基于核聚类的嗅觉神经网络对气味模式的识别

嗅觉系统是生物感觉神经系统中非常重要的组成部分。当嗅觉感受器接收到气味刺激时,其将化学信号转换为电信号并传递给嗅球,嗅球对信息进行整合与编码,继而将其传递到大脑嗅皮层,最终产生嗅觉。对于嗅觉神经网络的建模以及嗅觉信息处理的研究有助于理解嗅觉系统是如何有效区分不同种类与浓度的气味。本文在由僧帽细胞、颗粒细胞以及球旁细胞所构成的传统嗅球模型基础上,引人了嗅皮层来构建完整的嗅觉网络模型,并考虑了抑制性突触可塑性在网络接受刺激时的学习作用。其仿真结果表明抑制性突触可塑性可以平衡嗅皮层中兴奋性和抑制性的突触电流,从而使得嗅皮层对于气味刺激表现为特定的发放模式。嗅皮层对于不同种类的气味刺激表现为不同的发放模式,而对于同一种类不同浓度的气味刺激表现为相似的发放模式与不同程度的发放强度。同时提出了基于核方法的层次聚类和模糊聚类算法来实现对不同种类纯气味的识别和对混合气味中各种气味成分的识别。     

嗅觉神经网络在电子鼻识别多品牌绿茶中的应用研究

生物模式识别机理引入人工嗅觉系统将提高其仿生化程度,并被认为是有前途的传感阵列信息处理方法。本文尝试将一种嗅觉神经网络应用到电子鼻检测和识别多种品牌的绿茶气味。通过包含8个MOS型气敏传感器的自制电子鼻仪器,测量了来着不同地方的5种不同品牌的绿茶样品,在传感阵列信号稳态部分提取特征向量,并使用雷达图考察指纹图谱异同,验证传感阵列及特征提取方法的有效性。采用生物相似性学习算法训练该神经网络,考察了样本训练次数和识别率的关系,发现经过4～7次训练,该网络对这5种绿茶的识别率平均值都在97%以上。     

机器嗅觉系统气味识别算法

简单介绍机器嗅觉的发展概况,依据人鼻内嗅觉感受器细胞的嗅感原理提出气敏传感器阵列的信号模型;概括分析机器嗅觉的多变量模式分析技术,从其气味识别算法的角度,重点介绍统计模式分析(线性和非线性)和智能模式分析(人工神经网络和其它)的基本方法,对机器嗅觉系统的实现具有一定的指导意义.     

结合基因工程的生物电子鼻用于气味检测的研究

为了实现结合生物工程的仿生嗅觉传感器系统的多气味检测和目标气味的特异性识别和区分,使用生物工程化的大鼠嗅觉系统作为敏感元件,植入嗅球的多通道微丝电极作为换能器,采用无线数据采集系统收集和分析响应信号。最后,利用Matlab计算神经元平均放电率,对神经元的气味响应进行相关性分析和主成分分析,证实了该系统检测和区分不同气味的性能。创新性地提出将嗅觉受体基因整合、克隆到在体嗅觉细胞上,用于改造生物嗅觉系统,使之可以对目标气味分子产生敏感的响应信号。结果表明结合基因工程的生物电子鼻系统能够显著增强其检测特异性目标气味的性能,在环境监测、公共安全等领域中有很大应用潜力。     

卵磷脂类脂传感器

嗅觉是人和动物的化学感受之一,有着报警、识别、影响情绪进而影响行为等重要作用.科学家们长期以来期望制造出可代替鼻子的仪器,但因嗅觉的机制不清,所谓的“人造鼻”只不过是用气体传感器来测气味物质的气味传感器,但并不具有嗅觉的特点.1987年K.Kurihara~1等提出了生物膜的化学感受机制——嗅觉的脂质学说,认为嗅觉是嗅刺激物(气味分子)溶解或吸附于鼻粘膜细胞的类脂层中所引起细胞膜的电位变化,产生神经冲动而引起嗅刺激感受.据此,以生物材料或人造脂质材料为敏感膜的嗅觉、味觉传感器已出现~2,所用形式多为频率型的器件——石英振子及表面波(SAW)器件.未见报     

