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The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach.

The emerging paradigm of Network Function Virtualization (NFV) promises to shorten the renewal cycles of network functions and reduce the capital expenses by flexibly deploying virtualized network functions (VNFs) implementation on commodity servers. However, the required resource of each type (CPU, memory, etc.) for the running VNF should be provisioned to guarantee the performance when processing packets. This comes with different deployment cost especially in a heterogeneous cloud consisting of a large number of network function platforms from various vendors. To optimally operate VNFs, it is necessary for the network operator to dynamically deploy VNFs in the expensive cloud infrastructures. In this paper, we initiate the study of minimizing the deployment cost under multi-resource constraints in a heterogeneous cloud. We formulate multi-resource VNF deployment problem (MVDP) as an optimization program and prove its hardness. We propose an offline (1,d + 1)-bicriteria approximation algorithm and an (O(1),O(n . logn))-competitive online algorithm to deploy VNFs in a scalable manner, where d is the number of resource types and n is the number of required VNFs. Large-scale simulations and DPDK-based OpenNetVM implementation show that our algorithms can reduce the overall cost by 34% and improve the performance in terms of multi-resource allocation.

Virtual Coupling (VC) is proposed as an innovation solution to advance the concept of moving-block based signaling system. The control method for VC trains in multiple departure scenarios and the coupling time determining are still challenges. In this paper, the cooperative control problem for VC trains under fixed departure and coupling time is investigated. Firstly, three operating modes are proposed to describe the procedure of VC departure. Then it is shown that the trains can obtain reference information by the distributed observers under a jointly connected communication topology and reach the coupled state by the proposed fixed-time tracking controllers. The boundary of coupling time, which depends on controller parameters and waiting time, is also given. Finally, the control method's effectiveness is verified in three typical scenarios.

Reliability is reflected in product during manufacturing. However, due to uncontrollable factors during production, product reliability may degrade substantially after manufacturing. Thus, root cause analysis is important in identifying vulnerable parameters to prevent the product reliability degradation in manufacturing. Therefore, a novel root cause identification approach based on quality function deployment (QFD) and extended risk priority number (RPN) is proposed to prevent the degradation of product manufacturing reliability. First, the connotation of product manufacturing reliability and its degradation mechanism are expounded. Second, the associated tree of the root cause of product manufacturing reliability degradation is established using the waterfall decomposition of QFD. Third, the classic RPN is extended to focus on importance to reliability characteristics, probability, and un-detectability. Furthermore, fuzzy linguistic is adopted and the integrated RPN is calculated to determine the risk of root causes. Therefore, a risk-oriented root cause identification technique of product manufacturing reliability degradation is proposed using RPN. Finally, a root cause identification of an engine component is presented to verify the effectiveness of this method. Results show that the proposed approach can identify the root cause objectively and provide reference for reliability control during production.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

A novel topology multi-stage noise-shaping (MASH) delta-sigma modulator is proposed for 20-km digital mobile fronthaul (MFH) in this article. In the proposed MASH structure, a newly designed feedback unit is combined with a traditional fourth-order sturdy MASH structure to enhance the noise-shaping capacity. The detailed comparison between the conventional fourth-order single delta-sigma modulator (SDSM) and the proposed new topology MASH is presented in a 512/1024 quadrature amplitude modulation (QAM) orthogonal frequency division multiplexing (OFDM) transmission system with the bandwidth of 1.125 GHz. The OFDM signal is quantized to two bits by SDSM/MASH analog-to-digital conversion (ADC), and this digitized signal is transmitted over 20-km single mode fiber (SMF) in 20-Gbaud 4-level pulse amplitude modulation (PAM4) intensity modulation direct detection (IM/DD) system. The signal to noise ratios (SNRs) of the retrieved OFDM signal utilizing the proposed new topology MASH and the fourth-order SDSM ADCs are 38.7dB and 34.5dB, respectively. In the case of 1024-QAM PAM4 system, the error vector magnitude (EVM) floors of the proposed new topology MASH and the conventional fourth-order SDSM schemes are 1.64% and 1.96% over 20-km SMF transmission at off-line digital signal processing (DSP) reception, and 1.2 dB receiver sensitivity improvement is achieved.

Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. However, it remains challenging in a realistic scenario, i.e., limited number of labeled samples and sensors. This article proposes a novel deep learning method to achieve accurate and robust HAR with only a single inertial measurement unit (IMU) sensor. Our contributions are twofold. First, based on the skinned multiperson linear (SMPL) model, we build a large synthetic HAR dataset containing multimodal measurements: acceleration and angular velocity, which were generated according to the forward kinematics. Second, We propose a multiple-level domain adaptive learning model with information-theoretically stimulated constraints to simultaneously align the distributions of low- and high-level representations of virtual and real HAR data. The proposed mutual information constraints encourage the neural network to learn a disentangled representation for the multimodal sensing data. Comprehensive experimental results on three publicly available datasets demonstrate that the proposed method compares favorably with competing ones and has robust performance with variable labeled samples.

This letter proposes a joint-sparse index removal (JSIR) based algorithm to detect the index of space-frequency index modulation (SFIM) signal for MIMO-OFDM cognitive radio (MIMO-OFDM-CR) system. At first, the proposed algorithm calculates the inner-product matrix of the received signal and channel gain to measure the index information of each subcarrier on each antenna. Then, an antenna index metric is computed through a definition which is given by summing the metrics of all subcarriers on the antenna. According to the metric, the silent antenna indices are acquired by comparing the metric with a threshold derived from statistical distribution. Next, the index detection problem is simplified by removing the silent antenna indices, i.e., the joint-sparse indices of SFIM signal matrix. At last, the indices of active subcarriers on active antennas are obtained through solving the simplified problem via a compressed sensing reconstruction algorithm. The complexity analysis shows that the proposed JSIR algorithm has lower complexity under certain conditions, compared with other related methods. The simulation results verify the accuracy of the proposed method in terms of bit error rate (BER).

In cognitive radio (CR), the problem of spectrum hole detection has been extensively studied in single dimension, such as frequency domain, spatial domain, and so on. Recently, a class of two dimension spectrum hole detection methods, named as joint angle-frequency estimation (JAFE), has attracted much attention. Nevertheless, most of the existing approaches are only suitable for the scenario with matching frequency-angle pairs, rather than the non-matching scenario between the two parameters, like space division multiple access (SDMA) or frequency division multiplexing access (FDMA) communication mode which allows the same frequency band (or angle) to be reused in different angles (or frequencies). For the above two cases, including matching and non-matching scenarios, this paper develops an effective frequency-angle spectrum hole detection algorithm with Taylor expansion based focusing transformation (TFT-FASHD), on the basis of signal sparse representation by extending the array manifold from angle domain to frequency-angle domain. In the proposed method, for Fourier transform representation of the sparse model, a focusing transformation based on Taylor expansion is first performed to focus the signal subspaces at different frequencies to a single frequency, so as to carry out dimension reduction of dictionary in angular domain. TFT is derived by decomposing the array manifold with Taylor expansion, and further the optimum focusing frequency of focusing transform is discussed theoretically. Second, atoms with high representative performance are chosen by the presented TFT and compressed sensing (CS). Third, according to the low dimension dictionary, the TFT-FASHD is implemented by CS under multiple measurement vector (MMV) circumstances. The accuracy of the algorithm in non-matching scenario is verified by simulation results. For the matching scenario, compared with the related JAFE methods, the proposed algorithm has a lower computational complexity, smaller detection error, and higher energy efficiency, which are validated through simulation.

Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications., Designing efficient sensors for human machine interaction remains a challenge. Here, the authors present a soft robotic fingers system based on a triboelectric nanogenerator (L-TENG) sensor to capture the continuous motion of soft gripper and a soft tactile (T-TENG) sensor for tactile sensing, that can achieve an object recognition accuracy of 98.1%.

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security., Designing efficient and fast monitoring and response systems for smart building/home applications remains a challenge. Here, the authors propose a smart floor monitoring system developed through the integration of self-powered triboelectric sensing mechanism and deep learning data analytics.

