双耳分听汉语普通话声调研究

系统地探讨与研究了以汉语为母语的右利手被试者对汉语普通话声调感知的偏侧优势。选取40个常用的汉语普通话单音节词作为实验材料,在合适的反应时间和信噪比下采用双耳分听范式进行声调辨别听觉感知实验。实验要求被试者从同时、分别播放到左右耳的不同声调中选出一个最清楚听到的声调,而不考虑所选的声调来自左耳还是右耳。30位听力正常的右利手被试者参与了此项实验。实验结果表明,在本研究设定的双耳分听实验条件下,汉语为母语的右利手被试者对汉语普通话声调的感知存在显著的右耳(大脑左半球)优势,且对四个声调的感知具有相同的偏侧优势;左、右耳对四个声调间感知差异的趋势基本一致,且对3声的感知显著较其余声调差。     

多通道人机交互中感觉刺激的模拟方法研究综述

目的 对多通道人机交互中感觉刺激模拟方法的研究现状进行梳理,总结现有研究中的相关技术原理与方法,为后续的相关研究和实践提供参考。方法 利用文献调研和典型案例分析方法,梳理了触觉、嗅觉及味觉三种单通道感觉刺激模拟的相关技术;概述了多通道感觉刺激融合的认知学理论,并以视听触融合、视听嗅融合为例,总结了多通道感觉刺激融合的具体实现方法;介绍了多通道感觉刺激融合在医疗康复、虚拟购物、智能教育等典型场景中的应用。结论 多通道人机交互中感觉刺激的模拟方法研究有望支持低负荷、高效率的新型人机交互方式,甚至带来超越真实的交互体验,具备广泛的应用潜力。     

听力正常老年人皮层听觉诱发电位研究

文章研究了听力正常老年人的P1-N1-P2波的特征值及其影响因素,并初步探讨其与言语感知之间的相关性。对40例听力正常的老年人进行了不同频段言语刺激声条件下的P1-N1-P2波测试与不同信噪比条件下的言语识别率测试。使用非参数检验分析不同言语刺激声频段P1-N1-P2波各波潜伏期、幅值的差异性,使用斯皮尔曼(Spearman)相关性检验分析老年人年龄、纯音听阈与P1-N1-P2波潜伏期、幅值之间的相关性;使用Spearman相关性检验分析P1-N1-P2波各波潜伏期和幅值与言语识别率之间的相关性。结果表明,刺激声频段、年龄、纯音听阈对听力正常老年人P1-N1-P2波各波潜伏期、幅值有显著影响,听力正常老年人P1-N1-P2波特征值与言语感知具有相关性。     

视觉辅助信息对声学头模录音听感效果的影响

0引言近年来,智能手机和平板电脑等移动终端日益普及,用户对移动多媒体应用中音视频质量的要求也不断提高。作为适合耳机重放的双耳录音技术[1]因其逼真的声场还原能力越来越受到人们的重视。在实际应用中,音频一般会伴随着相应的视频信息同时出现,因此本文针对与声学头模双耳录音信号同步的视频信息,探究了视觉辅助信息对声学头模双耳录音听感效果的影响。     

急性高原低氧环境对大脑不同波段偏侧化的影响

目的探讨急性高原低氧对脑电(Electroencephalogram,EEG)功率的影响。方法采用低压低氧舱模拟海拔4300 m低氧环境,采集并分析EEG信号。结果(1)各频段功率受氧气环境影响显著,低氧环境下功率增强,尤其是alpha、delta、theta频段功率显著增强。(2)急性低氧后,常氧下额区活动呈左偏侧化的个体,偏侧化指标有所降低;常氧下额区活动呈右侧化的个体,偏侧化指标则略为上升。结论模拟低氧环境影响了个体的额区偏侧化指标,导致个体情绪调节灵活性变差。     

双耳差频声刺激下的脑波特征与心理变化

首先通过前期主观实验得到人耳对双耳差频声的感知频谱,随后通过两部分实验,分别探索双耳差频声刺激下被试的脑波特征与心理变化,选取人耳感知阈值较大的中频作为载波信号。经过分析脑波特征与心理变化之间的相关性,为通过测量与分析被试的脑波参数,预测其心理尺度提供一定的分析思路。分析结果表明:各脑波分析指标与疲劳、抑郁、精力这三个情绪以及情绪总估价分之间的相关性较为显著,相关系数绝对值均达到0.5以上;Delta/Theta波段功率比重与反应时间、正确率正相关,Beta波段功率比重与反应时间、正确率负相关。     

