Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading.

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

A duplex nested RT-PCR method for monitoring porcine epidemic diarrhea virus and porcine delta-coronavirus.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV) and porcine delta-coronavirus (PDCoV) are economically important pathogens that cause diarrhea in sows and acute death of newborn piglets. Moreover, the emerging PDCoV was reported to infect children. The current situation is that vaccine prevention has not met expectations, and emergency containment strategies following outbreaks cannot prevent the damages and losses already incurred. Therefore, a more sensitive detection method, that is both convenient and enables accurate and effective sequencing, that will provide early warning of PEDV and PDCoV is necessary. This will enable active, effective, and comprehensive prevention and control, which will possibly reduce disease occurrences. RESULTS: Duplex nested RT-PCR (dnRT-PCR) is an ideal method to achieve early warning and monitoring of PEDV and PDCoV diseases, and to additionally investigate any molecular epidemiological characteristics. In this study, two pairs of primers were designed for each virus based upon the highly conserved N protein sequences of both PEDV and PDCoV strains retrieved from the NCBI Genbank. After optimization of the reaction conditions, the dnRT-PCR assay amplified a 749-bp fragment specific to PEDV and a 344-bp fragment specific to PDCoV. Meanwhile, the specificity and sensitivity of the primers and clinical samples were tested to verify and establish this dnRT-PCR method. The limit of detection (LoD)for both PEDV and PDCoV was 10 copies/µL. The results showed that among 251 samples, 1 sample contained PEDV infection, 19 samples contained a PDCoV infection, and 8 samples were infected with both viruses, following the use of dnRT-PCR. Subsequently, the positive samples were sent for sequencing, and the sequencing results confirmed that they were all positive for the viruses detected using dnRT-PCR, and conventional RT-PCR detection was conducted again after the onset of disease. As these results were consistent with previous results, a detection method for PEDV and PDCoV using dnRT-PCR was successfully established. In conclusion, the dnRT-PCR method established in this study was able to detect both PEDV and PDCoV, concomitantly. CONCLUSIONS: The duplex nested RT-PCR method represents a convenient, reliable, specific, sensitive and anti-interference technique for detecting PEDV and PDCoV, and can additionally be used to simultaneously determine the molecular epidemiological background.

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network.

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 µ s. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

Novel SLC5A2 variants contribute to renal glucosuria in Chinese families: abnormal expression and dysfunction of variant SLC5A2.

Familial renal glucosuria (FRG) is characterized by persistent glucosuria despite normal serum glucose and the absence of overt tubular dysfunction. Variants in solute carrier family 5 (sodium-glucose cotransporter), member 2 (SLC5A2) have been reported in FRG patients. However, the functional and expression-related consequences of such variants have been scarcely investigated. In the current study, we studied five FRG families and identified six missense mutations, including four novel variants (c.1051T>C/.(C351R), c.1400T>C/p.(V467A), c.1420G>C/p.(A474P), c.1691G>A/p.(R564Q); RNA not analyzed) and two variants that had been previously reported (c.294C>A/p.(F98L), c.736C>T/p.(P246S); RNA not analyzed). The probands were either heterozygous or compound heterozygous for SLC5A2 variants and had glucosuria of 5.9%-19.6 g/day. Human 293 cells were transfected with plasmid constructs to study the expression and function of SLC5A2 variants in vitro. Western blotting revealed that the expression levels of SLC5A2-351R-GFP, SLC5A2-467A-GFP, SLC5A2-474P-GFP, and SLC5A2-564Q-GFP were significantly decreased compared with wild-type SLC5A2-GFP (37%-55%). Confocal microscopy revealed that three variants (c.1400T>C, c.1420G>C, c.1691G>A) resulted in a loss of the punctate membrane pattern typical of wild-type SLC5A2. All variants had a significantly lower transport capacity in than the wild-type control. The current study provides a starting point to further investigate the molecular mechanism of SLC5A2 in FRG families and provides functional clues for antidiabetes drugs.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints.

