2020年5月,教育部印发的《高等学校课程思政建设指导纲要》中提出,专业实验课程要增强学生的创新精神.结合电路基础实验教学的授课经验,以戴维南定理的验证为分析对象,提出"辨析型"实验教学模式的设计思想和执行方法.在基础类实验教学内容的设计过程中,给学生提供的电路模型或参数与理想情况下略有偏差,让学生根据实验所得数据,分析论证后得到相应结论,从而给学生提供独立思考、分析判断的机会,引导学生建立多角度、创新性的思维模式;同时借助在实验操作环节获得不理想实验数据的处理,引导学生建立诚信的学术人格,为培养诚实可靠的社会主义建设者和接班人,承担好育人责任.

分析了自动化类等相关专业嵌入式系统设计课程教学中存在的问题,进而对该课程的教学方法、教学手段、考核方式进行了改革和实践.在该课程的教学中引入慕课等线上资源,充分利用微信教学立方等现代教学手段进行课堂互动,并通过"任务驱动+项目导向"的教学模式督促学生自主完成学习任务.

随着互联网技术的快速发展,为了实施教育部的《教育信息化"十三五"规划》,针对学生的学习指导环节,建立了相关的制度文件,分析了互联网教学的特点,明确了各方面的职责,确定了学生的指导途径,从而提出了以"互联网+"下的混合式教学模式为基础,学业预警制度和检查监督制度为贯穿的全方位、多渠道的立体学生学习指导机制,通过3年的实施与对比,收到了较好的教学效果.

通过在自动控制原理课程中使用混合式教学模式,对ZOOM直播和智慧树翻转课支持下的混合教学模式的教学设计、具体实施过程、效果、教学反思进行了详细论述.对如何提高线上教学的教学效果进行了总结和展望,并通过这种不断地探索和实践形成可以推广应用的混合式线上教学模式.

在许多物理现象和生物化学处理的研究过程中,非接触传输和处理技术具有非常广阔的应用前景.以在平面阵列式超声悬浮装置中悬浮传输的聚苯乙烯小球为研究对象,建立声学有限元模型.通过有限元仿真与实验,分析小球在传输中的跳跃和振荡问题,提出用换能器阵列的相位和幅值调制驱动方法来提高小球的传输稳定性.实验结果表明,该方法可以显著减小物体在传输过程中的跳跃和振荡幅值,聚苯乙烯小球在传输过程中的跳跃距离降低64.9％,振荡幅值降低41.9％.

针对计算机控制技术课程本身实践性强的特点,采用基于创新项目的教学方式,分别从理论教学和实践教学两个方面进行改革.即在创新项目的选题方法、总体方案设计、具体实施、考核方式等几个方面进行了改革和探索.构建了工程教育专业认证和创新项目相融合的多元化教学模式,以满足专业认证标准对课程的要求.这种教学方式极大地激发了学生的学习兴趣,在专业课学习的同时,提高了其项目设计和研发的能力,进而增强分析问题与解决问题的能力,符合我校应用型人才培养的定位.

采用任务驱动+项目导向教学方法,在课程讲授过程中融入课程思政,改革作业形式及成绩评定办法,充分利用线上资源等.通过对三届学生的教学情况对比,显示改革取得初步成效.

在微装配、生物工程、制药等领域,悬浮技术因其具有非接触、微重力、可操控等特性成为一项重要的应用技术.由于声悬浮技术对被悬浮物体没有电磁学性质上的特殊要求,悬浮较稳定且容易控制,因而在各种悬浮技术中倍受青睐.对一种基于双换能器的超声悬浮传输装置进行有限元分析,明确该装置进行超声悬浮传输的机理,分析换能器与反射面之间的距离和换能器振速对传输过程的影响,并基于分析结果,提出一种操控颗粒进行水平悬浮传输的方法.

以工程教育专业认证通用标准为依据,分析自动控制原理课程教学当前存在的问题;以学生毕业要求能力的达成为目标,形成支撑更加紧密的课程目标,围绕课程目标对课程进行教学内容优化、教学方式改进、课程考核细化和量化,结合案例教学和网络平台,以激发学生学习兴趣和自主性提高教学质量,真正构成了以学生为中心、成果为导向的教学方式,并通过课程实践对毕业要求进行达成评价,评价效果良好.

根据工程教育专业认证的要求,结合我院电气工程及其自动化专业的特色,阐述了"可视化程序设计"课程毕业要求的指标点,根据毕业要求指标点,对"可视化程序设计"课程的理论教学、实验教学进行了达成度分析,并给出了课程的持续改进方法.课程达成度评价是衡量人才培养质量的一种手段,它对课程的持续改进有指导作用,两者又相辅相成,都是促进专业教学改革落实到课程教学的有效方法,也可以为有关高等院校和教师提供参考.

