Field Of Industrial Control Research Articles

Network Security Monitoring Method Based on Deep Learning

AbsrtactWith the rise and rapid development of mobile communication, big data and artificial intelligence technology, we are entering the era of mobile Internet. With the continuous intellectualization of network security and infrastructure, information technology has been widely used in the field of industrial control, making network security more and more open, which brings a new network security control system to the traditional relatively closed industrial control system. This paper mainly introduces the network security monitoring method based on deep learning. In this paper, the network security monitoring method based on deep learning is studied, and the network security monitoring planning is designed by using image analysis, and the feasibility of detection model and deep learning is analyzed reasonably. A network security monitoring method based on deep learning is designed. Through data collection, feature extraction and neural network model training of network security power consumption information, non-invasive network security monitoring is realized. This method can detect network security information attacks that cannot be found at the network level, and improve the security performance of network security. The experimental results show that the network security monitoring method for deep learning increases the network security efficiency by 24%. The limitations of the research on network security monitoring method for deep learning and the methods and paths to provide good network security monitoring planning for image analysis application are analyzed, discussed and summarized, so as to enrich the academic research results.

Research on Gating Scheduling of Time Sensitive Network Based on Constraint Strategy

With the continuous development of industry, information and intelligence have gradually become the keywords of industrial control. In modern industrial scenarios, more and more devices are connected to the factory network, and the complex network environment will greatly affect the end-to-end transmission performance of key control flows, which brings a huge impact to those high-precision control signal transmissions. With the development of Time Sensitive Network (TSN) in recent years, more and more people have introduced this technology to the field of industrial control. In this paper, we propose a scheduling scheme based on constraint strategy for the key technology of TSN—Time Aware Shaper (TAS). This scheme can derive the appropriate network constraint strategy and optimization scheme of network resource utilization according to the end-to-end Quality of Service (QoS) of multiple key data streams in the network, and finally obtain the configuration scheme of Gate Control Lists (GCLs).

Improvements in Labour Safety Management: Efficiency of Industrial Control Organization and Implementation, Occupational Risks Assessment (by the Example of Food Industry Enterprise)

The labour safety management system (LSMS) at many enterprises is absent or does not work effectively, although it is an essential component of the safe work organization. In this connection, one of the basic mechanisms is the systematization of activities in the field of industrial control, as well as in the occupational risk management. This article proposes a methodological approach to improving labour protection at a food enterprise. In the implementation if this approach the following tasks have been completed: the most significant information elements of the LSMS have been identified and the least studied information element have been identified; identification of hazards and risks assessment based on production control data and special assessment of working conditions was carried out; the ranking of the assessment of the conditions of possible risk at workplaces was carried out; a risk management strategy for the enterprise was developed. The results of the proposed research support the effectiveness and prospects of the developed approach.

Research and Analysis of Generalized Predictive Identification Algorithm Based On Nonlinear System in Control Field of Hydropower Industry

Due to the uncertainties, constraints, nonlinearity and the correlation among variables in the control of hydropower industry, it is difficult to obtain the accurate mathematical model. In this paper, a generalized predictive identification algorithm based on the nonlinear system in the control field of hydropower industry is presented, and a model of the generalized predictive identification algorithm based on the nonlinear system in the control field of hydropower industry is established AB simulation. The simulation results show that the algorithm is robust, can effectively overcome the system delay, and can be applied to the open-loop unstable non minimum phase system. It has strong applicability in the field of electric industry control and can produce good economic benefits.

