一种混叠语音清浊音判断方法

提出了一种混叠语音清浊音判断方法。该方法在分频带自相关函数的混叠语音基频分离提取算法中,引入了双周期度及频带组的分布度两个参数用于对混叠语音清浊判断,从而使基于分频带自相关函数的混叠语音基频分离提取方法不再局限于浊音,可推广到清音信号。实验结果证明算法有一定的清浊音判断能力。     

决策树的并行训练策略

随着生物科学技术的发展,其数据量的增长也非常迅速,很难在一定合理的时间内对数据进行建模和分析。因此,对并行数据挖掘算法的研究巳变成解决此问题的重要途径。决策树途径巳被广泛用作一种重要的分类工具,本文研究了几种决策树的并行训练策略并对它们的性能进行了比较。     

代价敏感的序贯三支决策方法

在现实决策中,代价敏感问题是影响人类决策的重要因素之一,许多研究者致力于降低决策的代价。现阶段,在粗糙集领域中,研究者多基于DTRS模型且仅考虑某一种代价,不够全面。针对以上问题,利用序贯三支决策模型对两种代价的敏感性,通过多层次粒结构可以有效降低决策总代价,且能够更好地模拟人类动态渐进的决策过程。在序贯三支决策模型的基础上,构造了多层次粒结构;将各个属性的测试代价与其分类能力相关联,从信息熵的角度为其设置测试代价;与此同时,将属性约简与序贯三支决策相结合,利用基于代价最小准则的属性约简去除冗余属性及不相关属性对代价的影响。在7个UCI数据集上的实验结果显示,在保证较高准确度的同时,决策的总代价平均下降了26%左右,充分验证了该方法的有效性。     

Learning Strategies for Mechanised Building of Decision Procedures

In this paper we present an investigation into whether and how decision procedures can be learnt and built automatically. Our approach consists of two stages. First, a refined brute-force search procedure applies exhaustively a set of given elementary methods to try to solve a corpus of conjectures generated by a stochastic context-free grammar. The successful proof traces are saved. In the second stage, a learning algorithm (by Jamnik et al.) tries to extract a required supermethod (i.e., decision procedure) from the given traces. In the paper, this technique is applied to elementary methods that encode the operations of the Fourier-Motzkin's decision procedure for Presburger arithmetic on rational numbers. The results of our experiment are encouraging.     

A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making

In developing autonomous agents, one usually emphasizes only (situated) procedural knowledge, ignoring more explicit declarative knowledge. On the other hand, in developing symbolic reasoning models, one usually emphasizes only declarative knowledge, ignoring procedural knowledge. In contrast, we have developed a learning model CLARION, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in [40]. CLARION learns and utilizes both procedural and declarative knowledge, tapping into the synergy of the two types of processes, and enables an agent to learn in situated contexts and generalize resulting knowledge to different scenarios. It unifies connectionist, reinforcement, and symbolic learning in a synergistic way, to perform on-line, bottom-up learning. This summary paper presents one version of the architecture and some results of the experiments.     

Knowledge-Based Adaptive Decision Tree State Tying for Conversational Speech Recognition

This paper presents a new method of constructing phonetic decision trees (PDTs) for acoustic model state tying based on implicitly induced prior knowledge. Our hypothesis is that knowledge on pronunciation variation in spontaneous, conversational speech contained in a relatively large corpus can be used for building domain-specific or speaker-dependent PDTs. In view of tree-structure adaptation, this method leads to transformation of tree topology in contrast to keeping fixed tree structure as in traditional methods of speaker adaptation. A Bayesian learning framework is proposed to incorporate prior knowledge on decision rules in a greedy search of new decision trees, where the prior is generated by a decision tree growing process on a large data set. Experimental results on the telemedicine automatic captioning task demonstrate that the proposed approach results in consistent improvement in model quality and recognition accuracy.     

无逆矩阵在线序列极限学习机

无逆矩阵极限学习机只能以批量学习方式进行训练,将其拓展为无逆矩阵在线学习版本,提出了无逆矩阵在线序列极限学习机算法(IOS-ELM)。所提算法增加训练样本时,利用Sherman Morrison Woodbury公式对新增样本数据后的模型进行更新,直接计算出新增隐含层输出权重,避免对已经分析过的训练样本的输出权重进行重复计算。给出了所提IOS-ELM算法的详细推导过程。在不同类型和大小的数据集上的实验结果表明,所提IOS-ELM算法非常适合在线方式逐步生成的数据集,在快速学习和性能方面都有很好的表现。     

自动售货机集群管理专家决策系统

主要讨论了专家决策系统在自动售货机集群管理中的应用,给出了一个通过无线网络联结的自动售货机集群的专家决策系统的系统结构,以及构造该系统的步骤过程,建立了自动售货机集群管理专家决策系统的模型库,利用决策模型对所采集的自动售货机中的数据进行分析,并根据专家系统的推理得出专家结论,给操作人员一个决策的参考,实验证明自动售货机专家决策系统的应用降低了售货机的运营管理成本且提高了运营效率。     

Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning

Most of the existing sequential learning methods for class imbalance learn data in chunks. In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). WOS-ELM is a general online learning method that alleviates the class imbalance problem in both chunk-by-chunk and one-by-one learning. One of the new features of WOS-ELM is that an appropriate weight setting for CIL is selected in a computationally efficient manner. In one-by-one learning of WOS-ELM, a new sample can update the classification model without waiting for a chunk to be completed. Extensive empirical evaluations on 15 imbalanced datasets show that WOS-ELM obtains comparable or better classification performance than competing methods. The computational time of WOS-ELM is also found to be lower than that of the competing CIL methods.     

