Autonomous Switching Research Articles

Propagating cortical waves coordinate sensory encoding and memory retrieval in the human brain.

Complex behavior entails a balance between taking in sensory information from the environment and utilizing previously learned internal information. Experiments in behaving mice have demonstrated that the brain continually alternates between outward and inward modes of cognition, switching its mode of operation every few seconds. Further, each state transition is marked by a stereotyped cascade of neuronal spiking that pervades most forebrain structures. Here we analyzed large fMRI datasets to demonstrate that a similar switching mechanism governs the operation of the human brain. We found that human brain activity was punctuated every several seconds by coherent, propagating waves emerging in the exteroceptive sensorimotor regions and terminating in the interoceptive default mode network. As in the mouse, the issuance of such events coincided with fluctuations in pupil size, indicating a tight relationship with arousal fluctuations, and this phenomenon occurred across behavioral states. Strikingly, concurrent measurement of human performance in a visual memory task indicated that each cycle of propagating fMRI waves sequentially promoted the encoding of semantic information and self-directed retrieval of memories. Together, these findings indicate that human cognitive performance is governed by autonomous switching between exteroceptive and interoceptive states. This apparently conserved feature of mammalian brain physiology bears directly on the integration of sensory and mnemonic information during everyday behavior.

Improving Fatigue Resistance and Autonomous Switching of pH Responsive Hydrazones by Pulses of a Chemical Fuel.

The chemically triggered reversible switching of pH-responsive hydrazones involves rotary motion-induced configurational changes, serving as a prototype for constructing an array of molecular machines. Typically, the configurational isomerization of such switches into two distinct forms (E/Z) occurs through the alteration of the pH the medium, achieved by successive additions of acid and base stimuli. However, this process results in intermittent operation due to the concomitant accumulation of salt after each cycle, limiting switching performance to only a few cycles (5-6). In this context, we introduce a novel strategy for the autonomous E/Z isomerization of hydrazones in acetonitrile using pulses of trichloroacetic acid as a chemical fuel. The use of this transient acid enabled reversible switching of hydrazones even after 50 cycles without causing significant fatigue. To test the broad viability of the fuel, a series of ortho/para-substituted hydrazones were synthesized and their switching performance was investigated. The analysis of kinetic data showed a strong dependency of switching operations including the lifetime of transient state, on the electronic properties of substituents. Finally, a distinct color change from yellow to orange due to reversible switching of the para-methoxy substituted hydrazone was employed for the creation of rewritable messages on commercially available paper.

Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to study the embedding representation to solve the incompleteness of knowledge graphs (KGs). Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been widely used in KGC tasks by capturing neighbor information of entities. However, Both GCNs and GATs based KGC models have their limitations, and the best method is to analyze the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, the representation quality of the embeddings can affect the aggregation of neighbor information (message passing). To address the above limitations, we propose a novel knowledge graph completion model with mixed geometry message and trainable convolutional attention network named MGTCA. Concretely, the mixed geometry message function generates rich neighbor message by integrating spatially information in the hyperbolic space, hypersphere space and Euclidean space jointly. To complete the autonomous switching of graph neural networks (GNNs) and eliminate the necessity of pre-validating the local structure of KGs, a trainable convolutional attention network is proposed by comprising three types of GNNs in one trainable formulation. Furthermore, a mixed geometry scoring function is proposed, which calculates scores of triples by novel prediction function and similarity function based on different geometric spaces. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of MGTCA is significantly improved compared to the state-of-the-art approaches.

Learnable convolutional attention network for knowledge graph completion

Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been used extensively in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, both GCNs and GATs have limitations in the KGC task, and the best method is analyzing the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, relations in KGs have specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network using a convolution operation before the attention mechanism to ensure structural information acquisition and avoid redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves MRR from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset).

Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network

Studies on developing future generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy consumption levels and achieving "greenness" remain key factors for the giant industry. Some research works have proposed deep reinforcement techniques to solve energy management (EM) issues in cellular networks. However, these techniques are inefficient in a distributive network environment and expose the devices to privacy issues. Federated learning (FL) is proven to enforce device privacy and train models distributively. Thus, this work proposes an autonomous switching mode framework for BSs based on federated-deep reinforcement learning (Fed-DRL) to address the aforementioned challenges encountered by prior studies. Specifically, we deploy multiple DRL agents to influence the decision of the BS for EM. On the other hand, to make DRL-based decisions feasible and satisfy device quality-of-service, we train the DRL agents distributively by employing the FL concept. The results show the effectiveness of our proposed framework under distributed network scenarios compared with other benchmark algorithms.

Switched Safe Tracking Control of Chain Tensioning System With Multiple Disturbances and Kinematic Constraints

In armored face conveyors, one of the significant challenges of moving from manual to automated chain tensioning is the safety of the interaction between the controlled system and complex environment. Multiple disturbances might put an automated chain tensioning system (CTS) at a high risk of collision and chain jumping due to excessive position/velocity in tension control. Therefore, a switched safe tracking control is proposed for the CTS with multiple disturbances and kinematic constraints. A hybrid switched tension/position control with hysteresis autonomous switching strategy is integrated into the safe tracking scheme. The switching control method enables the CTS to perform autonomous motions to follow a predefined safe trajectory when the position exceeds the safe bound. The barrier Lyapunov function is utilized in the controller design to prevent the violation of velocity constraints. Friction compensation, parameter adaption, and improved integrator are employed to deal with the multiple disturbances. To avoid the transient overshoot/oscillation around switching points, the negative influence of multiple disturbances is considered while designing the integrator term and autonomous switching strategy. Finally, comparative experiments are conducted to verify that the proposed control scheme can achieve excellent tension tracking performance while preventing the violation of kinematic constraints in multiple situations, such as startup phase, operation phase, and strong disturbance.

ASTS: Autonomous switching of task-level strategies

ASTS: Autonomous switching of task-level strategies

Absorption-dominated electromagnetic interference shielding assembled composites based on modular design with infrared camouflage and response switching

Absorption-dominated electromagnetic interference (EMI) shielding materials are ideal for preventing electromagnetic pollution, but they suffer from complex structural design, poor performance stability, and narrow operating bands. Here, silica/carbon nanotubes/polyimide-silver nanowires/cellulose nanofibers (SiO2/CNTs/PI-AgNWs/CNF) composites are prepared based on the principle of split conductive modular design. According to the use requirements, the electromagnetic module is reasonably assembled, thus achieving a high degree of unity in electromagnetic wave absorption and shielding. In detail, the SiO2/CNTs/PI foam with progressive long channel structure has good impedance matching and provides excellent electromagnetic wave absorption. The highly conductive AgNWs/CNF film ensures excellent EMI shielding performance. The reflection shielding effectiveness (SER) is as low as 0.023 dB at an average EMI SE of 110 dB for the assembled composite, corresponding to an ultra-low reflectivity (R) of 0.00538. In addition, the assembled composite also has an autonomous switching function between infrared camouflage and infrared response, due to the low infrared emissivity characteristic and Joule heating effect of AgNWs/CNF film, as well as the thermal insulation properties of SiO2/CNTs/PI foam. This flexible work will stimulate adaptive applications of EMI shielding materials in radar stealth and infrared camouflage environments.

Locomotion of Sensor‐Integrated Soft Robotic Devices Inside Sub‐Millimeter Arteries with Impaired Flow Conditions

One of the grand challenges in interventional cardiology and neuroradiology is to minimize the operation time and risk of damage during catheterization. These two factors drastically increase if the target location resides in small and tortuous vessels. Flow‐driven microcatheters are capable of rapidly and safely navigating small arteries with complex anatomy. However, their navigation relies on proper perfusion, which is an important bottleneck in the treatment of pathologies that cause impaired flow conditions. This work introduces the first endovascular sensor‐integrated soft robotic device that navigates sub‐millimeter arteries by extracting propulsive power from external magnetic fields. To this end, a number of innovations are described in the design, actuation, and control of flexible magnetic structures. The device is capable of advancing inside vasculature in an automated fashion using an open‐loop control scheme. Onboard sensors enable the real‐time monitoring of flow conditions, and autonomous switching between different modes of locomotion. The potential of the presented technology for minimally invasive diagnosis and therapy is demonstrated by achieving navigation inside coronary arteries of an ex vivo porcine heart under fluoroscopic guidance.

