Intelligent Processing Technology Research Articles

Reform of college students’ teaching management informatization under the background of big data and IoT

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students’ teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students’ teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students.

Urban fire emergency management based on big data intelligent processing system and Internet of Things

At present, the necessity to obtain and collect logistics information data has also shown explosive growth. Because the past data management technology cannot meet the development requirements of IoT industry under new circumstances, intelligent processing technology based on big data has emerged in a historic moment. Using this technology, people can easily and quickly identify, retrieve, track, monitor and manage products. So we need to learn more about the IoT, to be able to properly handle various data, improve operational efficiency, maximize the value of the Internet of Things system, and accelerate social and economic development. With the acceleration of urbanization and the gradual improvement of the level of urbanization, the scale and number of cities have grown rapidly, and various high-rise buildings, large-scale assembly halls, and underground buildings continue to appear in the city. The increasing importance of this city is due to the accumulation of population and wealth, but it also faces unprecedented security issues. Because various buildings and assemblies are crowded with people, are used frequently, and have complex risk factors, fire accidents continue to occur. Therefore, emergency management of urban fires is indispensable. Among all kinds of fires, building fires are the most serious and direct danger to people. It is a matter that countries all over the world pay close attention to and monitor in real time.

Visual video evaluation association modeling based on chaotic pseudo-random multi-layer compressed sensing for visual privacy-protected keyframe extraction

In current society, artificial intelligence processing technology offers convenient video monitoring, but also raises the risk of privacy leakage. Theoretically, the data used in intelligent video processing methods may directly convey visual information containing private content. For the above problem, this paper uses a multi-layer visual privacy-protected (VPP) coding method to blur private content in the video at the visual level, while avoiding the loss of important visual features contained in the video as much as possible. And this provides a guarantee of the quality of the subsequent keyframe extraction step. Then a visual evaluation algorithm is proposed for assessing the quality of VPP-encoded video privacy protection. And the experiment shows that the results are consistent with those of subjective evaluation. In addition, for VPP-encoded video, we propose an unsupervised two-layer clustering keyframe extraction method with corresponding performance evaluation index. Finally, an association model is established to balance the privacy protection quality and the keyframe extraction performance.

Design and Application of Intelligent Processing Technology for Animation Images Based on Deep Learning

The origins of anime can be traced all the way back to the Homo sapiens period of human civilization. Nowadays, anime is a record of life as well as a popular kind of entertainment and a source of ideal trust for many individuals. Children and individuals of all classes and ages enjoy anime. In the opinion of most people, anime is not simply a form of amusement and pleasure, but it can also express deeper meanings, transmit other cultures, and inspire individuals to pursue their aspirations. Image stylistic migration based on convolutional neural networks has developed as a central research path in recent years, and attempts on style migration have evolved as well. However, there are few studies on style migration. In this paper, we propose a deep learning-based solution to the problem of anime-style migration. Experiments on a relevant database show that our proposed method is effective and accurate and has commercial and academic significance.

Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates’ behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates’ entrepreneurship.

Data-Driven Teaching Model Design of College English Translation Using Intelligent Processing Technology

DDL (data-driven learning) advocates a deep integration of modern information technology and resources based on corpora and search engines with education and teaching, opening up a new path for college English translation instruction. The concept and characteristics of DDL theory are discussed in this paper. This system proposes an automatic scoring model of English translation based on mixed vector space, which comprehensively analyzes the target translation from two aspects: grammatical errors and semantic relevance, in order to address the shortcomings of current algorithms. The findings of this study show that using a combination of English fragmentation translation and adaptive resource push, learners can learn English reading using mobile terminals in fragmented learning situations, as well as receive targeted training for weak links in English reading in fragmented learning time and space, improving their learning efficiency and effect.

Research on Intelligent Processing Technology of Computer Image Recognition Based on Machine Vision

Although computer image recognition can distinguish very clear images, it is only limited to a certain number of years. Or it can only be clearly distinguishable from some range of graphs, and expanding the range or changing the range will not achieve the state that people want it to achieve. Nowadays, China’s equipment in this field can only conduct activity research in the two-dimensional space, and can only decompose and analyze the two-dimensional pictures. For high-dimensional and complex graphs, they still cannot be processed and the processing requirements are not up to standard. Moreover, the development of this technology is also affected by many human factors. The operation and implementation of this technology mainly rely on the high-energy and powerful processing power of the big data computing system. If we want to ensure the quality of image processing is very good, we must closely combine the recognition image and the related technology of the computer, and do not separate from each other. In this paper, the development of the technology from the status quo, the development of the principle and characteristics of the development of the technology for an in-depth study of the technology, but also the development prospect of the technology is described.