Augmented Online Sequential Quaternion Extreme Learning Machine

Neural Processing Letters - Online sequential extreme learning machine (OS-ELM) is one of the most popular real-time learning strategy for feedforward neural networks with single hidden layer due...     

智能机器伦理决策设计研究综述*

为了解决机器伦理决策困境,分析智能机器伦理决策设计的研究现状,对机器伦理决策设计方法进行分类总结。对基于规则推理和基于神经科学不同的伦理决策设计实现方式及优缺点进行了对比分析,并对现阶段无人驾驶伦理决策研究现状进行了归纳分析,最后总结了现阶段机器伦理决策的研究水平。基于上述分析研究,总结得出了解决机器伦理决策困境的技术难点有:机器伦理道德地位的确立、普适机器伦理决策、机器伦理决策评估。提出了未来研究方向:将人类道德规范应用到机器伦理决策设计中,运用机器学习实现伦理决策设计,利用法律及行业标准规范来实现机器伦理决策设计。为机器伦理决策及无人驾驶伦理决策的设计提供参考。     

Virtual Machine Communication for the Implementation of Decision Support Systems

This paper presents the use of the virtual machine concept as a software engineering tool. The focus is on techniques that allow the rapid integration and assimilation of existing data, models, analytical facilities, report generation facilities, and data base management systems. Since many of these models, programs, and facilities run under different operating systems, they often are incompatible with each other. By combining multiple virtual machines in a particular configuration, and allowing for communication among them, it has been possible to overcome these difficulties. The result is a set of software engineering tools that seem particularly useful in decision support systems. We present the application of these tools along with the software techniques used to implement them, and quantify some of their costs and benefits.     

板坯连铸机顺序控制分析

介绍了顺序控制在板坯连铸机生产中的应用,分析了铸机的运行状态转换及启动过程.该系统采用N-90分布式控制系统,将每个控制对象的整个复杂动作过程分解为符合工艺要求的具体步骤,软件设计由十分丰富的标准化模块功能码和图形库组态而成,编程灵活、实时性强、可扩展性好、能实现严格的逻辑顺序控制功能.该系统投运以来,控制准确,运行稳定可靠,保障了设备安全使用,在整个连铸控制中起到了重要作用.     

语音通信中信号的清浊音判别

文章采用了一种基于三参数组合的方法对语音信号进行清／浊音判决。在传统两参数（短时能量和过零率）算法的基础上增加了自相关函数作为判决参数,减少了清／浊音信号的误判率。经计算机仿真表明,算法切实可行,且算法误判概率小。     

UCAV对地多目标序次攻击决策研究

在增强无人机战斗力的研究中,针对无人作战飞机(UCAV)对地多目标序次攻击决策问题展开研究,建立多目标序次攻击的决策模型.为提高UCAV执行多目标攻击任务的成功率,决策模型综合考虑了UCAV在整个任务过程中所面临的战场风险,并将决策算法与航路规划问题分解,提出一种基于威胁分布密度的飞机生存概率评估方法,提高了决策效率.将序次决策问题转化为最优搜索问题,利用自适应蚁群算法求解决策问题并给出具体算法流程.仿真结果表明,多目标序次攻击决策方法能有效解决复杂战场环境分布所带来的目标排序优化问题,给出合理的多目标攻击时序,为方案设计提供了依据.     

一种训练支撑向量机的改进贯序最小优化算法

对于大规模问题,分解方法是训练支撑向量机主要的一类方法.在很多分类问题中,有相当比例的支撑向量对应的拉格朗日乘子达到惩罚上界,而且在训练过程中到达上界的拉格朗日乘子变化平稳.利用这一统计特性,提出了一种有效的缓存策略来加速这类分解方法,并将其具体应用于Platt的贯序最小优化(sequential minimization optimization,简称SMO) 算法中.实验结果表明,改进后的SMO算法的速度是原有算法训练的2~3倍.     

机器学习的可解释性

近年来,机器学习发展迅速,尤其是深度学习在图像、声音、自然语言处理等领域取得卓越成效.机器学习算法的表示能力大幅度提高,但是伴随着模型复杂度的增加,机器学习算法的可解释性越差,至今,机器学习的可解释性依旧是个难题.通过算法训练出的模型被看作成黑盒子,严重阻碍了机器学习在某些特定领域的使用,譬如医学、金融等领域.目前针对机器学习的可解释性综述性的工作极少,因此,将现有的可解释方法进行归类描述和分析比较,一方面对可解释性的定义、度量进行阐述,另一方面针对可解释对象的不同,从模型的解释、预测结果的解释和模仿者模型的解释3个方面,总结和分析各种机器学习可解释技术,并讨论了机器学习可解释方法面临的挑战和机遇以及未来的可能发展方向.     

Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization

Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical.     

一种高噪声环境下语音检测算法的仿真与应用

该文提出了一种高噪声环境下的自适应语音检测新算法。该算法利用了语音短时能量及帧内短时自相关的特性,两个自适应判决门限根据期望的误判率调整,无需事先给出噪声统计信息,适用于平稳噪声、缓变的非平稳噪声及脉冲噪声的情况,且可以实时实现。     

Learning Sequential Decision Rules Using Simulation Models and Competition

The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested.