Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Drug-induced liver injury and COVID-19:Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice

BACKGROUND Liver injury is a relevant condition in coronavirus disease 2019(COVID-19)inpatients.Pathophysiology varies from direct infection by virus,systemic inflammation or drug-induced adverse reaction(DILI).DILI detection and monitoring is clinically relevant,as it may contribute to poor prognosis,prolonged hospitalization and increase indirect healthcare costs.Artificial Intelligence(AI)applied in data mining of electronic medical records combining abnormal liver tests,keyword searching tools,and risk factors analysis is a relevant opportunity for early DILI detection by automated algorithms.AIM To describe DILI cases in COVID-19 inpatients detected from data mining in electronic medical records(EMR)using AI and the updated Roussel Uclaf Causality Assessment Method(RUCAM).METHODS The study was conducted in March 2021 in a hospital in southern Brazil.The NoHarm©system uses AI to support decision making in clinical pharmacy.Hospital admissions were 100523 during this period,of which 478 met the inclusion criteria.From these,290 inpatients were excluded due to alternative causes of liver injury and/or due to not having COVID-19.We manually reviewed the EMR of 188 patients for DILI investigation.Absence of clinical information excluded most eligible patients.The DILI assessment causality was possible via the updated RUCAM in 17 patients.RESULTS Mean patient age was 53 years(SD±18.37;range 22-83),most were male(70%),and admitted to the non-intensive care unit sector(65%).Liver injury pattern was mainly mixed,mean time to normalization of liver markers was 10 d,and mean length of hospitalization was 20.5 d(SD±16;range 7-70).Almost all patients recovered from DILI and one patient died of multiple organ failure.There were 31 suspected drugs with the following RUCAM score:Possible(n=24),probable(n=5),and unlikely(n=2).DILI agents in our study were ivermectin,bicalutamide,linezolid,azithromycin,ceftriaxone,amoxicillin-clavulanate,tocilizumab,piperacillin-tazobactam,and albendazole.Lack of essential clinical information excluded most patients.Although rare,DILI is a relevant clinical condition in COVID-19 patients and may contribute to poor prognostics.CONCLUSION The incidence of DILI in COVID-19 inpatients is rare and the absence of relevant clinical information on EMR may underestimate DILI rates.Prospects involve creation and validation of alerts for risk factors in all DILI patients based on RUCAM assessment causality,alterations of liver biomarkers and AI and machine learning.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

Multiple Knowledge Representation of Artificial Intelligence

In the 1970s,cognitive psychology recognized that the information in the long-term memory is scene and semantic[1]and may be encoded in parallel as verbal and mental imagery[2].In 1991,I pointed out that not all verbal propositions can be derived from the verbal system,and that many can only be transformed from the imagery system[3].I have proposed the concept of visual knowledge,which consists of visual concepts,visual propositions,and visual narratives[4].Visual knowledge can simulate the various spatiotemporal operations that a person can perform on a mental imagery in his/her brain,such as the design process[5].

Application of Blockchain Sharding Technology in Chinese Medicine Traceability System

Traditional Chinese Medicine(TCM)is one of the most promising programs for disease prevention and treatment.Meanwhile,the quality of TCM has garnered much attention.To ensure the quality of TCM,many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin.Although these schemes can ensure the integrity,sharability,credibility,and immutability of TCM more effectively,many problems are exposed with the rapid growth of TCM data in blockchains,such as expensive overhead,performance bottlenecks,and the traditional blockchain architecture is unsuitable for TCM data with dynamic growth.Motivated by the aforementioned problems,we propose a novel and lightweight TCM traceability architecture based on the blockchain using sharding(LBS-TCM).Compared to the existing blockchain-based TCM traceability system,our architecture utilizes sharding to develop a novel traceability mechanism that supports more convenient traceability operations for TCM requirements such as uploading,querying,and downloading.Specifically,our architecture consists of a leader shard blockchain layer as its main component,which employs a sharding mechanism to conveniently TCM tracing.Empirical evaluations demonstrated that our architecture showed better performance in many aspects compared to traditional blockchain architectures,such as TCM transaction processing,TCM transaction querying,TCM uploading,etc.In our architecture,tracing TCM has become a very efficient operation,which ensures the quality of TCM and provides great convenience for subsequent TCM analysis and retrospective research.

Correction of microwave pulse reflection by digital filters in superconducting quantum circuits

Reducing the control error is vital for high-fidelity digital and analog quantum operations.In superconducting circuits,one disagreeable error arises from the reflection of microwave signals due to impedance mismatch in the control chain.Here,we demonstrate a reflection cancelation method when considering that there are two reflection nodes on the control line.We propose to generate the pre-distortion pulse by passing the envelopes of the microwave signal through digital filters,which enables real-time reflection correction when integrated into the field-programmable gate array(FPGA).We achieve a reduction of single-qubit gate infidelity from 0.67%to 0.11%after eliminating microwave reflection.Real-time correction of microwave reflection paves the way for precise control and manipulation of the qubit state and would ultimately enhance the performance of algorithms and simulations executed on quantum processors.

