Intelligent Switching Research Articles

Observation of Temperature Dependent Atomic Off‐Centering in High Entropy Cubic I‐V‐VI2/IV‐VI Alloys with Ultralow Thermal Conductivity

AbstractI‐V‐VI2/IV‐VI compounds and their alloys are of great interest in the field of thermoelectricity due to their unique high‐symmetry crystal structures and unexpectedly low thermal conductivity, which is closely related to the off‐centering effect in the cubic lattice. However, the influence of such an effect on thermal transport across different temperatures remains largely elusive. Herein, using in situ pair distribution function analysis, it is directly observed that the sustained low thermal conductivity in n‐type (AgBiSe2)1‐x(PbS)x alloys stem from the dynamic intelligent switching of local structural distortions over wide temperature windows for the first time. Under ambient conditions, the local off‐centering of central atoms dominates the phonon scattering, while at high temperatures, the geometrical configuration of the ligands predominantly modulates thermal conductivity due to lattice expansion and diminished cation off‐centering. The complementary synergistic effect leads to an ultralow thermal conductivity (<0.64 W m−1 K−1) for (AgBiSe2)0.65(PbS)0.35 across the entire working temperature range and yields a lattice thermal conductivity of only 0.33 W m−1 K−1 at 800 K approaching the glass limit. Atomic‐scale insights into the thermal conductivity mechanism of I‐V‐VI2/IV‐VI alloys provide important guidance for designing high‐efficiency thermoelectric materials over broad operational temperature ranges.

Implementation of smart irrigation system

Current global technology significantly impacts agriculture, with automation playing a crucial role by executing procedures without assistance from any individual. This work aims to demonstrate how an individual can set up an affordable automatic irrigation system in a few hours by connecting various electronic components and materials. A sensor-based automated irrigation system has been designed and implemented, providing widespread use and significant advantages. This system aids daily activities by saving time and reducing labor. It makes use of a sensor technology in conjunction with a microcontroller, relay, DC motor, and battery. By measuring the moisture content of the soil and turning on irrigation as necessary, the device functions as an intelligent switch. The motor automatically turns on or off based on soil dryness. Sensor readings are transmitted to a computer for graph generation and analysis, allowing easy monitoring and control via a computer. Green roofs, nurseries, greenhouses, and gardens of all sizes can use this watering system.It conserves time, energy, reduces water wastage, and simplifies irrigation planning for farmers, enhancing their management of crops.

Multifunctional double network hydrogel with multi-directional actuation and high-precision sensing

Conductive hydrogels have gained great attention for applications in flexible electronics, electronic skins, and as human-machine interfaces. However, poor environmental tolerance of traditional hydrogels and defects in hydrogel actuator make them unfit for use in certain environments. Herein, a small natural molecule, proline (ZP), was introduced into the PAA/CS dual network system to prepare multifunctional hydrogel materials with high concentration of physically crosslinked metal coordination points. The elongation at break and mechanical strength of the hydrogel with optimized structure were 1277 % and 202 kPa, respectively. The effect of ZP on the destruction of hydrogen bonds of free water in the hydrogel provided the hydrogel with excellent temperature resistance. Multiple reversible interactions endowed the PAA/CS/Al0.5/ZP8 hydrogel with excellent self-healing properties at room temperature and even at −40 ℃. The PAA/CS/Al0.5/ZP8 hydrogel sensor with excellent comprehensive performance could be applied for the monitoring of a variety of human motions, small shape changes of flexible objects, and detection of vibrations in rigid pipes. The interactions between Fe3+, ascorbic acid, and -COOH groups on the hydrogel surface were used to record information and erase it repeatedly at low temperature and showed excellent shape memory. Based on the self-healing property, the hydrogel with gradients of swelling rates and water loss rates was assembled into a double-layer actuator, which could complete the actuation process in opposite directions in air and also underwater. This work provides a new idea for the design of soft robots and intelligent switches.

