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Based on the hyperbolic tangent shear deformation theory, free vibration and sound insulation of two different types of functionally graded (FG) honeycomb sandwich plates with negative Poisson’s ratio are studied in this paper. Using Hamilton’s principle, the vibration and vibro-acoustic coupling dynamic equations for FG honeycomb sandwich plates with simply supported edges are established. By applying the Navier’s method and fluid–solid interface conditions, the derived governing dynamic equations are solved. The natural frequencies and the sound insulation of FG honeycomb sandwich plates obtained in this work are compared with the numerical results by the finite element simulation. It is proven that the theoretical models for the free vibration and the sound insulation are accurate and efficient. Moreover, FG sandwich plates with different honeycomb cores are investigated and compared. The corresponding results show that the FG honeycomb core with negative Poisson’s ratio can yield much lower frequencies. Then, the influences of various geometrical and material parameters on the vibration and sound insulation performance are systematically analyzed.

The recommendation algorithm based on Singular Value Decomposition (SVD)++ is a widely used algorithm for its good prediction performance. However, with the rapid increase of data in smart societies, the poor computational performance of the SVD++ recommendation algorithm becomes a prominent disadvantage, for it takes a longer time to optimize the objective function during constructing the prediction model. The learning rate function is a significant factor in the prediction model based on the SVD++ recommendation algorithm. It can directly affect the convergence speed of the prediction model and the performance of the model. The traditional model uses an exponential function, natural exponential function or piecewise constant as its learning rate function. In this paper, a novel adaptive learning rate (ALR) function is proposed, which combines the exponential with linear functions, and the function is applied to the SVD++ recommendation algorithm. The highlights of the paper are as follows. First, with a larger initial value, the proposed function descends quicker and tends to the end with a less step. Second, the theoretical properties of the proposed learning rate function are verified through theoretical analysis, including the theoretical proof of its convergence and the iteration speed comparison. Compared to the existing learning rate functions, the proposed ALR function works better on the convergence speed through mathematical derivation. Finally, the novel ALR function is applied to the SVD++ recommendation algorithm as recommendation model ALRSVD++. Some existing learning rate methods are used as benchmarks for illustrating the computation and prediction performances of proposed ALR function and its ALRSVD++ model. Experimental results demonstrated that the SVD++ recommendation algorithm based on the proposed ALR function improved computational efficiency of the training model ALRSVD++ significantly. Especially, to the larger size training dataset, the iterations and training time based on the proposed ALR function and ALRSVD++ model reduced in a great deal, without greatly sacrificing the recommendation performance.

The inherent structure and connectivity of a group are important features of social networks. Finding the densest subgraphs of a graph directly maps to revealing the densest communities of a social network. Various techniques, e.g., edge density, k-core, near-cliques and k-cliques, have been developed to characterize graphs and extract the densest subgraphs of the graphs. However, as extraction of subgraphs with constraints is NP-hard, these techniques face a major difficulty of processing big and/or streaming data sets from social networks. This demands new methods from the expert and intelligent systems perspective for computation of the densest subgraph problem (DSP) with big and/or streaming data. The most recent method for this purpose is the “Sampling” method. It samples the big data sets, thus reducing the data space and consequently speeding up the DSP computation. But the sampled data inevitably miss out many useful data items. A new approach is presented in this paper for accelerated DSP computation with big and/or steaming data through data space reduction without loss of useful information. It uses a sliding window of small graphs with a fixed number of edges. Then, it filters out the least connected edges for each small graph. While the small graphs are processed, subgraphs are incrementally put together to reveal the densest subgraphs. Finally, the data space previously filtered out is checked for recovery of globally important edges. The approach is incorporated with existing subgraph extraction techniques for scalable and efficient DSP computation with improved accuracy. It is demonstrated for four subgraph extraction techniques over four Twitter data sets, and is shown to outperform the “sampling” method.