Research on Renewable Resources in Circular Economy based on Intelligent Processing Technology and Product Life Cycle Analysis

To achieve more real-time and efficient management of renewable resources and ensure the economic efficiency and environmental friendliness of the process, the renewable resources management mode based on intelligent processing and life cycle analysis was discussed. With the advantages of intelligent processing technology in perception, data collection, information network transmission and intelligent processing, the theory and method of life cycle analysis on environmental impact assessment were applied to the field of renewable resources management, so as to realize efficient and intelligent management of renewable resources. Meanwhile, the environmental effects of various renewable resources should be taken into account, and their respective advantages should be given full play to provide better services for the management of renewable resources.Finally, the combined application of the two was explored by taking the data as the integrating point.

On the Application of Big Data and its Intelligent Processing Technology in Internet of Things Industry

Big data and the Internet of Things are developing at a rapid pace, bringing great opportunities for the application of various technology fields, including smart cities, smart transportation, smart grid and telemedicine consultation. The wide application of the Internet of Things makes big data analysis challenging, as data is collected and processed by different sensors [1]. Based on this, this paper explores the application of big data and its intelligent processing technology in the Internet of Things industry.

Evaluation of Interior Design Schemes Based on Artificial Intelligence Processing Technology

This article analyzes the necessity of evaluating interior design schemes. The content of this article includes timely discovering the shortcomings of the plan, improving the comprehensive benefits brought by the plan, and reducing the appearance of construction changes. The composition combines the basic concepts and development of artificial intelligence processing technology, and studies the construction principles of artificial intelligence processing technology in a clear system. The author studied how to evaluate the content of the indicators, the integration of energy-saving function indicators, the calculation of the weights of different indicators, and the objective evaluation of specific application functions in the test plan. The author’s purpose is to improve the reliability of the evaluation results of the interior design plan and speed up the progress of the later plan implementation.

Analysis on the Algorithm and Practical Application of Computer Intelligent Image Processing Technology

With the continuous improvement of computer science and technology, the role of computer in the development of the whole society is more and more prominent. Based on this development background, people begin to pay attention to the research of computer intelligent processing technology, which can accurately identify and process objects in different environments. At present, the technology in this field has been greatly improved in China, and the application scope of computer intelligent image processing technology has been gradually expanded. The purpose of this paper is to realize the further breakthrough of the computer intelligent processing technology through the exploration of the algorithm and the analysis of the practical application. This article first to the intelligent image processing technology has carried on the summary of relevant, then combined with the existing research materials and the characteristics of the intelligent image processing technology, mathematical morphology of image processing algorithm is proposed, and the effectiveness of the proposed algorithm has carried on the inspection, finally the technology application in the field of industry, agriculture and transportation related instructions. The experimental results show that the image processing algorithm of mathematical form proposed in this paper can realize the accurate processing and recognition of the image and promote the improvement of the technology.

Research on Sea Clutter Reflectivity Using Deep Learning Model in Industry 4.0

The study of sea clutter reflectivity plays an important role in radar performance evaluations in the military industry. The industrial bodies are trying to apply the sea clutter intelligent processing technology to the radar system with the form of Internet of Things and Industry 4.0. Many sea clutter reflectivity models that have been proposed are difficult to fully adapt to the surrounding seas and different radar systems in China. This article proposes a model named multi-source input neural network (MSINN) sea clutter model using sea clutter collected by ultra high frequency (UHF) radar. In order to prepare sea clutter reflectivity data for training MSINN, the radar continuously collects sea clutter containing various disturbances. In the face of the challenges of preprocessing and storage of measured sea clutter big data, this article proposes a sea clutter preprocessing scheme based on yolov3-tiny model. Experimental results show that the average detection precision of test sea clutter Range-Pulse (RP) images is 75.3% and the effective region extraction time of sea clutter RP image is 0.003642 s, which can meet the requirement of real-time detection and data requirement of predicting sea clutter reflectivity based on MSINN. Compared with the traditional empirical model, the average prediction error of sea clutter reflectivity based on MSINN is 1.82 dB, which improves the prediction accuracy and is more suitable for the Yellow Sea in China.

A new method for detecting free liquid moisture on façade surfaces

Liquid water present on the surface of façades is significantly responsible for microorganism growth. This issue became especially important with the increasing use of thermal insulation. The formation of condensation on these façades is often increased due to low surface temperatures [1]. Therefore, the development of a method for controlling the wetness and drying of façades is necessary, since no suitable method is yet available for this purpose. In this paper, the development of an active façade moisture detection system is presented. This system consists of a surface humidity sensor network based on reflection from infrared (IR) radiation, data acquisition and intelligent processing technology. The system is tested and calibrated in the lab for different textured surfaces. Different newly developed coating systems are tested in-situ using this method. These coatings are evaluated with respect to their surface condensation in order to identify the resistance against condensation. The method provides long-term non-destructive measurements of surface wetness. Targeted measures to prevent or reduce the growth of algae can be initiated with the obtained data. In addition, the measured data make a valuable contribution to the development of facade materials and coatings, which have improved drying properties and balance between hydrophilic and hydrophobic surface. As a result, the use of biocides to protect the facade from algae growth can be reduced.

Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning

With the booming development of tourism, travel security problems are becoming more and more prominent. Congestion, stampedes, fights and other tourism emergency events occurred frequently, which should be a wake-up call for tourism security. Therefore, it is of great research value and application prospect to real-time monitor tourists and detect abnormal events in tourism surveillance video by using computer vision and video intelligent processing technology, which can realize the timely forecast and early warning of tourism emergencies. At present, although most of the video-based abnormal event detection methods work well in simple scenes, there are often problems such as low detection rate and high false positive rate in complex motion scenarios, and the detection of abnormal events can’t be processed in real time. To tackle these issues, we propose an abnormal event detection model in tourism video based on salient spatio-temporal features and sparse combination learning, which has good robustness and timeliness in complex motion scenarios and can be adapted to real-time anomaly detection in practical applications. Specifically, spatio-temporal gradient model is combined with foreground detection to extract 3D gradient features on the foreground target of video sequence as the salient spatio-temporal features, which can eliminate the interference of the background. Sparse combination learning algorithm is used to establish the abnormal event detection model, which can realize the real-time detection of abnormal events. In addition, we construct a new ScenicSpot dataset with 18 video clips (5964 frames) containing both normal and abnormal events. The experimental results on ScenicSpot dataset and two standard benchmark datasets show that our method can realize the automatic detection and recognition of tourists’ abnormal behavior, and has better performance compared with the classical methods.

构建绿色智慧城市的关键技术探讨

绿色智慧城市是未来城市发展的方向。本文分析探讨了建设绿色智慧城市的五大要核心技术：物联网、互联网、云计算、大数据和城市空间信息技术，提出了建立以智慧感知设施、智慧网络传输、智慧处理技术、智慧应用技术为基础的绿色智慧城市总体框架结构，指出可再生能源的高效利用需依托于能源互联网的发展，而能源互联网为绿色智慧城市的发展建设提供了动力。 Green smart city is the direction of the future development of the city. This paper explores the wisdom of urban green construction of the five core technologies: networking, Internet, cloud computing, data and urban space information technology and proposes building the green smart city overall frame structure based on wisdom perceived facilities, the intelligence transmission network, the intelligence processing technology and the intelligence application technology. It is pointed out that the use of the renewable energy needs to rely on the development of the Energy Internet and that the Energy Internet provides the impetus for development and construction of the green smart city.

The Use Of Computer Intelligent Processing Technologies Among Environmental Scientists In Nigerian Universities

This paper assesses the awareness and usage of a novel approach to data and information processing among scientists, researchers and students in the field of environmental sciences. In depth and structured interview was conducted, targeting a population who are working in a variety of environmental issues. The data generated were analysed using simple descriptive statistics. The results indicate that the level of awareness and responsiveness to this approach is very low (26.5%), and computer proficiency is also very low (30.8%). This suggests the need for a vigorous academic reorientation of all stakeholders through conferences, workshops and seminars.Key words: computer intelligent processing technologies, environmental scientists

Improvement of Robot Sensor by Integrating Information Using Neural Network

Recently, the improvement of robotics makes us feel the robot to be more familiar to human activities. For example, robot's motion similar to human movement is also developed. However, in order for a robot to play an active part in the medical and welfare field, the sensors of the robot will be important to have effective functions for robot's safety actions. Our approach is applying intelligent processing technology for the sensors by using neural networks. Our purpose is making recognition system with several sensors. We developed a sensory integrating system by the neural network and apply the system to the robotics. This robot can track the target by applying integrated neural system. We extend the robot system to avoid obstacles, which is based on a subsumption architecture.

Special Issue on Integration of Intelligence for Robotics in VLSI Chips

Intelligence is one of the most important subjects in information and electronics systems. In many applications such as multi media systems, home electronics systems, factory automation systems, security systems and aerospace systems, advanced intelligent processing technologies are more required to be developed as shown in Figure. There are two approaches to increase intelligence, although they are closely related each other and may not be separable. One is an algorithm-based approach to directly increase intelligence quality. The other is a computational-power-based approach to directly increase processing performance. Even if a single operation is very simple, its repeated operations often make the processing intelligent. The problem is how to increase the computational power. It is obvious that software acceleration using general-purpose microprocessors has some limitation. Therefore, special acceleration using newly developed chips is one of the most important solutions. In particular, real-world applications need to achieve very quick response for dynamically changing real-world environment. Therefore, special-purpose processors and special-purpose accelerators or engines, are essential to make the above applications realistic. Another words, ""to realize high speed processing intelligence"" On the other hand, solid-state circuits technology enabling single-chip systems have rapid advancement resulting in dramatic improvements in both performance and cost oer function. In fact, one-giga-bit DRAMs, ten SPECint95 microprocessors containing ten million transistors are being developed by recent VLSI technology. It is no more a dream to develop practical special processors using the recent VLSI technology. Moreover, new architecture and new concept circuits have been actively studied for the next-generation integration technology. From the above point of view, this special issue was planned to demonstrate the above important area. Especially, intelligent robot is a typical class of applications, soits intelligence technology makes also any other application promising. Finally, I would like to express my application to the authors for their efforts and contributions to this special issue and also the members of the Editorial Board for their useful comments.