With the rapid advances in wearable electronics and photonics, self-sustainable wearable systems are desired to increase service life and reduce maintenance frequency. Triboelectric technology stands out as a promising versatile technology due to its flexibility, self-sustainability, broad material availability, low cost, and good scalability. Various triboelectric-human-machine interfaces (THMIs) have been developed including interactive gloves, eye blinking/body motion-triggered interfaces, voice/breath monitors, and self-induced wireless interfaces. Nonetheless, THMIs conventionally use electrical readout and produce pulse-like signals due to the transient charge flows, leading to unstable and lossy transfer of interaction information. To address this issue, we propose a strategy by equipping THMIs with robust nanophotonic aluminum nitride (AlN) modulators for readout. The electrically capacitive nature of AlN modulators enables THMIs to work in the open-circuit condition with negligible charge flows. Meanwhile, the interaction information is transduced from THMIs' voltage to AlN modulators' optical output via the electro-optic Pockels effect. Thanks to the negligible charge flow and the high-speed optical information carrier, stable, information-lossless, and real-time THMIs are achieved. Leveraging the design flexibility of THMIs and nanophotonic readout circuits, various linear sensitivities independent of force speeds are achieved in different interaction force ranges. Toward practical applications, we develop a smart glove to realize continuous real-time robotics control and virtual/augmented reality interaction. Our work demonstrates a generic approach for developing self-sustainable HMIs with stable, information-lossless, and real-time features for wearable systems.

Optical phased arrays (OPAs) are promising in various applications owing to their excellent beam steering performance but suffer from the random initial phases ruining the beam patterns. In this work, an interference-based calibration method to align the random phases for OPA operations is proposed. Briefly, the phase differences are directly extracted from the interference fringes and used to build up the phase map. The feasibility and effectiveness of the proposed method were verified from an 8 × 8 OPA chip, in which the side-lobe suppression ratio of 10.1 dB was achieved. The proposed method can be utilized as a supplement of conventional calibration algorithms to reduce requirements for the sensitivity of the feedback system and avoid local optima, thus providing practical assistance for many OPA applications.

Blockchain networks have attracted tremendous attention for creating cryptocurrencies and decentralized economies built on peer-to-peer protocols. However, the complex nature of the dynamics and feedback mechanisms within these economic networks has rendered it difficult to reason about the growth and evolution of these networks. Hence, proper mathematical frameworks to model and analyze the behavior of blockchain-enabled networks are essential. To address this need, we establish a formal mathematical framework, based on dynamical systems, to model the core concepts in blockchain-enabled economies. Drawing on concepts from differential games, control engineering, and stochastic dynamical systems, this paper proposes a methodology to model, simulate, and engineer networked token economies. To illustrate our framework, a model of a generalized token economy is developed, where miners provide a commodity service to a platform in exchange for a cryptocurrency and users consume a service from the platform. We illustrate the dynamics of token economies by simulating and testing two different block reward strategies. We then conclude by outlining future research directions that will integrate additional methods from signal processing and control theory into the toolkit for designers of blockchain-enabled economic systems.

With the national goal of "carbon peak by 2030 and carbon neutral by 2060 in China", studies on carbon prices of China's Emissions Trading System (ETS) pilots have shown growing interest in the related fields. Carbon price fluctuations reflect the scarcity of carbon resources, and accurate prediction can improve carbon asset management capabilities. Therefore, in order to clarify the dynamics of carbon markets and assign carbon emissions allocation rationally, we propose a hybrid feature-driven forecasting model with the framework of decomposition-reconstruction-prediction-ensemble. In this paper, the non-stationary, nonlinear and chaotic characteristics of carbon prices in China's ETS pilots have been verified, and then the prediction model is built based on the tested features. Firstly, the original carbon price series are decomposed by Variational Mode Decomposition (VMD), and then reconstructed by Sample Entropy (SE). Next, Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) is conducted to predict the subsequences. Lastly, the forecasting series of every subseries are summed to obtain the final results. The empirical results based on carbon prices of China's ETS pilots proved that the proposed model performs more efficiently than the current benchmark models. As carbon prices are expected to increase across all ETS during the post-COVID-19 recovery stage, the new prediction model will be useful for improving the guiding principles of the existing government policies including the likely introductions of Border Carbon Adjustment (BCA) in the EU and the US, and governing the large global public companies to deliver their "net zero" commitments.