Cellulose-based multi-responsive soft robots for programmable smart devices

In the realm of advanced smart manufacturing, the development of fast, sensitive, stable soft robots that can be driven by multiple stimuli remains a significant challenge. In this study, we developed a cellulose-based soft robot featuring a dual-layer structure. The top layer is a hydrophilic composite membrane made from cellulose nanofibers (CNFs), MXene nanosheets, and carbon nanotubes (CNTs), henceforth referred to as the CNF@MXene-CNT (CMN) composite membrane, while the bottom layer consists of a hydrophobic Biaxially Oriented Polypropylene (BOPP) membrane. The soft robot exhibits highly sensitive and fast response towards humidity-, natural light and electrothermal stimuli, which can be attributed to the high humidity expansion (0.0005 (%RH)-1), photothermal conversion, and electrical thermal conversion of CMN composite membrane, also the distinctive expansion characteristics of CMN (−0.000757 °C−1) and BOPP (0.000137 (°C)-1). Remarkably, the soft robot can be actuated with merely 67 mW cm−2 of natural light or a power supply of 2.8 V, achieving a bending range of 360° with a corresponding bending curvature of 2.67 cm−1. The soft robot’s motion state and speed can be meticulously controlled, further emphasizing their vast applicability in various fields, including medical, industrial, aerospace, and beyond. The soft robot has been applied as a smart curtain, intelligent optical switch, and electrically controlled robot, demonstrating its exceptional multifunctionality and wide ranges of applications.

SOC chip based smart window curtain design

Relying on the rapid development of embedded systems, it becomes possible to integrate the system hardware into the same chip, and the system on chip SOC (System on Chips) is therefore birthed. This paper will be based on the SOC hardware and software co-design and verification, through the design of intelligent curtains for the specific implementation design of a kind of application in the al-in-one multi-functional machine motherboard design, the motherboard control core using SOC chip, integrated two MCU units, through the main control screen to display the control information to achieve the intelligent window switching conversion switching through the key such as manual mode, timer mode, light control mode. This design uses Keil as the software writing platform to realize the SOC master control system based on the on-chip bus interconnection. As well as this design can effectively reduce the degree of system cumbersomeness and provide smart window design ideas.

A novel framework for low-temperature fast charging of lithium-ion batteries without lithium plating

This paper proposes a novel framework for low-temperature fast charging of lithium-ion batteries (LIBs) without lithium plating. The framework includes three key components: modeling, constraints, and strategy design. In the modeling phase, a new electro-thermal coupled model is introduced, which integrates both frequency-domain and time-domain electro-thermal coupled models. They optimize the bidirectional pulsed current (BPC) heating parameters and charging current, respectively. Terminal voltage boundaries and lithium plating serve as constraints. Terminal voltage is confined within upper and lower limits to prevent battery overcharge and overdischarge. Lithium plating avoidance entails setting a minimum frequency for BPC heating. During charging, lithium plating is mitigated by maintaining negative potential above 0 V. To obtain calculated negative electrode potential, a three-electrode battery is employed to calibrate the electro-thermal coupled model parameters. Subsequently, a novel low-temperature fast charging strategy is devised. This strategy enables intelligent switching between BPC heating and charging, while also synergistically optimizing BPC heating parameters and charging current. The strategy also achieves optimization of both charging speed and energy consumption. Charging the battery SOC from 0.2 to 0.9 in 42 min at −10 °C, without triggering lithium plating, is feasible with this proposed strategy. Compared to strategies focusing solely on current amplitude optimization, heating followed by charging, and traditional methods, this heating strategy exhibits the highest charging speed.