In this work, we simultaneously obtained a large d33 and high TC in a lead-free piezoelectric system of (1-x)K0.5Na0.5NbO3–xBi0.5Na0.5Zr0.85Sn0.15O3 [(1-x)KNN–xBNZS]. This lead-free piezoelectric system was synthesized by conventional solid-state method. We investigated the microstructure and electrical properties of this system. The rhombohedral-tetragonal (R-T) phase coexistence is demonstrated in the ceramics with 0.05 ≤ x ≤ 0.06. Owing to the R–T phase coexistence as well as the enhancement of ferroelectric and dielectric properties, the ceramics with x = 0.05 showed a large d33 of ∼350 pC/N together with a high TC of ∼315 °C, thereby illustrating that it is an effective way to obtain both large d33 and high TC in KNN-based ceramics. It is believed that such a ceramic system with large d33 and high TC is one of the promising candidates for piezoelectric devices.

In chip multiprocessor scheduling, cache memory awareness is critical. However, most existing cache-aware multiprocessor scheduling methods are static and open-loop and thus do not work well in dynamic environments. To address this challenging problem, a control-theoretic approach is presented in this paper for adaptive cache-fair multiprocessor scheduling. The adaptive cache-fair multiprocessor scheduling uses process models to estimate the shared cache size requirements and the instruction count variations of the threads running on multiprocessor cores. Then, it feeds the estimated information back to an adaptive controller. Designed from the process models, the controller decides processor cycle re-allocation through model computation to maintain the instruction counts of the multiple threads at their desired values. The effectiveness of the adaptive cache-fair multiprocessor scheduling is demonstrated through examples.

KNN based lead free piezo-ceramics with new additive (Bi0.45La0.05K0.5)ZrO3 (BLKZ) doping have been prepared by conventional solid-state sintering method. The effect of BLKZ content on phase structure evolution was investigated through the X-ray diffraction and the Rietveld refinement method. Both the XRD and Rietveld refinement confirmed a coexistence of rhombohedral–orthorhombic–tetragonal (R–O–T) multiphase in the BLKZ adding KNN systems. The study of the BLKZ content on the phase transition temperature of the ceramics manifested that the BLKZ could modify the phase transition temperature of rhombohedral–orthorhombic (T R–O) and orthorhombic–tetragonal (T O–T) to room temperature simultaneously, thus, a new R–O–T multiphase coexistence boundary would be constructed in the KNN systems. A piezoelectric coefficient of d 33 ~ 385 pC/N with a T c of 245 °C were observed in the KNN ceramics when the BLKZ content is 3.5 mol%.

The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two examples, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.

In this work, lead free piezo-ceramics of (1−x)K0.4Na0.6Nb0.96Sb0.04O3–xBi0.5K0.5Zr0.9Hf0.1O3 [(1−x)KNNS–xBKZH] have been synthesized by conventional solid-state sintering method. The effect of BKZH content on the phase structure and electrical properties were systematically investigated. The results show that a coexistence of rhombohedral–orthorhombic–tetragonal (R–O–T) phases have been found when x = 0.04, indicating that BKZH doping can induce the formation of R–O–T phase boundary by increasing T R−O and decreasing T O−T simultaneously in KNN systems. In addition, an optimal electrical properties (e.g., d 33 = 451 pC/N, k p = 52%, T c = 258 °C, e r = 2489, tanδ = 3.6%, P r = 21.7 μC/cm2, and E c = 10.1 kV/cm) were obtained in the ceramic of x = 0.04, which proves that adding BKZH content is an efficientive way to promote the electrical properties of potassium–sodium niobate ceramics. As a result, we can believe that the (1−x)KNNS–xBKZH ceramics will become one of the promising lead-free candidates for piezoelectric applications.

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models.

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the electroencephalogram (EEG) signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposed a novel EEG stroke signal feature extraction method by combining fuzzy entropy and hierarchical theory. Fuzzy entropy not only took the advantages of sample entropy, but also had less dependence on the length of time series and possessed better robustness to noise signals. It measured the similarity of two vectors based on Gaussian function instead of Heaviside function, avoiding discontinuity problems of sample entropy and approximate entropy. Hierarchical theory efficiently took advantages of the approximation information in low-frequency and the detail information in high-frequency. This was benefit for capturing a wealth of dynamic information and retaining redundant components. Support vector machine (SVM) was further used as the stroke signal classification model for classifying ischemic stroke and hemorrhagic stroke. The experimental results showed that, compared with other benchmarks, the classification accuracy based on the features of hierarchical fuzzy entropy is much higher than those benchmarks methods. Compared with the features of fuzzy entropy without using hierarchical theory, the classifier based on the features of hierarchical fuzzy entropy gave a much more improvement in classification performance by increasing accuracy from 68.03% to 96.72%. It meant that the proposed EEG stroke signal hierarchical fuzzy entropy feature extraction method was an efficient measure in classifying ischemic and hemorrhagic stroke.