串列双幅典型断面三分力系数气动干扰效应

采用风洞试验与数值模拟相结合的方法对串列双幅典型断面（矩形断面、Π型断面及流线型断面）三分力系数和斯脱罗哈数的气动干扰效应进行了研究。首先针对宽高比为5的单幅矩形断面分别进行了三分力系数的数值模拟和风洞试验测试,数值模拟结果与风洞试验结果吻合良好;然后对串列双幅典型断面不同间距比D/B（D为双幅断面之间净间距,B为单幅断面宽度）对应的三分力系数及斯脱罗哈数进行了数值模拟。研究显示：上游断面阻力系数与单幅断面比较接近,下游断面阻力系数则随间距比D/B的增加而增加;上游断面升力系数、升力矩系数脉动根方差气动干扰因子明显小于下游断面升力系数脉动根方差气动干扰因子,两者均随间距比D/B先增加后减小。     

基于多感官交互的阿尔茨海默老人智能穿戴产品设计研究

目的 研究听觉、触觉与视觉多感官刺激因素介入阿尔茨海默病老人护理的反馈效果,借助智能软硬件技术增强其对音乐的感知,设计多感官交互的老人智能穿戴产品。方法 根据感知替换及通感原理,创建能够将音乐转化为振动及视觉信息的感官替代方法,并通过实验测试分别验证听–触觉和听–触–视觉两类多感官交互原型的可行性。结果 在融合听–触觉的基础上加入视觉设计,可以更好地激发老人的言语行为能力。在收集被试者使用反馈分析结论的基础上设计了一款指尖振动音乐可穿戴设备——指尖钢琴,通过提供以触觉、听觉、视觉融合的多感官体验来促进患者的主动行为。结论 多感官交互产品的设计对改善阿尔茨海默病患者的情绪和主动行为具有一定的效果,对老年患者身体健康和生活质量的提高具有积极意义。     

模拟耳道的传输特性分析

0引言双耳录音时录音点的位置一般选择在耳道入口处或鼓膜位置处。国外相关学者认为,耳道的传输特性是与方向无关的,但不同人研究成果中得到的与方向无关的频段各不相同,且对真人耳道测量中考察的方向仅为水平面上3、4个有限方向[1]。为考察声波导管理论对耳道的适用性,以及不同录音点位置选择对声源定位听感上的影响。本文设计制作了三种耳道模型,研究耳道传输特性以及录音点选取对听感定位的影响。     

空分产品的应用领域及其发展

论述了空分产品氧氮在钢铁、化工、煤气化中的典型应用,重点从保护环境、安全生产、提高生活质量和前沿产业四个方面论述了氧氮开拓中的应用领域,并对空分产品的应用进行了总结和展望。     

Adversarial Attacks and Defenses in Deep Learning

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans. Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality. Hence, adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years. In this paper, we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques. We then describe a few research efforts on the defense techniques, which cover the broad frontier in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke further research efforts in this critical area.     

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect identification process is slow and difficult, it is required to design a technique for a quick and automated facial sketch generation. Machine Learning (ML) and deep learning (DL) models find it useful to automatically support the decision of forensics experts. The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions. In this view, this study develops a new artificial intelligence (AI) based DL model with face sketch synthesis (FSS) for suspect identification (DLFSS-SI) in a big data environment. The proposed method performs preprocessing at the primary stage to improvise the image quality. In addition, the proposed model uses a DL based MobileNet (MN) model for feature extractor, and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization (QOFFO) algorithm. The proposed model automatically draws the sketches of the input facial images. Moreover, a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness. If there is a higher resemblance between the two sketches, the suspect will be determined. To validate the effective performance of the DLFSS-SI method, a detailed qualitative and quantitative examination takes place. The experimental outcome stated that the DLFSS-SI model has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), average actuary, and average computation time.     

Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a deep learning (DL) technique for the classification of arrhythmias. The proposed technique makes use of the University of California, Irvine (UCI) repository, which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes. In this research, our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset. The DL approach in the form of long short-term memory (LSTM) is an efficient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis. The goal of this research was to categorize cardiac arrhythmia patients by developing an efficient intelligent system using the LSTM DL algorithm. This approach to arrhythmia classification includes classification algorithms along with noise removal techniques. Therefore, we utilized principal components analysis (PCA) for noise removal, and LSTM for classification. This hybrid comprehensive arrhythmia classification approach performs better than previous approaches to arrhythmia classification. We attained a highest classification accuracy of 93.5% with the DL based disease classification system, and outperformed the earlier approaches used for cardiac arrhythmia classification.     

