Band 13, Heft 1

Self-adaptive systems (SASs) have the capability to evaluate and change their behavior according to changes occurring in the environment. Research in this field is being held since mid-60, and over the last decade, the importance of self-adaptivity is being increased. In the proposed research, colored petri nets (CPN) formal language is being used to model self-adaptive multiagent system. CPN is increasingly used to model self-adaptive complex concurrent systems due to its flexible formal specification and formal verification behavior. CPN being visually more expressive than simple, Petri Nets enable diverse modeling approaches and provides a richer framework for such a complex formalism. The main goal of this research is to apply self-adaptive multi-agent concurrent system (SMACS) for complex architectures. In our previous research, the SMACS framework is proposed and verified through traffic monitoring system. All agents of SMACS are also known as intelligent agents due to their self-adaptation behavior. Due to decentralized approach in this framework, each agent will intelligently adapt its behavior in the environment and send updates to other agents. In this research, we are choosing smart computer lab (SCL) as a case study. For internal structure of each agent modal, μ \mu -calculus will be used, and then a model checker TAPAs: a tool for the analysis of process algebras will be applied to verify these properties. CPN-based state space analysis will also be done to verify the behavioral properties of the model. The general objective of the proposed system is to maximize the utility generated over some predetermined time horizon.

The popularity of artificial intelligence applications is on the rise, and they are producing better outcomes in numerous fields of research. However, the effectiveness of these applications relies heavily on the quantity and quality of data used. While the volume of data available has increased significantly in recent years, this does not always lead to better results, as the information content of the data is also important. This study aims to evaluate a new data preprocessing technique called semi-pivoted QR (SPQR) approximation for machine learning. This technique is designed for approximating sparse matrices and acts as a feature selection algorithm. To the best of our knowledge, it has not been previously applied to data preprocessing in machine learning algorithms. The study aims to evaluate the impact of SPQR on the performance of an unsupervised clustering algorithm and compare its results to those obtained using principal component analysis (PCA) as the preprocessing algorithm. The evaluation is conducted on various publicly available datasets. The findings suggest that the SPQR algorithm can produce outcomes comparable to those achieved using PCA without altering the original dataset.

This article formalizes an algorithm that computes the minimum toll sets in an undirected graph. A core process in our algorithm is to check vertex subsets in order of size. We add a new flavor to the implementation of this process; when the k − 1 k-1 -vertex subsets are already constructed, our algorithm produces the k k -vertex subsets building on the k − 1 k-1 -vertex subsets rather than reconstructing the k k -vertex subsets from the ground up as the existing algorithms would do. Our implementation is usable in combinatorial minimization problems that require checking variable-size combinations in order of size.

Today, the rapid development of computer technology changes with each passing day. In the computer field, computer animation has rapidly grown from a new thing to a leading industry, and animation has entered the era of three-dimensional animation and computer graphics. This article aims to study the application of artificial intelligence-based style transfer algorithm in animation special effects design. It proposes methods such as adaptive loss function, style transfer process, animation special effect design, etc., and conducts related experiments on the application of style transfer algorithm in animation special effect design in the article. The experimental results show that the style transfer algorithm based on AI can effectively improve the effect of animation special effects. In this survey, more than 80% of the people are satisfied with the animation special effects design based on the style transfer algorithm.

In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.

The application of intelligent technology has realized the transformation of people’s production and lifestyle, and it has also promoted the development of the field of medicine. At present, the intensity of intelligence in the field of medicine is increasing. By using its cash methods and techniques combined with the mechanical field, this article proposes to use visual inspection technology to understand the fusion of the medical field and the mechanical field. It is helpful to analyze and solve objective problems such as low efficiency in current infusion and insufficient rigidity of large infusion plastic bottles. Drawing on the principles and laws of deep learning algorithms and neural networks, the technical research of intelligent robots for visual inspection is carried out to realize the intelligence of infusion robots. In the research accuracy of detection, the detection rate of standard particles higher than 85 µM has reached almost 100%, and the rate of 50 µM standard particles is lower and unstable. The detection effect of the control light bulb control was different, and the detection rate was between 50 and 80%, which was obviously worse than the detection robot effect. Therefore, the current research on the technology of intelligent robots is very important.

