System Construction Method Research Articles

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

The Application of MATLAB in the Mathematics Teaching of Computer Majors

Under the background of new engineering courses, professional mathematics teaching needs to keep pace with the times and improve the progressiveness and scientific nature of teaching methods.In order to improve the mathematics teaching effect of computer majors, This article constructs a new teaching environment through simulation system construction methods, analyzes the shortcomings of traditional teaching models through comparative analysis methods, and improves the teaching platform based on the actual needs of mathematics professionals in current society. The curve interpolation technology is improved, and a forward-looking real-time Matlab simulation algorithm is designed. Through the evaluation and analysis, it can be seen that the mathematics teaching system for computer majors based on MATLAB can effectively improve the intuitive effect of advanced mathematics teaching, and help to improve the teaching quality of advanced mathematics.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

Research on the Construction of IoT Engineering Major System in the Background of Emerging Engineering Education

Ideological and Political Education in majors is a new model of current ideological and political education work, and colleges and universities have successively carried out practice and innovation. Based on the professional talent training goals of the applied IoT engineering major under the background of Emerging Engineering Education, and by integrating the target requirements, a core value system is constructed, and a professional ideological and political major is proposed to be divided into IoT major groups and open semester-by-semester in-depth and continuously improved majors. The ideological and political closed-loop system construction method adopts a three-dimensional, multi-dynamic evaluation method, and is implemented and promoted in the three-dimensional education pattern to cultivate students into advanced applied talents with comprehensive development of moral, intellectual, physical, artistic and labor.

Skeleton-based coordinate system construction method for non-cooperative targets

Due to the lack of structure and part information in non-cooperative targets, the task of pose estimation suffers from unstable and unreliable initial coordinate system. In this paper, we introduce a curve-skeleton-based method to construct the robust coordinate system automatically for non-cooperative targets. First, a nonmanifold-Laplacian-based method is proposed to extract the curve skeleton from point clouds. The proposed method solves the over-contraction problem while preserves the topology and structure of skeleton well during point clouds contraction. Additionally, we create the skeleton graph to detect the skeleton structure and key points automatically. Then, the detected skeleton feature is used to construct target coordinate system, and improve the stability and robustness. Specifically, a real-scan dataset is constructed in this paper, and the proposed point cloud contraction method outperforms previous methods in structural representation and visualization. The mean rotation error of proposed coordinate system construct method performs 0.1249° and 1.2135° in 0%–30% noise ratio of distortions and outliers, and performs 7.04° and 4.21° in real-scan aircraft and satellite datasets. The results in complete and incomplete datasets verify the robustness and reliability of our skeleton-based coordinate system.

A benchmark construction method for large aperture circular segmented optical systems

A benchmark construction method for large aperture circular segmented optical systems

A Construction Method for Grade Protection System Based on STRIDE Threat Modeling

Abstract In recent years, against the backdrop of the national high attention to cybersecurity, the implementation of grade protection systems has been vigorously carried out in various industries within the Internet+ environment. However, the construction of grade protection systems has gradually revealed several issues: network product providers have a weak security awareness and lack of secure development experiences, leading to systems often failing to meet their grade protection requirements; Grade protection evaluation is complex and tedious, consuming a significant amount of time and labor costs. To address the issues above, this paper proposes a construction method for grade protection systems based on STRIDE threat modeling: by establishing the correspondence between threats and grade protection requirements, threats are eliminated during the system development process while implementing the content of grade protection requirements; through the security verification of threat elimination, materials for grade protection evaluation are accumulated, and work reuse is leveraged to alleviate the workload of grade protection evaluation. Implementing this method can effectively promote the construction of grade protection systems.