Asymmetric flexible graphene oxide papers for moisture-driven actuators and water level indicators

Smart actuators have shown great potential for a range of industries, including artificial muscles, sensors, and smart devices. However, traditional actuators often suffer from small deformation and complex materials and manufacturing processes. Graphene oxide (GO) has emerged as an ideal candidate for moisture-responsive actuators due to its excellent moisture sensitivity. In this study, we present the development of a moisture-responsive actuator using a simple and straightforward approach based on a monolayer graphene oxide (GO) paper. By utilizing the different affinity of its two sides for water, the graphene oxide actuator demonstrates excellent performance in response to humidity gradients, achieving a large bending amplitude with a curvature of 3 cm−1. Furthermore, the graphene oxide actuator was used as a humidity-sensitive switch to perform on and off switch movements, demonstrating its capability for autonomous detection for real-time humidity monitoring. This work provides a new avenue for the development of moisture-responsive materials and their potential applications in the design of intelligent control systems, actuators, soft robots, artificial muscles, and switches.

Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators

AbstractCooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.

Selective directional liquid transport on shoot surfaces of Crassula muscosa.

Directional liquid transport has been widely observed in various species including cacti, spiders, lizards, the pitcher plant Nepenthes alata, and Araucaria leaves. However, in all these examples the liquid transport for a specific liquid is completely restricted in a fixed direction. We demonstrate that Crassula muscosa shoot surfaces have the ability to transport a specific liquid unidirectionally in either direction. This is accomplished through the presence of asymmetric reentrant leaves with varying reentrant angles, which yields the variation in liquid meniscus heterogeneity. These findings enable engineered biomimetic structures capable of selective directional liquid transport, with functions such as intelligent flow direction switching, liquid distribution, and mixing.

Circuit design solution and algorithm of digital control of processes for a universal bidirectional transistor module of switching mode voltage converter

The problem of design a universal intelligent switching mode device based on SiC transistors with built-in digital control, capable of performing various types of electrical power conversion, is formulated. The results of a study of a computer model of this device are presented, confirming the correctness of the proposed solutions. Methods for implementing parallel operation of modules are considered.

A Novel Organic‐Inorganic Hybrid Material (BTTD)MnCl4 with Intelligent Switching Applications Derived from Unusual Wheels‐Like Motion

AbstractIn recent years, phase change materials (PCMs) have received extensive attention because of their applications in many fields, such as construction, semiconductor, energy and intelligent response switch. Organic‐inorganic hybrid materials (OIHMs) are wide applied in the preparation of PCMs due to their controllability of organic and inorganic components. In this study, we successfully synthesized an organic‐inorganic hybrid material (BTTD)MnCl4 (compound 1, BTTD=[(CH3)2NCH2Cl(CH2)3CH2ClN(CH3)2]. The purity of compound 1 was confirmed by PXRD. The crystal structure of compound 1 was analyzed at 300 K, 381 K and 400 K. The three different crystal structures prove the phase change of compound 1. According to the result of differential scanning calorimeter (DSC), the phase transition of compound 1 is reversible. Moreover, the measured bandgap (Eg) of 1 is 2.25 eV. The dielectric constant of compound 1 fluctuates near 370 K, indicating the changes of electrical properties, so compound 1 can be used to cut off circuit. This temperature is close to the boiling point of water under standard ambient conditions. Therefore, this work provides a new direction for the design of intelligent response switch for electric kettle.

Biomimetic protein structural transitions regulate activation and inhibition of the broad-spectrum bactericidal activity of cationic nanoparticles