A Methodology for Computed Tomography-Based Non-Destructive Geometrical Evaluations of Lattice Structures by Holistic Strut Measurement Approach

Abstract The presence of lattice structures is increasing in the manufacturing domain especially in the air/spacecraft and biomedical applications due to their advantages of high strength-to-weight ratios, energy absorption, acoustic and vibrational damping, etc. Dimensional accuracy of a lattice structure is one of the most important requirements to meet the desired functionality as there could be significant deviations in the as-produced part from the designed one. Evidently, an approach (non-destructive) to evaluate the dimensional accuracy of all the elements and eventually the lattice quality is of great significance. X-ray computed tomography (CT) has emerged as a promising solution in the field of industrial quality control over the last few years due to its non-destructive approach. In this work, we propose a methodology for geometrical evaluations of a lattice structure by measuring the deviation in the shape and size of its strut elements holistically. The acquired CT data of the complete lattice are extracted in the form of a point cloud and then segmented and stored as a single strut element with unique identification so that measurements can be performed on the strut individually. As demonstrated with a metallic BCCz-type lattice structure, the methodology helps in critical evaluation of its quality and the correlation with spatial position of the individual struts; e.g., the lattice exhibits large variations of shape among the inclined struts while the vertical struts possess consistency in their shape.

TiN NPs: a novel ethanol gas sensing material with ultrafast response and good stability

TiN, as a typical representative of transition metal nitrides (TMNs), has many excellent properties (higher electrical conductivity and chemical inertness). These features are of great importance for developing novel resistive chemical gas sensing. Here, we presented a facile ambient pressure chemical vapor deposition (APCVD) for the synthesis of TiN nanoparticles (NPs). Our result shows that TiN NPs exhibit excellent gas sensing behaviors towards ethanol gas. The high response and good stability towards ethanol gas was realized for the first time. Moreover, in order to elucidate the sensing mechanism, the carrier concentration and mobility in air and ethanol gas was studied for TiN NPs, offering a direct evidence for explaining the sensing behavior. Interestingly, the carrier concentrations play an important role in fast response ethanol gas. This study reveals that TiN could be a promising candidate for ethanol sensing, opening up new directions for applications in industrial control, environmental safety and other fields.

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.

Optimal Algorithm for Shared Network Communication Bandwidth in IoT Applications

In recent years, a variety of wired and wireless network communication protocols in the field of industrial control have become increasingly mature. The purpose of this paper is to provide a Shared network communication bandwidth optimization management algorithm for large-scale industrial networked control systems in Internet of things applications. This algorithm is based on the generalized geometric convex optimization method and can realize the optimal allocation of Shared network communication bandwidth resources. L2 networked control systems is used in this paper for the establishment of various numerical relations between the control performance and the communication network parameters. Based on the generalized geometric convex optimization method for the numerical relationship between convex analysis and fitting, convexity, and with the convex analysis and the numerical relationship between convexity fitting as constraint conditions, the results of integrity for networked control systems with large-scale resource allocation target will share the optimal management of network resources as a generalized geometric convex optimization problem. Using convex optimization software package for optimizing the optimal global solution of management problem, i. e. the optimal allocation of resources, the algorithm realizes the stability of each networked control system and achieve optimal L2 control performance. It is concluded that the predetermined transmission rate between the network node one and network node two, the data flow information sent by the network node two to the network node one is read, the delay time and packet loss rate between the two nodes are determined, the delay time is reduced by about 8 seconds, and the packet loss rate is greatly reduced by 78%.

An Aggressive Implementation Method of Branch Instruction Prefetch

Due to the demand in the field of industrial control, we developed a CPU core based on ARMv8m architecture [1], named TS400, and its performance is comparable to Arm Cortex-M33 [2]. The TS400 has four stages of pipelining, thus having a higher clock frequency than Cortex-M33 which has three stages. With the increase of pipeline stages, extra idle clock beats will be introduced when the pipeline is flushed, which will increase the clock cycles per instruction (CPI) value and decrease the score of CoreMark. Accurate instruction fetch and branch prediction can effectively reduce the impact of refreshing pipeline, at the cost of extra logic resources. In TS400, an aggressive branch instruction prefetch method is designed. Compared with branch prediction technology, this method does not need complex branch prediction logic and is suitable for the design of embedded CPU. The aggressive branch instruction prefetch method includes: 1) reducing the time cost of conditional branch target fetching to the minimum by taking the branch first and then confirming the branch taking result; 2) optimizing the bus control signal timing of the prefetch instruction to make the target address prefetch respond in time. The aggressive branch prefetch method reduces the impact of pipeline stall caused by the execution of conditional branch instructions as much as possible, thus achieving better running performance than Cortex-M33 at the same clock frequency, while the clock frequency performance is superior to Cortex-M33.

