For the intelligent dialogue system of customer service robots in power supply business halls, a large-scale dataset of power business user intents was constructed. The dataset includes 9 577 user queries and their labeling categories. First, the real voice data collected from the power supply business halls were cleaned, processed and filtered. In order to enable the data to drive the study of deep learning models related to intent classification, the data were labeled and augmented with high quality by the professionals according to the background knowledge of power business. In the labeling process, 35 types of service category labels were defined according to power business. In order to test the practicability and effectiveness of the proposed dataset, several classical models of intent classification were used for experiments, and the obtained intent classification models were put in the dialogue system. The classical Text classification model-Recurrent Convolutional Neural Network (Text-RCNN) was able to achieve 87.1% accuracy on this dataset. Experimental results show that the proposed dataset can effectively drive the research on power business related dialogue systems and improve user satisfaction.

Focusing on the issue that the impulse noise in Power Line Communication (PLC) system greatly affects the transmission performance, and most traditional de-noising algorithm can not effectively suppress the impulse noise, a time-frequency combination de-noising algorithm was proposed. Firstly, the impulse noise with large peak in the time domain received signal was detected and zeroed by selecting the appropriate threshold. Secondly, according to the symbols that had been decided in the frequency domain, the smaller impulse noise which had not eliminated in the time domain was reconstructed, and the accuracy of the noise reconstruction was improved by iteration. Finally, the reconstructed impulse noise was subtracted from the frequency domain received signal. Simulation experiments were conducted under the multipath channel of the power line. Compared with traditional time domain and frequency domain de-noising algorithms, the proposed algorithm could achieve the performance improvement of 2dB and 0.5dB respectively when the bit-error rate was 0.01. And as the bit-error rate decreased, the performance gap between them would be even greater. The simulation results show that the proposed time-frequency combination de-noising algorithm can improve the resistance of the PLC system to impulse noise.

In order to improve the accuracy of haze forecast and resolve the time lagging and low accuracy of temporal model, a mixed forecast method based on time series analysis and Karman filter was proposed. Firstly, the stability of time series was tested by graph analysis and eigenvalue analysis (ADF). Unstable time series were converted to stable ones by differential operation. A statistical function was established based on the stable time series. And then, the obtained model equations were used as the state and observation equation for Kalman filtering. Final haze forecast was based on recursion by Karman filtering. The experimental results showed that the accuracy of haze forecast is effectively improved by the mixed forecast method based on time series analysis and Karman filtering.

In order to reduce the relatively high probability of missed and false detection in traditional hard threshold setting algorithm, improve the anti-interference performance of Transform Domain Communication System (TDCS), an adaptive N-sigma amplitude spectrum shaping algorithm was proposed. The amplitude information of environment power spectrum was got according to spectrum sensing, the mean and standard deviation of the environment power spectrum were calculated. According to the correlation theory of normal distribution, the threshold was adaptively set. Therefore, when the electromagnetic environment changed, the mean and standard deviation would be readjusted and the threshold would be updated. The simulation results show that, compared to the traditional hard threshold setting algorithm, the threshold setting of the adaptive N-sigma amplitude spectrum shaping algorithm is more flexible and accurate, which can reduce the missed detection probability and false detection probability of interference and improve the overall anti-interference performance of the system.

To effectively utilize the depth information from RGB-D (Red Green Blue and Depth) images and enhance the scale invariance and rotation invariance of BRISK (Binary Robust Invariant Scalable Keypoints) algorithm, an improved BRISK algorithm combined with depth information was proposed. Firstly, the keypoints were detected by the FAST (Features from Accelerated Segment Test) algorithm and their Harris corner response values were computed. Then, the entire image was divided into the same size grids, and the keypoint with the maximum Harris corner response value was reserved by each grid. Next, the scale factor of the keypoint was directly computed with the depth information of the image. Finally, the intensity centroid of the circle centered on the keypoint was calculated, and the orientation of keypoint was computed by the offset from its intensity centroid. The comparison experiment analysis of several algorithms on the scale invariance and rotation invariance was performed. The experimental results show that, compared with the BRISK algorithm, the number of correctly matched keypoints of the improved algorithm improves by more than 90% when the image's scale is changed and raises by at least 70% when the image is rotated.

Focusing on the fading and shadowing effect in satellite channel, a Hybrid Satellite-Terrestrial Cooperative System (HSTCS) was presented, and the closed-form expression of the outage probability was evaluated using the Land Mobile Satellite (LMS) channel. A selective Decode-and-Forward (DF) scheme was implemented between a source node (the satellite) and a destination node (a terrestrial station), and signals from the satellite and terrestrial relay were combined at destination. The analytical expression of the outage probability was verified with the Matlab simulation. The results show that the system can improve the outage performance through the diversity gain, compared with the direct transmission.