As a typical cardiovascular disease, stroke is the world's second most deadly disease with the characteristics of high mortality, incidence, recurrence rate and disability rate. To improve the stroke predicting accuracy is a necessary measure for reducing the happening of stroke. As a non-invasive and portable method? Transcranial Doppler (TCD) can provide valuable information in diagnosis of cerebrovascular disease by measuring the blood flow speed in intracranial arteries. How to improve diagnosis accuracy performance based on TCD data set is a necessary demand for realizing cerebrovascular disease TCD intelligent diagnosis system. But TCD data set often presents an imbalanced phenomenon for less patients' data over all examiners. This imbalanced phenomenon easily makes a lower predicting accuracy of cerebrovascular patients. In this paper, an improved fuzzy support vector machine model is proposed by combining class center distance and information entropy. The model efficiently utilizes the advantages of class center distance and information entropy. Moreover, the imbalanced fuzzy support vector machine is used by combining an adaptive imbalanced regulator. The different influence of the imbalance regulating factor value on the classification effect were discussed in detail for finding a suitable regulating factor value. A practical clinical imbalanced TCD dataset is used by collecting clinical TCD data for identify the performance of the proposed method. The experimental results showed that the proposed method can get a better classification performance with higher accuracies and G-means over imbalanced TCD dataset than other benchmark methods.

Nano-grained ceramics have their unique mechanical characteristics that are not commonly found in their coarse-grained counterparts. In this study, nano-grained YAG transparent ceramics (NG-YAG) were prepared by low-temperature high-pressure technique (LTHP). The peak profile analysis of the X-ray diffraction was employed to investigate the compressive yield strength of NG-YAG. During the temperature at 450 °C, the residual micro-strain (RMS) increased with increasing loading pressure. However when the loading pressure was exceeded to 4.0 GPa the RMS exhibited a severe negative slop. The temperature effects on the compressive yield strength were also studied. It shows that the compressive yield strength of NG-YAG is 4.0 GPa and 5.0 GPa respectively at 450 °C and 350 °C. More importantly according to this investigation, a feasible technique to study the nano-grained ceramics is provided.

Ensemble learning is a wildly concerned issue. Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers. They fail to address the ensemble task where only unlabeled data are available. A label propagation based ensemble (LPBE) approach is proposed to further combine base classification results with unlabeled data. First, a graph is constructed by taking unlabeled data as vertexes, and the weights in the graph are calculated by correntropy function. Average prediction results are gained from base classifiers, and then propagated under a regularization framework and adaptively enhanced over the graph. The proposed approach is further enriched when small labeled data are available. The proposed algorithms are evaluated on several UCI benchmark data sets. Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

In the recommender system, users often label multiple same tags on the interesting items which other users have labeled. It can cause that many same tags are often repeatedly labeled, resulting in the tag redundancy situation on the user-item-tag data set. In this paper, a tag recombinant approach based on Apriori algorithm is proposed for reducing the tag redundancy situation. The approach first utilized the Apriori algorithm to preprocess the original tag data for searching the frequent itemsets of tags, and then recombined the new tags to form a new data set. The experimental results demonstrated that based on the proposed approach, the newly formed data set significantly reduced the number of the tuples, and at the same time the relationship among the user, item and tag can be reflected more clearly.

Conventional approaches to acoustic source localization simply based on the received microphone signals, are often vulnerable to adverse acoustic conditions, such as low signal-to-noise ratio (SNR) or high reverberation. But, approaches based on Pattern Recognition and Machine Learning Technology can increase accuracy to locate source in adverse acoustic environment. The advantage of the algorithm is that it requires no calibration of microphone arrays. And Naive Bayes Classifier is simple, fast, and has a small error rate. This paper proposed an improved localization algorithm based on classification of cross-correlation functions (GCC). The weighted cross power spectrum of GCC is smoothed by a smooth filter to formed smooth generalized cross-correlation (SGCC). Then, the classifier model is obtained in each location and form the feature vector. Finally, acoustic source location is estimated by Naive- Bayes classifier. We also proposed in this study the source localization system that based on merely two microphones to input sound signals, combined with improved and optimal methods proposed above. Real-data experiments have demonstrated that algorithm with SGCC has higher localization accuracy than with GCC by 20% in the proposed system at least. The system has good ability to acoustic source localization.