The development of cationic polymers as alternative materials to antibiotics necessitates addressing the challenge of balancing their antimicrobial activity and toxicity. Here we propose a precise switching strategy inspired by biomimetic voltage-gated ion channels, enabling controlled activation and inhibition of cationic antimicrobial functions through protein conformational transitions in diverse physiological environments. Following thermodynamic studies on the specific recognition between mannose end groups on polycations and concanavalin A (ConA), we synthesized a type of ConA-polycation nanoparticle. The nanoparticle was inhibited under neutral conditions, with cationic moieties shielded by ConA's β-sheet. This shielding suppresses their antimicrobial activity, thereby ensuring satisfactory biocompatibility. In mildly acidic environments, however, the transition of a portion of ConA to an α-helix conformation exposed cations at the particle periphery, activating antibacterial functionality. Compared to inhibited nanoparticles, those in the activated state exhibited a 32–256 times reduction in the minimum bactericidal concentration against bacteria and fungi (2–16 µg/mL). In a murine acute pulmonary infection model, intravenous administration of inhibited nanoparticles effectively reduced bacterial counts by 4-log within 12 h. The biomimetic design, regulating cationic antimicrobial functionality through the alteration in protein secondary structure, significantly retards bacterial resistance development, holding great promise for intelligent antimicrobial materials. Statement of significanceCationic antimicrobial polymers exhibit advantages distinct from antibiotics due to their lower propensity for resistance development. However, the presence of cationic moieties also poses a threat to healthy cells and tissues, significantly constraining their potential for clinical applications. To address this challenge, we propose a biomimetic strategy that mimics voltage-gated ion channels to activate the antimicrobial functionality of cations selectively in bacterial environments through the conformational transitions of proteins between β–sheets and α–helices. In healthy tissues, the antimicrobial functionality is inhibited, ensuring satisfactory biocompatibility. Antimicrobial cationic materials capable of intelligent switching between an activated state and an inhibited state in response to environmental changes offer an effective strategy to prevent the development of resistance and mitigate potential side effects.

An accurate trajectory tracking method for low-speed unmanned vehicles based on model predictive control

Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy. Therefore, a trajectory tracking method considering road curvature based on MPC is proposed in this paper. In this method, the controller can automatically switch between MPC types. Linear model predictive control (LMPC) is selected for small road curvatures, while nonlinear model predictive control (NMPC) is employed for large road curvatures. In addition, the NMPC algorithm in this work considers the effect of road curvature on tracking accuracy, making it suitable for tracking time-varying curvature roads. To verify the feasibility of the algorithm, simulation comparisons with the basic MPC model were carried out at different testing roads and vehicle longitudinal speeds. The results indicate that the method significantly improves trajectory tracking accuracy, all while ensuring real-time calculations. The intelligent switching capability of control models based on road curvature allows its application to track trajectories on arbitrarily complex roads.

Traffic Flow Optimization for Metropolitan Cities

This research paper presents the design and implementation of an adaptive traffic signal timer system that utilizes real-time traffic density calculation and an intelligent signal switching algorithm to optimize traffic flow at intersections. The proposed system leverages computer vision techniques like object detection to count and classify vehicles, and then dynamically adjusts the green signal durations based on the detected traffic density. A simulation module is also developed to visualize the system's performance and compare it to a static traffic signal implementation. The results demonstrate the effectiveness of the adaptive approach in reducing vehicle waiting times and improving overall intersection throughput

Exploring an Intelligent Classification Model for the Recognition of Automobile Sounds Based on EEG Physiological Signals

The advancement of an intelligent automobile sound switching system has the potential to elevate the market standing of automotive products, with the pivotal prerequisite being the selection of automobile sounds based on the driver’s subjective perception. The subjective responses of diverse individuals to sounds can be objectively manifested through EEG signals. Therefore, EEG signals are employed herein to attain the recognition of automobile sounds. A subjective evaluation and EEG signal acquisition experiment are designed involving the stimulation of three distinct types of automobile sounds, namely comfort, power, and technology sounds, and a comprehensive database of EEG signals corresponding to these three sound qualities is established. Then, a specific transfer learning model based on a convolutional neural network (STL-CNN) is formulated, where the method of training the upper layer parameters with the fixed bottom weights is proposed to adaptively extract the EEG features related to automobile sounds. These improvements contribute to improving the generalization ability of the model and realizing the recognition of automobile sounds fused with EEG signals. The results of the comparison with traditional support vector machine (SVM) and convolutional neural network (CNN) models demonstrate that the accuracy of the test set of the STL-CNN model reaches 91.5%. Moreover, its comprehensive performance, coupled with the ability to adapt to individual differences, surpasses that of both SVM and CNN models. The demonstrated method in the recognition of automobile sounds based on EEG signals is of significance for the future implementation of switching driving sound modes fused with EEG signals.

