Intelligent Switching Research Articles

Carbon nanotube/polyurethane core–sheath nanocomposite fibers for wearable strain sensors and electro-thermochromic textiles

Color-changing has a wide application including electronic display, military camouflage, intelligent switch, intelligent sensor and intelligent fabric. However, slow response, poor controllability and stability are the challenges affecting the currently application of flexible thermochromic materials. This work aims at designing and constructing hierarchical structure of core-coated electro-thermochromic carbon nanotube/polyurethane composite fibers (ECPF) by impregnation and electrospinning, resulting in color-rich and electro-driven thermochromic composite fibers. The results showed that the temperature on the surface of the composite fiber increased from 25 °C to 66.4 °C and the color changed from red to yellow with the voltage of 9 V applied to both ends of ECPF respectively. The color spectrum of ECPF can be achieved by adjusting the type of color-changing inks, and different patterns of discolored textiles can be realized by weaving. In addition, ECPF composite fibers have excellent conductivity, high sensitivity, a wide strain range (0%–310% strain) and excellent durability (>2000 cycles). The ECPF composite fibers can not only detect human micro-movement, but also exhibit a large strain range more than twice the original length. This work contributes a novel strategy of developing multi-functional electro-thermochromic nanocomposite fibers with color-changing performance and wearable electronic applications for health and human movements monitoring.

Experimental Research on the Sensitivity of Partial Discharge Ultra High Frequency Sensor Structure Parameters

With the development of intelligent high voltage switch, online gas insulated switchgear discharge detection technology has been more and more widely applied. Because of the high sensing sensitivity and good antiinterference performance of build-in partial discharge sensor, it is currently required to install them on gas insulated switchgear products with voltage levels of 220 kV and above at the factory. The structural parameters of the build-in partial discharge sensor is of great importance to its sensing sensitivity. Therefore, in this paper, the influence of the structure change on the sensitivity of axisymmetric gas insulated switchgear build-in partial discharge sensor is experimentally studied. A practical gas insulated switchgear test model was established in the laboratory, and the frequency domain measurement system based on Spectrogram analyzer and synchronous sweep signal generator was adopted. By comparing the insertion loss generated by the build-in sensor when receiving sweep signal, the influence of structural parameter changes on the sensing sensitivity was analyzed. The results show that the diameter of the build-in sensor should be matched with the diameter of the hand-hole. The match of the larger diameter of the hand-hole and the smaller diameter of the sensor can increase the sensing sensitivity, but when the diameter of the sensor is close to the diameter of the hand-hole, the sensitivity will be significantly reduced. In addition, metal bolts with the sensor electrode and its insulating support fixed to the hand-hole cover can also result in a significant reduction in sensing sensitivity. Using the same measuring principle and system, the propagation attenuation characteristics of partial discharge ultra high frequency signal in gas insulated switchgear typical structures (linear and L-shaped structures) are also experimentally studied. The results show that the average attenuation of partial discharge ultra high frequency signal in gas insulated switchgear along the linear distance is 0.3 dB/m, and the attenuation through every L-shaped structure is 4.2 dB.

Attenuation-based hybrid RF/FSO link using soft switching

Due to high data rates, license-free spectrum, and immunity to electromagnetic interference, free-space optical (FSO) links are being considered as a potential candidate to meet the ever-increasing traffic demands of users. The FSO links remain less explored, as their performance depends on environmental conditions such as dust, fog, and clouds. Such conditions do not affect the radio frequency (RF) links in a similar way; however, RF resources are scarce. As an ultimate solution to this performance/scarcity dilemma, we propose a fuzzy logic-based hybrid architecture of FSO and RF links, which can be used to enhance reliability and resource efficiency. We present an intelligent soft switching mechanism between FSO and RF links using a fuzzy inference system to achieve the maximum link reliability and provide heterogeneous wireless services.

Research on Key Technologies of Intelligent Control Switches in Distribution Networks

With the development and widespread application of high-power power electronics technology, there is an interaction between distribution network voltage and reactive power and power quality. It is prone to operating over-voltage and closing inrush when closing, and reignition often occurs during opening. The voltage is too high, which far exceeds the voltage and insulation performance that the equipment can withstand, resulting in damage to the equipment. In response to the above problems, this article proposes an intelligent control switch closing and opening strategy to reduce the impact on the equipment during the reactive power compensation switching process. Considering the different dispersion of the intelligent control switch, the process of closing the parallel capacitor is simulated, and the effect of the intelligent control switch on the overvoltage and inrush current limitation is compared, which proves that the strategy is effective.

