Football Video Feature Extraction and Coaching Significance Based on Improved Huff Coding

Call center is one of the most important means for telecom operators to provide after-sales service for customers. Every day, call center can produce a large number of voice data. But these data do not bring significant value to the enterprise. Recent years, the development of intelligent voice technology has brought a turning point for the operator call center. By using the intelligent voice technology, telecom operators can analyze user behavior characteristics. And then they can develop a behavioral warning, precision marketing and other business strategies. In this article, we introduce the key technologies of intelligent voice technology which could be used in call center. And base on the cloud computing technology, we point out the overall architecture and working mechanism of the intelligent voice analysis system.

With the development of intelligent theory of computer vision and the popularization of surveillance cameras, intelligent video analysis and surveillance technology has been widely used. The traditional video monitoring system will produce a large amount of video information in the application process, which is not conducive to the storage and reference of information, and it can not carry out real-time warning, and has poor robustness to the bad environment. In view of these disadvantages, this paper proposes an improved video enhancement and concentration method, which has a good effect in ensuring the video concentration ratio and the target loss rate, and can improve the shortcomings brought by manual reference. Aiming at the problem of real-time early warning, an intelligent video analysis method is proposed to realize the functions of event alarm and detection analysis. It is of great significance to improve the video monitoring management level of the substation and realize the unattended operation of the substation.

With the continuous development of video detection technology, the video analysis technology based on campus security has become an important part of the construction of safe campus. As the college students still are a group that has poor ability of security protection, campus security issue is closely related to the stability of society and family happiness, and has become a topic of concern to the whole society. The intelligent vision-based campus public safety monitoring system is an important means to achieve security monitoring, it can automatically analyze the video image sequence, and detect, track and identify objects in the monitoring scene without human intervention, and make high-level understanding and analysis of behaviors on this basis. Most of the existing visual monitoring systems can collect and store video data, and the real-time event detection task can automatically be generated through background analysis. Intelligent visual monitoring system should not only be used for accident investigation, but also be used to prevent potential disasters and accidents. The system is consisted of system management platform, event mining and analysis, monitoring and extraction of moving targets, forecasting and tracking targets. The paper makes an in-depth study on the application of intelligent visual detection technology on campus. Based on the intelligent visual video analysis, hidden Markov model is adopted in the paper for video event detection and analysis, motion features and shape features are taken as the observation data, and segmentation method is adopted to analyze the influence of video viewing height and angle on the detection result.

This paper aims to implement an distributed intelligent video analysis system based on TI multimedia Digital Signal Processor (DSP) TMS320DM642, at the same time, the algorithm optimization is also described. The intelligent video analysis system we proposed provides users with fast and precise video analysis services. The video image is transmitted to the image processing board by analog channels and IP cameras, then the DSP processing the video flow. In this system, target detection, segmentation, feature extraction, alarm and automatic tracking can be implemented. In this system, several optimization techniques are also used in algorithms, including the algorithm level optimization (ALO), the program level optimization (PLO) and the instruction level optimization (ILO). Experimental results show the exellent result in reducing the CPU load.

The traditional sky screen has been mainly used to detect the projectile’s information, but it mainly uses the detection environment with natural light as the background. As natural environmental illumination changes with the time of a day, the environmental illumination can significantly change. When the environmental illumination is lower than 1500 lx, the detection ability of the traditional sky screen is extremely low, and accurate detection cannot be achieved. To solve this problem, this article proposes a method of using a high-power near-infrared line laser to form a laser detection plane as a background light source, considering that the line laser divergent plane coincides with the detection screen plane to realize the near-infrared laser detection screen and presents the parameter design and calculation analysis of the receiving optical path. The proposed method considers the geometric structure of the near-infrared laser detection screen and the spatial position of a projectile. A laser reflection characteristics’ calculation model is constructed using bidirectional reflection distribution function (BRDF), and the calculation function of the total spectral irradiance of the projectile’s reflection laser is derived. By combining the optical and structural parameters and the spectral irradiance of the projectile reflection laser, the theoretical calculation function of the detection ability is derived based on the number of electrons that come from the projectile’s surface reflection laser. The experimental verification shows that the designed active laser detection screen instrument can solve the problem of detection and testing of a projectile’s parameter under the condition of low environmental illumination.

