计算机与人工智能

本文从"数据处理"到"知识处理"层面介绍了计算机对人工智能的影响,算法危机对人工智能的期待,人脑与电脑、人类智能与人工智能的关系.     

五子棋算法探讨

让电脑像人脑一样的思考，一直以来是人工智能发展的最终目标。人工智能运用的领域可谓数不胜数。随着软件业的快速发展，各种的棋类软件也具有越来越高的人工智能，电脑籍助这些人工智能已经有了相当水平的棋力，大有与人脑分庭抗礼之势。然而电脑真的能像人一样思考吗，目前来说，还不可能，电脑能做到“思考”无非是if…then…else。     

人工智能时代编程思维的重要性

结合人工智能技术特点,对电脑编程内容进行分析,总结了人工智能时代编程思维运用的重要性,旨在通过各项技术的研究及协调,提高人工智能在计算机网络中的使用价值,满足网络信息技术的发展需求。     

让计算机也能感知世界 英特尔RealSense 3D交互技术点评

正触控技术的兴起,大大缩短了人与电脑间的距离,让用户对人机互动的渴望得到满足,语音控制能够成为市场关注的焦点,其同样拉近了人与电脑间的距离。人们渴望与电脑之间进行人与人一般的交流,更希望未来电脑能够感知世界,最终完成人工智能的进化,在众多交互技术中,英特尔RealSense 3D无疑成为市场关注的焦点。     

基于人工智能的计算机网络技术应用研究

随着电脑的普及和互联网的快速发展,人们的生活越来越方便。目前,网络已成为一个时代主题,人们离不开网络。为了使网络为人们提供优质服务,满足人们的需求,需要将人工智能技术引入计算机网络技术。基于此,在大数据和互联网快速发展的背景下,研究了计算机网络中如何使用人工智能技术,介绍了人工智能的概念、发展,列举了具体的人工智能技术,并通过案例探索计算机网络中人工智能技术的具体应用。     

Delphi 程序设计实弹演习之五——为五子棋程序注入智慧

博弈是研究使自己取胜、战胜对手的策略。电脑下棋，也就是计算机博弈，是人工智能(AI)的一个重要的研究领域。“深蓝”与国际象棋世界冠军帕斯帕罗夫的人机博弈大战一直为人们津津乐道。     

为爸妈打造无忧电脑

老爸爱聊QQ,老妈爱听歌,可电脑出问题了就只有干瞪眼,本来家里我最擅长用电脑,可我远在北京上学,家里的电脑真出问题了我也鞭长莫及。上次家里电脑出现问题,长时间不能使用,等我暑期回家才将系统装好,爸妈可高兴了。马上又要开学,即将远离爸妈,我最担心的就是家里的电脑出问题了怎么办？难道又要长时间闲置不成？现在,我就来对家里的电脑进行设置,让它具有人工智能电脑的部分功能,让老爸不再为电脑问题而发愁。     

关于电脑“智能”的两则思考

关于电脑“智能”之一:人工智能的逻辑———电脑真能比人脑聪明?     

机械电子工程与人工智能的关系分析

0引言
　　机械电子工程与人工智能技术的结合,强调的是机械电子工程的特点与传统机械系统的功能及能量的连接,重点突出了信息连接的作用。换言之,即为在机械电子工程中,引入人工智能整合集成的理念,使机械电子的操作变得更加精确。本文通过对机械电子工程与人工智能技术分别进行阐述,重点对二者之间的关系进行研究,强调人工智能技术在机械电子工程中的实际应用效果,对我国机械电子工程的智能化发展起到了促进作用。     

人工智能技术在电力基建管理中的应用

人工智能为电力基建管理工作提供了相应的技术保障。电力系统中人工智能的使用使电力系统更加智能,提高了电力系统的效率,基于此,本文对人工智能技术、人工智能在电力系统中的应用现状以及人工智能技术在电力基建管理中的措施进行了分析。     

Reducing machine heat and noise by a special enclosure

Some improvements on a machine used in the steel cord industry are described. The main ergonomic problems found in a department with such machines are the high noise and heat. The noise produced by the machine and by its components was studied and an enclosure containing the most noisy parts was built. In order to reduce the heat released by the machine at the man's level ( approximately 2600 kcal/h) a system which carries the hot air up to 2.5 meters was studied. Such a system, which reduces by about 50% the heat released at the man's level, works by exploiting some of the energy absorbed by the machine itself.     

