变精度下近似算子与程度上近似算子的逻辑与运算模型

基于精度与程度的逻辑与需求,提出了变精度下近似算子与程度上近似算子的逻辑与运算模型。在该模型中,得到了变精度下近似算子与程度上近似算子的逻辑与运算的精确描述与基本性质,提出了宏观算法与微观算法,进行了算法分析与比较,得到了微观算法更具空间优势的结论。最后用医疗实例对模型与算法进行了说明。变精度下近似算子与程度上近似算子的逻辑与运算模型,部分拓展了变精度粗糙集模型、程度粗糙集模型和经典粗糙集模型,并在这些模型中得到了近似算子的相应性质。     

计算机技术与虚拟技术的协同发展

计算机技术的实现与迅速发展,带动了诸多领域的信息技术变革,拓宽了虚拟技术在社会领域的应用。文章从计算机技术与虚拟技术的实践化应用角度出发,简要概述了计算机技术与虚拟技术的相关概念,分析了虚拟技术工作原理,研究了计算机技术与虚拟技术协同发展的特点,并在此基础上探讨了计算机技术与虚拟技术的应用现状与基于虚拟技术的多台计算机协同发展。计算机技术作为一种技术手段,在与虚拟技术有机融合并协同发展的过程中,必将为人们的生产与生活带来了极大的便利。     

变精度上近似与程度下近似粗糙集模型的正域及其算法

针对变精度近似与程度近似的结合问题及正域的核心地位,组建了变精度上近似与程度下近似粗糙集模型,并定义了其中的正域概念。研究了模型正域与精度量化指标和程度量化指标关联的内涵及意义,得到了模型正域的精确刻画与性质。为了计算模型正域,提出了自然算法与原子算法,并进行了算法分析与算法比较,得到了自然算法与原子算法具有相同的时间复杂性,而原子算法却具有更优的空间复杂性的结论。最后用一个医疗实例对模型正域及其算法进行了分析与说明。变精度上近似与程度下近似粗糙集模型的正域,从膨胀的优势方向完全扩展了经典粗糙集模型的正域,对与精度参数和程度参数相关的必然性知识发现具有意义。     

VB与Matlab混合编程的仿真应用

介绍了Visual Basic与Matlab混合编程方法在可靠性与维修性仿真中的应用。此方法突破了以往利用单一语言实现系统可靠性与维修性仿真的限制,更广泛的利用了现有技术成果,降低了编程难度,加快了编程速度。详细介绍了Visual Basic与Matlab混合编程的方法及实现步骤。包括Matlab编译环境的设置、Matlab程序编写、Matlab COM组件的编译与发布以及如何在Visual Basic中使用Matlab COM组件。详细叙述了混合编程每个步骤实现的技术关键,为可靠性与维修性仿真解决了软件实现的技术问题。最后一部分给出了利用Visual Basic与Matlab混合编程方法进行可靠性与维修性仿真的示例。     

人性化可折叠助行车设计

助行车是帮助行走不便的老年人与腿部有残疾的人群辅助行走与实现腿部功能恢复训练的设备。讨论了人性化设计理念,研究与分析了行走不便的老年人与腿部残疾人群的生理与心理需求,进行了助行车人性化设计的人机关系分析,给出了助行车关键部位设计尺寸,在考虑了稳定性与舒适性要求的基础上完成了可折叠助行车的总体设计与细节设计。所设计的助行车除了具备助行功能,还具备一定娱乐功能与报警等功能。助行车可在不需行走状态变换成座椅休息,助行车可折叠存放与搬运。给出了助行车的助行状态设计方案、座椅休息状态设计方案、折叠存放状态设计方案以及细节设计方案。     

ERP用户行业和地区分布统计

从以上表格中ERP用户的行业分布数据可以算出,IT与电信行业的用户占到了11.5％,电子与家电行业的用户占到了10.4％,汽车与航空行业的用户占到了7.9％,服装与纺织行业的用户占到了2.5％,机械与机电行业的用户占到了10.3％,能源与化工行业的用户占到了8.2％,医药行业的用户占到了9.5％,金融、流通与物流行业的用户占到了20.6％,其它行业的用户占到了19.1％。     