Aiming at the lack of semantic description for Service-Level Agreement (SLA) elements used in negotiation and the negotiation process in the SLA auto-negotiation, a negotiation mechanism based on the semantic Web technology was proposed, At first, a negotiation ontology named Osn was proposed, which was used for the description of SLA elements directly used in negotiation;the mapping function and the evaluation function of negotiation for these SLA elements were designed and described in this Osn, and the formal description of the main concepts and the relationship between these concepts was given based on description logic to provide a satisfiable semantic model for the Osn. Then a bargain model was put forward for SLA negotiation, and it was illustrated that a Pareto optimal offer could be generated by adopting this model through the proof of the related proposition and theorem;the service ontology was designed for SLA negotiation based on the mapping between OWL-S and Unified Modeling Language (UML) using this bargain model. The result of case study shows that the knowledge can form the sequence of offers which satisfied the need to maximize the interest of negotiation participants. It is illustrated that Osn can provide the service ontology with the parameter type support for the negotiation of an arbitrary SLA;the SLA negotiation oriented bargain model can generate the SLA accepted by both negotiation participants.

Towards the large frequency offset caused by Doppler effect in high speed moving environment, a dynamic state space model of Orthogonal Frequency Division Multiplexing (OFDM) was built, and a kind of frequency offset tracking and estimation algorithm in OFDM based on improved Strong Tracking Unscented Kalman Filter (STUKF) was proposed. By combining strong tracking filter theory and UKF together, the fading factor was introduced during the process of calculating the measurement predictive covariance and cross covariance. The frequency offset estimation error covariance was adjusted; meanwhile, the process noise covariance was also controlled, and the gain matrix was adjusted in real-time. So the tracking ability to time-varying frequency offset was enhanced and the estimated accuracy was raised. The simulation test was carried out in time-invariant and time-varying frequency offset models. The simulation results show that the proposed algorithm has better tracking and estimation performance than the UKF frequency offset estimation algorithm, the Signal-to-Noise Ratio (SNR) raises about 1dB under the same Bit Error Rate (BER).

The effect of the existing Total Variation (TV) method for image denoising is not ideal, and it is not good at keeping the characteristics of image edge and texture details. A new method of image denoising based on rational-order differential was proposed in this paper. First, the advantages and disadvantages of the present image denoising methods of TV and fractional differential were discussed in detail, respectively. Then, combining the model of TV with fractional differential theory, the new method of image denoising was obtained, and a rational differential mask in eight directions was drawn. The experimental results demonstrate that compared with the existing denoising methods, Signal Noise Ratio (SNR) is increased about 2 percents, and the method retains effectively the advantages of integer and fractional differential methods, respectively. In aspects of improving significantly high frequency of image and keeping effectively the details of image texture, it is also an effective, superior image denoising method. Therefore, it is an effective method for edge detection.

With regard to the characteristics of randomness and fuzziness in service trust under computing environment, and lack of consideration in timeliness and recommend trust, a service trust evaluation method based on weighted multiple attribute cloud was proposed. Firstly, each service evaluation was given weight by introducing time decay factor, the evaluation granularity was refined by trust evaluation from multiple attribute of service, and direct trust cloud could be generated using the weighted attribute trust cloud backward generator. Then, the weight of recommender could be confirmed by similarity of evaluation, and recommended trust cloud was obtained by recommend information. Finally, the comprehensive trust cloud was obtained by merging direct and recommended trust cloud, and the trust rating could be confirmed by cloud similarity calculation. The simulation results show that the proposed method can improve the success rate of services interaction obviously, restrain malicious recommendation effectively, and reflect the situation of service trust under computing environment more truly.

Concerning the classification problems of complex data distribution of scientific practice, economic life and many other fields, the correlation between variables could not be well described by using traditional Support Vector Machine (SVM), which would influence the classification performance. For this situation, Q-Gaussian function that was a parametric generalization of Gaussian function was put forward as the kernel function of SVM, and a financial early-warning model based on SVM with Q-Gaussian kernel was presented. Based on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets, T-2 and T-3 financial early-warning model were constructed in experiments, the significance test was used to select some suitable indicators and the Cross Validation (CV) was used to determine model parameters. Compared to SVM model with Gaussian kernel, the forecasting accuracies of T-2 and T-3 model constructed by SVM with Q-Gaussian kernel were improved about 3%, and high-cost type I errors were reduced by at most 14.29%.

Mobility model is the basis of protocol design and evaluation for mobile Ad Hoc network. A multiple states entity random mobility model was proposed according to the requirements of entity mobility modeling, which could reflect the realistic node movement and had more controllable parameters. Several familiar mobility models were derived from it by adjusting some parameters. The mobility model could be applied to simulation of mobile Ad Hoc network for its flexibility and versatility.