Radio frequency identification (RFID) is a broad rapidly evolving skill in the past few years. It is characterized by non-contact identification, fast read and write speed, small label size, large data storage capacity, and other technical advantages. RFID technology for goods movement has completely changed the traditional supply chain management, greatly improved the operational efficiency of enterprises, and has become an important method for the development of supply chain logistics. This work mainly studies and analyzes the RFID supply chain, introduces the development and application of RFID supply chain sector technology, and discusses the operation of the supply chain in detail. Then, according to the existing RFID supply chain, a RFID supply chain artificial intelligence (AI) based approach to technology is proposed, and the data analysis of RFID supply chain is introduced in detail. In this work, through the research experiment of AI technology RFID supply chain data analysis, the experimental data show that there are several time-consuming links in the supply chain system. The time consumed in the AI RFID system is 9.9, 3.4, 3.5, and 29.9 min, respectively, while each link in the original system takes 13.4, 4.9, 4.9, and 34.9 min. It can be seen from the above data that the amount of time in each system link of the AI RFID supply chain system is less than that of the original supply chain system, which shortens the entire product passing cycle and greatly improves work efficiency.

Cloud computing is a system development method based on dynamic sharing, which allows a large number of systems to be combined to provide services. The purpose of this work is to study the design and implementation of a dynamic virtual resource allocation system in cloud computing, whose architecture allows load balancing between virtual resource pools and reduces resource wastage. Using the cluster network topology, the resource usage of the dynamic system cluster can be monitored in real time, and the total cluster load can be automatically determined based on the monitoring data. The experiment is divided into two parts. Performance testing and scenario testing. Performance tests examine execution time, processor, and memory performance. In the scenario test, JMeter is used to simulate the occurrence of a large number of concurrent application access requests, the loss rate, and processing time of these requests on the cloud platform, and load balancing tests are performed. The test results show that the system running time is about 22–27 ms, the CPU utilization is about 90–95%, and the RAM is about 3.5 ms. The results show that cloud technology can improve resource scheduling of large tasks and optimize resource load balance.

In the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher requirements for criminal technology. The current criminal technology equipment is relatively backward, and the technical level is not high enough, resulting in a low utilization rate of trace material evidence extraction, which directly affects the role of criminal technology in the investigation and solving of cases. In recent years, fingerprint recognition algorithms and image edge detection algorithms have been widely used in various fields. This work studied the application of fingerprint image fuzzy edge recognition algorithm in criminal technology, in order to improve the level of criminal technology and the utilization rate of physical evidence extraction. The criminal technology system is upgraded and optimized by combining fingerprint recognition algorithm and image edge detection algorithm. And fuzzy theory is added to ensure the feasibility of the research. The experimental results show that the fuzzy edge recognition algorithm of fingerprint image can improve the level of criminal technology and the utilization rate of material evidence to a certain extent. The utilization rate is increased by 7.04%. The recognition accuracy of the fuzzy recognition method is also 13.2% higher than that of the methods in the literature.

In order to improve the ability of computer network intrusion detection, the big data technology for computer intrusion detection was studied. This research uses big data technology to build a network intrusion detection model, using clustering algorithms, classification algorithms, and association rule algorithms in data mining to automatically identify the attack patterns in the network and quickly learn and extract the characteristics of network attacks. The experimental results show that the recognition effect of the classification algorithm is obviously better than that of the clustering algorithm and the association rule. With the increase in the proportion of abnormal commands, the accuracy rate can still be maintained at 90%. As a compromise between the classification algorithm and the clustering algorithm, the accuracy rate of the association rule algorithm is basically maintained at more than 75%. It is proved that the big data technology oriented to computer intrusion detection can effectively improve the detection ability of computer network intrusion.

