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The traditional Chinese folk paper-cutting art has a long history. It is a traditional folk art form created by working people to satisfy their spiritual and material pursuits. It originates from life and returns to life. With the pace of the times, folk paper-cutting has undergone an innovative transformation and development through the interweaving, penetration and integration of contemporary design concepts. Through the combination of network big data, it provides a large number of references for modern graphic design decoration and function combination, graphic context inheritance, color language, modeling language, etc.

The paper presents an information system for calculating the main paper-forming parameters of the pulp for given technological and design parameters of the grinding plant. The operation of the system is based on mathematical regression models of the processes that were obtained in the course of experimental studies of knife sets of various types. As input parameters in these models, the rotor speed, knife gap, concentration and degree of grinding of the mass are used. The output parameters are the grinding time, water retention capacity and average fiber length. The developed information system is a network web application with a modular structure. The modules are united by the main interface for the user. Each module implements a regression model for a specific type of headset. At the moment, the system has two modules for calculating the parameters of a percussion type headset and a headset with a curvilinear shape of knives. In the future, it is planned to add the ability to calculate indicators for headsets of other designs. It will also add the ability to solve optimization problems by finding the minimum or maximum value of the output parameters.

Paper artifacts are contaminated by external factors in the process of preservation such as foxing. For the problem of backward technology of rapid detection of foxing on paper artifacts, a method based on hyperspectral imaging technology is proposed to detect foxing spots on paper artifacts. After selecting the region of interest and obtaining the corresponding average reflectance, the difference in the average reflectance is found after comparing the healthy regions with the diseased regions. Using band operation and minimum noise fraction to observe the characteristics of foxing image, although there is overlap in different parts, the distribution distinction between moldy and healthy regions is obvious; K-nearest neighbor method and BP neural network are applied to establish the spectral discrimination model of paper artifacts with foxing spots, and the overall discrimination rate of the two methods is 73.3% and 85%, respectively. The results show that hyperspectral imaging can be used for the identification of foxing spots, but the distinction between different parts is not good, and the discrimination effect still needs to be improved.

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.

At present, green environmental protection has become a common topic of concern all over the world, and the principle of “low carbonization” is also the focus of designers in the design of paper children’s furniture in the future. How to implement the 4R principle into children’s furniture has become the most important issue for designers at present. This paper summarizes the design principles of children’s furniture, puts forward the design scheme of children’s furniture based on 4R principle, and forecasts the future children’s furniture market.

In Today’s world technologies such as Internet of Things, Cloud Computing as well as Fog Computing are growing at an exponential rate which depend upon each other directly or indirectly. The Internet of Things can be described as a network of substantial matter such as cars, washing machines, refrigerator which can interact with each other through internet. Billions of devices will be IoT enabled in near future and generate enormous amount of data but IoT devices has some limitations like storage capabilities, processing capabilities and utilization of resources which can only be handled by integrating it with cloud technology. Cloud model provide environment in which software, Infrastructure, sharable pool of configurable resources, virtual environment, sensors, hardware and database is provided as a utility for IoT devices and users. In cloud computing paradigm some limitations exist for example distance of the data source from multi-hop, geological unified structure, latency, heterogeneity and many more. To address such limitations, Fog computing approach can be used to bring computing assets nearer to IoT devices. Fog computing is an enhancement of the cloud-based Network and computing services. It provides computational and storage services of cloud proximate to IoT devices. This paper provides an overview regarding the cloud computing uses in IoT devices and issues or problems that occur during integration. Handling of problems that occurs during integration of cloud with IoT can be done through fog computing. The purpose of this survey is to understand the concept of fog computing to improve the existing system of Integration of Cloud with IoT.

A hypervisor is a computer programme or software that facilitates to create and run multiple virtual machines. It is also known as Virtual Machine Manager (VMM). Due to their popularity, it exploits the attack surface, because the Hypervisor code contains much vulnerability. Since the Hypervisor is a core element of any cloud computing service, it is always on the top priority of the attackers. There are many Software (open source) and Hardware-based Solutions are available in the market to monitor and control the hypervisor activities. This paper summarizes various types of attacks, vulnerabilities, security issues and challenges related to hypervisor and virtual machines.