多形式特征向量脑网络分类方法研究

目前已有的脑网络分类方法大多是通过处理收集的信号来构建脑网络,并根据一个或多个脑区之间的脑网络特征属性来进行分类。该分类方法只考虑一个特征属性,忽略了脑网络的其他特征属性,而被忽略的特征属性很可能会对实验结果产生较大的影响。为了克服已有分类方法的缺陷,文中考虑多种特征属性提出了一种基于多形式特征向量的脑网络分类方法并使用了新型图核,该分类方法由4步构成：将原始实验数据经过预处理后完成脑网络构建;根据不同的阈值来提取脑网络中多种脑网络属性值;利用支持向量机训练所有数据,根据训练结果的优劣,在每种网络属性值里挑选分类效果最优的阈值参数,并将它们进行特征融合;使用支持向量机训练融合后的特征向量。通过实验数据分析并与已有分类方法进行了对比,验证该方法在轻度认知障碍数据集上脑网络分类的有效性。     

基于机器学习的脑网络分析方法及应用

脑网络学习旨在从整体上研究大脑各功能区的交互,对于人类深入了解大脑功能和结构以及对一些脑疾病的诊断都具有非常重要的作用。作为脑网络分析的重要工具,机器学习由于能够从数据中学习规律并对未知数据进行预测,已成为近年来脑网络分析领域一个新的研究热点。本文综述了近年来基于机器学习技术在脑网络分析中的典型研究方法和应用,主要从网络的构建、特征学习和分类预测等3个方面加以介绍。最后,总结全文并展望未来研究方向。     

Estimation of time-varying delay time in nonstationary linearsystems: an approach to monitor human operator adaptation in manualtracking tasks

Adaptability is one of man's advantages over machines. Perhaps one of the reasons for our limited understanding about human adaptation during manual tracking tasks is that we have only limited tools to identify the model coefficients (especially delay time) of an adapting human operator. In this paper, we introduce a discrete time recursive delay identifier (RDI) capable of simultaneously estimating a human operator's nonstationary delay time and linear model coefficients. At its core lies the extended Kalman filter (EKF). Our goal to obtain fractional delay time estimates was realized by using the bicubic interpolation scheme as part of the EKF to provide subsample magnitude and derivative estimates of the observed input/output time series. While this theoretically limits the RDI applicability to band-limited or differentiable signals, this is seldom a concern in practice. Based on data from simulated and experimental time varying tracking tasks, we show the RDI's potential to substantially increase our understanding about human adaptations thus perhaps offering new avenues for machine adaptation     

面向阿尔茨海默病的脑网络多频融合图核

复杂网络分析与机器学习方法相结合的阿尔茨海默病辅助诊断研究受到了越来越多的关注,其通常采用脑功能网络的方法来描述大脑活动的信息.然而,现有的成果大多基于时域信号匹配构建脑功能网络,忽略了脑活动信息在各个频段下的差异.因此,本文提出了脑网络多频融合图核的阿尔茨海默病诊断方法.首先,将功能磁共振成像产生的图像通过小波变换的方法进行分频段处理;其次,分别计算得到的各频段图像中任意两个脑区间的互信息,并设定阈值与互信息值进行比较进而构造出多频脑网络模型;然后,基于此提出面向多频脑网络模型的融合图核;最后,基于多频融合图核、采用核极限学习机在ADNI(Alzheimer’s Disease Neuroimaging Initiative)公开数据库中获取的一组数据以及在OASIS(Open Access Series of Imaging Studies)公开数据库上获取的一组数据进行阿尔茨海默病的诊断.同时,还通过实验验证了不同参数设置对诊断结果的影响.两组数据集的实验结果表明,提出的多频融合图核的辅助诊断方法能够取得最佳性能,且该方法的辅助诊断准确率在两种数据集上比对比方法的最好结果分别提高了13.79%和15.29%.     