基于DSP的悬浮转子的控制

为满足陀螺转子自动控制的需求,设计了以DSP微处理器中的TMS320F2812为硬件核心的悬浮转子自动控制系统,完成了系统的自动控制及状态监测,与上位机的数据与控制信号通讯。采用CPLD作为扩展接口实现了DSP与数据输入输出等外围设备之间的联接,完成了对转子信号及运行参数的采集控制与存储,多串行口的扩展等功能。与传统转子控制系统相比,提高了系统的安全性和通信的高效性与抗干扰性能,实现了转子控制的自动化、智能化,为与其他系统的整体运作提供了可能性。     

一种新型限幅器

本文介绍了为自动控制系统设计的一种新型限幅与钳位电路。论述了设计的必要性,给出了电路的组成并分析了工作原理,叙述了调整方法,给出了限幅与钳位特性曲线。给出了应用实例并概述了工作原理及性能特点。由于巧妙地利用了运算放大器的输出电平与中间级电位的关系,使得限幅与钳位性能的调节灵活方便,同时具有电路简单,限幅范围广,限幅值与钳位点任意调节等特点。     

校企共建实验室的合作模式与运行机制探讨

经济与科技改革背景下,校企共建实验室为高校与企业提供了重要的科研实践平台,加速了重要科研成果的转化与推广,实现了高校与企业之间的资源共享,是实验室建设发展的重要方向。本文系统的探究了目前我国校企共建实验室的合作模式,分析了校企共建实验室的运行机制,并针对存在的弊端提出了一系列的建议,为校企共建实验室的建设与发展提供重要的理论参考与智力支持。     

一种PP型无线供电系统的分析

本文旨在对于小功率,短距离的无线充电场景下,以PP型MCR-WPT系统作为模型基础,提出了在模型发射端,只以单个MOS管作为驱动核心的电路设计,并在此基础上分析了该种电路的原理与特性。通过仿真分析了谐振频率与效率,耦合系数与谐振频率,驱动频率与谐振频率,负载与传输效率最佳频率与传输效率的关系。通过仿真验证了在该种电路设计下,存在一个占空比域度与传输效率最佳频率,耦合系数与谐振频率成反比,驱动频率约大于谐振频率,在最佳频率下,负载与传输效率为反比的关系。最后通过制作实物验证了仿真的正确性。     

基于谐波的乐纹提取和音乐检索

提出了一种基于节拍内音乐谐波特性的乐纹特征提取方法,首先求得每帧音乐的谐波信息,再利用跟踪得到的节拍,计算节拍内所有帧的谐波信息的均值,构成此节拍的乐纹特征矩阵。为了提高音乐检索的效率,设计了一个二级音乐检索算法：根据节拍信息,将与查询音乐片段的每分钟节拍数相近的音乐作为候选音乐,再逐节拍计算所查询音乐的乐纹和候选音乐乐纹的相似度,选择相似度最高的音乐作为检索结果。实验结果表明,提出的乐纹特征和音乐检索算法有效地提高了检索准确率和检索效率。     

Emotion-based music recommendation by affinity discovery from film music

With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user’s preference on music. However, sometimes, it might better meet users’ requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average.     

Algorithmic compositions based on discovered musical patterns

Computer music composition is the dream of computer music researchers. In this paper, a top-down approach is investigated to discover the rules of musical composition from given music objects and to create a new music object of which style is similar to the given music objects based on the discovered composition rules. The proposed approach utilizes the data mining techniques in order to discover the styled rules of music composition characterized by music structures, melody styles and motifs. A new music object is generated based on the discovered rules. To measure the effectiveness of the proposed approach in computer music composition, a method similar to the Turing test was adopted to test the differences between the machine-generated and human-composed music. Experimental results show that it is hard to distinguish between them. The other experiment showed that the style of generated music is similar to that of the given music objects.     

