Teaching chemical engineering using Jupyter notebook: Problem generators and lecturing tools

The primary objective of this study is to develop teaching materials for chemical engineering students (or students pursuing other related B.Sc./M.Sc. degrees) that encourage self-learning to facilitate understanding and the development of problem-solving skills. Tools are oriented to support the teaching of “Introduction to Chemical Engineering” courses to 1^st year B.Sc. students in Chemical Engineering, 2^nd year B.Sc. students in Chemistry, and 3^rd year B.Sc. students in Biochemistry at the Universidad Complutense de Madrid (UCM). Problem generators of standard exercises, as an additional complement to the exercises that are used in the lectures, seminars, and assignments to facilitate the learning of the students, have been developed. These generators provide exercises to students with “unlimited” initial values (with certain restrictions). The software that was used to develop these materials was Jupyter Notebook, which runs under the Python 3.6 language. The problem generators can be classified into fixed problem generators, case-based generators, and random problem generators. Additionally, lecturing tools have been developed to support teaching using live/interactive examples. These examples can learning understanding of various topics of the courses.     

以课题制为核心的化工专业实习方式改革与实践

为培养学生的工程素质,我校化工专业采用以课题制为核心的实习方式，将企业生产中的工程问题梳理、凝练成小课题，让学生分组完成，企业技术人员和学校教师共同指导，采用课题报告、答辩、过程表现、安全知识考核等评价学习效果。这种实习方式能有效培养学生的自学能力和研究能力，增强学生的团队合作、沟通交流能力和社会责任感，显著提高学生的工程素质，有助于学生在学科竞赛和课程学习中取得优异的成绩。     

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.     

Adaptive hierarchical artificial immune system and its application in RFID reader collision avoidance

This paper proposes an efficient adaptive hierarchical artificial immune system (AHAIS) for complex global optimization problems. In the proposed AHAIS optimization, a hierarchy with top-bottom levels is used to construct the antibody population, where some antibodies with higher affinity become the top-level elitist antibodies and the other antibodies with lower affinity become the bottom-level common antibodies. The elitist antibodies experience different evolutionary operators from those common antibodies, and a well-designed dynamic updating strategy is used to guide the evolution and retrogradation of antibodies between two levels. In detail, the elitist antibodies focus on self-learning and local searching while the common antibodies emphasize elitist-learning and global searching. In addition, an adaptive searching step length adjustment mechanism is proposed to capture more accurate solutions. The suppression operator introduces an upper limit of the similarity-based threshold by considering the concentration of the candidate antibodies. To evaluate the effectiveness and the efficiency of algorithms, a series of comparative numerical simulations are arranged among the proposed AHAIS, DE, PSO, opt-aiNet and IA-AIS, where eight benchmark functions are selected as testbeds. The simulation results prove that the proposed AHAIS is an efficient method and outperforms DE, PSO, opt-aiNet and IA-AIS in convergence speed and solution accuracy. Moreover, an industrial application in RFID reader collision avoidance also demonstrates the searching capability and practical value of the proposed AHAIS optimization.     

货币银行学课程自主性学习的教学设计和实践

货币银行学是理论和实践兼具的经济管理类本科专业核心课程,在传统教学模式和教学手段下,难以促进学生自主性学习。本文旨在对金融市场和商业银行的教学内容进行模拟教学设计,并创立课程博客以搭建师生交流互动平台,以期对自主性学习方式进行有益的教学实践探索。     

Making on-line logistics training sustainable through e-learning

The purpose of this study is to investigate the possibility of using an online logistics certification learning environment as a training tool to equip future logisticians with required logistics skills. This study incorporates an online logistics certification website that was constructed for college students to familiarize themselves with the certification. In addition, this study also performed comparison tests on students before and after their interaction with the web-based learning environment system to ascertain the system’s effectiveness. Our findings suggest that such a system might motivate students to familiarize themselves with logistics-related certification information and can enhance students’ professional capabilities. In addition, the web-based learning environment might possibly motivate students to join logistics related industries in the future.     

A PID-type fuzzy controller model for machine control applications

This paper introduces a PID-like fuzzy controller model, which consists of only five fuzzy rules. Each fuzzy rule is designed to carry out a single control task. A self-learning procedure is used to determine the input scale factor for auto-tuning the system. A new performance criterion is suggested as an object function for the auto-tuning procedure. The features of this fuzzy controller, used in position control applications, are demonstrated by the simulation results of two different types of plants — a d.c. motor and an inverted pendulum.Nomenclature adjusting parameter of AIAE - C _I integral control constant in ruleR _I - C _L leading control constant in ruleR _zn andR _zp - C _P proportional control constant in ruleR _P andR _N - D _ZE domain of membership function _ZE - K _a adaptive adjusting constant ofC _L - K _H adjusting parameter for high-border of dynamic domain space - K _L adjusting parameter for low-border of dynamic domain space - P _R ramp input command - P _S step input command - S _de scaling factor for input variable de(change of error) - S _e scaling factor for input variablee(error) - S _o controller output scaling factor - U _max maximum output limit of fuzzy controller     

单体模糊神经网络自学习问题研究

本文采用的单体模糊神经网络在智能控制中兼有模糊控制与人工神经网络控制的特点，不仅具有良好的控制特性，而且能够进行模型自学习和在线自调整。结合本身结构，人工神经网络理论和模糊理论，可得到ＭＦＮＮｓ自学习的一系列算法。     

基于人工生命系统的计算框架

人工生命是通过对自然生命现象的模拟来研究行为如何变得智能、自适应的学科，其本质在于大量生命之间相互作用产生的自下而上的自组织行为。人工生命已经在许多行业得到了广泛的应用，从人工生命发展得到了众多的算法。但这些算法大都是专用的算法，各种算法之间的相似性很差，这极大地限制了人工生命算法的应用广度和深度。该文将对人工生命的本质作一个描述，然后提出一种人工生命算法的统一框架结构，同时通过此算法框架在TSP问题求解过程中的应用验证其有效性。最后对算法框架将来的研究作简单的讨论。     

自学习模糊滑模控制及其在电液伺服系统中的应用

本文针对一类非线性系统的跟踪控制问题,基于滑模等价控制的基本概念,提出了一种具有在线学习能力的模糊滑模控制方法,利用学习控制实现模糊控制规则的动态更新,使得模糊控制的输出逐步逼近滑模等价控制,最终实现渐近稳定的滑模控制。