Improving the Energy-Conversion Efficiency of a PV–TE System With an Intelligent Power-Track Switching Technique and Efficient Thermal-Management Scheme

A photovoltaic-thermoelectric (PV-TE) hybrid system can be used for efficient thermal energy utilization from the generated waste heat in PV devices. In this article, an efficient PV-TE hybrid system with intelligent power-track switching technique and thermal management based on energy conversion is proposed. To make the output power of PV-TE system stable and normalized, an incorporated stable voltage circuit is designed based on energy conversion. In addition, a control-and-monitoring strategy is launched in the system to realize the normal collecting for the output power of PV-TE system. Finally, a battery protection circuit is performed to ensure that the energy converted by the entire system is effectively stored. The experimental results show that more electrical energy about 84 034 J was obtained with our energy harvesting system than that of a single photovoltaic (PV) cell. Besides, the thermal gradient of PV cells is indirectly reduced the operation of the whole system, which is automatically monitored due to the proposed intelligent power-track switching technique.

Enhancing reactant mass transfer inside fuel cells to improve dynamic performance via intelligent hydrogen pressure control

Due to internal mass transfer resistance, the dynamic response of fuel cells often lags the demand power in vehicular powertrains. In this study, an intelligent hydrogen pressure control method based on short-term vehicular power demand prediction is proposed to enhance mass transfer inside diffusion layer and improve dynamic performance. Sensitivity analysis confirmed the positive effect of hydrogen pressure on the dynamic response of fuel cells and revealed the necessity of adaptive switching of hydrogen pressure according to power demand. The short-term power demand is predicted using an iterative learning framework (ILF). Then, the variable-pressure control strategy is presented to realize the intelligent mode switching of hydrogen pressure according to the predicted power demand via a three-way solenoid valve. The effectiveness of the proposed method is validated experimentally. The results show that ILF achieves the best predictability for short-term power demand compared with non-iterative learning framework, thus guaranteeing high decision correctness of the prediction-based control strategy up to 99.9%. The dynamic performance of fuel cell is improved effectively, and its delivered energy increases by 6.98%, such that the battery discharge energy can be reduced by 45.4% in a 35-s test, which confirms the viability of the proposed hydrogen pressure control method.

A robust regenerated cellulose-based dual stimuli-responsive hydrogel as an intelligent switch for controlled drug delivery

Constructing robust hydrogels with biodegradability and dual stimuli-responsive by utilizing natural polymer as raw materials remains a sustaining challenge. Herein, we proposed an interpenetrating strategy in which N-isopropyl acrylamide (NIPAM) and acrylamide (AM) block copolymers were introduced as the second network into the carboxymethyl cellulose single network gel (CMC gel) to construct a dual-network robust hydrogel (CMC/PNIPAM-co-PAM). The dual-network design strategy effectively improves the mechanical strength of CMC gel. The hydrogel suggests intelligent dual stimuli-responsive behavior to pH and temperature. Furthermore, the copolymerization of NIPAM and AM regulated the low critical solution temperature (LCST) of the hybrid hydrogel, making it close to the physiological temperature of the human body. With the aim of evaluating its application in drug delivery, we loaded tetracycline into the dual-network hydrogel and simulated its release process under the pH microenvironment of the small intestine and the physiological temperature to infer its potential application in intestinal inflammation treatments. Moreover, it is proved that the strong hydrogel possesses good cytocompatibility in vitro biocompatibility testing. In addition, the embedding of tetracycline makes the hydrogel excellent antioxidant performance. This dual-stimulus response integrated hydrogel is expected to play a critical role in drug delivery and targeted therapy.

