艺术设计实践教学模式的探寻与创新实践——以环境艺术设计实践改革为例

本文简述欧美艺术设计实践教学模式的发展及优秀经验,阐述我国艺术设计实践教学模式的现状。以我校环境艺术设计实践教学创新改革为切入点,介绍两个实践案例。     

科氏力质量流量计的工作原理及应用

科氏力质量流量计能直接测量质量流量，在我国自20世纪90年代初进入工业测量领域以来，应用越来越广泛。主要讨论科氏力流量计的工作原理及应用。     

超低功耗智能监护报警器研究与设计

该文针对现有国内婴幼儿智能护理产品的优缺点,深入分析了如何防止睡觉受凉和尿床等问题,并对国外婴幼儿护理产品和婴幼儿护理理论进行了深入研究,设计了一种新型的智能化监护报警器。该报警器以低成本的松翰单片机为核心,采用纽扣电池供电,实现了超低功耗设计。实验表明,该装置集尿湿报警、走失报警、踢被报警、发烧报警和人工呼救多功能于一体,在具有使用安全、性能可靠、低功耗、报警及时、准确等优点的同时,还具有极好的性价比。     

Investigation on film cooling characteristics from a row of converging slot-holes on flat plate

Experimental and numerical researches were conducted to investigate the cooling performance of a single row of consoles (converging slot-hole) on a large-scale flat-plate model. The results show that the coolant flow from a row of consoles shows good lateral uniformity of adiabatic effectiveness, with regions of slightly enhanced cooling occurring between the consoles. For the console cooling geometry, the interaction between coolant jet from inclined console and the mainstream flow results in reasonable vo...     

高职学生学习主动性意识的缺失与重塑

分析高职学生的特点和学习主动性意识缺失的症状,从学生的学习自觉自信、教师的课堂教学反思、学校的学生评价机制等方面,对高职学生学习主动性意识的重塑策略进行探讨,并提出相应的对策。     

Experimental Study of Microstructure Evolution during Tempering of Quenched Steel and Its Application

LARGE-SIZED FORGINGS are usually intensivelyquenched to obtain the desired microstructuredistribution,high strength and hardness.Becauseresidual stresses are high in quenched large-sizedforgings,tempering processes follow quenchingprocesses to reduce the residual stress and obtain goodmechanical properties.Several differentmicrostructures are generated in quenched steelforgings,such as martensite,bainite and retainedaustenite.Modeling of microstructure evolution inquenched steel during …     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.