NIR-Mediated Deformation from a CNT-Based Bilayer Hydrogel.

Shape-shifting polymers are widely used in various fields such as intelligent switches, soft robots and sensors, which require both multiple stimulus-response functions and qualified mechanical strength. In this study, a novel near-infrared-light (NIR)-responsible shape-shifting hydrogel system was designed and fabricated through embedding vinylsilane-modified carbon nanotubes (CNTs) into particle double-network (P-DN) hydrogels by micellar copolymerisation. The dispersed brittle Poly(sodium 2-acrylamido-2-methylpropane-1-sulfonate) (PNaAMPS) network of the microgels can serve as sacrificial bonds to toughen the hydrogels, and the CNTs endow it with NIR photothermal conversion ability. The results show that the CNTs embedded in the P-DN hydrogels present excellent mechanical strength, i.e., a fracture strength of 312 kPa and a fracture strain of 357%. Moreover, an asymmetric bilayer hydrogel, where the active layer contains CNTs, can achieve 0°-110° bending deformation within 10 min under NIR irradiation and can realise complex deformation movement. This study provides a theoretical and experimental basis for the design and manufacture of photoresponsive soft actuators.

Polarization and incident angle independent multifunctional tunable terahertz metasurface based on graphene

Motivated by the imperative demand for design integration and miniaturization within the terahertz (THz) spectrum, this study presents an innovative solution to the challenges associated with singular functionality, limited application scope, and intricate structures prevalent in conventional metasurfaces. The proposed multifunctional tunable metasurface leverages a hybridized grapheme–metal structure, addressing critical limitations in existing designs. Comprising three distinct layers, namely a graphene–gold resonance layer, a Topas dielectric layer, and a bottom gold film reflective layer, this terahertz metasurface exhibits multifunctionality that is both polarization and incident-angle independent. The metasurface demonstrates a broadband circular dichroism (CD) function when subjected to incident circularly polarized waves. In contrast, under linear incidence, the proposed design achieves functionalities encompassing linear dichroism (LD) and polarization conversion. Remarkably, graphene's chemical potential and the incident light’s state can be manipulated to tune each functional aspect's intensity finely. The proposed tunable multifaceted metasurface showcases significant referential importance within the terahertz spectrum, mainly contributing to advancing CD metamirrors, chiral photodetectors, polarization digital imaging systems, and intelligent switches.

Research on fault diagnosis of electromagnetic active suspension based on double observer intelligent switching

Electromagnetic active suspension can adjust stiffness and damping and is widely used in suspension. When the electromagnetic active suspension fails, the observer can be used for fault detection. In order to realize suspension fault detection under all road conditions, a double observer switching strategy is designed and simulated using MATLAB. Firstly, the switching strategies of the Kalman observer, unknown input observer (UIO) and double observer are designed, respectively. Then, for random roads, the Kalman observer is used for fault detection, while for bump road surfaces, the UIO is used for fault detection. The residoubles of the dynamic deflection of the suspension are used as switching conditions. Finally, a simulation analysis is carried out with MATLAB, and the conclusion shows that the switching strategy of double observers is better than that of the Kalman observer and UIO in full road conditions.

FedRDR: Federated Reinforcement Distillation-Based Routing Algorithm in UAV-Assisted Networks for Communication Infrastructure Failures

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value.

CNN-LSTM Base Station Traffic Prediction Based On Dual Attention Mechanism and Timing Application

Abstract Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch timing. The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal attention mechanism is added to the LSTM to enhance the importance of temporal information. Moreover, the particle swarm optimization K-Means algorithm is proposed in order to cluster the traffic prediction results, output the corresponding time points of the lower traffic value and to obtain the optimal switch-off periods of the base station. Extensive experiments across multiple base stations over an extended period of time have validated our approach. The results show that this method offers accurate traffic prediction with minimal average errors in traffic prediction and the on/off timings of the base stations are in line with the “tide effect” of traffic, thereby achieving the goal of energy savings.