Age of Loop Information with Flexible Transmission Enabled Communication and Control Co-Design in Industrial IoT

Precise and low-latency information transmission through communication systems is essential in the Industrial Internet of Things(IIoT).However,in an industrial system,there is always a coupling relationship between the control and communication components.To improve the system's overall performance,exploring the co-design of communication and control systems is crucial.In this work,we propose a new metric±Age of Loop Information with Flexible Transmission(AoLI-FT),which dynamically adjusts the maximum number of uplink(UL)and downlink(DL)transmission rounds,thus enhancing reliability while ensuring timeliness.Our goal is to explore the relationship between AoLI-FT,reliability,and control convergence rate,and to design optimal blocklengths for UL and DL that achieve the desired control convergence rate.To address this issue,we first derive a closed-form expression for the upper bound of AoLI-FT.Subsequently,we establish a relationship between communication reliability and control convergence rates using a Lyapunov-like function.Finally,we introduce an iterative alternating algorithm to determine the optimal communication and control parameters.The numerical results demonstrate the significant performance advantages of our proposed communication and control co-design strategy in terms of latency and control cost.

Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes

The High Altitude Detection of Astronomical Radiation(HADAR)experiment,which was constructed in Tibet,China,combines the wide-angle advantages of traditional EAS array detectors with the high-sensitivity advantages of focused Cherenkov detectors.Its objective is to observe transient sources such as gamma-ray bursts and the counterparts of gravitational waves.This study aims to utilize the latest AI technology to enhance the sensitivity of HADAR experiments.Training datasets and models with distinctive creativity were constructed by incorporating the relevant physical theories for various applications.These models can determine the type,energy,and direction of the incident particles after careful design.We obtained a background identification accuracy of 98.6%,a relative energy reconstruction error of 10.0%,and an angular resolution of 0.22°in a test dataset at 10 TeV.These findings demonstrate the significant potential for enhancing the precision and dependability of detector data analysis in astrophysical research.By using deep learning techniques,the HADAR experiment’s observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S.at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies.In addition,our experiment offers a new approach for dealing with strongly connected,scattered data.

Interface microstructure and mechanical properties of selective laser melted multilayer functionally graded materials

Functionally graded material(FGM)can tailor properties of components such as wear resistance,corrosion resistance,and functionality to enhance the overall performance.The selective laser melting(SLM)additive manufacturing highlights the capability in manufacturing FGMs with a high geometrical complexity and manufacture flexibility.In this work,the 316L/CuSn10/18Ni300/CoCr four-type materials FGMs were fabricated using SLM.The microstructure and properties of the FGMs were investigated to reveal the effects of SLM processing parameters on the defects.A large number of microcracks were found at the 316L/CuSn10 interface,which initiated from the fusion boundary of 316L region and extended along the building direction.The elastic modulus and nano-hardness in the 18Ni300/CoCr fusion zone decreased significantly,less than those in the 18Ni300 region or the CoCr region.The iron and copper elements were well diffused in the 316L/CuSn10 fusion zone,while elements in the CuSn10/18Ni300 and the 18Ni300/CoCr fusion zones showed significantly gradient transitions.Compared with other regions,the width of the CuSn10/18Ni300 interface and the CuSn10 region expand significantly.The mechanisms of materials fusion and crack generation at the 316L/CuSn10 interface were discussed.In addition,FGM structures without macro-crack were built by only altering the deposition subsequence of 316L and CuSn10,which provides a guide for the additive manufacturing of FGM structures.

Simulation study on performance optimization of a prototype scintillation detector for the GRANDProto35 experiment

As a proposed detector,the giant radio array for neutrino detection(GRAND)is primarily designed to discover and study the origin of ultra-high-energy cosmic rays,with ultra-high-energy neutrinos presenting the main method for detecting ultra-high-energy cosmic rays and their sources.The main principle is to detect radio emissions generated by ultra-high-energy neutrinos interacting with the atmosphere as they travel.GRAND is the largest neutrino detection array to be built in China.GRANDProto35,as the first stage of the GRAND experiment,is a coincidence array composed of radio antennas and a scintillation detector,the latter of which,as a traditional detector,is used to perform cross-validation with radio detection,thus verifying the radio detection efficiency and enabling study of the background exclusion method.This study focused on the implementation of the optimization simulation and experimental testing of the performance of the prototype scintillation detector used in GRANDProto35.A package based on GEANT4 was used to simulate the details of the scintillation detector,including the optical properties of its materials,the height of the light guide box,and position inhomogeneity.The surface of the scintillator and the reflective materials used in the detector was optimized,and the influence of light guide heights and position inhomogeneity on the energy and time resolutions of the detector was studied.According to the simulation study,the number of scintillator photoelectrons increased when changing from the polished surface to the ground surface,with the appropriate design height for the light guide box being 50 cm and the appropriate design area for the scintillator being 0.5 m^(2).The performance of the detector was tested in detail through a coincidence experiment,and the test results showed that the number of photoelectrons collected in the detector was$84 with a time resolution of~1 ns,indicating good performance.The simulation results were consistent with those obtained from the tests,which also verified the reliability of the simulation software.These studies provided a full understanding of the performance of the scintillation detector and guidance for the subsequent operation and analysis of the GRANDProto35 experimental array.