Digital image processing has numerous applications in many sectors of the world. It expands from initial information registration into methods and thoughts combining pattern recognition, computer vision, and machine learning. The wide utilization has pulled in many researchers to integrate with a range of related specializations. This work gives a study of the latest development and theoretical ideas clarifying an improvement of computer vision particularly with pattern recognition and image processing, utilizing various regions of their field implementation. Digital image processing causes researchers to analyze images to get important data and comprehend information. It utilized a technique of multi-range implementation and huge information analysis. This work aims to focus on the latest studies related to image processing, pattern recognition, and computer vision. In this paper, Computer vision standard has been categorized into groups. For example, pattern recognition, image processing, and AI. Additionally, we give a short clarification of the forward-thinking data about the methods and their realization. This survey is restricted to computer vision, and more research can include detecting the conduct and properties of the object including people actions.

Wireless technology these days is very quickly becoming crescent. A network that was recently wired to get online was required. Just wired phones have been a thing of the past. In the past four decades, mobile networks have thrived immensely. The starting point was the 1G cellular concept where ‘G’ stances for generation linkages. This had fully developed very rapidly, generating 1G, 2G, 3G and gradually transferring into 4G from generation to generation. And now people are utilizing the 4G networks. 5G network will nearly stretch its wings to conquer this complex world of cell technology. Integrated 5G work is continuing, with complete service planned in 2020. The development of 5G technology is the perfect solution for the many problems facing us today with today’s innovations. 5G will become an intelligent technology that will limit to a single global uniform body the number of different innovations. This paper is mostly about the evolution of mobile wireless networks like 1G and 5G as well as how they vary and their benefits and drawbacks.

Natural Science Lessons emphasize providing direct experience, to develop competencies in order to explore and understand the natural surroundings scientifically. It is taught to equip students to have knowledge (knowing various ways) and skills (how to do it) that can help students to understand natural phenomena in depth. However, in the learning process, students still have difficulty understanding of the solar system. Inadequate media and conventional learning processes make students’ enthusiastic in participating in learning still low, and this makes learning not as expected by the teacher. In the solar system topic, the teacher expects students to be able to understand the position of the planets in the solar system in order and correctly. The strategy used in the learning process is to apply two-dimensional mind mapping using mase paper balls made of pulp and flashcards made by the teacher. The goal that is expected in the learning process using these strategies and media is to increase the effectiveness of student learning. The research was conducted using the R and D (Research and Development) method using the ADDIE model development method (Analysis, Design, Development, Implementation, Evaluation). Effective learning is active, creative and fun learning for students so that learning outcomes increase. Effectiveness is the level of success achieved from implementing a strategy and learning media, in this case measured from student learning outcomes. The results of the final assessment are in the form of a process assessment and a final assessment.

The results of CFD research using the VOF method with Open Foam open source are presented. In the process of study, we built a digital model allowing us to analyze and predict the process of interaction of the dampening solution with substrate (paper) surface in the measuring cell of the PDA ultrasonic device. Discretization of the area for calculation was created using the blockMesh utility. A block-structural hexahedral computational grid has been constructed. The computational grid area is presented in such a way that the number of cells increases in the direction of the paper sample. The visualization of calculations in the ParaView package coincides with the time intervals obtained on the PDA ultrasonic measuring device. The practical significance of the study lies in the realization of the possibility to evaluate the dampening solution parameters, to control the modes of its supply, taking into account the use of modern papers and printing systems.

Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

In the previous decade, the mixing between chaotic supposition and cryptography frames considers a significant field of data security. Chaos-based image encryption is given a lot of attention in the exploration of data security moreover a great deal of picture encrypting calculations have been proposed concerning chaotic maps. Because of some inveterate highlights of media like information limit and high information excess, the encryption of images not quite the same as that of texts; accordingly it is hard to deal with them by conventional encryption strategies. This paper presents a short review of robust digital watermarking systems that used chaotic algorithms such as Logistic, Tent, Baker, Hyper, Fibonacci, and Arnold maps for encryption of the data presented in several years.

All of 1,647 top papers for Computer Science, Information Systems from 2009 to 2019, which included 1,642 highly cited papers and 32 hot papers. All papers were written in English, from 69 countries/territories and listed in 115 journals. Top 5 core journals were IEEE Communications Surveys and Tutorials, Information Sciences, IEEE Access, IEEE Internet of Things Journal, MIS Quarterly. The research was separated 9 clusters.