A novel adaptive Kalman filter for Euler-angle-based MEMS IMU/magnetometer attitude estimation

This paper introduces a novel Euler-angle-based adaptive attitude filter for micro-electro-mechanical system inertial measurement unit (IMU)/magnetometer fusion. An intelligent coordinate switch algorithm is proposed to overcome the singularity problem that exists in the Euler-angle-based attitude calculation approach. This method rotates the physical IMU body frame and forms a quasi-computational coordinate to perform the attitude and filter update. In addition, a novel noise estimation theorem based on redundant measurement systems is introduced, proofed and employed to adaptively tune the measurement covariance matrix R in the filter. This algorithm uses gyroscope short-term accuracy to evaluate the attitude performance derived from different sensors and optimally assigns weight in the filter update. Simulated and practical experiments are carried out to test the validation of the proposed adaptive attitude filter. The results achieved demonstrate that this approach can provide a promising orientation solution.

Conductive MXene/melamine sponge combined with 3D printing resin base prepared as an electromagnetic interferences shielding switch

With the rapid development of wireless communication and electronic devices, current electromagnetic interference (EMI) shielding materials cannot meet the demands of smart EMI shielding in the next generation of electronic equipment. Herein, we developed an intelligent EMI shielding switch to realize microwave shielding and transmitting functions for one material simultaneously. The EMI shielding switch is composed of MXene/melamine sponge, and 3D printed resin base. The EMI shielding switch can smartly regulate its absorption-dominant EMI SE from ~0 dB to ~43.1 dB in S-band through simply operation, to fully realize microwave transmitting and shielding functions simultaneously. The EMI shielding switch provides a novel way to fabricate efficient EMI shielding materials for actual wireless communication demands.

Smart Soft-Switching Switchover Circuit for Dual-Power PV-grid System Energy

—The dual-power PV-grid system is introduced to reduce the installation cost of a PV energy system. However, in the implementation of the dual-power PV-grid system, there is an interruption occurs during the transition of power process. To avoid the harmful effect during the transition, a small delay is inserted between the transition. The delay affects sensitive electronic equipment or industrial processes relying on constant electrical power. An intelligent control and switching scheme are required to solve the transition and synchronization problems. The objective of this project is to develop the soft-switching switchover circuit for dual-power PV-grid energy system. A new integration of hardware components and software developments is proposed to monitor PV current, to decide the power supply source, and to transfer the supply power at zero voltage and current. The proposed system involves three circuits, supervisory controller, switchover circuit and soft-switching circuit. Simulation and hardware implementation of the system proves by utilizing smart soft-switching switchover circuit, problem encountered during the transition of power supply could be eliminated

Application of intelligent technologies for modeling of controlled switching systems

The development of instrumental and methodological support for modeling nonlinear control systems with switching is an urgent problem. Various modifications of numerical optimization methods and artificial intelligence technologies are used to solve this problem. The purpose of this article is to develop algorithmic software for modeling controlled switching systems based on the use of intelligent technologies and numerical optimization methods. Algorithmic software for the synthesis of feedback controls using a PID controller is developed. Intelligent technologies for modeling controlled switching systems are characterized. An algorithm using global parametric optimization methods is proposed for tuning the PID controller. Models of nonlinear dynamic switching systems are studied. An algorithm for finding optimal trajectories based on neural network automata is proposed. The basis for further research is developed, in which it is planned to create a software implementation of the switching algorithm and the neural network algorithm. The practical significance of the results is that the developed algorithmic software will allow analyzing the influence of various parameters on the quality and speed of functioning of intelligent control switching systems. The obtained results can be used in various problems of modeling and global optimization of controlled systems: technical systems with switching modes of operation, transport systems, as well as in problems of neural network modeling and machine learning.

Intelligent Multi-homing Switching for Vehicles Connectivity

The spread of the internet applications has affected the vehicle connectivity requirements where connected vehicles can communicate with other systems to exchange data outside the vehicles. The continuous connectivity of smart vehicles is becoming very important for supporting various user applications. The challenges arise from the moving nature of vehicles on the road and the ability of providing a stable network connection due to areas where the network coverage is not available. The use of predictive multi-homed switching can eliminate the effect of network coverage holes. A switching decision can be supported by the intelligence of a connectivity gateway that analyzes network connectivity metrics collected from the vehicles such as round time delay and packet loss rate. In this paper we study the use of predictive multi-homed switching and the associated connectivity gateway in order to evaluate the improvement on the service connectivity of the vehicles. The gateway provides information ahead of time to prepare the vehicle to utilize alternative connectivity methods on different areas along the path of the vehicle. The system’s reaction to the elimination of network coverage holes is assessed where we show the improvement in the continued connectivity key performance indicators.