Recent research progress of computer science and technology and its application in intelligent art analysis systems is studied in this paper. According to the functions of computer networks, logically speaking, its main purpose is to transmit effective information and to connect general various computer systems through the communication lines to then form the good computer network information transmission system. Inspired by the recent work, the fuzzy clustering, neural network and the convex optimization are considered to construct the evaluation system for the intelligent art analysis. The advantages and drawbacks are jointly studied. The designed model is efficient.

In this paper, we have developed a novel framework to enable more effective investigation of large-scale news video database via knowledge visualization. To relieve users from the burdensome exploration of well-known and uninteresting knowledge of news reports, a novel interestingness measurement for video news reports is presented to enable users to find news stories of interest at first glance and capture the relevant knowledge in large-scale video news databases efficiently. Our framework takes advantage of both automatic semantic video analysis and human intelligence by integrating with visualization techniques on semantic video retrieval systems. Our techniques on intelligent news video analysis and knowledge discovery have the capacity to enable more effective visualization and exploration of large-scale news video collections. In addition, news video visualization and exploration can provide valuable feedback to improve our techniques for intelligent news video analysis and knowledge discovery.

This paper is mainly based on the video monitoring of unmanned substation, with the detection and tracking object of moving target, combined with the inter-frame difference method and background difference method, in order to achieve effective detection of moving targets through improving the traditional algorithm; combined with cam- shift algorithm and mean-shift algorithm to reduce the calculated amount and improve the accuracy of target tracking, in order to achieve intelligence level of substation video monitoring. This paper uses the computer vision and video analysis method to further analyze and understand the image sequences recorded by the camera, and links with related equipment to do simple processing while timely giving an alarm and feedback to monitoring personnel. In this way, the monitoring personnel can offer centralized processing for accident alarm, thus greatly reducing erroneous judgment and missing judgment of accidents, and improving the reliability, accuracy and timeliness of the alarm. Therefore, the intelligent video analysis is applied to substation attending, and combined with the traditional monitoring system, thus having a very important significance on security monitoring of the substation.

Surgery is one of the larger wounds in conventional surgery, and patients often experience different pain and postural discomfort after surgery. With the ever-changing standards of medical care and patient care requirements, providing high-quality care to postoperative patients is an important measure to reduce complications and promote rapid recovery. However, in the traditional postsurgical nursing methods, there are often the phenomenon that wrong patients are connected, patient data is messy, and medicines are counted incorrectly, which directly leads to a rapid decline in nursing efficiency. In the context of the rapid development of artificial intelligence and big data, intelligent medical data analysis technology has gradually been integrated into the medical field. This paper analyzes and studies the application effect of intelligent medical data analysis technology in postoperative nursing. It is aimed at changing the traditional postoperative nursing methods and improving nursing efficiency, and it provides important suggestions for the development of postoperative nursing in the new era. Combining big data and Internet of Things technology, this paper builds a smart medical Internet of Things framework and an intelligent postoperative care system and uses machine learning algorithms to preprocess relevant medical data. The final experimental results show that the intelligent medical data analysis technology has a good effect in improving the nursing efficiency after surgery, and the nursing efficiency has increased by 6.9%.

We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.

