Scientific Programming

In recent years, with the popularization of computer and information network technology, the core business of more and more industries has basically realized informatization office. Although the informatization office has brought a lot of convenience to people, the increasingly complex informatization infrastructure equipment has also buried many hidden dangers for people. More and more security incidents are caused by unfamiliarity with these infrastructures. How to use the information infrastructure correctly has become a problem considered by more and more enterprise information security departments, and the position of IT operation and maintenance is born to solve this hidden danger. The IT (Internet Technology) operation and maintenance personnel of each enterprise formulate a set of operational risk quantitative assessment models for the enterprise based on the internal information infrastructure equipment of the enterprise so that the enterprise staff can use these infrastructure equipment efficiently and safely. This paper focused on the quantitative assessment model of IT operation and maintenance operation risk constructed by the digital twin model. The quantitative assessment model of IT operation and maintenance operation risk constructed by the digital twin model was compared with the quantitative assessment model of IT operation and maintenance operation risk constructed by other traditional models. The comparison results showed that the output results of the evaluation model constructed by the digital twin model were closer to the actual results, and the accuracy rate was 35.9% higher than that of the previous operational risk assessment model.

This paper provides an analysis of the combining effect of novel activation function and loss function based on M-estimation in application to extreme learning machine (ELM), a feed-forward neural network. Due to the computational efficiency and classification/prediction accuracy of ELM and its variants, they have been widely exploited in the development of new technologies and applications. However, in real applications, the performance of classical ELMs deteriorates in the presence of outliers, thus, negatively impacting the precision and accuracy of the system. To further enhance the performance of ELM and its variants, we proposed novel activation functions based on the psi function of M and redescend the M-estimation method along with the smooth 2-norm weight-loss functions to reduce the negative impact of the outliers. The proposed psi functions of several M and redescending M-estimation methods are more flexible to make more distinct features space. For the first time, the idea of the psi function as an activation function in the neural network is introduced in the literature to ensure accurate prediction. In addition, new robust 2 norm-loss functions based on M and redescending M-estimation are proposed to deal with outliers efficiently in ELM. To evaluate the performance of the proposed methodology against other state-of-the-art techniques, experiments have been performed in diverse environments, which show promising improvements in application to regression and classification problems.

An intelligent manufacturing environment employs internet-based communication and monitoring technologies for fault detection, diagnosis, and monitoring of industrial machines. The monitoring and fault detection are performed remotely without human intervention that predicts faults and ensures specific operational control. This article introduces a rational fault diagnosis process (RFDP) best suited for remote fault detection and diagnosis of CNC machine tools. The proposed process monitors different operational segments of the machine and extracts related data to validate its performance. The interconnection between the segments and fault impact are identified using the transfer learning process. The previously identified faults are used in the state training process to improve detection and diagnosis accuracy. Depending on the operational control continuity, the performance is assessed post the fault diagnosis. The learning paradigm is trained using the machine’s efficiency and rational data processing to predict the transfer states’ faults. The transfer states are modulated based on the efficiency and minimum-maximum control recommended for the CNC machine. This process’s performance is validated using detection accuracy, diagnosis recommendation, downtime, data processing rate, and processing time. From the experimental analysis, it is seen that for the varying data extraction rates, the proposed process improves detection accuracy by 10.14%, diagnosis recommendation by 8.58% and data processing rate by 7.95%, reducing the downtime by 8.85%, and processing by 11.24%.

Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields.

Including shared bicycles, shared chefs, shared ideas, shared printing, and even shared human resources, the vocabulary of sharing has quietly entered people’s lives, affecting everyone’s necessities. “Everyone can participate” and “everything can be shared” are all familiar words in the sharing economy field. The implementation of the sharing economy is carried out by relying on the sharing platform. Knowledge, as a special resource, can exert its maximum benefit only when it is widely disseminated and shared, and the process of knowledge sharing and transformation itself contains knowledge innovation. By linking the multiparticipants of the platform, sharing, cocreation, and win-win are realized. The research value of this paper lies in the use of microservices to solve the problems of high coupling, poor scalability, and difficulty in rapid iteration in traditional monolithic applications, effectively identifying and blocking spam that may appear on the platform through a relatively simple solution. Meanwhile, it has been expected that under the trend of sharing economy, with the development of blockchain and 5G technology, an Internet life and health platform can be built. The upstream and downstream in the medical and health industry can be linked to share resources between the upstream and downstream in the industry, which can create a common nakedness, thus improving the operational efficiency and profitability of the entire medical and health industry. Finally, a spam identification scheme combining the improved Aho-Corasick algorithm (AC) and the naive Bayesian model (NBM) has been proposed, and a comparative experiment was conducted between this scheme and the scheme directly using NBM. The experimental results showed that the macro F1 value of the improved scheme on the bad evaluation dataset of platform A was 3.6% higher than that of NBM alone, and the macro F1 value of the improved scheme was 1.7% higher than that of NBM alone on the B platform review dataset. The overall performance of the improved NBM algorithm was stable and better than the traditional algorithm, which verified the feasibility of the scheme.

With the increasing import volume of China’s oil and natural gas, the structure of China’s energy supply chain (ESC) has become more and more complex. How to use the “Belt and Road” (OBOR) layout to promote China’s regional energy cooperation and ensure China’s energy security has become an important international issue facing China. This paper constructs an ESC network model in the context of the OBOR, and finds out the position of China in this network model. This paper also finds out the role law of each risk influencing factor, analyzes the degree of its influence, and puts forward some suggestions on the risk management of ESC according to the analysis results. This paper proposes to use decision tree algorithm to guide the development and construction of Sino-Lao economic and trade cooperation, which will help analyze and help the construction and research of Sino-Lao economic and trade cooperation and sustainable energy freight corridor. Based on the energy cooperation between China and the countries along the “Belt and Road,” this paper analyzes the network structure of energy cooperation under the “Belt and Road” background based on the small-world network theory. This paper analyzes the important role of core countries in the ESC through the analysis of the basic parameters of the small-world network, the efficiency of energy cooperation, and the stability of energy cooperation. According to the value of each vertex degree obtained by the calculation formula of entropy value, the entropy value of the ESC network structure under the background of OBOR can be obtained as 2.408. It calculates that the maximum and minimum entropy of the ESC network structure at this time are 3.09 and 2.21 respectively.