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

To provide users with high throughputs, the fifth generation (5G) and beyond networks are expected to utilize the Massive Multiple-Input Multiple-Output technology (MMIMO), i.e., large antenna arrays. However, additional antennas require the installation of dedicated hardware. As a result the power consumption of a 5G MMIMO network grows. This implies, e.g., higher operator costs. From this angle, the improvement of Energy Efficiency (EE) is identified as one of the key challenges for the 5G and beyond networks. EE can be improved through intelligent antenna switching, i.e., disabling some of the antennas installed at a 5G MMIMO Base Station (BS) when there are few User Equipments (UEs) within the cell area. To improve EE in this scenario we propose to utilize a sub-class of Machine Learning techniques named Reinforcement Learning (RL). Because 5G and beyond networks are expected to come with accurate UE localization, the proposed RL algorithm is based on UE location information stored in an intelligent database named a Radio Environment Map (REM). Two approaches are proposed: first EE is maximized independently for every set of UEs’ positions. After that the process of learning is accelerated by exploiting similarities between data in REM, i.e., REM-Empowered Action Selection Algorithm (REASA) is proposed. The proposed RL algorithms are evaluated with the use of a realistic simulator of the 5G MMIMO network utilizing an accurate 3D-Ray-Tracing radio channel model. The utilization of RL provides about 18.5% EE gains over algorithms based on standard optimization methods. Moreover, when REASA is used the process of learning can be accomplished approximately two times faster.

Research and implementation of condition monitoring and evaluation for process switch of intelligent substation

There are some problems in the regular inspection of switch, such as lack of real-time, lack of maintenance and over maintenance, this paper proposes a method for monitoring and evaluating the status of process layer intelligent switches . According to IEC61850 standard, the information model of switch is established to obtain the status information of the switch. The status information data is analyzed through the monitoring model of over-limit, synchronization, mutation and trend warning, and the status of switch is quantitatively evaluated by the fuzzy comprehensive evaluation algorithm. The example shows that this method can effectively evaluate the state of switch, which provides application technical support for state maintenance of process layer intelligent switches.

The artificially intelligent switching framework for terminalaccess provides smart routing in modern computer networks

The main page of the web interface provides access to various switch settings and displays all the necessaryinformation about the device. Admins can quickly view device status, performance statistics, etc., as well asmake necessary settings. The switch’s command-line interface is accessed by connecting a terminal or personalcomputer with a terminal emulation program installed on the switch’s console port. In this paper, the intelligentswitching framework was introduced for terminal access to provide smart routing. This access method is mostuseful when connecting to the switch first when the Internet protocol (IP) address is unknown or not set, whenpassword recovery is required, or when performing advanced switch settings. The command line interface can alsobe accessed over the network using the Telnet protocol. The user can use any administrative interface convenientto him to configure the switch because the set of functions available through the different management interfacesis the same for each.

Intelligent switching control of hybrid electromagnetic active suspension based on road identification

The hybrid electromagnetic actuator integrates a magnetorheological damper, and a linear motor can effectively reduce the energy consumption of linear electromagnetic active suspension by switching the control parameters with the same dynamic performances. Thus, the intelligent switching control of hybrid electromagnetic active suspension (HEMAS) is investigated with a fast–slow switching method for full road conditions for the first time, which uses the road level and system state variables as the switching rules. First, the intelligent switching control system architecture of HEMAS is designed, and the switching logic is refined. Then, the modified skyhook control strategy is adopted, and the control parameters (including overfull and transient objective control parameters) are optimized for different driving conditions. Then, the simultaneous estimation method of road profile and system state variables and the identification method of road level are studied. The identification result is used as a prerequisite for intelligent switching of system control parameters. Last, a road adaptive intelligent switching control system is designed, and the stability of the switching system is analyzed. Furthermore, a hardware-in-the-loop bench test is performed. The test results verify the effectiveness of the intelligent switching control system in improving the comprehensive performances (dynamic and energy-saving performances) of HEMAS.