In order to solve the problem of tracking failure when the target is near the background color or the target is occluded,an improved Camshift target tracking algorithm is proposed in this paper.First,the calculation of histogram for the improved algorithm model used the probability distribution histogram with the fusion of color and texture and hence it solved the problem that using a single color model is difficult to adapt to the change of background objects caused by large range of motion and occlusion.Secondly,the weight image can be calculated from the square root of the ratio of the feature probability of target model to that of candidate target model.The calculated weight was used to further estimate the position and direction of the target.It overcomes the shortcomings of the original Camshift algorithm that only relies on the target model in the calculation of weight image and greatly reduces the influence of background features on tracking.At last,the state of moving object is estimated by particle filter to overcome the occlusion,interleaving or overlap,and then the tracking accuracy of target position is improved.The average success rate of the improved algorithm is more than 50％,and the average central position error is less than 20％.Experimental results showed that the algorithm can obviously improve the performance of target tracking,and achieve the target tracking effectively and accurately.

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

In order to surmount the major difficulties in multi-target tracking, one was that the observation model and target distribution was highly non-linear and non-Gaussian, the other was varying number of targets bring about overlapping complex interactions and ambiguities. We proposed a kind of system that is able of learning, detecting and tracking the multi-targets. In the method we combine the advantages of two algorithms: mixture particle filters and Multiple Instance Boosting. The key design issues in particle filtering are the selecting of the proposal distribution and the handling the problem of objects leaving and entering the scene. We construct the proposal distribution using a compound model that incorporates information from the dynamic models of each object and the detection hypotheses generated by Multiple Instance Boosting. The learned Multiple Instance Boosting proposal distribution makes us to detect quickly object which is entering the scene, while the filtering process allows us to keep the tracking of the simple object. An automatic multiple targets tracking system is constructed, and it can learn and detect and track the interest object. Finally, the algorithm is tested on multiple pedestrian objects in video sequences. The experiment results show that the algorithm can effectively track the targets the number is changed.

To solve the problem of particle filter' s degradation and lower tracking robustness with single feature application, a kernel particle filter tracking method was proposed by multi-feature fusion. Firstly, the method of new weight updating in kernel particle filter was put forward. Then the robust tracking was achieved by integrating the color and texture feature under the framework of kernel particle filter method. Spatiograms and integral histogram was used respectively to calculate color and texture feature. The disadvantages of their own were effectively overcome by the two kinds of calculation methods for two characteristics. The sampling efficiency was improved by using the algorithm and the larger calculation problem of particle filter and particle degradation was solved. Finally target tracking experiment was conducted by adopting the methods for complex background and serious occlusion circumstances. Experimental results show that the proposed algorithm can track target accurately and may well deal with object occlusion.

In order to better meet the requirement of engineering education professional certification training target ,the teaching way of visual programming course is reformed .After analyzing the problems of visual programming in current teaching thing ,this paper sticks to the thought "students as the center" in the professional certification . Starting with "Reforming course content and revising outline" ,"Combining theory with practice teaching" ,"Establishing a course library"and"Carrying out project-driven teaching" , this paper enhances the subjective initiative of students and improve innovation ability of students .Then it improves the ability of the students in applying visual programming to analyze and solve complex problems in the professional field . And it makes visual programming course that can plays an important role in engineering culture education .

为了更好地满足工程教育专业认证培养目标的要求,对自动控制原理实验教学进行了改革.分析了电气工程专业在工程教育专业认证中当前教学所存在的问题,坚持专业认证中"以学生为中心"的思想,从实验教学内容的设置、教学内容设计、实验教学方法、增加实验演示、开展课题研究和科技竞赛等几方面出发,增强学生的主观能动性、提高学生创新能力,进而提高学生分析和解决控制领域复杂问题的能力,使电气自动化类课程在工程文化教育中发挥重要作用.

In view of the shortcomings of traditional particle filter which is lacking of utilizing current observational information, this paper proposes a multi-featured fusion tracking algorithm based on simulated annealing to improve particle filter. The proposed method solves the problem of large amount of computation and lack of particle number in high dimensional state. A hierarchical random search annealing method is used to generate a better proposal distribution in the Monte Carlo importance sampling. In the likelihood approximation, this paper integrated image feature attribute of colors and edges to generate weight function in the different annealing layer by weighting. Using this method to track the moving objects with complex background and occlusion, the experimental results show that the proposed method has high tracking accuracy and strong stability.

The behavior recognition algorithm based on block matrix is proposed, which mainly includes six kinds of abnormal behavior of jumping, accelerated running, falling down, squatting, waving and bag. Firstly, inter frame difference method is applied to on video stream to extract contour features of the moving object and the mathematical morphology method was carried out to deal with it. Then block bilateral two dimensional linear discriminate analyses are applied to extract features for the obtained contour. Finally, nearest neighbor classifier is used for classification through template matching method. Experiment results show that the method has a certain practical value in the identification of the abnormal behavior given samples in this paper.

设计了一种利用Arduino技术与集成电路(STM8L152)构成的燃气定时开关系统.通过Arduino构成远程控制模块,可利用Wi-Fi等无线方式传递控制信号.信号传递给STM8L15和LCD笔段液晶-3位构成定时显示装置.收到定时关闭信号后启动定时和报警装置,待定时器时间结束后,电磁阀自动旋紧,关闭燃气通道.

基于移动机器人的运动机理,对其运动控制进行研究.确立机器人控制系统的电机模型;然后通过对机器人系统的动力学特性和移动控制特性的分析,给出基于模糊自适应PID控制的机器人运动控制方法,仿真实验结果表明该算法可实现机器人运动控制系统的有效控制.