This paper describes the problem of intelligent paper grouping and its mathematical model. By optimizing and improving the traditional dynamic programming algorithm, its space complexity is reduced from O(nb) to O(b). At the same time, the flexibility of dynamic programming algorithm is increased by using marker function and tracking algorithm, and the result composition is tracked to obtain the optimal solution. Finally, through several experiments, the improved dynamic programming algorithm is compared with the greedy algorithm and brute force algorithm, and it is found that the improved dynamic programming algorithm has a very good result and is with high efficiency when applied to the simple test paper. It is the most recommended algorithm among the two algorithms compared in this paper.

This article analyzes the common classifications of computer test paper algorithms. This article includes random method, backtracking method, genetic algorithm, random location search method, ant colony algorithm, gene expression algorithm, etc. The author obtains the expected calculated values by studying the technologies of setting control parameters, creating initial populations, calculating individual fitness, optimizing preservation strategies, performing genetic operations, and generating new populations. The author’s purpose is to improve the application effect of the computer test paper algorithm and the practicability of the information in the question bank.

Examination paper analysis is important for students to improve their learning efficiency. Traditional paper examination papers are difficult to sort out, which makes collecting mistakes for future review both time-consuming and laborious, either by handwriting or existed software tools. To easy the process, we propose a layout analysis method combined with the conditional generative adversarial network (CGAN). The traditional semantic segmentation structure is improved and used as a generator in the network, while a discriminator is designed to make the segmentation results more accurate. The motivation is that the discriminator can judge the authenticity of the image, so it can help reduce the unreasonable phenomenon in the semantic segmentation results of the generator. The experimental results show that by this method the examination paper image can be welly split into various components, which provides convenience for further sorting and analysis of the examination paper images.

The paper parameters were selected for a computer model using open source software to predict their interaction with the dampening solution in an offset printing machine and in an ultrasonic measuring device. The indices characterizing the penetration of alcohol-free and alcohol dampening solutions with 5% and 10% content of isopropyl alcohol into the structure of offset coated papers on PDA c02 ultrasonic measuring device were obtained and analyzed. The surface profiles of coated offset papers are obtained on a Micro Measure 3D Station three-dimensional non-contact profilometer for constructing the surface geometry of papers in a graphical interface. A tstartswelling value was found characterizing the time of the beginning of fiber swelling in the paper structure, which will allow setting the time for calculating the numerical model. The obtained t95 and USI 70 indices are recommended for an indirect assessment of the identity of a computer model to the actual conditions for the onset of interaction and penetration of dampening solutions into the paper pores.

Improved streamflow forecasting is considered an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data from rainfall. Hence, this is important to make the estimation of rainfall as accurate as possible result in achieve an economical design of watershed management, water budget studies, reservoir operation, and flood forecasting and control. Most of the previous research was highlighted, an optimal rain gauge network is necessary to provide high quality rainfall estimates. The goal of this paper is to provide a concise review of several studies on the optimal design of a rain gauge network models to enhance the accuracy of streamflow forecasting. This study had two components. First, the design of an optimal rain gauge network using the kriging-based geostatistical approach based on the variance reduction framework. Second, the uses of optimization technique for minimizing the kriging variance in order to optimize rain gauge networks. Additionally, a discussion of both techniques to design an optimal rain gauge network is presented. A well designed rain gauge network is capable of providing accurate rainfall estimates with an optimal number of rain gauge network density. This paper closes with a set of recommendations for what observations and capabilities are needed in the future to advance our understanding of an optimal rain gauge network design and their location for improving the estimate of aerial rainfall.

The article deals with the actual problem of managing the paper printing properties using factor analysis methods. The analysis of the stone debris method allowed us to prove the combination of data in three ways: the interaction of the ink with the paper surface, which controls the optical density of the print, the value of dot gain; structural properties of the surface, which determine the effusing ability of the paper, printing slip; features of the distribution of the ink layer, which change the parameters of the prints resolution, font resolution threshold, print contrast. The identified factors allowed to simplify the task of rational decision making. As a result of the analysis of a set of technological parameters, i.e. the composition by fiber, filling, surface treatment and mass sizing, production of paper within the improved printing properties was achieved. Changes have been made in the technology of producing uncoated printing paper under the conditions of IUE “Paper Mill” Goznak. The analysis of the quality of management was carried out on the basis of the integral method and made up 92%.