To enable smart homes and relative applications, the floor monitoring system with embedded triboelectric sensors has been proven as an effective paradigm to capture the ample sensory information from our daily activities, without the camera-associated privacy concerns. Yet the inherent limitations of triboelectric sensors such as high susceptibility to humidity and long-term stability remain a great challenge to develop a reliable floor monitoring system. Here we develop a robust and smart floor monitoring system through the synergistic integration of highly reliable triboelectric coding mats and deep-learning-assisted data analytics. Two quaternary coding electrodes are configured, and their outputs are normalized with respect to a reference electrode, leading to highly stable detection that is not affected by the ambient parameters and operation manners. Besides, due to the universal electrode pattern design, all the floor mats can be screen-printed with only one mask, rendering higher facileness and cost-effectiveness. Then a distinctive coding can be implemented to each floor mat through external wiring, which permits the parallel-array connection to minimize the output terminals and system complexity. Further integrating with deep-learning-assisted data analytics, a smart floor monitoring system is realized for various smart home monitoring and interactions, including position/trajectory tracking, identity recognition, and automatic controls. Hence, the developed low-cost, large-area, reliable, and smart floor monitoring system shows a promising advancement of floor sensing technology in smart home applications.

With the increase of train density on the line in high-speed railways (HSR), delay propagation becomes easy to occur. In this paper, we investigated a real-time rescheduling problem to restore the HSR operation from delay caused by disturbance. A real-time rescheduling model considering the quasi-moving block rules and the train speed profiles was constructed. The objective of the proposed model was to minimize the total delay when disturbance occurred. We developed a bi-level iterative algorithm (BIA) which combined CPLEX with loop iteration. The calculating time is greatly decreased by reducing constraints and variable numbers. Two experiments were presented to demonstrate the validity of the proposed model and the effectiveness of the proposed algorithm. The simulation results showed that, by applying the BIA approach, we have succeeded in reducing the total delay time by 17%. Meanwhile, compared with using CPLEX to solve job shop model, the calculating time is greatly decreased by reducing constraints.

An artificial intelligence motor fault diagnosis system based on STM32 single-chip microcomputer and deep learning is designed. After algorithm optimization and a small number of sample learning, the real-time recognition rate of fault diagnosis can reach 96.07%. First, the hardware circuit is designed, the ADXL335 acceleration sensor is used to collect the motor running vibration signal, and the Kalman filter is used to improve the sampling accuracy. Then the Kalman filtered signal is output to the host computer through the serial port, and then converted into the deep learning model for training and recognition using FFT. Finally, experiments are carried out on a self-made motor fault diagnosis simulation experiment platform, and the results show that the system has a better recognition effect.

Many older adults with Alzheimer's disease and patients with conditions such as spinal nerve injuries have symptoms of urinary incontinence. In order to improve their quality of life, B-mode ultrasound and drainage tubes are often used to measure the urine accumulation in the human bladder. In this paper, an ultrasonic urine system based on A-mode ultrasound is developed for measuring the urine accumulation in the human bladder. The ultrasound urine system contains three parts: the collection device, the mobile terminal, and the upper data processing software. In the collection device, the STM32 microcontroller sends out a pulse signal with a rate of 2.5MHz to excite the ultrasonic transducer through the ultrasonic transmitter circuit. Ultrasonic transducer receives the echo signals by the ultrasonic receiving circuit processing into digital signals, STM32 microcontroller transmits them to the mobile terminal. The mobile terminal transmits the digital signals to the upper data processing software for processing and receives the calculation results for display. We conducted tests on a bladder simulator. We conducted three sets of experiments and measured the transducer to baffle distance of 30mm, 40mm, and 50mm. The accuracy of the system measurements in three sets of experiments were 99.8%, 99.65%, and 99.52%. Experimental results verify the effectiveness of the system.

