Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.     

Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.     

反应型次氯酸荧光探针

生物体内的次氯酸是氯离子和过氧化氢(H2O2)由髓过氧化物酶(MPO)催化生成的,是生物体内最重要的活性氧(ROS)之一,在生理过程中发挥了至关重要的作用.但是,过量的次氯酸会导致一系列生理疾病,因此有效的识别和检测次氯酸备受研究者们的青睐.与传统检测方法相比,荧光探针具有选择性好、灵敏度高、可实时监测等诸多优势,因此...     

基于小波的光照补偿及其在人脸识别中的应用

提出了一种基于小波分析的人脸图像光照补偿技术,通过舍弃图像中的低频分量,仅用高频分量进行图像重构,便可较好地消除原始人脸图像中光照的影响.并将该技术应用于特征脸法人脸识别,在Yale库和自建库图像中,实验测得在较大尺度的情况下,图像识别率都有明显提高.     

光谱法研究水溶性杯[8]芳烃对伊红的分子识别作用

采用荧光光谱法和紫外-可见光谱法研究了水溶性对-二甲氨甲基-杯[8]芳烃(简称杯[8]胺)对伊红的分子识别作用。研究发现,对-二甲氨甲基-杯[8]芳烃与伊红存在较强的静电作用,两者之间形成了2:1型的络合物,络合常数为2.1×109Lmol-1,小牛胸腺DNA对杯[8]胺-伊红络合物的稳定性有较大影响,DNA能夺取杯[8]胺-伊红络合物中的杯[8]胺,导致伊红游离,预示着杯[8]胺是良好的药物载体分子。同时考察了β-环糊精、几种常见有机溶剂和溶液的pH值等对杯[8]胺与伊红相互作用的影响,初步探讨了两者相互作用的机理。     

Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory’s viability and performance were implemented using IBM’s Qiskit library.     

New Chiral Calixarene Derivatives： Syntheses and Their Chiral Recognition Toward Amino Acids by UV-Vis Spectroscopy

Three novel types of chiral calixarene derivatives 5, 8, and 10 were designed and synthesized by introducing chiral units to parent calixarenes. Their chiralities were confirmed by rotational analysis. Chiral recognition properties of these host compounds towards L- and D-threonine were studied by UV-Vis spectroscopy. The results indicated that calixarene derivatives 5 and 8 exhibited good chiral recognition capabilities toward L- or D-threonine. Although calixarene derivative 10 had no evident chiral recognition ability, the supramolecules of calixarene derivative 10 with L- or D-threonine showed a hypochromic effect or hyperchromic effect respectively. Therefore, calixarene derivative 10 might serve as a good chiral UV-indicator.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

视频图像记录判读系统

光电经纬仪的摄影胶片图像和CCD摄像机的视频图像的数字化处理是兵器试验靶场中始终未能很好解决的问题。主要源于两个问题:一是视频图像的数字化采集和转换;二是数字图像的自动处理。介绍了根据靶场广泛应用的光学图像记录、判读需求而研制的视频图像记录判读系统,并对所采用的视频图像实时存储技术、字符识别技术、目标识别与定位技术等几项主要的关键技术进行了说明,并给出了应用结果。结果表明,图像采集记录灵活方便,速度快,不丢帧;目标图像处理精度高,实时性强,自动处理能力好,使光测数据处理的精度和效率都有了很大的提高。