Motor Restoration Based on the Brain–Machine Interface Using Brain Surface Electrodes: Real-Time Robot Control and a Fully Implantable Wireless System

The brain–machine interface (BMI) is a new approach to the man–machine interface, which enables us to control machines and to communicate with others without input devices, but directly using brain signals. We describe our integrative approach to develop a BMI system using brain surface electrodes for motor and communication control in severely disabled people. This includes effective brain signal recording, accurate neural decoding, robust robot control, a wireless fully implantable device, a non-invasive evaluation of surgical indications, etc. In addition, the inspection and addressing of neuroethical issues is indispensible when undertaking work in this field.     

基于影像组学和机器学习的脑部胶质瘤分级模型研究

本文将影像组学的方法和机器学习算法结合起来,对脑部胶质瘤进行分级预测。利用BraTS2019公开数据集,从多模态MRI图像中分别提取肿瘤的448维影像组学特征:肿瘤形态学特征、一阶灰度特征、纹理特征等;然后通过最小绝对收缩和选择算子(Lasso)算法筛选出15个最佳的影像组学特征;最后根据筛选出的最佳特征集,利用随机森林分类算法构建脑部胶质瘤的分级预测模型。基于机器学习建立的模型在训练组患者中预测胶质瘤级别的准确率达到95.6%,ROC曲线下面积(AUC)达到0.99;在验证组患者中预测胶质瘤级别的准确率达到89.3%,AUC达到0.96。可见,基于机器学习算法,利用影像组学的方法可以对脑部肿瘤的高低级别进行准确的预测和分类。     

神经网络结构搜索在脑数据分析领域的研究进展

神经网络结构搜索(neural architecture search,NAS)是自动化机器学习的重要组成部分,已被广泛应用于多个领域,包括计算机视觉、语音识别等,能够针对特定数据、场景、任务寻找最优的深层神经网络结构.将NAS引入至脑数据分析领域,能够在图像分割、特征提取、辅助诊断等多个应用领域大幅度提升性能,展现低能耗自动化机器学习的优势.基于NAS进行脑数据分析是当前的研究热点之一,同时也具有一定挑战.目前,在此领域,国内外可供参考的综述性文献较少.对近年来国内外相关文献进行了细致地调研分析,从算法模型、研究任务、实验数据等不同方面对NAS在脑数据分析领域的研究现状进行了综述.同时,也对能够支撑NAS训练的脑数据集进行了系统性总结,并对NAS在脑数据分析中存在的挑战和未来的研究方向进行了分析和展望.     

Internal models of reaching and grasping

One of the most distinguishing features of cognitive systems is the ability to predict the future course of actions and the results of ongoing behaviors, and in general to plan actions well in advance. Neuroscience has started examining the neural basis of these skills with behavioral or animal studies and it is now relatively well understood that the brain builds models of the physical world through learning. These models are sometimes called 'internal models', meaning that they are the internal rehearsal (or simulation) of the world enacted by the brain. In this paper we investigate the possibility of building internal models of human behaviors with a learning machine that has access to information in principle similar to that used by the brain when learning similar tasks. In particular, we concentrate on models of reaching and grasping, and we report on an experiment in which biometric data collected from human users during grasping was used to train a support vector machine. We then assess to what degree the models built by the machine are faithful representations of the actual human behaviors. The results indicate that the machine is able to predict reasonably well human reaching and grasping, and that prior knowledge of the object to be grasped improves the performance of the machine, while keeping the same computational cost.     

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.