Research on Security Technology of Network Communication Information Based on Double Encryption

Data is transmitted from the server to the terminal or the terminal to the server during the transmission process, the data information is easily monitored or tampered with, which will cause information leakage or data display false data. If the wrong instruction information may be received in the industrial control field, it will bring a lot to industrial production. Hazards and losses, so in order to ensure the reliability and data security of the network communication between the terminal and the server, industrial equipment and management system, it is very necessary to use secure communication connections and reliable data encryption algorithms to ensure communication and data security.This paper proposes an improved AES and MD5 hybrid encryption scheme, which uses SSL to encrypt the transmission channel during the communication process, uses the improved symmetric encryption algorithm AES to encrypt the data in network transmission, and encrypts the data at the sending end Then use the improved MD5 to generate a digital fingerprint for the encrypted data. At the receiving end, use MD5 to generate the same fingerprint for the encrypted data sent, and use the digest fingerprint verification to determine whether the data has been tampered with or lost during transmission. is possible to greatly improve the security and reliability of system communication and data.

The Use of ZigBee Wireless Communication Technology in Industrial Automation Control

With the accelerating process of world industrialization, countries pay more attention to industrial modernization. In order to solve the problems of traditional control technology and system with low accuracy, high energy consumption, and high delay in industrial automation control and the difficulty in adapting to the new industrial production process, the new industrial automation control system is discussed and studied. The industrial automation control is studied, and a new monitoring system is designed for automation control by using ZigBee wireless communication technology. The design of the system data communication scheme is completed by the in‐depth study and summary of ZigBee wireless communication technology. The hardware structure and software structure of ZigBee wireless communication module are designed, and the wireless monitoring system using ZigBee is established. Taking the production workshop of a pharmaceutical factory as the monitoring object, the working stability and data transmission efficiency of the model are tested. The experiment shows that the ZigBee wireless transmission model has lower temperature and better stability than the wired transmission system in the working process. Compared with the traditional wired control system, the temperature of ZigBee wireless control system is lower when it works. The difference between the highest temperature and the lowest temperature is 0.1°C. In the overall work flow, the system temperature is lower than that of the wired control. ZigBee wireless control has good stability. Subsequently, the method of manually setting obstacles is used to test the effectiveness of data transmission. The experiment found that in the absence of barrier, the maximum effective transmission distance was 40 meters, and the packet loss rate was very low, which could ensure the timeliness of data. Therefore, this system can be applied to industrial automation control. The research results can provide some reference for the design of new control systems in other industrial control fields. The improved system can also be applied to other industries. ZigBee is a short‐range technology for wireless sensor networks, and it is a two‐way communication technology with low power consumption, low data rate, and low cost. It can be applied to automatic control and remote control. This provides a platform for wireless networking and control of small and cheap devices. ZigBee will not compete with connection technologies such as Wi‐Fi. Meanwhile, it will greatly stimulate the progress of the industrial market.

Research on Energy Saving and Emission Reduction Technology Based on Field Communication and Embedded Control Computer Data Exchange Algorithm

In addition to meeting the basic characteristics of traditional transactions, the concurrent control of embedded mobile real-time transactions should also focus on priority inversion, unnecessary transaction restart and global data consistency, performance of hybrid transaction systems, energy saving and emission reduction. When using traditional control algorithms, scheduling control is mainly based on data structure characteristics, and it is easy to cause data stack congestion. In order to solve the above problems, a stack scheduling control method of embedded data structure using multi-parameter neural network is proposed. Combined with the widely used switched Ethernet technology in defences, communications, aerospace, industrial control and other fields, a prototype of a star topology embedded software test system is introduced.