Graph-based two-level indicator system construction method for smart city information security risk assessment

<p>The rapid development of urban informatization has led to a deep integration of advanced information technology into urban life. Many decision-makers are starting to alleviate the adverse effects of this informatization process through risk assessment. However, existing methods cannot effectively analyze internal and hierarchical relationships because of the excessive number of indicators. Thus, it is necessary to construct an indicator's dependency graph and conduct a comprehensive hierarchical analysis to solve this problem. In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. To verify the performance, we took China's 25 smart cities as examples. For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. Results showed that the two-level indicator system is superior to the general indicator system for risk assessment.</p>

Natural Frequency of Lightweight Composite Slabs Based on Experimental Study and Numerical Modelling

Recently, lightweight composite slabs have become increasingly popular. Lightweight composite slabs are an innovation that provides a better and more convenient construction method for floor systems. Under dynamic loads, lightweight composite slabs may experience meagre inertia forces due to poor stiffness or low mass. Compared to conventional composite slabs, lightweight composite slabs are 40% lighter and more susceptible to structural resonance. Therefore, the vibration behaviour must be controlled to avoid discomfort issues. This study investigates the natural frequency of lightweight composite slabs through experimental study and numerical modelling. In the experimental study, lightweight composite slabs were prepared for the hammer-impact test. The slab thickness ranges from 100 mm to 200 mm. In numerical modelling, lightweight composite slabs were modelled in SAP2000 using a unique technique called the simplified equivalent plate model. The effective material properties were derived from the rule of mixtures and depend exclusively on elastic properties with strength characteristics. The results of the experimental study and numerical modelling agree positively. The natural frequency decreased with slab thickness, signifying that the natural frequency is dominated by mass rather than stiffness. Overall, the natural frequency of lightweight composite slabs is around 27.23Hz to 31.45Hz.

Research on the construction of weaponry indicator system and intelligent evaluation methods

To decrease subjective interference and improve the construction efficiency of the traditional weapon and equipment index system, an index system construction method based on target detection is proposed in combination with the equipment test video data. The three-level index system of combat effectiveness of a certain type of equipment is established, and various intelligent assessment methods are proposed. Firstly, an optimaized IPSO-BP network model is proposed, in which dynamic weights are set to improve the particle search network, and adaptive learning factors are introduced to optimize the update speed. Secondly, an improved DS evidence-parallel neural network assessment method is proposed, setting multiple parallel neural networks with different parameters, and improving the angle cosine to weaken the numerical nonlinear attributes in DS evidence fusion and increase the model's assessment operation stability. Thirdly, the three types of view features corresponding to the index item images are extracted to train the base classifiers. The integrated CNN network based multi-view feature integration assessment model is constructed and the improved residual network block is introduced to optimize the network gradient. Comparison with existing evaluation methods shows that the proposed methods achieve efficient and intelligent construction and evaluation of the indicator system and enrich the evaluation of indicator data.

Experimental and Numerical Investigation on Pile Foundation Underpinning Structure System in Urban Overpass.

In view of the complexity of the pile foundation underpinning structure system and the stringent requirements of the construction process, this paper briefly describes the necessity of introducing epoxy resin reinforcing adhesive of planting rebar in the design of pile foundation underpinning beam structure to improve the mechanical properties of the reinforced beam new and old concrete joint surfaces and proposes a new type of pile foundation replacement beam system construction method by "chiseling + prestressed reinforcement + epoxy resin reinforcing adhesive". This paper uses an actual pile foundation underpinning project of an urban overpass as a prototype, designs and creates a model structure with a similarity ratio of 1/6, and performs repeated progressive static loading tests to study the load carrying capacity, displacement change, and other properties of the strengthened replacement structure, as well as analyses and distorts the overall working performance and failure mode of them. On this basis, the prototype structure's finite element analysis model was built, and the finite element analysis results were compared with the test results to obtain the mechanical properties and deformation characters of the actual pile foundation underpinning structure system corresponding to the actual underpinning beam load. This paper's study can lay the theoretical and experimental foundation for the smooth development of similar projects.

