mask (RM) module to refine all features and mask the unimportant features to optimize the membrane potential of spiking neurons, which in turn drops the spiking activity. Inspired by the fact that not all events in spatio-temporal streams are task-relevant, we execute the RM module in both temporal and channel dimensions. Extensive experiments on seven event-based benchmarks, DVS128 Gesture, DVS128 Gait, CIFAR10-DVS, N-Caltech101, DailyAction-DVS, UCF101-DVS, and HMDB51-DVS demonstrate that under the multi-scale constraints of input time window, RM-SNN can significantly reduce the network average spiking activity rate while improving the task performance. In addition, by visualizing spiking responses, we analyze why sparser spiking activity can be better. Code

Two adaptive enhancement algorithms for high gray-scale RAW infrared images based on multi-scale fusion and chromatographic remapping

As an important part of unmanned platform, the images taken by vehicle-mounted infrared camera are less affected by night, strong light and bad weather, which are widely used in the field of path planning and decision control in complex scenes. However, infrared images also have some shortcomings, such as high dynamic range, low contrast, high noise and blurred details, which make it difficult to enhance images and detect targets. In order to solve the above problems, two high grayscale infrared image enhancement algorithms were proposed in this paper. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.Firstly, a high grayscale infrared image enhancement algorithm based on multi-scale fusion was proposed by combining the idea of debate and exposure fusion. Secondly, to solve the problems of poor visual effect and limited self-adaptation ability in the current pseudo-color ehancement algorithm, a pseudo-color enhancement algorithm for high gray-scale infrared images based on chromatographic remapping was proposed. Thirdly, qualitative and quantitative experiments were carried out by using the published FLIR data set. The results show that the two methods proposed in this paper can improve the overall quality of infrared images more effectively and highlight the details of the image. The objective image quality evaluation indexes are superior to most of the existing mainstream traditional algorithms. Then, two algorithms were used to enhance SAR images, and the experimental results show that this algorithm has good universality. Finally, the images enhanced by the two algorithms in this paper was sent to the YOLOv4 network for target detection, which improved the accuracy of target detection. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.The algorithm can be applied in the image enhancement and target detection of vehicle-borne infrared images, SAR images, medical images and industrial film images, and provide reliable technical support for applications in related fields.

An immune-based multi-agent system for flexible job shop scheduling problem in dynamic and multi-objective environments

In this paper, a biological immune-based multi agent system (IMAS) is developed to overcome the multi-objectivity and dynamicity of the multi-objective dynamic flexible job shop scheduling (MODFJSS) problem. In modeling the MODFJSS problem, four objectives, i.e., makespan, total weighted tardiness, maximal machine workload, and schedule stability, are considered for simultaneous optimization. In the proposed IMAS architecture, immune agents are coordinated to identify the environment, generate a set of non-dominated schedules, and select a utility optimal schedule. In addition, these agents have the ability of self-adaptation and flexible coordination to adapt to the environment of MODFJSS problem. The performance of the proposed method was evaluated on dynamic versions of the five commonly used benchmark instance sets in comparison with three state-of-the-art algorithms. The obtained results showed that the proposed IMAS outperformed the competitors in most of the experiments conducted in this study.

Frequent pattern-based parallel search approach for time-dependent agile earth observation satellite scheduling

To address the time-dependent agile earth observation satellite (AEOS) scheduling problem more effectively, the frequent pattern-based parallel search (FPBPS) algorithm is proposed, which is composed of a parallel local search procedure, a competition-based algorithm and operator adaptive selection procedure and the new solutions construction method based on frequent pattern. First, the algorithm and the operator are selected from the algorithm pool and operator pool and run in parallel. As a result, some high-quality and diverse solutions are obtained in a short time. Second, we update the probability of each algorithm and operator to strengthen the self-adaptive ability of the algorithm. Third, frequent pattern mining method is used to extract knowledge with respect to AEOS scheduling to construct new solutions, and parallel local search is applied to further improve these solutions. Finally, extensive experiments prove that the FPBPS algorithm has a better performance than other comparison algorithms in the quality of solution, computation time, and robustness.