Intra-Domain Heuristic Traffic Scheduling Algorithm for Time-Sensitive Networks

Time-Sensitive Network (TSN) is a new network transmission technology, which has the advantages of high data transmission and priority setting. Researching and improving the performance of TSN is of great significance to the development of future industrial communication and automation control fields. However, in practical applications, due to complex scenarios and difficulty in TSN traffic scheduling, traditional network traffic scheduling algorithms are not fully applicable to TSN, which may lead to timeout and inefficiency of traffic scheduling, and it is difficult to guarantee the quality of scheduling. In response to this situation, a routing and scheduling algorithm suitable for TSN is proposed. Firstly, TSN is modeled and analyzed, and the balanced routing algorithm based on bi-directional A* algorithm (BR-BidA*) and the modified greedy randomized adaptive search procedures (MGRASP) are proposed to solve the TSN routing and scheduling respectively. Compared with the contrast algorithm, the simulation results show that the proposed method can improve the performance of the algorithm, and greatly improve the solution quality and scheduling success rate.

A Novel Model for Train Operation Adjustment in High-Speed Railway based on Max-plus Algebra

The running time of high-speed train is generally not late, and it can run normally in most cases. However, when severe weather conditions, train components and equipment, accidents or emergencies occur, it will lead to train operation delay and traffic congestion. Therefore, when an accident occurs, we need to adjust the train time or route timely and accurately. As an important algebraic system, max-plus algebra is widely used in the field of industrial production control. In industrial production, the most production mode is the discrete system , but the characteristics and the ability of discrete systems depends on the periodic of system and the number of workpiece produced by the system in unit time, and the characteristics of the system are closely related to the properties of the matrix, especially, the eigenvalues and eigenvectors of the matrix in the sense of max-plus algebra. Therefore, this paper studies the max-plus algebra theory and the solution of eigenvalues and eigenvectors of matrices in the sense of max-plus algebra, establish the operation time matrix to optimize the train operation adjustment model of high-speed railway, and analyze the failure propagation model.

Control Method Based On Long Time-delay System

In today’s industrial control field, the control system often has long time-delay property, which makes the sensor unable to acquire real-time information and the control system difficult to control. A control method is proposed here, on the one hand, which uses Fuzzy-PID control to adjust control parameters in real time, improves control accuracy and strengthen the robustness of the control system; On the other hand, this method uses particle swarm algorithm to calculate the optimal fuzzy control parameters to make the system in the optimal control state, and at the same time, uses Smith algorithm to overcome the system delay. Finally, through the simulation, This control strategy has higher precision than traditional PID control, and has a shorter reflection time, which achieves precise control of systems for long time-delay system.

Simulation analysis of communication performance of PROFIBUS in single and multiple master mode

With the application of digital information technology and communication technology, industrial intelligence and integration also began to develop rapidly, and the importance of the research on bus communication in industrial control communication also increased. As the first international bus standard, PROFIBUS has been widely used in the field of industrial control; in recent years, it was mainly used to connect devices to the control layer and convert data through token transmission. Based on the understanding of the transmission mechanism of PROFIBUS fieldbus protocol, this article carries out a simulation study on its communication performance. Through analysis of the data link layer, changing the target token cycle time (high and low priority) and random setting each site message number, as well as the number of master station and slave station, how the main token latency and packet loss rate and efficiency of bus affect the change of performance parameters was observed. For multiple communication systems, the relationship between the address value on the main token ring and transport performance is found by experiments on the site address value on the main token ring and the target token cycle time value. Finally, the scheduling algorithm is applied to the communication scheduling of PROFIBUS fieldbus. The results confirm that the scheduling algorithm has better transmission efficiency than the traditional MAC media access protocol under the condition that the load rate does not exceed 100%, which makes up for the shortcomings of the original PROFIBUS bus system and makes the communication performance better.

