This study explores the progression of global healthcare and medical waste (HMW) disposal technologies and emerging practices in China including the COVID-19 pandemic period through patent technology innovation analysis. Trends were identified through both the Derwent Innovation Index database and bibliometric methods. Based on the bibliometric analysis of 4128 patents issued from 2002 to 2021, the development status and research trends of HMW disposal technology were revealed. Regarding patents, China significantly advanced post-2011. However, a large number of applications are filed only in China and are more focused on domestic rather than overseas markets. As the pandemic remains a threat, and increasing amounts of medical waste are generated, new technologies are being sought in China that will be safer for humans and the environment, and will also be in line with the zero waste technology trend. Incineration and waste crushing are core methodologies in medical waste disposal. Fut... [more]

A Distributed Control System is a concept of Network Control Systems whose applications range from industrial control systems to the control of large physical experiments such as the ALICE experiment at CERN. The design phase of the Distributed Control Systems implementation brings several challenges, such as predicting the throughput and response of the system in terms of data-flow. These parameters have a significant impact on the operation of the Distributed Control System, and it is necessary to consider them when determining the distribution of software/hardware resources within the system. This distribution is often determined experimentally, which may be a difficult, iterative process. This paper proposes a methodology for modeling Distributed Control Systems using a combination of Finite-State Automata and Petri nets, where the resulting model can be used to determine the system’s throughput and response before its final implementation. The proposed methodology is demonstrated... [more]

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, onlin... [more]

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection ti... [more]

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals.... [more]

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, e... [more]

In some applications of microgrids and distributed generation, it is necessary to feed islanded or stand-alone loads with high-quality voltage to provide low total harmonic distortion (THD). To fulfil these demands, an LC filter is usuallyconnected to the output terminals of power electronics converters. A cascaded voltage and current control loop with pulse-width modulation schemes are used to regulate the voltages and currents in these systems. However, these strategies have some drawbacks, particularly when multiple-input−multiple-output plants (MIMO) are controlled using single-input−single-output (SISO) design methods. This methodology usually produces a sluggish transient response and cross−coupling between different control loops and state variables. In this paper, a model predictive control (MPC) strategy based on the concept of optimal switching sequences (OSS) is designed to control voltage and current in an LC filter connected to a three-level neutral-point clamped converter... [more]

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures... [more]

A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Control mode switching on the attack-free process at steady-state may induce transients and generate false alarms in the detection scheme. To minimize false alarms, we subsequently developed a control mode switch-scheduling condition for processes with an invertible output matrix. In the current work, we utilize a reachable set-based detection scheme and use randomized control mode switches to augmen... [more]

A fault is a common geological structure encountered in underground coal mining. Interactions between the discontinuous structure of a fault and mining activities are the key factors in controlling the rock bursts induced by the fault. It is of great importance to study the rock burst mechanism of an extra-thick coal seam under the combined influence of reverse faults and coal mining for the prediction and prevention of rock burst. In this study, we establish a sliding dynamics model of rock mass in a fault zone and analyze the mechanical distribution of fault-induced rock bursts under the combined action of mining disturbances. Additionally, we utilize theoretical calculation and a 3D numerical simulation method to clarify the rockburst mechanism in an extra-thick coal seam controlled by a thrust fault under mining disturbance and a fault. The results showed that the distribution range of the shear stress increment in the fault footwall was larger than that in the hanging wall, reveal... [more]

Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to control multiple agents to drive effectively and safely in a simulation, demonstrating the applicability of MARL in autonomous driving. However, several studies have indicated that MARL is vulnerable to poisoning attacks. This study proposes a ’locality-based action-poisoning attack’ against MARL-based continuous control systems. Each bird in a flock interacts with its neighbors to generate the collective behavior, which is implemented through rules in the Reynolds’ flocking algorithm, where each individual maintains an appropriate distance from its neighbors and moves in a similar direction. We use this concept to propose an action-poisoning attack, based on the hypothesis that if an agent is performing significantly different behaviors from neighboring agents, it can disturb the driving stability of the entirety of the agents. We demonstrate that when a MARL-based continuous control system is trained... [more]