A non-contact temperature measurement and recognition device based on OpenMV camera module and MLX90614 temperature recognition module is designed. OpenMV is used for identification and MLX90614 module is used for initial temperature measurement. The measured data can be compensated and posted on the LCD screen. Identity recognition adopts multi-template storage, LBP value and threshold value comparison and matching method, after multiple verifications, the accuracy rate reaches 94%. Temperature measurement adopts Kalman filter algorithm to reduce instrument error. The error range of measured object temperature after algorithm compensation is closed to 0.2 centigrade, and the error range of body temperature measurement is 1.7 centigrade. Finally, if the identification process fails or the temperature measurement value is higher than the preset value, the buzzer and LCD screen will issue a warning.

The intelligent question answering over the knowledge graph aims to automatically answer natural language questions via locating the correct entities in the knowledge graph. Aside from the former progresses, it is still challenging to answer the multi-relation questions because of the variety and complexity of the natural language, as well as the combinatorial explosion on possible candidates. In this paper, we propose a novel embedding-based approach named SPE-QA to address these issues. It answers a question by identifying its most semantic like question-answer path from the candidate topic-entity-centric subgraph, and locating this path's tail entity as the final answer. In order to limit the scale of the candidate sub graph and thus reduce the neural network's training complexity, it is essential to filter the explicit noises as much as possible. Therefore, we employ a sub graph separation method to decide the scale of the sub graph, and remove the false question-answer paths with two different pruning policies. The experimental results on two widely used benchmarks approve that, our mechanism can not only reduce the subgraph's scale dramatically, but also obtain better performance on the multi-relation questions, compare to the stat-of-the-art approaches.

This paper investigates the multi-channel access and rendezvous problem in unmanned aerial vehicle (UAV) relaying system in the absence of pre-allocated control channel. Both the geographical sensing range and the spectrum sensing bandwidth of each UAV are limited due to onboard payload constraints, hence it becomes challenging to design effective and efficient channel rendezvous mechanisms. To address the challenge, this paper first observes and analyzes the effects of UAV relaying network topology and geographical sensing range on the rendezvous, and it is found that a joint exploitation of motion and frequency control is essential to achieve efficient rendezvous. Based on the important insight, this paper formulates the UAV relaying rendezvous problem and proposes a novel joint space-frequency rendezvous (JSFR) method for multi-UAV networks, incorporating with distributed reinforcement learning techniques. The simulation results show that the JSFR method may significantly improve the effectiveness and efficiency of rendezvous in UAV relaying networks, in terms of rendezvous probabilities and convergence rates.

Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital‐twin‐based remote interactive system for advanced robotic‐enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T‐TENG) and length (L‐TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T‐TENG and L‐TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital‐twin‐based virtual shop is successfully implemented to provide the users with real‐time feedback about the details of the product. In general, by offering a more immersive experience in human–machine interactions, the proposed remote interactive system shows the great potential of being the advanced human–machine interface for the applications of the unmanned working space., A smart soft robotic manipulator is developed with a self‐powered multifunctional sensory system for simultaneously deformation, tactile, and temperature perception. With machine learning analysis, automatic recognition of grasped objects can be realized with high accuracy. By leveraging the artificial intelligence of things (AIoT) technology, a digital‐twin‐based virtual shop is successfully implemented to provide users with a more immersive shopping experience.

Lower‐limb motion monitoring is highly desired in various application scenarios ranging from rehabilitation to sports training. However, there still lacks a cost‐effective, energy‐saving, and computational complexity‐reducing solution for this specific demand. Here, a motion capturing and energy harvesting hybridized lower‐limb (MC‐EH‐HL) system with 3D printing is demonstrated. It enables low‐frequency biomechanical energy harvesting with a sliding block‐rail piezoelectric generator (S‐PEG) and lower‐limb motion sensing with a ratchet‐based triboelectric nanogenerator (R‐TENG). A unique S‐PEG is proposed with particularly designed mechanical structures to convert lower‐limb 3D motion into 1D linear sliding on the rail. On the one hand, high output power is achieved with the S‐PEG working at a very low frequency, which realizes self‐sustainable systems for wireless sensing under the Internet of Things framework. On the other hand, the R‐TENG gives rise to digitalized triboelectric output, matching the rotation angles to the pulse numbers. Additional physical parameters can be estimated to enrich the sensory dimension. Accordingly, demonstrative rehabilitation, human‐machine interfacing in virtual reality, and sports monitoring are presented. This developed hybridized system exhibits an economic and energy‐efficient solution to support the need for lower‐limb motion tracking in various scenarios, paving the way for self‐sustainable multidimensional motion tracking systems in near future., A motion capturing and energy harvesting hybridized lower‐limb system is proposed for low‐frequency biomechanical energy harvesting and lower‐limb motion sensing. Combining with the Internet of Things framework, the hybridized system exhibits an economic and energy‐efficient solution to support the need for lower‐limb motion tracking in various applicable scenarios, paving the way for self‐sustainable multidimensional body motion tracking systems in near future.