With the rapid development of social informatization, the Internet has become an important channel for global information dissemination. The wireless network space and the real space are intertwined and have a significant impact on the political, economic, and cultural aspects of international society. For example, it can effectively solve the problem of information exchange between cities, regions, and countries. Wireless network crime and wireless network space security issues involve the development of information technology in various countries and, more importantly, involve all aspects of national security. In the age of science and technology, the current situation of cybercrime can be expressed by data as follows: the online crime rate is high. According to relevant statistics, nearly 5 million Internet users are involved in various cases every year due to the use of the Internet. This article starts with the digital forensics of wireless network crime and wireless network security management systems and discusses the composition, characteristics, existing problems, and future development directions of the current wireless network security management system. By comparing the wireless network security laws of China and the United States, using the latent Dirichlet allocation (LDA)-Gibbs model and the k -means algorithm to analyze the data, this article provides guidance for future research on wireless network space governance. The combination of digital forensics analysis based on cybercrime and space governance can improve the level of public security work. At present, the world has made some progress in cyberspace, but it still faces severe challenges. In the face of new situations and new measures, it is necessary to strengthen legislation, improve relevant systems, and strengthen supervision capacity. At the same time, it is necessary to establish perfect technical means and safeguards to ensure that this goal can be achieved.

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

As the rapid advancement of information security company is developing quickly and the technology including big information, big computer, large cloud, and artificial intelligence are being widely used, network security has entered a new era. While ushering in huge development opportunities, it also faces severe tests. Network security is a major issue related to the comprehensive realization of a well-off society and national security, and has risen to the national strategic level. The current financial industry network security construction mainly focuses on team, process construction, and the research and development of individual tools and equipment. It lacks system research and implementation guidelines for defense technology construction based on industry IT characteristics. At the same time, there is also a lack of objective and unified measurement and evaluation standards for enterprise security defense capabilities, which restricts the improvement of cybersecurity capabilities in the financial industry to a certain extent. In terms of actual combat exercises, in various actual combat exercises over the years, the technical architecture of the bank’s network security defense has withstood the test of high-intensity confrontation. The defense process achieved zero deductions, and stood out among the participating defending teams through traceability and countermeasures, which effectively improved the network security large-scale group operations and security protection capabilities. The effectiveness of the technical architecture design is verified in actual combat, which shows that the control of the bank’s internet financial security protection system is effective. With digital computing, all banking transactions are fully automatically implemented and the bank’s clientele is systematically self-managed. It predicts that using this system, the speed can be increased by 98% and the accuracy can be increased by 12%.

With the development of urbanization, urban public safety is becoming more and more important. Urban public safety is not only the foundation of urban development, but also the basic guarantee for the stability of citizens’ lives. In the context of today’s artificial intelligence (AI), the concept of smart cities is constantly being practiced. Urban public safety has also ushered in some new problems and challenges. To this end, this article aimed to use AI technology to build an efficient public safety data resource management system in a smart city environment. A major goal of AI research was to enable machines to perform complex tasks that normally require human intelligence. In this article, a data resource management system was constructed according to the city security system and risk data sources, and the data processing method of neural network (NN) was adopted. Factors affecting urban public safety were processed as indicator data. In this article, the feedforward back-propagation neural network (BPNN) was used to predict the index data in real time, which has realized the management functions of risk monitoring and early warning of public safety data indicators. The BPNN model was used to test the urban risk early warning capability of the constructed system. BPNN is a multi-layer feed-forward NN trained according to the error back-propagation algorithm, which is one of the most widely used NN models. The results showed that the average prediction accuracy of the BPNN model for indicator prediction was about 89%, which was 16.1% higher than that of the traditional NN model. The average risk warning accuracy rate of the BPNN model was 90.3%, which was 16.5% higher than that of the traditional NN model. This shows that the BPNN model using AI technology in this article can more efficiently and accurately carry out early warning of risk and management of urban public safety.