High utility itemsets (HUIs) mining is A creation worry in records mining, that understands finding all itemsets having AN application total a customer demonstrated least programming viewpoint min_util. Visit sets see an imperative portion of in an extremely couple of measurements driving undertakings that decide to peer intriguing purposes of concentrate from databases, like connection guidelines. The mining of connection guidelines is one all educated the basic paying practically no personality to what you show up as though at it issues with those. Move out of mining from a static dealings dataset, the spilling case has liberally additional actualities to search for when and incomprehensibly reasonably essential diverse nature to facilitate. In-visit matters can convey to a close go to some time later thus can’t be overlooked. For the term of this paper we keep an eye on present day situation with obtaining for Frequent Itemset Mining estimations. The outline signally information the deficiencies of the open estimations for HUI and proposes specific picks to overcome the flaws.

One of some ways to summarize scientific papers is by employing rhetorical structure of sentences. Determining rhetorical sentence itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing semantic similarity words. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by using selected features, added previous label, and Word2Vec to capture semantic similarity words. Then, this paper shows the result of employing resampling for balancing the existing instances per class and combining resampling and Word2Vec representation itself. Every experiment is tested in two classifiers, namely IBk and J48 tree. It shows that the use of previous label, Word2Vec (Skip-Gram), and resampling improves performance. After doing all the experiments in the 10-fold cross-validation, the highest performance of F-measure is achieved 84.97% by combining Word2Vec (Skip-Gram), all features, and resampling.

In 2010, the LHC experiment produced 7 TeV and heavy-ions collisions continually, generating a huge amount of data, which was analyzed and reported throughout several performed studies. Since then, physicists are bringing out papers and conference notes announcing results and achievements. During 2010, 37 papers and 102 conference notes were published and until September 2011 there are already 131 papers and 189 conference notes in preparation. This paper presents the ATLAS Analysis Papers and ATLAS Analysis Conference Notes systems, developed to monitor the entire publication procedure up to the final submission and to promote the communication among the collaboration members. The software supports the paper elaboration process, tracking the analysis results status and improvement of the paper initial version, presenting a step-by-step procedure overview and promoting communication among collaborators. Along with the increasing flow of papers and conference notes, one of the issues is the way to guarantee that all members who participate in the analysis studies are aware of not only the discussion deadlines but also of the publication process, which involves 17 steps, split in 3 different phases for papers and 10 steps in 1 phase for conference notes. By sending notifications based on predefined rules the systems inform members to approve each step and provide further information such as the approval conditions and the documents in which the publication is based on. Through the software it is also possible to manage dates and members of the editorial team. The data processing is performed by using the Glance System, the main data retrieval platform used for ATLAS information management.

This paper addresses the limitations of existing Solar Envelope (SE) methods to explore the trade-offs of solar radiation and urban shading, and to simultaneously account for several different Key Performance Indicators (KPIs). It offers an alternative parametric workflow - the Solar Block Generator (SBG) - which is based on an additive voxelization method by which multiple solar-driven massing alternatives are generated and evaluated for a given site, corresponding to a set of user-defined environmental KPIs. This method is tested here on an urban redevelopment case study in the Mediterranean (Tel Aviv). The results help achieve a more holistic approach for solar driven urban design.

The aim of this study is to compare and validate a building energy simulation model of a university building with monitored data. In addition, different building services engineering concepts are compared with each other regarding their ecological life cycle-based performance using Life Cycle Assessment (LCA) methods. Optimizing these performances regarding sustainability indicators plays an essential role in realizing a climate-neutral building stock. A university building from the 90s, which consists of almost 900 thermal zones, is used as a case study. Detailed heating consumption data are available for the inspected building and are compared to the simulation results. Different energy supply concepts are first subjected to conducting LCA and then to a detailed energy performance simulation. This paper presents a procedure that enables decision-makers to examine building services engineering issues and to derive conclusions in a time-saving manner regarding appropriate sustainable actions. The focus on sustainable energy supply systems is an essential milestone for the realization of climate-neutral building stocks. The scientific innovation is in the detailed reproduction of the existing building and the comparison between simulation data and real-time data as well as an innovative and experimental approach to building and Heating, ventilation, and air conditioning (HVAC) system simulation.