Design and implementation of a process-aware accounting information system to improve business process management

Abstract As one of the core components of enterprise information system, accounting information systems is facing double pressure from the enterprise management environment and the development of accounting discipline, and the functional application-oriented accounting information system is unable to meet the needs of modern enterprise management. To improve the accounting efficiency of enterprises, this paper proposes a process-aware accounting information system based on data mining to improve business process management, hoping to provide some reference values for the design of the accounting information system of enterprises. Based on this paper, the advantages and disadvantages of typical accounting information system construction methods are analyzed and compared, and the feasibility of process-aware accounting information systems for improving business process management is analyzed in terms of enterprise efficiency, accounting cost, accounting time, risk, and control quality. The feasibility of improving the process-aware accounting information system for business process management is analyzed in terms of enterprise efficiency, accounting cost, accounting time, risk, and control quality. The results show that the process-aware accounting information system with improved business process management is economically and effectively feasible, saves time, reduces accounting risks, and improves management control quality of the enterprise. The process-aware accounting information system with improved business process management reduces the accounting cost of enterprise Y. The accounting cost before is between 100,000 and 200,000, while the accounting cost after using the new system is between 80,000 and 100,000. Company Y’s accounting behavior, logic, and convenience satisfaction increased by at least 10-20% with the new system. So, in general, the process-aware accounting information system for improving business process management helps companies to grow and manage.

Method to construct the initial structure of optical systems based on full-field aberration correction.

To meet full-field image quality requirements for extremely low aberration optical systems, an initial structure construction method for reflective optical systems based on full-field aberration correction is proposed. The aberration of the full field is used as the main evaluation criterion in this method. A multi-field evaluation function is established using the aberration values of multiple characteristic field points to represent the full-field imaging quality, and spatial ray tracing is introduced to constrain the optical system structure. Multi-objective optimization of the evaluation function is performed using a combinatorial nondominated sorting and metaheuristics algorithm; an initial optical system with a reasonable structure and corrected third-order aberrations over the full field is subsequently obtained. After optimization, an extreme ultraviolet lithography objective with a numerical aperture of 0.33 and root-mean-square wavefront error of 0.128 nm ($1/105\lambda, \lambda = 13.5\;{\rm nm}$) is obtained.

Autonomous Following for Unmanned Ground Vehicles on Unstructured Scenario: Risk and Performance Assessment

Military unmanned ground vehicles are often used in wild natural environment, autonomous following and accompanying security are typical functions. In order to make a quantitative assessment about the autonomous following performance of unmanned ground vehicles on unstructured scenarios, a set of evaluation index system construction method is proposed based on the combination of risk analysis and key performance indexes. First, the kinematics model of autonomous vehicle following in straight and curve based on meta-trajectory is established. Second, the key indexes affecting the following performance are analyzed and obtained from collision risk and lost risk. Third, the evaluation index system of autonomous following performance is constructed from five dimensions: average following speed, collision risk, collision hazard degree, following stability and lost risk. Finally, two unmanned vehicles are tested, the results show that the conclusion based on the constructed evaluation index system can significantly reflect the difference of autonomous following performance, which is basically consistent with the subjective evaluation of the test personnel.

Construction ideas for supply chain management major under the background of new liberal arts education

In response to the problem of a lack of high-level supply chain management professionals, under the background of new liberal arts education, this article takes the construction of the supply chain management major at Wuhan Institute of Technology as an example. It explores the talent cultivation system for supply chain management professionals from seven aspects: talent cultivation mode construction, teaching content and characteristic course system construction, textbook construction and teaching method reform, practical teaching system construction, faculty construction, teaching quality assurance system construction, and reform evaluation system construction. Through the exploration of professional teaching in teaching practice, relevant references can be provided for the cultivation of supply chain management professionals.

공장 자동화 환경에서 센서 고장 진단을 위한 시스템 설계에 관한 연구

This study proposes a standard system that can be applied to existing legacy facilities. In terms of system construction methods, data acquired by standardized protocols from industrial IoT sensors were transferred from PLC-based to RDBMS that was divided from the upper application layer to the filed and management layers. Through the proposal in this study, small- and medium-sized companies that require the establishment of facility foresight preservation in smart factories are expected to be able to build systems at low cost.