电子鼻—智能气体传感器

论述了应用模式识别技术和气体传感器阵列所研制的模拟嗅觉系统。气体传感器阵列由８个金属氧化物气敏器件组成，它们对不同的气味和气体有不同的响应；传感器阵列输出的信号经过微机处理和识别。作者对实验过程进行了详细的讨论，实验结果表明该系统可成功地识别多种不同的气体。     

基于嗅觉神经网络采用时间序列的电子鼻模式分类方法

从传感器阵列响应曲线中提取有效特征是传统人工神经网络在电子鼻模式识别应用中的第一步.文中提出将传感器阵列时间序列信号直接输入到一种具有丰富动力学特性的嗅觉神经网络中进行模式分类的方法.该方法不仅在仿生角度上使电子鼻进一步模拟了生物嗅觉系统信息处理过程,而且与以前所用的特征提取加神经网络的方法相比,在6种有机挥发物的分类识别中表现得更佳.     

基于模糊神经网络生物嗅觉的智能电子鼻的研究

通过研究鼻道结构对生物嗅觉的影响,构造了装有传感器阵列的电子鼻流道和控制装置,实现了嗅觉区域气体流量和气味分子浓度的主动控制,提高了嗅觉灵敏度;根据生物嗅觉系统的模糊性质在嗅觉感知中所起的关键作用,构建了更接近生物嗅觉的模糊优化神经网络算法,使电子鼻系统更具仿生特性,实现了电子鼻动态检测的目标;实验结果表明,该电子鼻不仅具有辨识的快速性,还提高了自适应辨识精度,从而能够准确做出预报。     

基于三维空间均匀矩形阵列的MIMO系统研究

主要研究MIMO天线阵列系统性能,包括MIMO空间时间相关性和天线阵列配置。推导了三维多径信道中均匀矩形阵列在多种角能量分布下空间衰落相关性公式。采用多重信号分类算法对MIMO系统波达信号方向进行空间谱估计,推导了多种天线阵列空间谱通用公式。通过计算机模拟仿真验证分析结果,仿真结果表明方位角扩展是天线间空间衰落相关性的主要决定因素,在低方位角扩展时,俯仰角扩展对性能影响也是明显的。结果表明在同样的参数情况下估计MIMO系统空间谱时,采用三维均匀矩形阵列是有优势的。     

随机振动多窗口谱分析法模型建立及统计分析

针对随机振动中谱估计算法,分析了周期图估计算法对噪声系列的功率谱相关系数及加窗造成的信息损失,在此基础上分析了由直接谱估计算法构建的多窗口谱分析法,建立了其噪声模型,推导了估计方差减小与参与运算的窗口数的关系。针对白噪声序列,得到MTSA估计器避免加窗而造成信息损失时窗口数与信号序列的关系,最后推出了MTSA估计器的统计特征。     

Predicting odor mixture's responses on machine olfaction sensors

One of the challenging issues in current research on machine olfaction devices, which are often called electronic noses (e-noses), is how to approximate or predict the sensor response to odor mixtures. When each odor is produced by its own unique set of odorant compounds, combinations of these unique odorant sets create a sensing challenge for the e-noses with a limited number of elements in its sensing array. One possible approach proposed in the literature is based on an “additive law of mixing” model but it fails in a complex odor mixtures. Another method adopted a specific hardware solution called odor recorder developed by using active odor sensing system. In this study, signal decomposition/reconstruction based on wavelet analysis and support vector regression are adopted to predict a sensor's response to mixtures of odors. The prediction results of our method are investigated and compared with the real sensor responses collected from a commercial e-nose machine, the AppliedSensor NST 3320. We find that the proposed method provides good prediction when applied to different mixing ratios of some coffees and green tea.     

电子鼻在棉织物异味检测中的应用研究

基于电子鼻传感器检测技术,对棉织物中5种异味整体性质的人工智能评价进行探究,根据传感器检测数据曲线和数据主成分分析(PCA)分析,结果表明:各传感器对不同异味成分的响应性不同,PCA分析法处理数据能够有效区分布样中不同的异味组分,为纺织品异味的快速、有效、客观检测评定奠定了一定的基础.     