Sensory and nerve systems play important role in mediating the interactions with the world. The pursuit of neuromorphic computing has inspired innovations in artificial sensory and nervous systems. Here, an all‐in‐one, tailorable artificial perception, and transmission nerve (APTN) was developed for mimicking the biological sensory and nervous ability to detect and transmit the location information of mechanical stimulation. The APTN shows excellent reliability with a single triboelectric electrode for the detection of multiple pixels, by employing a gradient thickness dielectric layer and a grid surface structure. The sliding mode is used on the APTN to eliminate the amplitude influence of output signal, such as force, interlayer distance. By tailoring the geometry, an L‐shaped APTN is demonstrated for the application of single‐electrode bionic artificial nerve for 2D detection. In addition, an APTN based prosthetic arm is also fabricated to biomimetically identify and transmit the stimuli location signal to pattern the feedback. With features of low‐cost, easy installation, and good flexibility, the APTN renders as a promising artificial sensory and nervous system for artificial intelligence, human–machine interface, and robotics applications., An all‐in‐one, tailorable artificial perception, and transmission nerve is developed for mimicking the biological sensory and nervous ability to detect and transmit the location information of mechanical stimulation. The as‐mentioned process consumes no electrical energy at all. In addition to detecting 2D stimuli by a single‐electrode mode, a self‐powered prosthetic arm is also demonstrated.

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

The identification of lithology from well log data is an important task in petroleum exploration and development. However, due to the complexity of the sedimentary environment and reservoir heterogeneity, the traditional lithology identification methods can not meet the needs of real-time and accurate prediction and identification with logging while drilling (LWD) equipment. The basic data of this paper are derived from conventional wireline logging (CWL) data and the LWD data in Yan'an Gas Field. The main research goal is to compare and analyse three popular machine learning algorithms, which are one-versus-rest support vector machines (OVR SVMs), one-versus-one support vector machines (OVO SVMs) and random forest (RF), and to optimize a more practical method in the field for LWD systems. To reduce the dimensions of the input data, the characteristic parameters of the training data are obtained by a correlation analysis of the logging data. The optimal parameter values of each algorithm are determined by grid search method and 10-fold cross-validation method. On this basis, the lithology predictions of the actual LWD data are carried out by using three classifiers. Considering the time consumption of the model training and the lithology identification accuracy of the model, the best lithology identification model while drilling is selected. The results show that the characteristic parameters of the training data after the correlation analysis are AC, CAL, GR, K, RD and SP logs. The overall classification and recognition performance of the RF classifier is better than that of the other two classifiers, and its accuracy is even greater than 90%. The evaluation matrix shows that the OVR SVMs and RF classifiers yield lower prediction errors than the OVO SVMs classifier in each single lithology identification, but the RF classifier spends much less time in the training process. Based on the comprehensive comparative analysis, it is considered that the RF classifier has the characteristics of a short training time and high recognition accuracy in practical production applications, so it is an ideal optimization classifier for lithology identification while drilling. The research results provide not only a theoretical basis for the drilling geosteering of oilfield development but also valuable information for future basic research.