A co-word analysis of digital library field in China

The aim of this study is to map the intellectual structure of digital library (DL) field in China during the period of 2002–2011. Co-word analysis was employed to reveal the patterns of DL field in China through measuring the association strength of keywords in relevant journals. Data was collected from Chinese Journal Full-Text Database during the period of 2002–2011. And then, the co-occurrence matrix of keywords was analyzed by the methods of multivariate statistical analysis and social network analysis. The results mainly include five parts: seven clusters of keywords, a two-dimensional map, the density and centrality of clusters, a strategic diagram, and a relation network. The results show that there are some hot research topics and marginal topics in DL field in China, but the research topics are relatively decentralized compared with the international studies.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

人工智能在电力系统中应用的近期研究热点介绍

大数据驱动下的新一代人工智能由传统知识表示转向深度、自主知识学习,不再需要人的过多干预,展现出了更加智能的一面。近年来,深度学习成为人工智能研究热点。该文重点介绍了深度学习的特点、原理及其在电力系统中的应用研究现状,分析了新一代智能方法研究趋势及在应用过程中存在的问题,并提供了基于分布式机器学习和增量学习的解决方法,旨在为相关研究工作者提供参考。     

Navigational conversation impairs concurrent distance judgments

Dual-task performance as it relates to driving, such as tuning a radio or manipulating a cellular phone, forces drivers to divide their attention between the traffic demands and the in-car task. The present study investigated how concurrent spatial or non-spatial cognitive distractions mediated proximity judgments using vehicular stimuli. Utilizing a modified version of the task employed by [Elias, L.J., Robinson, B. in press. Drive on the right side of the road: perceptual asymmetries for judgments of automobile proximity. International Journal of Neuroscience.] the current study examined how mental navigation (spatial distraction) affected accuracy and response time for depth judgments on vehicular stimuli in each visual field. These were compared to a control condition in which no distraction was present, as well as when a semantic (non-spatial) distraction was present. We found that conversation of a navigational nature (i.e., spatial distraction) most negatively impacted accuracy and response time when processing dynamically changing vehicle proximity. Further, these deleterious effects appeared to be uniform throughout the visual field. Findings are related to driving while being distracted, with particular emphasis on the role of cerebral lateralization in dual-task performance.     

聚-DL/L-丙交酯(30DL/70L)纳米纤维的电纺研究

对生物可吸收聚-DL/L-丙交酯(30DL/70L)体系进行了静电纺丝.研究了聚-DL/L-丙交酯(30DL/70L)的浓度,加料速度,电压,喷头与接收体之间的距离等因素对纤维形态的影响,制备出纳米纤维膜,并用扫描电镜(SEM)等对纤维膜进行表征.结果表明,电纺溶液浓度和溶剂对纤维直径影响比较明显,减小电纺溶液浓度和采用复合溶剂CHCl3/DMF可得到更细的纳米纤维;一定范围内适当的增加电压、减小距离和减小加料速度有利于减小纤维直径.在聚-DL/L-丙交酯(30DL/70L)浓度为5g/100mL溶剂、加料速度1mL/h、喷头与接收体之间的距离6cm、电压15kV电纺条件下,可制备直径50nm左右的聚-DL/L-丙交酯(30DL/70L)纳米纤维膜.     

Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter unprecedented data in dataset. Semi-supervised Learning is a way to exploit unla-beled data effectively to reduce over-fitting in DL. In this paper, we extend Genera-tive Adversarial Networks (GANs) to the semi-supervised learning will show it is a method can be used to create a more data-efficient classifier.     

Deformation luminescence produced during application and release of pressure on to gamma-irradiated CaF2:RE crystals

Calcium fluoride CaF2 is an interesting host lattice for rare earth (RE) activators. CaF2 crystals doped with different concentrations of Dy, Ce, Er and Gd have been grown by the Bridgman technique and their deformation luminescence (DL) induced by room temperature gamma irradiation has been recorded. When a uniaxial pressure is applied on to gamma-irradiated CaF2:RE crystals, initially the DL intensity increases with time, attains a maximum value and then it decreases with time. Although the DL intensity produced during the release of pressure is less, its rise and decay behaviours are similar to that obtained during the application of pressure. The DL intensity depends on dopant, concentration of dopant, irradiation doses and mass of the load or applied pressure. It is suggested that the moving dislocation produced during deformation of crystals capture holes from hole trapped centres (like perturbed Vk centre) and the subsequent radiative recombination of the dislocation holes with electrons give rise to DL.