A Systematic Approach for Making 3D-Printed Patient-Specific Implants for Craniomaxillofacial Reconstruction

Craniomaxillofacial reconstruction implants,which are extensively used in head and neck surgery,are conventionally made in standardized forms.During surgery,the implant must be bended manually to match the anatomy of the individual bones.The bending process is time-consuming,especially for inexperienced surgeons.Moreover,repetitive bending may induce undesirable internal stress concentration,resulting in fatigue under masticatory loading in v iv o and causing various complications such as implant fracture,screw loosening,and bone resorption.There have been reports on the use of patient-specific 3D-printed implants for craniomaxillofacial reconstruction,although few reports have considered implant quality.In this paper,we present a systematic approach for making 3D-printed patientspecific surgical implants for craniomaxillofacial reconstruction.The approach consists of three parts:First,an easy-to-use design module is developed using Solidworks®software,which helps surgeons to design the implants and the axillary fixtures for surgery.Design engineers can then carry out the detailed design and use finite-element modeling(FEM)to optimize the design.Second,the fabrication process is carried out in three steps:0 testing the quality of the powder;(2)setting up the appropriate process parameters and running the 3D printing process;and (3)conducting post-processing treatments(i.e.,heat and surface treatments)to ensure the quality and performance of the implant.Third,the operation begins after the final checking of the implant and sterilization.After the surgery,postoperative rehabilitation follow-up can be carried out using our patient tracking software.Following this systematic approach,we have successfully conducted a total of 41 surgical cases.3D-printed patient-specific implants have a number of advantages;in particular,their use reduces surgery time and shortens patient recovery time.Moreover,the presented approach helps to ensure implant quality.

Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network

Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction.In order to improve the rehabilitation training effect based on the human motion mechanism,this paper proposes a humanoid sliding mode neural network controller based on the human gait.A humanoid model is constructed based on the human mechanism,and the parameterised gait trajectory is used as target to design the humanoid control system for robots.Considering the imprecision of the robot dynamics model,the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy.Moreover,the sliding mode control in the system improves the response speed,tracking performance,and stability of the control system.The Lyapunov stability analysis proves the stability of the control system theoretically.Meanwhile,an evaluation method using the similarity function is improved based on joint angle,velocity,and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably.Finally,to verify the effectiveness of the proposed method,simulations are carried out based on experimental data.The results show that the control system could accurately track the target trajectory,of which the robot is highly similar to the human.

Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.

Research on thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane based on differential scanning calorimetry

In order to test the thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane(RDX),the linear temperature rise experiment of RDX was carried out by differential scanning calorimeter under different heating rate conditions.The kinetic calculation of RDX thermal decomposition curve was carried out by Kissinger and Ozawa methods,respectively,and the thermal analysis software was used to calculate the parameters such as self-accelerating decomposition temperature.The results show that the initial decomposition temperature range,decomposition peak temperature range,and decomposition completion temperature range of RDX are 208.4-214.2,225.7-239.3 and 234.0-252.4℃,respectively,and the average decomposition enthalpy is 362.9 J·g^-1.Kissinger method was used to calculate the DSC experimental data of RDX,the apparent activation energy obtained is 190.8 kJ·mol^-1,which is coincident with the results calculated by Ozawa method at the end of the reaction,indicating that the apparent activation energy calculated by the two methods is relatively accurate.When the packaging mass values are 1.0,2.0 and 5.0 kg,respectively,the self-accelerating decomposition temperatures are 97.0,93.0 and 87.0℃,respectively,indicating that with the increase of packaging mass,the self-accelerating decomposition temperature gradually decreases,and the risk increases accordingly.

A novel trigger algorithm for wide-field-of-view imaging atmospheric Cherenkov technique experiments

The high-altitude detection of astronomical radiation(HADAR)experiment is a new Cherenkov observation technique with a wide field of view(FoV),aimed at observing the prompt emissions ofγ-ray bursts(GRBs).The bottleneck for this type of experiment can be found in determining how to reject the high rate of nightsky background(NSB)noise from random stars.In this work,we propose a novel method for rejecting noise,which considers the spatial properties of GRBs and the temporal characteristics of Cherenkov radiation.In space coordinates,the map between the celestial sphere and the fired photomultiplier tubes(PMTs)on the telescope's camera can be expressed as f(δ(i,j))=δ'(i',j'),which means that a limited number of PMTs is selected from one direction.On the temporal scale,a 20-ns time window was selected based on the knowledge of Cherenkov radiation.This allowed integration of the NSB for a short time interval.Consequently,the angular resolution and effective area at 100 GeV in the HADAR experiment were obtained as 0.2°and 10^(4)m^(2),respectively.This method can be applied to all wide-FoV experiments.