In this paper, we address the problem of adaptive tracking control for a teleoperation system with uncertainties in both kinematics and dynamics. Its solution is difficult to establish as the real control torque will be wrapped in the coupling of kinematic and dynamic uncertainties. To overcome this difficulty, we developed an adaptive control approach with the aid of fuzzy logic systems designed to approximate uncertain dynamics so that the real control can be separated from the coupling uncertainties. With our scheme, the boundedness of all the closed-loop signals is ensured, and at the same time the tracking errors go to a residual around zero as time tends to infinity. The effectiveness of the obtained results will be illustrated through experimental tests.

Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers’ daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers. Though wearable gloves are widely used for gesture associated applications (e.g. sign language recognition), sentence identification of sign language remains a challenge. Here, the authors report AI-enabled recognition system helps barrier-free communication between signers and non-signers.

The application of the unmanned aerial vehicles (UAV) in future wireless networks is getting more and more popular. This article investigates the flight trajectory optimization problem with minimum energy consumption when the UAVs are communicating with the ground terminals (GT) for data collections. The specific flying speed is determined to minimize the energy consumption of the whole flying process. In addition, the algorithms to find the optimal trajectory are proposed. Experimental results are presented to show the effectiveness of our proposed algorithms.

In the past few years, triboelectric nanogenerator-based (TENG-based) hybrid generators and systems have experienced a widespread and flourishing development, ranging among almost every aspect of our lives, e.g., from industry to consumer, outdoor to indoor, and wearable to implantable applications. Although TENG technology has been extensively investigated for mechanical energy harvesting, most developed TENGs still have limitations of small output current, unstable power generation, and low energy utilization rate of multisource energies. To harvest the ubiquitous/coexisted energy forms including mechanical, thermal, and solar energy simultaneously, a promising direction is to integrate TENG with other transducing mechanisms, e.g., electromagnetic generator, piezoelectric nanogenerator, pyroelectric nanogenerator, thermoelectric generator, and solar cell, forming the hybrid generator for synergetic single-source and multisource energy harvesting. The resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations, toward achieving a maximum, reliable, and stable output generation. Hence, in this review, we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems, in the aspects of operation principles, structure designs, optimization strategies, power management, and system integration. The recent progress of TENG-based hybrid generators and hybridized systems for the outdoor, indoor, wearable, and implantable applications is also provided. Lastly, we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring, human activity sensation, human-machine interaction, smart home, healthcare, wearables, implants, robotics, Internet of things (IoT), and many other fields.

The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser. Our framework consists of two novel components: 1) an AMR based semantic graph aggregator to let the candidate entity and event trigger nodes collect neighborhood information from AMR graph for passing message among related knowledge elements; 2) an AMR guided graph decoder to extract knowledge elements based on the order decided by the hierarchical structures in AMR. Experiments on multiple datasets have shown that the AMR graph encoder and decoder have provided significant gains and our approach has achieved new state-of-the-art performance on all IE subtasks.

In this article, the problem of distributed cooperative fault detection (FD) for multi-agent system with packet dropout is described. Firstly, fault modeling is carried out for this system with packet dropout. Secondly, the best reference model without packet dropout is established, and the residual generator is constructed by the model matching strategy, so that the residual signal can approach to the reference model. According to the above work, an information fusion rule based on Dempster-Shafer evidence theory for conflict evidence is used to fuse the confidence of nodes in local sub-network, and the distributed cooperation residual is calculated. Finally, combined with residual evaluation, a simulation test is carried out. The comparison of simulation results with and without cooperation shows that distributed cooperation between nodes can reduce the impact on fault detection with packet dropout and improve the property of fault detection.

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.

The traditional method of designing wayfinding systems has been used in tourism management for decades. However, the absence of quantitative evaluations for the wayfinding system at tourist resorts may affect information transmission and cause visitors to feel lost. This article proposes a quantitative approach and applies it to the wayfinding system at the Shanghai Disneyland Resort from 2017 to 2019, where the layouts and the contents of wayfinding signboards are systematically evaluated. The analytic hierarchy process is used in conjunction with expert advice to rank 11 elements of content design, and four reasonable indices are calculated to explore the spatial&ndash, topological relationships at the tourism resort. According to both the field and expert investigation and tourists&rsquo, responses to designed questionnaires, the results show that this method containing content and layout evaluation can enhance tourist satisfaction in certain aspects, thus helping to improve tourism management by considering important indices in wayfinding signage design and modification.