Active Sludge particles in wastewater or sewage water treatment plants are analyzed. Based on the analysis report, the water is treated to improve the quality of water so that it can be reused. Microscopic images of water samples are obtained from municipal Active Sludge wastewater treatment plants for analysis, so that particles can be viewed clearly. A method that exactly determines the length of filamentous bacteria and flocs segmented from the images is proposed in this paper. Texture and shape features are extracted from the filaments and flocs. An SVM classifier is used to classify the flocs and filaments using the features extracted. Based on the distribution of sludge particles in a particular area, Artificial intelligence is used to find the process of chemical treatment. This helps in treatment of wastewater for further reuse.

With the development of higher education in China, the number of college students on campus is increasing, and the number of people eating in college cafeterias also increases. On the one hand, the problem of crowded lines in school cafeterias during peak dining periods is becoming more and more serious, and students have a poor dining experience. On the other hand, it is often difficult to establish an effective communication mechanism between students and cafeterias. The traditional canteen model is difficult to meet the current needs, and the upgrade of the canteen system in universities is urgent. This paper designs a set of waste reduction intelligent cafeteria system based on Raspberry Pi to reduce waste. The whole system effectively solves the problems of traditional canteens, and establishes a certain reward mechanism to encourage students to use up the purchased dishes and reduce waste. The university canteen provides a practical model for improving service quality and reform and transformation.

Combined with the basic theoretical knowledge of Kansei Engineering and clothing materials, the perceptual cognition of consumers for different dyed fabrics was studied, and the perceptual evaluation of dyed fabrics of different materials was compared. SPSS software was used for statistical analysis of the survey data in order to find the characteristics of dyed cotton fabric. The perceptual evaluation results of plant dyed fabric and industrial dyed fabric were discussed. The relationship between the preferences of consumers towards plant dyed and industrial dyed cotton fabrics and the perceptual evaluation were analyzed through regression analysis.

The standard fatigue estimation procedure is implemented in Model Predictive Control via externalization of the Rainflow algorithm from the optimization problem. Additionally, stress history is considered in a consistent manner by employing a so-called stress residue. The formulation is implemented in the state-of-the-art MPC framework acados and tested in closed-loop with the 5MW onshore turbine in OpenFAST. Simulation results indicate that the new formulation outperforms conventional PID- and MPC-controllers over the entire wind regime, and that the consideration of stress history is highly beneficial.

In recent years, many research works from institutions that participated in Formula SAE had highlighted on suspension systems. The aim is to improve the system in term of performance and robustness. However, every suspension system for a racing car is tailored to the car itself. Thus, this paper proposes a new design for front suspension system for UniART FSAE car. The new design was than being compared to the previous suspension system for enhancement. The analysis covered in this paper based on several conditions such as braking, cornering and bumping condition and was carried out using finite element analysis. Each main component for the suspension system such as lower arm, upper arm and knuckle has been analysed in term of strength and performance. From the results, the proposed new design of the suspension system has improved in term of strength and performance compared to the previous suspension system.

A system based on the HACCP principles represents an effective tool for poultry processing. This system provides risk identification and corrective actions for it reducing. A low level of metrological assurance of CCP effects on risk identity because it depends on accuracy of measurement information. MSA method for measurement assessment of CCP in poultry production is recommended. The metrological analysis of CCP for technology process is developed. The researches of temperature of cooled poultry meat is traced in real-time production. At the first step with the use of control diagrams the stable measurement process was established. Second step showed the absolute and relative meanings for deviation in measurement process. At the third stage the assessment of measurement deviation was leaded. At the final stage over result grading the top ways of reducing measurement deviation were estimated. The analysis showed that measurement process should be improved. Changes the properties of meat samples is the major du ring temperature measurement. The second place is for measurement repeatability. The measurement method is important, that’s why for reducing the measurement deviation it is recommended to change the method or choose another measuring instrument. The role of mechanic was not recognized. Proposed assessment approach for measurement process of CCP based on HACCP principles in order to identify risks within the food safety control and to minimize technique regulation procedure.