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

The Internet of Things(IoT)has numerous applications in every domain,e.g.,smart cities to provide intelligent services to sustainable cities.The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment.The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network.At the medium access control(MAC)layer,mitigating channel collision is still one of the main challenges of future IoT networks.Similarly,the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts(ETX),which often does not adapt to the dynamic and lossy environment and impact performance.The ranking mechanism also requires large control overheads to update rank information.The resource-constrained IoT devices operating in a low-power and lossy network(LLN)environment need an efficient solution to handle these problems.Reinforcement learning(RL)algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors.Thus,in this paper,an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised.The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer.The proposed protocol also enhances the operation of the trickle timer algorithm.A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function.Based on a simulation using Contiki 3.0 Cooja,the proposed intelligent scheme achieves a lower packet loss ratio,improves throughput,produces lower control overheads,and consumes less energy than other state-of-the-art mechanisms.

Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities

Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

Robust H_(∞)state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks

We investigate the dynamic event-triggered state estimation for uncertain complex networks with hybrid delays suffering from both deception attacks and denial-of-service attacks.Firstly,the effects of time-varying delays and finitedistributed delays are considered during data transmission between nodes.Secondly,a dynamic event-triggered scheme(ETS)is introduced to reduce the frequency of data transmission between sensors and estimators.Thirdly,by considering the discussed plant,dynamic ETS,state estimator,and hybrid attacks into a unified framework,this framework is transferred into a novel dynamical model.Furthermore,with the help of Lyapunov stability theory and linear matrix inequality techniques,sufficient condition to ensure that the system is exponentially stable and satisfies H∞performance constraints is obtained,and the design algorithm for estimator gains is given.Finally,two numerical examples verify the effectiveness of the proposed method.

Determined Reverberant Blind Source Separation of Audio Mixing Signals

Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.

A Morphological Study of Two Young Multipolar Planetary Nebulae

We carry out an optical morphological and infrared spectral study for two young planetary nebulae(PNs)Hen2-158 and Pe 1-1 to understand their complex shapes and dust properties.Hubble Space Telescope optical images reveal that these nebulae have several bipolar-lobed structures and a faint arc with a clear boundary is located at the northwestern side of Pe 1-1.The presence of this arc-shaped structure suggests that the object interacts with its nearby interstellar medium.Spitzer IRS spectroscopic observations of these young nebulae clearly show prominent unidentified infrared emission features and a weak silicate band in Pe 1-1,indicating that Hen 2-158 is a carbonrich nebula and Pe 1-1 has a mixed chemistry dust environment.Furthermore,we construct two three-dimensional models for these PNs to realize their intrinsic structures.The simulated models of the nebulae suggest that multipolar nebulae may be more numerous than we thought.Our analyses of spectral energy distributions for Hen 2-158 and Pe 1-1 show that they have low luminosities and low stellar effective temperatures,suggesting that these nebulae are young PNs.A possible correlation between typical multipolar young PNs and nested nebulae is also discussed.

Design and motion control analysis of double helix wall climbing robot

For the detection environment of complex walls such as high-rise buildings,a double helix wall climbing robot(DHWCR)with strong adsorption force and good stability is designed and developed,which uses symmetrical propellers to provide adsorption force.The symmetrical driving structure can provide smooth thrust for the DHWCR,so that the robot can be absorbed to the wall surface with different roughness.A left and right control frame with multiple degrees of freedom is designed,which can adjust the fixed position of the brushless propeller motor in the front and back directions,realize the continuous adjustable thrust direction of the robot,and improve the flexibility of the robot movement.Using the front wheel steering mechanism with universal joint,the steering control of the DHWCR is realized by differential control.In the vertical to ground transition,the front and rear brushless motors can provide the pull up and oblique thrust,so that the DHWCR can smoothly transition to the vertical wall.The motion performance and adaptability of the DHWCR in the horizontal ground and vertical wall environment are tested.The results show that the DHWCR can switch motion between the horizontal ground and vertical wall,and can stably adsorb on the vertical wall with flexible attitude control.The DHWCR can move at a fast speed.The speed on the horizontal ground is higher than that on the vertical wall,which verifies the feasibility and reliability of the DHWCR moving stably on the vertical wall.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Digital Twin-Assisted Semi-Federated Learning Framework for Industrial Edge Intelligence

The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.

Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).