Simulation of football based on PID controller and BP neural network

Wheel robot soccer speed control system using a ball object detection method and PID controller. A control system is based on the object detection system's behavior based on the robot position's orientation to the target position. PIDs are instruments, pressure, speed, and other operational factors used in control, temperature adjustment flow, and industrial control applications. The PID controller uses control loop feedback dynamics to control functional variables and is the most accurate and stable controller. The robot position is held by placing the ball vertically. When the robot's work is perpendicular to the ball, the robot moves with a certain speed controlled by the PIT controller based on the robot's distance and the ball. Standard conditions (standard ball) test results show that the robot can detect the ball material while in the vertical position, whether on the robot's right or left. In the random test that changes direction, the robot can move more dynamically as the ball's change in place.     

Application of iot medical image detection and prenatal genetic testing in obstetric clinic

The use of new techniques and methods can be significantly improved during prenatal examination and screening of fetal genetic abnormalities. Expanding the number of vectors in screening and screening for single-cell gene diseases in fetal DNA will increase the probability of success of the whole screening test.Recently, sub chromosomal abnormalities have been introduced into prenatal care. The lack of suitable tools makes it very difficult to obtain information about collection, management, and prenatal genetic testing. The purpose of this study is to improve the accessibility of nutritional information by using the advantages of advanced medical image detection and integrating the IoT (Internet of Things) and prenatal genetic testing platforms in obstetric outpatient clinics. Records are kept that the platform will allow IoT to interact with sensor practitioners and provide immediate access to medical images prior to delivery. This proposed system provides an IoT application for managing obstetric outpatient information and prenatal genetic testing requirements. The requirement for establishment is the realization of the development of an IoT platform for complete medical image detection information management with IoT input required for integration with existing medical information systems. In response, the purpose of the study is to use integrated IoT applications to help nutrition professionals, researchers, patients, and especially mothers, to enhance their collection of medical imaging and prenatal genetic testing IoT sensor based information transfer.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

Development of an index based on fish,macroinvertebrates, phytoplankton,and physicochemical properties to assess urban aquatic ecosystems in Jinan,China

Because of the rapid growth in urbanization over recent decades, urban aquatic ecosystems have been subjected to unprecedented threats and challenges. In this study, we visited 48 sites, of which 14 were reservoir sites, in water bodies in Jinan, China, on six occasions in May, August, and October of 2014 and 2015. We sampled fish, macroinvertebrates, and phytoplankton assemblages and physicochemical factors at each site. We used the richness and diversity of fish, macroinvertebrates, and phytoplankton to evaluate the condition of the aquatic ecosystems of the river sites. We also evaluated the water quality of the reservoir sites using the physicochemical factors of the State Environmental Protection Administration of China. We classified the results into five levels—excellent, good, moderate, poor, and bad—with the comprehensive evaluation index (CEI). More than 40% of the sampling sites were in either poor or bad condition. Nearly half of the excellent or good sites were in the south of Jinan. The CEI was significantly different between the reference and impaired sites; it was significantly and positively correlated with the qualitative habitat evaluation index and was significantly and negatively correlated with conductivity, total alkalinity, and ammonia nitrogen.     

Theoretical analysis of a new segmented anchoring style in weakly cemented soft surrounding rock

According to the tensile failure of rock bolt in weakly cemented soft rock, this paper presents a new segmented anchoring style in order to weaken the cumulative effect of anchoring force associated with the large deformation. Firstly, a segmented mechanical model was established in which free and anchoring section of rock bolt were respectively arranged in different deformation zones. Then, stress and displacement in elastic non-anchoring zone, elastic anchoring zone, elastic sticking zone, softening sticking zone and broken zone were derived respectively based on neural theory and tri-linear strain softening constitutive model of soft rock. Results show that the anchoring effect can be characterized by a supporting parameter b. With its increase, the peak value of tangential stress gradually moves to the roadway wall, and the radial stress significantly increases, which means the decrease of equivalent plastic zone and improvement of confining effect provided by anchorage body. When b increases to 0.72, the equivalent plastic zone disappears, and stresses tend to be the elastic solutions. In addition, the anchoring effect on the displacement of surrounding rock can be quantified by a normalization factor δ.     

基于模糊理论的非处方药的满意度评价系统

为了帮助顾客从众多对症的非处方药中选出最满意的药品,提出了开发1个基于Web的非处方药满意度评价系统。系统根据顾客提供的典型症状,采用疾病与症状相结合的选药策略,查找出所有对症的非处方药,并以药品安全性、疗效、稳定性、易用性、价格作为评价因素,根据顾客的关注角度和关注程度确定各因素权重,用模型b=W·R对对症药品进行满意度评价,按满意度高低依次推荐。经实际应用,系统推荐准确度在70%以上。     

Do social media literacy skills help in combating fake news spread? Modelling the moderating role of social media literacy skills in the relationship between rational choice factors and fake news sharing behaviour

This study modelled the rational factors that predict fake news sharing behaviour. It also tested the moderating role of social media literacy skills. The focus was on social media users in Nigeria. An online survey was conducted to gather the responses from participants across Nigerian geopolitical zones. Structural equation modelling (SEM) Smart PLS 3.6 was used to analyse the data. We found that information sharing, the news finds me perception, trust in social media and status-seeking lead to fake news sharing among social media users in Nigeria. Specifically, trust in social media and status-seeking had a greater effect on fake news sharing behaviour. We also found that social media literacy skills significantly moderate the relationship between information sharing, status-seeking, the news finds me perception, trust in social media and fake news sharing in such a way that the effects/relationships are stronger among those with low social media literacy skills. This outcome contributes to theory and practice which was highlighted in the concluding aspect of this study.     

智能输液器设计浅析

智能输液器是以单片机控制的微型电机代替普通的重力输液,能够精确的控制输液速度和输液量;对输液过程进行监测,当出现管路堵塞、有危险气泡等异常情况时,能够迅速自动关断输液通路,防止输液事故发生;输液结束后自动停机,并通过声光提醒病人或护理人员;具有携带方便、操作简单等特点。