One of the most popular algorithms for classification is the decision tree. However, existing binary decision tree models do not handle well the minority class over imbalanced data sets. To address this difficulty, a Cost-sensitive and Hybrid attribute measure Multi-Decision Tree (CHMDT) approach is presented in this paper. It penalizes misclassification through a hybrid attribute measure, which is defined from the combination of the Gini index and information gain measure. It further builds a multi-decision tree consisting of multiple decision trees each with different root node information. The overall objective of the approach is to maximize the classification performance with the hybrid attribute measure while minimizing the total misclassification cost. Experiments are conducted over twelve KEEL imbalanced data sets to demonstrate the CHMDT approach. They show that the classification performance of the minority class is improved significantly without sacrifice of the overall classification accuracy of the majority class.

In this study, a robust classification method for emotional speech single-trial event-related potential (ERP) signal was developed. The classification method based on compression sensing (CS) theory. First, we use CS theory to reduce the dimensionality of the ERP signal. Second, the ERP signal was reconstructed by using K-SVD method to construct the over-complete redundant dictionary. Finally, the ERP signal was classified by calculating the residuals between the reconstructed samples and the test samples. The experimental results show that the proposed algorithm can effectively classify the noisy ERP signal and avoid the feature extraction process in the signal recognition.

As CMOS process is advancing towards the high-k/metal gate (HK/MG) technology, dummy poly gate removal (DPGR) process, one of key steps in gate-last technology, poses the challenge for its multiple-film related etching process. Its subsequent photo-resist (PR) strip process is also very difficult due to rigorous process requirements and rather limited wet clean resource. The DPGR process in this work includes two separate DPGR processes for n/p-MOS structures. In the 1st DPGR PR strip process, PR strip capability significantly improves with higher power, higher temperature and higher hydrogen flow-rate. Unlike its PR strip process, post etch treatment (PET) with lower temperature is proven helpful for the 2nd DPGR PR strip. In addition, both bias temperature instability (BTI) and time-dependent dielectric breakdown (TDDB) have been enhanced with the optimized PR strip processes.

A two-step post-metallization annealing (PMA) process has been developed and it has demonstrated its significant benefit for high-k metal gate (HKMG) in reducing post chemical mechanical polish (CMP) Al corrosion defects. After metal gate CMP, samples are firstly treated with N2O plasma in a 400°C PECVD (plasma enhanced chemical vapor deposition) chamber for surface oxidization, before which the chamber has been cycled with pumping through N2 and noble gas mixture so as to remove moisture and/or particles on chamber wall. Then, N2O gas switched off and NH3/N2 mixture plasma turned on to nitridize the metal oxides. A multi-variable (e.g. Gas ratio, RF power, plasma treatment time, chamber pressure, etc.) statistical design of experiment (DOE) was performed to optimize processes for growing ultra-thin metal oxynitride (e.g. AlON, TiON, TaON, etc.) on top surface of the metal gate, which will work as the 2nd etch stop layer (ESL) as well. TEM results showed that metal has been dragged upwards desirably and 30∼60A self-aligned insulated film grown upon Al/HfO2 capacitor, but 50–100A diffusing layer was also observed unavoidably, which may induce metal loss and lead to a higher electrical resistance. With PMA treatment, PMOS post-CMP corrosion defects have been reduced significantly by ∼85% in isolated SRAM (Static Random Access Memory), by ∼50% in dense area, and by ∼75% at the gate boundary area. The correlation of IOFF v.s IDSAT and IDSAT v.s VDSAT (saturation voltage at sub-threshold regime) demonstrated a comparable performance with that of non-PMA process. In light of the findings from this study, a trade-off between defect reduction and N/O atom diffusion downward metal gate should achieve with further PMA optimization.