The twenty-first century is the century of marine resources. The ocean is a treasure of biological resources, energy, water resources and mineral resources, and it would gradually become the “second space” of mankind. In the next few years, it would be more and more relevant to human life. Many scholars have realized the importance of the ocean and began to vigorously develop and use the ocean. Underwater robot is a means for human beings to explore and develop the ocean, and it would be widely used in this field. The development and promotion of underwater vehicles are of great significance to resource development, economic development, and national security. With the increasing shortage of land resources, the development and utilization of marine resources have received increasing attention. The direct exploitation of marine resources by humans would have adverse effects, so the underwater robot technology has developed rapidly in recent years. However, at present, most underwater robots are driven by electric turbines. The underwater working environment requires that the underwater motor has good sealing performance, so its structure is complex and expensive, and it is rarely used in ordinary underwater operations. In recent years, intelligent robots have been used more and more, but because of the complexity and uncertainty of the underwater working environment, there are many uncertain factors. Therefore, it is very meaningful to carry out stability control for it. The research results showed that the displacement, stability, and other corresponding test curves of each component can be obtained by establishing a simple model with software and through ADAMS (Automatic Dynamic Analysis of Mechanical Systems) simulation analysis. This can simulate the movement of real objects in the real environment and find the existing problems, so as to provide a reference for the actual underwater robot design. In this way, the development cycle and production costs can be reduced. This article analyzed the structure and control system of the underwater vehicle based on ADAMS simulation. The results showed that the dynamic stability of the underwater vehicle based on ADAMS simulation analysis was improved by 4.67% compared with the underwater vehicle before optimization.

Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.

With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.

With the continuous development of big data (BD) and Internet of Things (IoT) technology, research in the fields of network security and IoT is also deepening. Big data provides more data support for network security and the IoT, while also bringing more security risks. Therefore, how to ensure the security of big data, prevent network attacks, and improve the security and reliability of the IoT has become a major issue in the current field of network security and the IoT. This article aims to analyze the research results of network security and the IoT in the context of big data and explore how to ensure big data security and improve the security and reliability of the IoT from a multidimensional perspective. Therefore, this article proposes BD technology, that is, through information mining, to ensure network security from the perspective of controlling information flow. At the same time, this article also proposes an LED lightweight encryption algorithm in the IOT, which is used to achieve secure communication between ordinary nodes and gateway nodes, effectively solving the security issues of data distribution in the network, resisting virus attacks and man-in--in-the--the-middle attacks, and has higher security and efficiency. Both of these methods can effectively protect network security: one is to control data flow, and the other is to start with communication protocols. Finally, this article analyzed the adoption of network security protection measures by netizens and found that only 13% of netizens frequently take network security protection measures, while 35% of netizens never take network security protection measures. This is also one of the important reasons for the increasing number of current network security issues.

Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.

The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.

Blockchain technology is a widely used emerging technology. It can integrate cloud computing technology and big data to form a distributed cloud computing system, providing efficient services for local enterprises and governments. In addition, local cloud computing is also widely used, and there are many big data in these applications. Blockchain and local cloud computing technology offers safe and reliable information exchange for data exchange and provides a practical method for analyzing big data. This article aims to study how to analyze and research the application analysis method of big data based on blockchain technology and improve the classical apriori algorithm (CAA). This article compares and analyzes the performance of CAA and improved apriori algorithm (IAA) in big data applications. When the number of key words in the query are 20 and 100, the result search time of the CAA are 1.08 and 9.24 s, respectively, and the IAA are 0.76 and 7.58 s, respectively. The result search cost of the CAA is 12.43 and 91.55 kB, respectively, and the IAA is 5.05 and 63.72 kB, respectively. It is not difficult to see that applying the IAA to the blockchain-based government data-sharing scheme had relatively excellent performance and was worth further promotion and application.

For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.