A dynamic taint tracking optimized fuzz testing method based on multi-modal sensor data fusion

The safety of the Industrial Internet Control Systems has been a hotspot in the information security. To meet the needs of communication, a large variety of proprietary protocols have emerged in the field of industrial control. The protocol field is often trusted in the implementation of industrial control terminal code. If attackers modify the data of these fields using the protocol defect, the operation of the program can be controlled and the entire system will be affected. To cope with such security threats, academia and industry generally adopt fuzz test methods. However, the current industrial control protocol fuzz test methods generally have low code coverage, where unified description models are missing and test cases are not targeted. A method of fuzzification processing combined with dynamic multi-modal sensor communication data is proposed. To track the program execution, the dynamic pollution analysis is used to search for the input fields that affect the execution of the conditional branch and capture the dependencies between the conditional branches to guide the grammar generation of test cases, which can increase the chances of executing deep code. The experimental results show that the proposed method improves the validity and code coverage of test cases to a certain extent and greatly increases the probability of anomaly detection in the protocol implementation.

Influence of resolution on the X-ray CT-based measurements of metallic AM lattice structures

Additive manufactured (AM) lattice structures have become very prominent in recent times especially in air and spacecraft industry for their lower weight and specific mechanical properties. Their stiffness and strength can be controlled by their geometrical properties, such as the shape and dimensions of the unit cell. Geometrical and dimensional accuracy of the AM lattices is therefore one of the most important requirements to meet the desired functionality as there could be significant deviations in the as-produced part from the designed model; thus, their measurements are of great significance. X-ray computed tomography (CT) has emerged as a promising solution in the field of industrial quality control over the last few years due to its non-destructive approach. However, CT measurement accuracy depends on various parameters (part material, system, operator, environment, data post-processing), among which the resolution or voxel size of the CT data is crucial. In this work, the influence of resolution on the measurement of metallic lattice structure is studied by means of simulations and real CT experiments. The optimized CT acquisition settings are obtained with the help of simulated radiographs and design-of-experiment approach. Three different resolutions are achieved by placing the part at different positions from the X-ray source. The computer-aided design (CAD) model comparison reveals that the majority of surface has a deviation of ± 0.2 mm and the results are slightly affected by the resolution. The wall thickness analysis provides a global observation of the strut and node thicknesses. Individual struts are measured with representative regions of interest (ROIs) considering the manufacturing direction. The measurement results are significantly affected by the resolution (or voxel size) of the CT data. Simulated CT scans with different resolutions have been performed for systematic error estimation in relation to the voxel size.

Design of Multi-point Intelligent Temperature Monitoring System for Transformer Equipment

With the development and progress of industrial technology, the temperature range of factory substations is required strictly, and most of the temperature measuring devices widely used in the market at present are single-point measurements, with the disadvantages of slow transmission speed and imprecision, etc., it is disadvantageous for the controller of the substation equipment to make a decision in time according to its temperature change. This subject is based on single-chip microcomputer as the core of the control components, the acquisition temperature through the acquisition channel multiplexing technology, to achieve multi-point temperature monitoring and recording. A temperature measurement and control system is composed of a / D conversion photosensitive triode, a temperature sensor and a light intensity acquisition, detection and display. The temperature sensor is controlled by a single-chip microcomputer to collect the temperature, and the temperature is fed back to the single-chip microcomputer to make corresponding analysis and processing, it has powerful data processing function, intuitive display, friendly interface and high cost performance, and can be widely used in industrial and agricultural control, mechatronics engineering, smart home and many other fields.

RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

Abstract Real time speech recognition technology, as a key cross technology in the field of artificial intelligence in recent years, has been widely used in the fields of intelligent voice toys, industrial control and intelligent rehabilitation. Because the real-time speech recognition technology based on embedded technology has obvious advantages in the volume, power consumption and research and development cost of the system, it has become a hot carrier to achieve efficient speech recognition technology. In order to realize a simple and practical real-time speech recognition system based on embedded system, this paper designs a basic framework of machine learning based on Markov random field theory combined with machine learning theory, and studies the algorithm of real-time speech vocabulary matching recognition based on this framework. In detail, the algorithm proposed in this paper will process the speech signal from the aspects of preprocessing, signal detection, feature extraction and quantization. Finally, this paper will build a real-time speech recognition system based on DSP processor. The experimental results show that the real-time speech recognition algorithm proposed in this paper can improve the real-time recognition speed of the system. The corresponding speed changes from about 12 s to about 200 ms, and the corresponding real-time accuracy rate increases to about 95%.