A design concept for radiation hardened RADFET readout system for space applications

Instruments for measuring the absorbed dose and dose rate under radiation exposure, known as radiation dosimeters, are indispensable in space missions. They are composed of radiation sensors that generate current or voltage response when exposed to ionizing radiation, and processing electronics for computing the absorbed dose and dose rate. Among a wide range of existing radiation sensors, the Radiation Sensitive Field Effect Transistors (RADFETs) have unique advantages for absorbed dose measurement, and a proven record of successful exploitation in space missions. It has been shown that the RADFETs may be also used for the dose rate monitoring. In that regard, we propose a unique design concept that supports the simultaneous operation of a single RADFET as absorbed dose and dose rate monitor. This enables to reduce the cost of implementation, since the need for other types of radiation sensors can be minimized or eliminated. For processing the RADFET's response we propose a readout system composed of analog signal conditioner (ASC) and a self-adaptive multiprocessing system-on-chip (MPSoC). The soft error rate of MPSoC is monitored in real time with embedded sensors, allowing the autonomous switching between three operating modes (high-performance, de-stress and fault-tolerant), according to the application requirements and radiation conditions.

Herding in the Chinese and US stock markets: Evidence from a micro-founded approach

Joining WTO accelerates the process of integrating China into the world financial market and should improve the development of the Chinese financial markets. Herding is one of the key aspects gauging the level of market development. Existing literature often uses aggregate level measures to investigate herding and therefore lacks the capability to differentiate autonomous market consensus from herding. This paper applies the Simulated Method of Moment estimator proposed by Chen and Lux (2018) to investigate the herding behavior in the Chinese and US stock markets. It is found that the asset pricing process is driven by fundamental factor and sentiment change due to autonomous switching and herding of the individual investors. We find evidence of herding behavior of individual investors in both stock markets. The Chinese stock market is mainly driven by behavioral sentiment dynamics due to the switching behaviors of investors while the US market is by fundamental factors. The severity of herding in the Chinese market is reduced after China joined the WTO.

Hierarchical autonomous switching control of a multi-modes omnidirectional mobile robot

Adequate control flexibility and tracking precision of the omnidirectional mobile robot (OMR) are difficult to be guaranteed with a fixed control mode. To address this challenge, this paper presents a modified hierarchical autonomous switching control scheme for an OMR with multiple maneuver-modes, which is capable of switching the alternative modes to adapt to the operational conditions and performing a satisfactory trajectory tracking control. Utilizing a hierarchical switching signal, the design begins by formulating a hybrid discrete OMR system with multiple subsystems cascading to its alternative kinematic states. Under the integrated system constraints, a new hierarchical switched fractional-order receding horizon control is presented to offer more adjusting flexibilities, which constructs a novel fractional-order cost function and then derives a numerical solvable solution. Meanwhile, with a Lyapunov-principle-based supervisory variable, an autonomous switching law is proposed so that the preferred subsystem can be selected to enhance the moving maneuverability. Under certain average dwell time conditions, the asymptotic stability of the resultant system is guaranteed. Finally, comparative experiments implemented on the real-world scenarios are provided to demonstrate the superior tracking performance and enhanced robustness of the proposed hierarchical autonomous switching control method as compared to traditional control schemes.

Analysis of the Vibration Suppression of Double-Beam System via Nonlinear Switching Piezoelectric Network

In this paper, a method to suppress the vibration of a double-beam system with nonlinear synchronized switch damping on the inductor via a network (SSDI-net) is proposed. Unlike the classical linear piezoelectric shunt damping, SSDI-net is a nonlinear piezoelectric damping. A double-beam system with SSDI-net was simplified to a lumped parameter electromechanical coupling model and analyzed by using the multi-harmonic balance method, at first with alternating frequency–time techniques (MHBM/AFT). Then, a new lower-power autonomous switching control circuit board was designed, based on SSD technique, and vibration control experiments using a double-beam system with an SSDI network are conducted, to verify the validity of the proposed analysis method and its calculation results. The nonlinear switching piezoelectric network proposed in this article can increase the voltage inversion factor. Furthermore, future applications of this switching piezoelectric network technology in the vibration suppression of bladed-disk structures in aero engines can reduce the number of switches by at least half and obtain almost the same damping effect.