面向推荐系统的音乐特征抽取

音乐推荐系统是指根据用户的历史浏览数据,从候选库中推荐给用户可能喜欢的音乐的一种新型网络服务。该系统的关键在于需要对整个数据库按照音乐风格进行分类,基于此提出一种新的音乐特征处理方法来完成音乐库分类,以有效实现音乐推荐。该方法首先为候选音乐库构建常规的音乐特征数据集,然后基于分形理论对数据集进行属性约简,获取每一首音乐的推荐特征向量,并且依据特征向量的特点,定义了一种新的距离度量方法。在包含六种风格的音乐数据库的实验中,仿真结果证明了提出的音乐推荐特征和距离度量的有效性,与现有的基于内容的音乐检索研究相比,音乐推荐特征的使用极大地降低了对数据库存储量的需求,对音乐推荐系统的网络开发具有很好的应用价值。     

基于小波变换的电子音乐信号降噪算法

电子音乐信号受到多种因素的影响,包含了大量的噪声,直接影响电子音乐信号的后续处理。为了提高电子音乐信号质量,改善信号的信噪比,针对当前电子音乐信号降噪算法存在的局限性,设计了一种基于小波变换的电子音乐信号降噪算法。首先分析电子音乐信号降噪的研究现状,指出各种电子音乐信号降噪算法的弊端,然后采集电子音乐信号;对其进行截尾操作,提取最有效的电子音乐信号,最后引入小波变换对电子音乐信号进行多尺度分解和重构操作,过滤掉电子音乐信号中的噪声。并采用Matlab 2018编程实现电子音乐信号降噪仿真实验。结果表明,小波变换克服了当前电子音乐信号降噪算法的不足,电子音乐信号降噪的精度高,能够有效识别噪声,使得降噪后的电子音乐信号具有更高的信噪比。     

A novel method for personalized music recommendation

With the development of digital music technologies, it is an interesting and useful issue to recommend the ‘favored music’ from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the ‘favored music’ which has not been rated by anyone, (b) how to avoid repeatedly recommending the ‘disfavored music’ for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users’ interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users’ logs on music listening records. By the feedback of the user’s options, the proposed methods accommodate the variations of the users’ musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the ‘favored music’ to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.     

融合CNN-BiLSTM和自注意力模型的音乐情感识别

随着音乐科技研究的不断深入,音乐情感识别已被广泛实践和应用在音乐推荐、音乐心理治疗、声光场景构建等方面。模拟人类感受音乐表现情感的过程,针对音乐情感识别中长短时记忆神经网络的长距离依赖和训练效率低的问题,提出一种新的网络模型CBSA（CNN BiLSTM self attention）,应用于长距离音乐情感识别回归训练。模型使用二维卷积神经网络获取音乐情感局部关键特征,采用双向长短时记忆神经网络从获取的局部关键特征中提取序列化音乐情感信息,利用自注意力模型对获取的序列化信息进行动态权重调整,突出音乐情感全局关键点。实验结果表明,CBSA模型可缩短分析音乐情感信息中数据规律的训练时间,有效地提高音乐情感识别精确度。     

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

     

计算机音乐制作与数字音频

近年来,随着计算机的快速发展,利用计算机的音乐制作以及数字音频的制作得到了很广泛的应用。分析了现代音乐的创作过程,通过计算机的智能算法作曲功能,详细解释了不同计算机语言在识别音乐时的相关理论基础。同时,在计算机的辅助功能下,让音乐的旋律实现了音乐的二维可视化。最后,根据音乐旋律中的响度特征实现了音乐的自动识别,分析了音乐旋律与计算机的关系,为后续的学者分析提供了相应的参考。     

Comparison of content-based music recommendation using different distance estimation methods

With rapid growth in the online music market, music recommendation has become an active research area. In most current approaches, content-based recommendation methods play an important role. Estimation of similarity between music content is the key to these approaches. A distance formula is used to calculate the music distance measure, and music recommendations are provided based on this measure. However, people have their own unique tastes in music. This paper proposes a method to calculate a personalized distance measure between different pieces of music based on user preferences. These methods utilize a randomized algorithm, a genetic algorithm, and genetic programming. The first two methods are based on Euclidean distance calculation, where the weight of each music feature in the distance calculation approximates user perception. The third method is not limited to Euclidean distance calculation. It generates a more complex distance function to estimate a user’s music preferences. Experiments were conducted to compare the distance functions calculated by the three methods, and to compare and evaluate their performance in music recommendation.