Emotion recognition based on physiological signals is affected by noise and other factors， resulting in low accuracy and weak cross-individual generalization ability. Concerning the issue， a multimodal emotion recognition method based on ElectroEncephaloGram （EEG）， ElectroCardioGram （ECG）， and eye movement signals was proposed. Firstly， physiological signals were performed multi-scale convolution to obtain higher-dimensional signal features and reduce parameter size. Secondly， self-attention was employed in the fusion of multimodal signal features to enhance the weights of key features and reduce feature interference between modalities. Finally， a Bi-directional Long Short-Term Memory （Bi-LSTM） network was used for extraction of temporal information of fused features and classification. Experimental results show that， the proposed method achieves recognition accuracies of 90.29%， 91.38%， and 83.53% for valence， arousal， and valence/arousal four-class recognition tasks， respectively， with improvements of 3.46-7.11 and 0.92-3.15 percentage points compared to the EEG single-modality and EEG+ECG bimodal methods. The proposed method can accurately recognize emotion with better recognition stability between individuals.

Due to complex structure of Three-Dimensional Stacked Integrated Circuit （3D SIC）， it is more difficult to design an efficient test structure for it to reduce test cost than for Two-Dimensional Integrated Circuit （2D IC）. For decreasing cost of 3D SIC testing， a Three-Dimensional Linear Feedback Shift Register （3D-LFSR） test structure was proposed based on Linear Feedback Shift Register （LFSR）， which can effectively adapt to different test phases of 3D SIC. The structure was able to perform tests independently in the pre-stacking tests. After the stacking， the pre-stacking test structure was reused and reconfigured into a test structure suitable for the current circuit to be tested， and the reconfigured test structure was able to further reduce test cost. Based on this structure， the corresponding test data processing method and test flow were designed， and the mixed test mode was adopted to reduce the test time. Experimental results show that compared with the dual-LFSR structure， 3D-LFSR structure has the average power consumption reduced by 40.19%， the average area overhead decreased by 21.31%， and the test data compression rate increased by 5.22 percentage points. And， using the hybrid test mode reduces the average test time by 20.49% compared to using the serial test mode.

Considering the issues of physiological signal emotion recognition， a bimodal emotion recognition method based on Graph Neural Network （GNN） and attention was proposed. Firstly， the GNN was used to classify ElectroEncephaloGram （EEG） signals. Secondly， an attention-based Bi-directional Long Short-Term Memory （Bi-LSTM） network was used to classify ElectroCardioGram （ECG） signals. Finally， the results of EEG and ECG classification were fused by Dempster-Shafer evidence theory， thus improving the comprehensive performance of the emotion recognition task. To verify the effectiveness of the proposed method， 20 subjects were invited to participate in the emotion elicitation experiment， and the EEG signals and ECG signals of the subjects were collected. Experimental results show that the binary classification accuracies of the proposed method are 91.82% and 88.24% in the valence dimension and arousal dimension， respectively， which are 2.65% and 0.40% higher than those of the single-modal EEG method respectively， and are 19.79% and 24.90% higher than those of the single-modal ECG method respectively. It can be seen that the proposed method can effectively improve the accuracy of emotion recognition and provide decision support for medical diagnosis and other fields.

In order to solve the problem of unbalanced dataset in credit assessment and the limited classification effect of single classifier on unbalanced data, a Majority Weighted Minority Oversampling TEchnique-Random Forest (MWMOTE-RF) credit assessment method was proposed. Firstly, MWMOTE technology was applied to increase the samples of minority classes in the preprocessing stage. Then, on the preprocessed balanced dataset, random forest algorithm, one of supervised machine learning algorithms, was used to classify and predict the data. With Area Under the Carve (AUC) used to evaluate the performance of classifier, experiments were conducted on German credict card dataset from UCI database and a company's car default loan dataset. The results show that the AUC value of MWMOTE-RF method increases by 18% and 20% respectively compared with random forest method and Naive Bayes method on the same data set. At the same time, random forest method was combined with Synthetic Minority Over-sampling TEchnique (SMOTE) and ADAptive SYNthetic over-sampling (ADASYN), respectively, and the AUC value of MWMOTE-RF method increases by 1.47% and 2.34% respectively compared with them. The results prove the effectiveness and the optimization of classifier performance of the proposed method.