Aiming at the problem of account risk discrimination and prediction in financial system, an intelligent analysis and processing technology based on K-means clustering algorithm study the analysis and processing technology of financial data in detail, this paper fully analyzes various management requirements of the financial system, and completes the automatic execution of a large number of standardized financial work and the identification, prediction and analysis of account risk by using process automation technology and neural network algorithm. In addition, by introducing k-means algorithm, this paper studies and classifies, so as to realize the comprehensive analysis and evaluation of various abilities such as debt repayment, development, operation and profitability of financial accounts, so as to avoid the one sidedness and subjectivity of artificial risk evaluation to a great extent.

This paper presents the methodology and results of a recent research project SCETIST (2010-2013) devoted to foresight of selected artificial intelligence technologies. The AI trends and scenarios have been elicited using expert Delphi as well as via a simulation of a hybrid discrete-time and discrete-event control system. The system components include a complex information society model, which describes the evolution of social, economic and scientific factors relevant to the production and adsorption of intelligent technologies. The discrete-event component is tailored to model the and science and technology (S&T) development processes in each area under consideration. We specifically investigated the development of intelligent internet technologies, including e-health, e-government and expert systems. Special attention has been paid to decision support systems, digital creativity, and recommenders. Other project work-packages encompass neurocognitive and computer vision systems, as well as quantum and molecular computing. Finally, we will present the results related to the evolution of rule-based and creativity support systems in the context of global economic and research trends.

With the rapid development of intelligent manufacturing technology, the mechanical processing industry is facing an urgent need to transform from traditional modes to high efficiency, precision, and flexibility. However, there are problems in traditional processes such as the disconnect between standardization and intelligence, and limited local optimization efficiency. Therefore, this article focuses on the intelligent transformation and design of mechanical processing technology, and proposes an integrated solution that integrates standardized benchmarks and intelligent technology. This plan constructs a framework of “standardized foundation construction intelligent technology empowerment closed-loop collaborative optimization”, based on a standardized system, integrating industrial Internet of Things, machine learning, and digital twin technologies to achieve real-time data collection, dynamic parameter optimization, and virtual real collaborative simulation. Experimental verification shows that this scheme can significantly improve machining performance: surface roughness is reduced by 44%, machining cycle is shortened by 36%, and equipment failure warning accuracy reaches 95%. The research results confirm that by ensuring data quality and operational consistency through standardization, combined with intelligent technology to achieve dynamic control of the entire process, it can effectively break through the limitations of traditional processes and provide a feasible path for the intelligent upgrade of mechanical processing technology.

The subject of research is the activities of state institutions aimed at the development of digital technologies in the economy and other spheres of society life, particularly intelligent technologies for decision support and making. The purpose of research was to determine the prospects for using intelligent technologies in the federal risk management system. In the context of the Quality of Life problem solution, an approach to creating a factor model of the problem domain of ensuring security and sustainable socio-economic development is shown. The model identifies 12 spheres combining 72 interrelated factors. Problems and threats to be solved and overcome in the process of development and wide promotion of intelligent information technologies in the practice of managing socio-economic systems are considered. It is concluded that intelligent systems used for managing socio-economic systems must undergo strict verification of not only their adequacy but also to expose possible user manipulation devices embedded in them.

One of the biggest hurdles in developing an innovative, intelligent failure analysis system that caters to the needs of the manufacturing industry, is the lack of manufacturing data to train a deep learning driven system. The Finite Element Method (FEM) has the potential to bridge this data gap by developing simulations that encapsulate the underlying physical phenomena in the form of complex differential equations and solve them numerically. This work presents a detailed study comparing the results obtained for pulse thermography experiments and the FEM. Samples with artificially introduced defects in the form of flat bottom holes are fabricated and used for this study. The concept of defect signatures based on 2D axisymmetric FEM is introduced to significantly reduce the computation time for transient thermal simulations. The method for parameter estimation and boundary conditions for the FEM is explained. The evaluation of the temperature evolution of sound and defect regions, surface temperature distribution and thermal contrast shows, that the FEM results are an excellent estimate of the experimental setup and are a good fit for generating varied data for data augmentation to aid the development of a deep learning based image segmentation model.