The exploitation of ultradeep, fractured, and low-porosity gas reservoirs often encounters challenges from water invasion, exacerbated by the presence of faults and fractures. This is particularly evident in the Kelasu gas reservoir group, located in the Kuqa Depression of the Tarim Basin. The complexity of the water invasion patterns in these reservoirs demands a thorough investigation to devise effective water control measures. To elucidate the water invasion patterns, a combined approach of large-scale physical modeling and discrete fracture numerical simulations was adopted. These models allowed for the identification and categorization of water invasion behaviors in various gas reservoirs. Furthermore, production dynamic analysis was utilized to tailor water control strategies to specific invasion patterns. The large-scale physical simulation experiment revealed that water invasion in gas reservoirs is primarily influenced by high-permeability channels (faults + fractures), and th... [more]

Motor vehicle exhaust is an important cause of atmospheric pollution. Nowadays, mainstream exhaust emission aftertreatment technologies, such as TWC, DOC, SCR, and DPF, usually require sufficient temperature to perform good purification or maintain normal working conditions. Compared with exhaust gas heating technologies such as engine enrichment and fuel injection, electric heating technology can quickly increase the temperature of exhaust gas aftertreatment devices without adverse effects on engine operating conditions. This article introduces the research and progress of electric heating technology combined with traditional aftertreatment devices on major types of vehicles, such as gasoline vehicles, diesel vehicles, motorcycles, and hybrid vehicles, to improve exhaust purification efficiency and its accompanying fuel consumption impact. In addition, the common structure and characteristics of electric heaters, as well as the current status and development trend of electric heating... [more]

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (PIDD2-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm... [more]

This paper proposes an improved fault diagnosis algorithm that combines a modified fast kurtogram (FK) method with the lightweight convolutional neural network GhostNet. The FK algorithm can adaptively select resonance demodulation bands for envelope demodulation to extract fault features, but it may be disturbed by non-Gaussian noise. Hence, the fast average kurtogram (FAK) method based on sub-band averaging was introduced. This method effectively weakens the impact of pulse noise on the kurtosis graph by splitting the signal into equal-length sub-signals and calculating the average kurtosis value of all sub-signal filters. Simultaneously, to fully utilize the advantages of deep learning technology in feature extraction and classification, this study used the FAK to convert vibration signals from one-dimensional to two-dimensional kurtosis graphs as the input for the GhostNet model. This combination not only achieved accurate fault diagnosis and classification but also showed signific... [more]

The waterflooding effect evaluation of carbonate fractured−vuggy reservoirs constitutes a comprehensive multiple-information decision-making process involving quantitative unascertained measure theory. This paper establishes a novel comprehensive methodology to evaluate the waterflooding effects of carbonate fractured−vuggy reservoirs for the first time. A new evaluation grading criteria is proposed using the Mahalanobis distance method based on the multi-index comprehensive unascertained measure theory derived from a modified five-scale analytical hierarchy process−entropy weight method. The actual field data from the carbonate fractured−vuggy reservoirs and the nine evaluation indices are specifically applied to demonstrate the calculation process for the construction of the grading system model on the waterflooding effects and to verify the accuracy of the Mahalanobis distance method by comparing the calculation results with the Minkowski and Euclidean distance methods. The proposed... [more]

Heterogeneous composite flooding has performed well with regard to enhanced oil recovery after polymer flooding in recent years. In order to significantly increase oil recovery, the development parameters should be designed differently for each well. However, it is difficult to rapidly allocate development parameters through the lowering of computational costs. Therefore, the authors of this paper carried out research to clarify the main controlling factors of parameter allocation. Firstly, the numerical simulation domain was separated into several regions, with injection wells and production wells at the center of each region. The statistical parameters of each region were calculated. Then, the water injection rate, liquid production rate, and chemical agent concentration were allocated based on the proportion of statistical parameters in each region. A large number of development schemes were designed by combining different injection and production allocations that were calculated ba... [more]

Due to the large inertia and strong impact accompanying the free-falling hook process of crawler cranes, it is difficult to meet the demand for flexible and smooth braking control under different weight load conditions. Therefore, this paper takes the free-fall hook system as the research object and combines system operation characteristics and control theory to carry out research on flexible braking control of the free-fall hook system. Firstly, a joint simulation platform of MATLAB (version 2018b) and AMESim (version 2019.1) software is built to theoretically analyze the key components of the free-fall hook system (proportional pressure-reducing valve, winch reducer, and wet clutch). Secondly, a mathematical model of the braking process is established, and the pressure control demand is clarified to analyze the reasons for the existence of dead zones and hysteresis loops in the system. Meanwhile, it is found that the dead zones and hysteresis loops existing in the pressure output of... [more]