This research focuses on a cooperative control problem for networked multi-agent systems (NMASs) under time-variant communication constraints (containing time-variant communication delays and time-variant data losses) in the forward and feedback channels. From the perspective of high-order fully actuated (HOFA) system theory, a HOFA system model is adopted to describe the NMAS, which is called the networked HOFA multi-agent system (NHOFAMAS). Because of complicated working scenarios over the network, the states of NMASs are immeasurable and the communication constraints are always present, such that an observer-based HOFA predictive control (OB-HOFAPC) method is designed to implement the cooperative control when existing the immeasurable states and time-variant communication constraints. In this method, a HOFA observer is established to estimate the immeasurable states for constructing a consensus control protocol. Then, an incremental prediction model (IPM) in a HOFA form is developed via a Diophantine equation to take the place of a reduced-order prediction model. Through this IPM, multi-step output ahead predictions are derived to optimize the cooperative control performance and compensate for time-variant communication constraints in real-time. The depth discussion gives a sufficient and necessary criterion to analyze the simultaneous consensus and stability for closed-loop NHOFAMASs. The capability and advantage of OB-HOFAPC method are illustrated via numerical simulation and experimental verification on a cooperative flying-around task of three air-bearing spacecraft simulators.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Secure Predictive Coordinated Control of High-Order Fully Actuated Networked Multiagent Systems Under Random DoS Attacks.

This research addresses a coordinated control problem for high-order fully actuated networked multiagent systems (HOFANMASs) under random denial-of-service (DoS) attacks. A type of Bernoulli processes is exploited to denote the successful rate of launching random DoS attacks happened to the forward and feedback channels. When acting these attacks successfully, random data losses and disorders are caused in the forward and feedback channels. A high-order fully actuated (HOFA) secure predictive coordinated control scheme is provided to achieve the security coordination. In this scheme, a dynamic model of networked multiagent system is established with the help of a HOFA system model, which is called the HOFANMAS. Then, a prediction model in an incremental HOFA (IHOFA) form is developed by means of a Diophantine equation, which aims at constructing the multistep ahead output predictions for the optimization of coordinated control performance and the compensation of random data losses and disorders. Furthermore, a necessary and sufficient condition is proposed to analyze the consensus and stability of closed-loop HOFANMASs. The effectiveness and superiority of HOFA secure predictive control scheme can be demonstrated via simulated and experimental results of formation control for three air-bearing spacecraft (ABS) simulators.

Data-Driven Distributed Predictive Control for Voltage Regulation and Current Sharing in DC Microgrids With Communication Constraints.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy.

In today's cyber-physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids' communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber-physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems' stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Predictive control for networked high-order fully actuated systems subject to communication delays and external disturbances.

This research is concerned with an output tracking problem for networked high-order fully actuated (NHOFA) systems subject to communication delays and external disturbances, where communication delays occur in sensor to network node and network node to actuator. A HOFA system model, as a novel system representation, is applied to establish the dynamics of networked control systems (NCSs). Accordingly, a disturbance observer based HOFA predictive control approach is proposed to address this problem. In the proposed approach, a disturbance observer is utilized to cope with the external disturbances, and then a local HOFA feedback with disturbance compensation is designed to adjust the closed-loop system performances. Further, a Diophantine Equation is applied to establish an incremental HOFA (IHOFA) prediction model to substitute a reduced-order prediction model, such that multi-step ahead predictions are derived to minimize a cost function involving the optimization of tracking performance and the compensation of network-induced communication delays. A necessary and sufficient criterion is given to discuss the stability and tracking performance of closed-loop NHOFA systems, it is simple to use in system analysis and extend in practice. The availability of the proposed approach is demonstrated via simulated and experimental results for tracking control of air-bearing spacecraft (ABS) simulator.

Priori-guided and data-driven hybrid model for wind power forecasting.

To overcome the high uncertainty and randomness of wind and enable the grid to optimize advance preparation, a priori-guided and data-driven hybrid method is proposed to provide accurate and reasonable wind power forecasting results. Fuzzy C-Means (FCM) clustering algorithm is used first to recognize the characteristics of the weather in different regions. Then, for the purpose of making full use of both priori information and collected measured data, a three-stage hierarchical framework is designed. First, via fuzzy inference and dimension reduction of Numerical Weather Prediction (NWP), more applicable wind speed information is obtained. Second, the accessible wind power generation patterns are served as a guide for mining the actual power curve. Third, the forecasted power is derived through the recorded data and the predictable wind conditions via data-driven model. This forecasting framework ingeniously introduces a gateway that can import priori knowledge to steer the iterative learning, thus possessing both adaptive learning ability and Volterra polynomial representation, and can present forecasted outcomes with robustness, accuracy and interpretability. Finally, a real-world dataset of a wind farm as well as an open source dataset are used to verify the performance of the proposed forecasting method. Results of the ablation analyses and comparative experiments demonstrate that the introduction of domain knowledge improves the forecasting performance.