A Multi-Objective Optimization Method of a Mobile Robot Milling System Construction for Large Cabins

Constructing mobile robot milling systems with multiple mounting surfaces for large cabins still has several unsolved issues, such as huge economic and time costs, unpredictable milling accuracy and milling time. Hence, a multi-objective optimization method for constructing a mobile robot milling system of large cabins is proposed in the current paper. Firstly, mathematical models of constructing the system and the optimization objective function are established. Thereafter, a multi-objective optimization method for the mobile robot milling system construction based on NSGA-II (Fast Non-dominated Sorting Genetic Algorithm) is proposed. Finally, feasibility and validity of the proposed method are verified through comparing the optimization result with two practical mobile robot systems. Results show that the proposed method is able to estimate different combinations’ milling accuracy, cost and time consumption.

A study of interactive robot architecture through the practical implementation of conversational android.

This study shows an autonomous android robot that can have a natural daily dialogue with humans. The dialogue system for daily dialogue is different from a task-oriented dialogue system in that it is not given a clear purpose or the necessary information. That is, it needs to generate an utterance in a situation where there is no clear request from humans. Therefore, to continue a dialogue with a consistent content, it is necessary to essentially change the design policy of dialogue management compared with the existing dialogue system. The purpose of our study is to constructively find out the dialogue system architecture for realizing daily dialogue through implementing an autonomous dialogue robot capable of daily natural dialogue. We defined the android’s desire necessary for daily dialogue and the dialogue management system in which the android changes its internal (mental) states in accordance to the desire and partner’s behavior and chooses a dialogue topic suitable for the current situation. The developed android could continue daily dialogue for about 10 min in the scene where the robot and partner met for the first time in the experiment. Moreover, a multimodal Turing test has shown that half of the participants had felt that the android was remotely controlled to some degree, that is, the android’s behavior was humanlike. This result suggests that the system construction method assumed in this study is an effective approach to realize daily dialogue, and the study discusses the system architecture for daily dialogue.

Research on Risk Assessment and Prediction of RMB Internationalization Based on the PCA-SA-BPNN Model.

This paper combines principal component analysis, a BP neural network, and a simulated annealing algorithm, to construct a PCA-SA-BPNN risk forecast model to evaluate and predict the RMB internationalization risk status of China. First, we analyze the risk of RMB internationalization and its transmission mechanism from the perspective of the economic characteristics of neighboring countries and trading partner countries. Second, we use the FASP index system construction method for reference to construct a forecast index system for macro- and microrisks brought about by RMB internationalization. Then, the weight of each index is determined through index common degree analysis and principal component analysis, and the risk of RMB internationalization is divided. On this basis, the risks of RMB internationalization in China from 2000 to 2019 are divided into four categories. Based on the BP neural network algorithm optimized by the simulated degradation algorithm, the PCA-SA-BPNN model of RMB internationalization risk forecast is constructed. Finally, the validity of the model is verified by experimental verification, and the risk status of RMB internationalization in 2020 is simulated and predicted. The research results show that the risk status of RMB internationalization in 2020 is basically safe, and the risks of RMB internationalization mainly come from macroeconomic growth risks and systemic risks of the financial system.

Evaluation method of a new power system construction based on improved LSTM neural network

The evaluation of new power system construction is the research foundation for improving the flexible regulation ability and comprehensive operational efficiency of new power systems, and achieve the comprehensive goals of safe power supply, green consumption, and economic efficiency. However, the existing research on the evaluation index system of new power system construction can not fully reflect the main objectives of new power system construction. Therefore, this paper first develops a source-load and green-intelligence multi-level and multi-dimensional evaluation system for new power system construction from source-load side equipment, green power, reliable power supply, and intelligent power consumption. Secondly, a hybrid optimization algorithm is proposed based on fluid search algorithm (FSO) for improving the Long Short-Term Memory (LSTM) neural network parameter updating method. Then, the improved LSTM neural network is applied to the construction evaluation of the new power system. Finally, the simulation results show that the evaluation error of the new power system construction evaluation method is 0.0063, which has a high evaluation.