The fish processing industry applies a small number of automation technologies compared to other industries. Their number should be increased despite automated food processing problems and hygiene requirements. Automation and implementation of robots can reduce production costs and improve product quality. The issues of designing automation systems for the fishing industry are analyzed. Automatic control systems can control quality of fish and fish products. Automated systems receive, freeze, sort, cut, wash, salt, dry, smoke, press, cool, package and store fish and fish products. Modern technological tools are equipped with automatic control systems. Some of them include specialized automation and robotic units equipped with microprocessor control systems. Application and implementation of automation systems for processing fish are described. Future trends are discussed.

Food is essential for all the human being. In the food we are gained the more number of energy to our body. But now days, Lot of chemical substances that are added in the food. That makes human being to be unhealthy person. Children are also affected due to unhealthy food. In our proposal, we used to find the quality of food using the pH sensor. The pH sensors are used to find the pH value of the food and also find the bacterial contamination of the food. This project is used for the government to find the quality of food in the restaurant and display their result in the server and also check the quality of food and display the output pH value in the message using GSM. In additional, we can also share the location of the restaurant.

The article provides a comparison of the main valuation techniques for a regulated parameter of printability of the offset paper by current standards GOST 24356 and ISO 3783: 2006. The results of development and implementation of a complex test scale for management and control the quality of printed production are represented. The estimation scale is introduced. It includes normalized parameters of print optical density, print uniformity, picking out speed, the value of dot gain, print contrast with the added criteria of minimizing microtexts, a paper slip, resolution threshold and effusing ability of paper surface. The results of analysis allow directionally form surface properties of the substrate to facilitate achieving the required quality of the printed image parameters, i. e. optical density of a print at a predetermined level not less than 1.3, the print uniformity with minimal deviation of dot gain about the order of 10 per cents.

A mathematical model for fibrous structures using a direction dependent scaling law is presented. The orientation of fibrous nets (e.g. paper) is analysed with a method based on the curvelet transform. The curvelet-based orientation analysis has been tested successfully on real data from paper samples: the major directions of fibrefibre orientation can apparently be recovered. Similar results are achieved in tests on data simulated by the new model, allowing a comparison with ground truth. peerReviewed

With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.

Under the pressure of energy and environmental protection, we will promote the technological progress and demonstration of electric vehicles, and the construction of charging facilities will continue. Charging facilities planning and orderly charging, as two major research directions of electric vehicle infrastructure, are of great significance for the future development of electric vehicles. The optimal charging of electric vehicles can effectively improve the safe and economic operation ability of distribution network, which is of great significance to its safe operation. Therefore, this paper proposes the outsourcing test experiment and processing of urban electric vehicle public charging network based on 5G and big data. In this paper, through the analysis of the development status of urban electric vehicles, this paper proposes to optimize the charging mode of electric vehicles by combining the charging network forward and backward algorithm. In the outsourcing test experiment, the electrical safety test shows that when the current reaches 1.1-37.1kw: 5000A, when the power factor is 0.8 ∼ 0.9, when the short-circuit current impact is tolerated, the connection device will not affect the breaking operation by contact fusion welding, and the insulation protection will not be invalid. Through investigation and analysis, the satisfaction degree of electric vehicle optimization algorithm is increasing year by year. Through the analysis of the test results, the research in this paper has achieved ideal results and made a contribution to the research of urban electric vehicle public charging network.

Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.

This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.

With the environmental pollution and the shortage of oil resources becoming more and more serious, the development and application of new energy vehicles have attracted more and more attention. Engine is an important part of new energy vehicles, and its performance has a great impact on the vehicle. Compared with traditional industrial motors, new energy vehicle engines have higher requirements on power density, and the improvement of power density poses new challenges to the design of motor cooling system. The purpose of this paper is to study the engine cooling device of new energy vehicles and improve the overall performance of the vehicle. The main research content of this paper is to lay a foundation for the theoretical basis of the engine cooling device, elaborate the working principle of the motor cooling system and the loss of the motor in operation. Then, the heat dissipation system of permanent magnet synchronous motor based on heat pipe is studied experimentally. Aiming at the problem of only considering the temperature rise and ignoring the pressure loss in the flow channel design, a flow channel design method considering the motor temperature rise and the flow channel pressure loss is proposed, and the motor flow channel is optimized. The test results show that the maximum temperature rise at the end is close to 16.56 °C, which is in good agreement with the simulation results. It shows that the heat pipe based heat dissipation system can effectively reduce the temperature rise of motor winding, which provides a new idea for the heat dissipation design of permanent magnet synchronous motor

Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.