Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.     

Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder

Generally, an experienced therapist continuously monitors the affective cues of the children with Autism Spectrum Disorders (ASD) and adjusts the course of the intervention accordingly. In this work, we address the problem of how to make the computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. Two computer-based cognitive tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism intervention. A large set of physiological indices are investigated that may correlate with the above affective states of children with ASD. In order to have reliable reference points to link the physiological data to the affective states, the subjective reports of the affective states from a therapist, a parent, and the child himself/herself were collected and analyzed. A support vector machines (SVM)-based affective model yields reliable prediction with approximately 82.9% success when using the therapist's reports. This is the first time, to our knowledge, that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.     

DNA甲基化微阵列的非参数贝叶斯聚类算法

面向 Illumina GoldenGate 甲基化微阵列数据提出了一种基于模型的聚类算法. 算法通过建立贝塔无限混合模型, 采用 Dirichlet 过程作为先验, 实现了基于数据和模型的聚类结构的建立, 实验结果表明该算法能够有效估计出聚类类别个数、 每个聚类类别的混合权重、每个聚类类别的特征等信息, 达到比较理想的聚类效果.     

一种用于线性光谱解混算法验证的模拟数据生成方法

线性光谱解混已成为一种通用的光谱解混方法并已发展出大量算法,对这些算法进行客观评价,是该算法得到推广应用的重要基础。由于实测数据获取困难且花费很大,大多数研究中广泛采用模拟数据进行算法验证。针对目前常用的基于Dirichlet分布的模拟方法在实际应用中遇到的问题,结合实验分析了Dirichlet分布的参数设置对模拟结果的影响,进一步结合高光谱数据在其特征空间中的单形体几何特征,探讨了实际应用中参数取值受到的限制,并通过分析Dirichlet分布概率密度函数值的空间分布特征,提出参数的合适取值范围为(0,1.5)。     

Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data

Computational aspects concerning a model for clustered binary panel data are analyzed. The model is based on the representation of the behavior of a subject (individual panel member) in a given cluster by means of a latent process. This latent process is decomposed into a cluster-specific component and an individual-specific component. The first component follows a first-order Markov chain, whereas the second is time-invariant and is represented by a discrete random variable. An algorithm for computing the joint distribution of the response variables is introduced. The algorithm may be used even in the presence of a large number of subjects in the same cluster. An Expectation-Maximization (EM) scheme for the maximum likelihood estimation of the model is also described together with the estimation of the Fisher information matrix on the basis of the numerical derivative of the score vector. The estimate of this matrix is used to obtain standard errors for the parameter estimates and to check the identifiability of the model and the convergence of the EM algorithm. The approach is illustrated by means of an application to a data set concerning Italian employees’ illness benefits.     

基于自组织映射模型的数据挖掘算法在对二甲苯吸附分离过程中的应用

以PX吸附分离过程为研究对象，运用基于SOM模型的数据挖掘算法对其进行分析研究．SOM模型在整个挖掘过程中起了关键性的作用．一方面，SOM模型作为探索性数据分析的有效工具，为进一步的挖掘提供了依据．另一方面，SOM模型为聚类算法提供参数指导和数据支持．最终，通过数据挖掘实现了两个目标，得到了在不同负荷情况下操作参数的稳态优化区域；建立了可用于指导操作员改进操作的可视化实时评估模型．     

图像分割中改进空间约束贝叶斯网络模型的应用

针对马尔可夫链蒙特卡罗方法普遍存在的迭代收敛性问题,在具有空间平滑约束的高斯混合模型条件上提出改进空间约束贝叶斯网络模型并在图像分割领域进行具体应用。所提模型应用隐狄利克雷分布（LDA）概率密度模型和高斯-马尔可夫定理的随机域参数混合过程来实现参数平滑。所提方法根据空间信息先验平滑变换操作,在待处理像素点的上下文混合结构中引入LDA符合多项式分布,用来替换传统期望最大化算法中映射操作。LDA参数采用闭合形式将有利于准确估计最大后验概率（MAP）框架与上下文混合结构的相关比例。实验结果表明,应用PRI、VoI、GCE和BDE指标进行效果比较,该方法比联合系统工程组（JSEG）、当前变换矩阵（CTM）和最大后验概率-最大似然法（MM）方法的图像分割应用效果较好,高斯噪声对于该算法的鲁棒性影响较小。     

基于模型的工业过程控制系统假数据注入攻击识别方法设计

随着国家两化融合的不断推进,网络安全问题也日益突出。在控制系统闭环辨识基础上,设计了一种基于模型的工业过程控制系统假数据注入攻击识别方法。该方法能够结合工业过程控制系统特点,通过在线闭环辨识过程模型,更加准确清晰地对系统中存在的攻击信号进行监测和识别。仿真结果证明,提出的方法可识别假数据注入攻击的具体类型,具有较高的准确性。     

一类神经网络数据预处理的二种方法的比较

介绍了主元分析法和粗集理论对原始数据进行压缩处理的基本算法。对一组边坡工程数据,分别利用主元分析法和粗集理论对数据预处理后,送给BP神经网络对边坡状态进行逼近。对检验样本进行仿真比较,说明了粗集理论在此种分类神经网络数据预处理上优于主元分析法。     

Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel

A novel approach to characterise the model prediction errors using a Gaussian mixture model is proposed. The motivation for this work lies behind many data models that are developed through prediction error minimisation with the assumption of a normal noise distribution. When the noise is non-normal, which may often be the case in complicated data modelling scenarios, the model prediction errors may contain rich information, which can be further exploited for model refinement and improvement. The key contents presented in this paper include: choosing the relevant variables to form the error data, optimising the number of Gaussian components required for the error data modelling, and fitting the Gaussian mixture parameters using an expectation-maximisation algorithm. Application of the proposed method for further model improvement, within the framework of hybrid deterministic/stochastic modelling, is also discussed. Preliminary results on the real industrial Charpy impact energy data for heat-treated steels show its effectiveness for model error characterisation, and the potential for model performance improvement in terms of prediction accuracy as well as providing accurate prediction confidence intervals.