Electrochemical Circuits for Autonomous Microfluidic Solution Processing

Electrochemical Circuits for Autonomous Microfluidic Solution Processing

An Autonomous Frequency Reconfigurable Antenna Using Slotline Open-Loop Resonators

This paper presents a slotline open-loop resonators based frequency reconfigurable antenna with autonomous switching of frequency bands. The dual-port slot antenna is designed to operate at 2.1 GHz when excited at port-1, and when port-2 is excited, the antenna can be reconfigured to operate at 2.85 GHz and 5.52 GHz, respectively. Port-1 is used to receive the control signal, which is converted to a DC signal using a rectifier. The rectified control signal is used to switch the operating frequency bands of the port-2. The proposed antenna configuration gives the flexibility of remote/wireless control of the operating frequency. The antenna prototype is fabricated and measured for observing the frequency switching at port-2. The measured results show that the antenna has been effectively switched from lower band frequency (2.8 GHz) to upper band frequency (5.41 GHz) when the PIN diode is ON. The proposed antenna in its final configuration can be potentially suitable for transmitter reconfigurable antenna without the need for external DC bias voltage.

Stability analysis of discrete-time LPV switched systems

This paper addresses the stability problem for discrete-time switched systems under autonomous switching. Each mode of the switched system is modeled as a Linear Parameter Varying (LPV) system, the time-varying parameters can vary arbitrarily fast and are represented in a polytopic form. The Lyapunov theory is employed to get new conditions in the form of parameter-dependent LMIs. The constructed Lyapunov function takes advantage of using an augmented state vector with shifted states in its construction. In this sense, the Lyapunov function employed in this paper can be viewed as a discrete-time LPV switched Lyapunov function. Numerical experiments illustrate the efficacy of the technique in providing stability certificates.

On Optimal Control of Flat Hybrid Automata

Recently, Flat Hybrid Automata (FHA) were introduced as a new model class of hybrid systems. In order to answer the evident question of an optimal operation of the new class of FHA, a well-posed optimization problem definition for FHA is presented. This problem formulation also includes costs on discrete–state transitions and switching actions. We present a solution for a reduced problem: FHA with autonomous switching. For these, a new algorithm is introduced which solves the optimal control problem. The application of this optimization algorithm is shown with an electrical network example.

Compact Self-Powered Wireless Sensors for Real-Time Monitoring of Power Lines

This paper presents a highly efficient self-powered sensor with a radio-frequency (RF) transmission range of a few hundred meters. Magnetic energy from power lines was harvested and converted into 3.3 V pulsed voltage using a voltage doubler and an autonomous switching circuit. A ferrite core with a high relative permeability of 3200 was used for magnetic harvesting, and a split core was employed to allow live-line installation without causing a power outage. Low power sensors, such as a temperature sensor, a current sensor, and a three-axis accelerometer, were included in the module to monitor the power lines in real time. Even under a very low magnetic field of 40 µT, average harvested power of 1.8 mW was achieved and wireless multi-sensing was successfully demonstrated over 500 m.

Stability of switching linear systems with switching signals driven by stochastic processes

This paper presents conditions to assure the exponential stability in probability for autonomous switching linear systems. The switching signal acting on the autonomous system produces intervals that follow independent, identically distributed stochastic processes—the stability then follows by verifying simple-to-check linear matrix inequalities.

A Method for Autonomous Power Balance of DC Microgrid with Photovoltaic and Storage System

DC microgrid with photovoltaic and energy storage has been attracted widespread attention due to its high reliability and flexibility. In order to solve the problem that the power of DC microgrid cannot be self balanced due to the no uniformity and uncertainty of the output of photovoltaic power in the photovoltaic DC storage microgrid. In this paper, a power balance control method based on the DC bus voltage signal autonomous switching mode is proposed, this method can effectively stabilize the power fluctuation and maintain the power balance in the network, and enhance the safe and reliable operation ability of the optical storage DC microgrid. Finally, the feasibility and effectiveness of the proposed control method are verified by experiments.