Switchable Terahertz Band-Pass/Band-Stop Filter Enabled by Hybrid Vanadium Dioxide Metamaterial

To date, little research has been carried out on the integration of switchable and diversified functionalities into a single metamaterial in the terahertz (THz) range. Here, a hybrid vanadium dioxide (VO2) metamaterial was designed with switchable properties of band-pass filter and band-stop filter in the frequency range of 0.3–1.6 THz. Simulations demonstrated that under TE polarization, the proposed system acted as band-stop filter with the center frequency of 0.95 THz when VO2 is in the insulating state. Upon the transformation of VO2 into the metallic state, the proposed system behaved as a band-pass filter with a transmittance of >80%. The physical mechanism of the band-pass/band-stop conversion was examined by analyzing the surface current distribution of the designed device. The switchable characteristics of this structure can enable its wide application in tunable THz functional components such as amplitude modulators, polarization control, and intelligent switches.

Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

With the incoming of 5G communications, Vehicular Networks have the hope to achieve ultra-high data transmission rate with extremely low end-to-end delay. However, the dynamic nature of transportation traffic and increased data bandwidth demands are the major obstacles to achieve high transmission rate in Vehicular-to-Anything (V2X) Networks. To overcome these obstacles, this paper presents a novel Software Defined Networking (SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz). To improve the network management, Road Side units (RSUs) are used to segregate the V2X network into different clusters. Stability-aware clustering (SAC) scheme is also used for cluster formations. Our intelligent V2X is based on three features enabled machine learning approach: (1) To predict future 3D positions of the vehicles in the Cluster Heads (CHs) using Deep Neural Network with Extended Kalman Filter (DNN-EKF) algorithm for real-time, high-resolution prediction. (2) For THz communications, 0.3 THz to 3 THz band is selected for short-distance super-fast data transmissions. The THz band detection is performed by the CR-enabled Road Side Units (cRSUs). A Genetic Algorithm (GA)-based Improved Fruit Fly (GA-IFF) scheme is proposed to achieve an optimal route selection in THz communications. (3) In mmWave-based V2X communications, optimal beam selection is performed by the multi-type2 fuzzy inference system (M-T2FIS). By using these three intelligent designs approaches, we are able to achieve ultrahigh- rate and minimized transmission delay for short-range (in THz bands) and middle-range (in mmWave) communications. Finally, the proposed SDN-controlled, CR-enabled V2X Network is modeled and tested for performance evaluations with the metrics of delivery ratio, routing delay, protocol overhead, and data rate.

4D printed zero Poisson's ratio metamaterial with switching function of mechanical and vibration isolation performance

The unusual properties of mechanical metamaterials are determined by the configuration of artificial periodic structures. However, the mechanical performance of conventional metamaterials is irreversible and cannot perceive and respond to the changes in the environment. In present study, a zero Poisson's ratio metamaterial with intelligent switching mechanical properties and vibration isolation effect is proposed. Based on a 4D printing method of shape memory polymer, this metamaterial is created that can sense temperature changes and switch mechanical properties. The macroscopic deformation and the morphology change of the metamaterial during compression tests are analyzed using experimental and finite element methods. The irregular buckling distortion of the metamaterial is eliminated by cylindrical design, and controllable and adjustable local deformation and stress-strain curve are achieved based on microstructure gradient design. Subsequently, this work focused on the vibration isolation performance of metamaterials, and found fascinating shock absorption performance. Compared with traditional linear spring, this metamaterial spring can effectively reduce the vibration amplitude of certain frequency bands before reaching the resonance peak, which provides a new realization method for low-frequency vibration isolation design.

Design of Intelligent Low-Voltage Load Switch for Remote Control System in Smart Grid

Current low-voltage load switches do not support remote disconnect/connect and real-time monitoring of a disconnect/connect state. Addressing to these issues, this paper presents a low-voltage load switch for a smart remote control system, which uses a one-chip microcontroller board and a DC step motor drive mechanism and provides the feedback on the switch status also. Arrears disconnect and full-pay connect control is implemented via the microcomputer and Hall sensor. The low-power supply and state feedback circuit are designed to reduce the power consumption. And the motor drive mechanism is optimized to improve the performance of motor. Finally, a test environment includes the system control and feedback is created to illustrate the feasibility of the designed low-voltage switch. The test results that the design supports disconnect/connect control and state feedback in various conditions validate the feasibility of this designed system, which has high practical value and is worthy of promotion.