基于电子鼻技术的烤烟过程气味变化研究

为了掌握烟叶烘烤过程中气味变化的规律,用金属氧化物半导体传感器阵列组成的电子鼻对烤烟烟气进行实时监测。详细阐述了自制的电子鼻系统和实验过程,在对样本数据进行预处理后,采用主成分分析法对样本进行分析。分析结果表明:利用电子鼻技术得出的气味综合曲线能够真实有效地反映烟叶烘烤过程中的气味整体变化趋势,并且分析得出的烤烟过程中气味变化规律与现有烟叶烘烤理论较好地吻合。     

Odor emoticon: An olfactory application that conveys emotions

Olfaction is an associative and emotional sense. The brain processes odors together with internal states, and odor-related memories contain strong emotional contents. This study focuses on people's emotional interpretations of odors, and proposes an olfactory application that uses odors as emoticons to convey emotions. This application offers an olfactory route for emotion communication, which can enhance communication experiences. Nine odor emoticons were designed, and validated through experiments. The effects of odor emoticons were also examined. A prototype system—Olfacticon—that emits odor emoticons was developed and applied in two contexts: online text chatting and voicemail receiving. Results suggested that odor emoticons induced more chatting, were easy to use, and helped participants perceive and convey emotions.     

Comparison of odor detection thresholds and odor discriminablities of a conducting polymer composite electronic nose versus mammalian olfaction

Response data from an array of conducting polymer composite vapor detectors that form an electronic nose were collected for the purpose of comparing selected, quantitatively measurable, phenomena in odor detection and classification to the olfactory characteristics of monkeys and humans. Odor detection thresholds and discriminability between structurally similar pairs of odorants were the two primary quantities evaluated for this comparison. Comparisons were only made for volatile organic vapors as opposed to aroma active odorant vapors. Electronic nose detection thresholds for a homologous series of n-alkane and 1-alcohol odorants were determined and the results were compared to literature values for the mean olfactory detection thresholds observed in psychophysical experiments on humans exposed to these same vapors. The trends in odor detection thresholds of the electronic nose towards the tested analytes were very similar to those exhibited by humans. The discrimination performance of the electronic nose for distinguishing between pairs of odorants within incrementally varying series of esters, carboxylic acids and alcohols were also compared to the published data of Laska and co-workers on the psychophysical performance of humans and monkeys for these same odorant pairs. Similar trends were generally observed between the humans, monkeys, and the electronic nose in that discrimination performance increased as the compounds of an odorant pair became more structurally dissimilar. With use of the Fisher linear discriminant algorithm for classification of these test pairs of odorants, the electronic nose exhibited significantly better discriminability than humans or monkeys for the odorant pairs evaluated in this work under the test conditions for which the discriminability was evaluated.     

Learning classification in the olfactory system of insects

We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size of the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.     

Active gas/odor sensing system using automatically controlled gas blender and numerical optimization technique

As measurement of a vapor mixture composition is a difficult technique, no method using a sensing system has yet been established in spite of great effort by many researchers. In this paper, the authors propose a new gas/odor sensing system using a gas blender and a nonlinear numerical optimization algorithm by which the concentration of each component in an unknown vapor can be quantified. The component vapors are internally blended and the mixture ratio is modified by the system so that the sensor array output pattern of the blended vapor can be made equal to that of the unknown one. After several iterations, convergence is obtained and the vapor concentration of each component is determined from the mixture composition of the blended vapor. Although the conventional system is passive, this system is considered as an active one as it performs exploratory behavior prior to recognition. Here, gasoline vapor concentration is measured under the condition that one or two interference vapors exist together. Gasoline vapor has been adopted as an example of odors in the passenger compartment of a car, since it sometimes smells unpleasant. The measurement is essential for designing a car in order to keep it comfortable for passengers. The sensors used here are three semiconductor gas sensors and two electrochemical sensors, which are chosen in order to obtain high sensitivity to gasoline. The nonlinear numerical optimization techniques used are the simplex method and the gradient descent method and these two methods are compared here. It is found that the quantification error is within ten ppm for two- or three-component vapors.