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment.Concur-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human intervention.However,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration.The findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection.Recent investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)attacks.Moreover,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application plane.Additionally,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

The FRB-searching Pipeline of the Tianlai Cylinder Pathfinder Array

This paper presents the design,calibration,and survey strategy of the Fast Radio Burst(FRB)digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array.The array,consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds,is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere.The FRB digital backend enables the formation of 96 digital beams,effectively covering an area of approximately 40 square degrees with the 3 dB beam.Our pipeline demonstrates the capability to conduct an automatic search of FRBs,detecting at quasi-realtime and classifying FRB candidates automatically.The current FRB searching pipeline has an overall recall rate of88%.During the commissioning phase,we successfully detected signals emitted by four well-known pulsars:PSR B0329+54,B2021+51,B0823+26,and B2020+28.We report the first discovery of an FRB by our array,designated as FRB 20220414A.We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation.

Immune algorithm for discretization of decision systems in rough set theory

Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Method of Inequalities-based Multiobjective Genetic Algorithm for Optimizing a Cart-double-pendulum System

This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system （CDPS）. The designed controller, which is based on the human-simulated intelligent control （HSIC） method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based （MoI） multiobjective genetic algorithm （MMGA） is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

Effects of microstructure characteristics on the tensile properties and fracture toughness of TA15 alloy fabricated by hot isostatic pressing

Powder hot isostatic pressing(HIP) is an effective method to achieve near-net-shape manufacturing of high-quality complex thinwalled titanium alloy parts, and it has received extensive attention in recent years. However, there are few reports about the microstructure characteristics on the strengthening and toughening mechanisms of powder hot isostatic pressed(HIPed) titanium alloys. Therefore, TA15powder was prepared into alloy by HIP approach, which was used to explore the microstructure characteristics at different HIP temperatures and the corresponding tensile properties and fracture toughness. Results show that the fabricated alloy has a “basket-like structure” when the HIP temperature is below 950℃, consisting of lath clusters and surrounding small equiaxed grains belts. When the HIP temperature is higher than 950℃, the microstructure gradually transforms into the Widmanstatten structure, accompanied by a significant increase in grain size. The tensile strength and elongation are reduced from 948 MPa and 17.3% for the 910℃ specimen to 861 MPa and 10% for the 970℃ specimen.The corresponding tensile fracture mode changes from transcrystalline plastic fracture to mixed fracture including intercrystalline cleavage.The fracture toughness of the specimens increases from 82.64 MPa·m^(1/2)for the 910℃ specimen to 140.18 MPa·m^(1/2)for the 970℃ specimen.Specimens below 950℃ tend to form holes due to the prior particle boundaries(PPBs), which is not conducive to toughening. Specimens above 950℃ have high fracture toughness due to the crack deflection, crack branching, and shear plastic deformation of the Widmanstatten structure. This study provides a valid reference for the development of powder HIPed titanium alloy.

Moving Target Landing of a Quadrotor Using Robust Optimal Guaranteed Cost Control

Dear Editor,This letter proposes a robust control strategy for the autonomous landing of a quadrotor on a moving target.Specifically,a force command that consists of a cascade dynamics estimator and an optimal guaranteed cost control law is exploited for the position-loop tracking.Then,an orientation constraint torque command is employed for the attitude-loop tracking such that the quadrotor refrains from flipping during the landing operation.

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm.The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory.The unmodeled dynamics of the system are considered,and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network.The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory.The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod consists of a part for detecting the worker’s helmet and mask and apart for verifying the worker’s identity. An algorithm for helmet and maskdetection is generated by transfer learning of Yolov5’s s-model and m-model.Both models are trained by changing the learning rate, batch size, and epoch.The model with the best performance is selected as the model for detectingmasks and helmets. At a learning rate of 0.001, a batch size of 32, and anepoch of 200, the s-model showed the best performance with a mAP of0.954, and this was selected as an optimal model. The worker’s identificationalgorithm consists of a facial feature extraction part and a classifier partfor the worker’s identification. The algorithm for facial feature extraction isgenerated by transfer learning of Facenet, and SVMis used as the classifier foridentification. The proposed method makes trained models using two datasets,a masked face dataset with only a masked face, and a mixed face datasetwith both a masked face and an unmasked face. And the model with the bestperformance among the trained models was selected as the optimal model foridentification when using a mask. As a result of the experiment, the model bytransfer learning of Facenet and SVM using a mixed face dataset showed thebest performance. When the optimal model was tested with a mixed dataset,it showed an accuracy of 95.4%. Also, the proposed model was evaluated asdata from 500 images of taking 10 people with a mobile phone. The resultsshowed that the helmet and mask were detected well and identification wasalso good.

Design of three-dimensional imaging lidar optical system for large field of view scanning

Three-dimensional(3D)lidar has been widely used in various fields.The MEMS scanning system is one of its most important components,while the limitation of scanning angle is the main obstacle to improve the demerit for its application in various fields.In this paper,a folded large field of view scanning optical system is proposed.The structure and parameters of the system are determined by theoretical derivation of ray tracing.The optical design software Zemax is used to design the system.After optimization,the final structure performs well in collimation and beam expansion.The results show that the scan angle can be expanded from±5°to±26.5°,and finally the parallel light scanning is realized.The spot diagram at a distance of 100 mm from the exit surface shows that the maximum radius of the spot is 0.506 mm with a uniformly distributed spot.The maximum radius of the spot at 100 m is 19 cm,and the diffusion angle is less than 2 mrad.The energy concentration in the spot range is greater than 90%with a high system energy concentration,and the parallelism is good.This design overcomes the shortcoming of the small mechanical scanning angle of the MEMS lidar,and has good performance in collimation and beam expansion.It provides a design method for large-scale application of MEMS lidar.