Cloud-based predictive formation control of networked multi-agent system and its application to air bearing spacecraft simulators.

This paper studies a class of networked multi-agent systems with communication delays. A centralized cloud predictive control protocol is proposed to realize formation control of multiple agents and especially the predictive method is introduced to actively compensate for the delays in the network. The analysis of closed-loop networked multi-agent systems provides the necessary and sufficient condition of stability and consensus. Finally, the proposed cloud-based predictive formation control scheme is verified by its application to 3-degree-of-freedom air-bearing spacecraft simulators platform. The results show that the scheme can effectively compensate for the delays in the forward channel and the feedback channel and can be applied to the networked multi-agent systems well.

Differentially Private Average Consensus With Logarithmic Dynamic Encoding-Decoding Scheme.

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and ϵ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.

Digital twin based monitoring and control for DC-DC converters.

The monitoring and control of DC-DC converters have become key issues since DC-DC converters are gradually playing increasingly crucial roles in power electronics applications such as electric vehicles and renewable energy systems. As an emerging and transforming technology, the digital twin, which is a dynamic virtual replica of a physical system, can potentially provide solutions for the monitoring and control of DC-DC converters. This work discusses the design and implementation of the digital twin DC-DC converter in detail. The key features of the physical and twin systems are outlined, and the control architecture is provided. To verify the effectiveness of the proposed digital twin method, four possible cases that may occur during the practical control scenarios of DC-DC converter applications are discussed. Simulations and experimental verification are conducted, showing that the digital twin can dynamically track the physical DC-DC converter, detect the failure of the physical controller and replace it in real time.

Event-Based Optimal Stealthy False Data-Injection Attacks Against Remote State Estimation Systems.

Security is a crucial issue for cyber-physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.

Relative States-Based Consensus for Sampled-Data Second-Order Multiagent Systems With Time-Varying Topology and Delays.

In this article, the consensus problem of sampled-data second-order integrator multiagent systems with switching topology and time-varying delay is studied. And, a zero rendezvous speed is not required in the problem. Two new consensus protocols that employ no absolute states are proposed, depending on the presence of delay. Sufficient synchronization conditions are obtained for both protocols. It is shown that consensus can be reached, provided there is a sufficiently small gain and periodically joint connectivity in the sense of scrambling graph or spanning tree. Finally, both numerical and practical examples are supplied for illustrative purpose, and both show the effectiveness of the theoretical results.

Ruptured small pancreaticoduodenal artery aneurysm-clinical features similar to pancreatitis: A case report.

BACKGROUND: Pancreaticoduodenal artery aneurysm (PDAA) is rare and has high rupture risks. PDAA rupture has a wide range of clinical symptoms, including abdominal pain, nausea, syncope, and hemorrhagic shock, which is difficult to differentiate from other diseases. PATIENT CONCERNS: A 55-year-old female patient was admitted to our hospital due to abdominal pain for 11 days. DIAGNOSIS: Acute pancreatitis was initially diagnosed. The patient's hemoglobin decreased compared to before admission, suggesting that active bleeding may occur. CT volume diagram and maximum intensity projection diagram show that a small aneurysm with a diameter of about 6 mm can be seen at the pancreaticoduodenal artery arch. The patient was diagnosed with a rupture and hemorrhage of the small pancreaticoduodenal aneurysm. INTERVENTIONS: Interventional treatment was performed. After the microcatheter was selected for the branch of the diseased artery for angiography, the pseudoaneurysm was displayed and embolized. OUTCOMES: The angiography showed that the pseudoaneurysm was occluded, and the distal cavity was not redeveloped. CONCLUSION: The clinical manifestations of PDAA rupture were significantly correlated with the aneurysm diameter. Because of small aneurysms, the bleeding is limited around the peripancreatic and duodenal horizontal segments, accompanied by abdominal pain, vomiting, and elevated serum amylase, similar to the clinical manifestations of acute pancreatitis but accompanied by the decrease of hemoglobin. This will help us to improve our understanding of the disease, avoid misdiagnosis, and provide the basis for clinical treatment.

Application of multilabel learning using the relevant feature for each label in chronic gastritis syndrome diagnosis.

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.