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real t... [more]

In this study, groundbreaking software has been developed to automate the generation of equations of motion for manipulator robots with varying configurations and degrees of freedom (DoF). The implementation of three algorithms rooted in the Lagrange−Euler (L-E) formulation is achieved through the utilization of .m files in MATLAB R2020a software.This results in the derivation of a symbolic dynamic model for industrial manipulator robots. To comprehend the unique features and advantages of the developed software, dynamic simulations are conducted for two 6- and 9-DoF redundant manipulator robots as well as for a 3-DoF non-redundant manipulator robot equipped with prismatic and rotational joints, which is used to simplify the dynamic equations of the redundant prototypes. Notably, for the 6-DoF manipulator robot, model predictive control (MPC) is employed using insights gained from the dynamic model. This enables optimal control by predicting the future evolution of state variables: spe... [more]

In engineering, cost minimization, especially in Computer Numerical Control (CNC) machining like pocket milling, is crucial. Existing tool path definition software often lacks optimization, particularly at critical starting and ending points. This study optimizes CNC machine tool paths for energy-efficient multi-pocket milling, utilizing the Symbolic Discrete Control Synthesis (SDCS) method for formal correctness. In our work, the tool path generation is formulated as a traveling salesman problem. We introduce a modeling framework to adapt SDCS to multi-pocket-milling processes, aiming to enhance precision and efficiency for potential cost savings, including energy and time, in engineering applications. This study reports experimental and comparative results, where comparative evaluations were made using metaheuristic algorithms. Our proposed approach improves CNC machining processes for multi-pocket milling. We experimentally evaluate our control algorithms and demonstrate and validat... [more]

Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of a literature review and applied research. This four-phase strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities a... [more]

Concerning the application of high-precision, enormous rotating equipment under harsh working conditions, the advantages of dry gas sealing technology are increasingly obvious. Herein, research on the dynamic stability of dry gas seals is reviewed based upon their operating mechanisms. The influence of the dry gas seal structure, vibration response, and dynamic followability on the reliability of the shaft end sealing system of rotating machinery is the focus of current dry gas sealing technology. This work reviews the research history; analyzes the key coefficient of the instability of the sealing system under external disturbances, and the existing research on stability models; discusses the influence of starting and stopping characteristics, working conditions, and groove parameters on the stability of dry gas seals; and points out the shortcomings in the existing research. In addition, potential developments in dynamic stability are proposed, including improving model accuracy, imp... [more]

This paper proposes an observer-based proportional Derivative (O-BPD) fuzzy controller for uncertain discrete-time nonlinear descriptor systems (NDSs). Representing NDSs with the Takagi−Sugeno fuzzy model (T-SFM), the proportional derivative (PD) feedback method can be utilized in the fuzzy controller design via the Parallel Distributed Compensation (PDC) concept, such that the noncausal problem and impulse behavior are avoided. A fuzzy observer is proposed to obtain unmeasured states to fulfill the PD fuzzy controller. Moreover, uncertainties and transient response performances are taken into account for the NDSs. Then, a stability analysis process and corresponding stability conditions are derived from the Lyapunov theory with the robust control method and the pole constraint. Different from existing research, the Singular Value Decomposition (SVD) and the projection lemma are utilized to transfer the stability conditions into the Linear Matrix Inequation (LMI) form. Because of this... [more]

Batch processing is a widely utilized technique in the manufacturing of high-value products. Traditional methods for quality assessment in batch processes often lead to productivity and yield losses because of offline measurement of quality variables. The use of soft sensors enhances product quality and increases production efficiency. However, due to the uneven batch data, the variation in processing times presents a significant challenge for building effective soft sensor models. Moreover, sensor failures, exacerbated by the manufacturing environment, complicate the accurate modeling of process variables. Existing soft sensor approaches inadequately address sensor malfunctions, resulting in significant prediction inaccuracies. This study proposes a fault-tolerant soft sensor algorithm that integrates two Long Short-Term Memory (LSTM) networks. The algorithm focuses on modeling process variables and compensating for sensor failures using historical batch quality data. It introduces a... [more]