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.

This paper proposes a new method for the determination of optimal control strategy with considering multiple time constraints. For the train on a level track, the optimal train control model is firstly formulated. Then, with the given control sequences, Kuhn-Tucker condition is used to deduce the necessary conditions for a strategy of optimal type and an analytical solution with the minimum energy consumption is proposed to calculate the optimal control strategy which contains the specific switching points for the given control sequence. Finally, two realistic examples will be applied to illustrate the numerical calculation procedures and prove the effectiveness of the proposed approach.

The cooperative control problem for high speed trains under transmission delay is discussed in this paper. The control law is constructed guaranteeing that all the trains can operate at the desired distance and speed profile cooperatively. An active compensation approach is used to handle the transmission delays when each train communicates with its neighbors and gets information. In addition, adaptive laws are designed to deal with parametric and environmental uncertainties. Simulation results are displayed to demonstrate the effectiveness of the proposed method.

SummaryscATAC-seq is a powerful approach for characterizing cell-type-specific regulatory landscapes. However, it is difficult to benchmark the performance of various scATAC-seq analysis techniques (such as clustering and deconvolution) without having a priori a known set of gold-standard cell types. To simulate scATAC-seq experiments with known cell-type labels, we introduce an efficient and scalable scATAC-seq simulation method (SCAN-ATAC-Sim) that down-samples bulk ATAC-seq data (e.g., from representative cell lines or tissues). Our protocol uses a consistent but tunable signal-to-noise ratio across cell types in a scATAC-seq simulation for integrating bulk experiments with different levels of background noise, and it independently samples twice without replacement to account for the diploid genome. Because it uses an efficient weighted reservoir sampling algorithm and is highly parallelizable with OpenMP, our implementation in C++ allows millions of cells to be simulated in less than an hour on a laptop computer.AvailabilitySCAN-ATAC-Sim is available at scan-atac-sim.gersteinlab.org.Contactpi@gersteinlab.orgSupplementary informationSupplementary data are available at Bioinformatics online.

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers&rsquo, characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers&rsquo, characteristics and the following car&rsquo, s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers&rsquo, characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers&rsquo, characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles&rsquo, mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers&rsquo, characteristics into account under the V2V communication situation for car-following theory.

A triboelectric interacting patch with only four sensing electrodes is presented as flexible and multifunctional human-machine interface for the detection of various human-machine interactions. By leveraging the predefined operation areas, position detection under finger operations can be achieved through the output relationship of the four electrodes. The accurate sensing capability of the triboelectric patch is compatible with common finger motions such as finger tapping and sliding, opening up broad application in various interacting areas, e.g., writing interface, security code system, intuitive intelligent control, virtual/augmented reality, and robotics, etc.

Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.

Adopting advanced control strategy is one of the effective ways to ensure the safety and reliability for the automatic train operation of high-speed train. With the purpose to achieve the accurate position and velocity tracking control, this paper proposes a state-feedback control method based on integral reinforcement learning (IRL) and parameter identification methods. The IRL part is utilized to online find the optimal state-feedback control policy through policy evaluation and policy improvement, which does not involve complete knowledge of system dynamics, while, parameter identification part based on adaptive control laws is to solve the problem of the train model parameters’ uncertainty. The state-feedback system is proved to be stable in the sense of Lyapunov stability theorem and satisfactory performance of position and velocity tracking control is demonstrated by numerical simulations.

Frequency hopping communication and spread spectrum communication are the common means of anti-jamming, and these means have their own limitations. In the case of unpredictable jamming, reinforcement learning algorithm can provide good guidance for communication frequency. Firstly, this paper introduces the framework of communication system and the jamming modes commonly used in communication. Then the basic principle of Q-Learning algorithm is briefly introduced. Meanwhile, the meanings of the parameters in the algorithm are explained. In the part of numerical simulation, different jamming modes are used to test the performance of the algorithm. It proves that Q-Learning algorithm has good memory in sweeping, sinusoidal and Gaussian random jamming. It can provide better policy for communication frequency in real time.