User health data such as heart rate and blood glucose generated by wearable devices in real time is of great significance for health monitoring and disease diagnosis. However, health data is private information of users. In order to publish the average value of numerical stream data for wearable devices and prevent the leakage of users’ privacy information, a new differential private average publishing method of wearable devices based on adaptive sampling was proposed. Firstly, the global sensitivity was introduced which was adaptive to the characteristic of small fluctuation of stream data average for wearable devices. Then, the privacy budget was allocated by the adaptive sampling based on Kalman filter error adjustment, so as to improve the availability of the published data. In the experiments of two kinds of health data publishing, while the privacy budget is 0.1, which means that the level of privacy protection is high, the Mean Relative Errors (MRE) of the proposed method on the heart rate dataset and blood glucose dataset are only 0.01 and 0.08, which are 36% and 33% lower than those of Filtering and Adaptive Sampling for differential private Time-series monitoring (FAST) algorithm. The proposed method can improve the usability of wearable devices’ stream data publishing.

Focusing on the issue that collecting multi-dimensional numerical sensitive data directly from wearable devices may leak users' privacy information when a data server was untrusted, by introducing a local differential privacy model, a personalized local privacy protection scheme for the numerical sensitive data of wearable devices was proposed. Firstly, by setting the privacy budget threshold interval, a users' privacy budget within the interval was set to meet the individual privacy needs, which also met the definition of personalized local differential privacy. Then, security domain was used to normalize the sensitive data. Finally, the Bernoulli distribution was used to perturb multi-dimensional numerical data by grouping, and attribute security domain was used to restore the disturbance results. The theoretical analysis shows that the proposed algorithm meets the personalized local differential privacy. The experimental results demonstrate that the proposed algorithm has lower Max Relative Error (MRE) than that of Harmony algorithm, thus effectively improving the utility of aggregated data collecting from wearable devices with the untrusted data server as well as protecting users' privacy.

To solve the problem that the China new-generation Doppler weather radar named CINRAD is easy to lose small shear in radial or tangential direction, a new alerting algorithm of low-level wind shear based on Fuzzy C-Means (FCM) was proposed for wind shear identification of front and tornado. In order to achieve high shear and low shear warning, the core idea of this algorithm was to use 8-neighborhood system, according to the wind speed divergence characteristics to identify varying degrees of shear. Firstly, the Total Variation (TV) model was used in radar velocity base data denoising while maintaining the details of the data. Secondly, the 8-neighborhood system was convoluted in turn with 4-direction template to obtain the omni directional velocity gradient. Then, in order to achieve different intensity of wind shear altering, the FCM algorithm was used to classify the gradient values into two categories. Using the measured data provided with the Wuhan Rainstorm Research Institute to test and verify, the small shear was identified. The results show that the algorithm to detect wind shear is superior to the wind shear recognition algorithm based on radial or tangential direction in terms of both position accuracy and edge recognition, which has important guiding significance to judgment of position and intensity and analysis of wind shear caused by different weather.

The binary brain network modularization is not enough to describe physiological features of human brain. In order to solve the problem, a modularization algorithm for weighted brain network based on Fast Newman binary algorithm was presented. Using the hierarchical clustering idea of condensed nodes as the base, a weighted modularity indicator was built with the main bases of single node's weight and entire network's weight. Then the modularity increment was taken as the testing index to decide which two nodes should be combined in weighted brain network and realize module partition. The proposed method was applied to detect the modular structure of the group average data of 60 healthy people. The experiment results showed that, compared with the modular structure of the binary brain network, the brain network modularity of the proposed method was increased by 28% and more significant difference between inside and outside of modules could be revealed. Moreover, the modular structure found by the proposed method is more consistent with the physiological characteristics of human brain. Compared with the other two existing weighted modular algorithms, the proposed method can also slightly improve the modularity and guarantee a reasonable identification for human brain modular structure.

According to the shape features of wind shear images extracted by wavelet invariant moment based on cubic B-spline wavelet basis, an improved Genetic Algorithm (GA) was proposed to apply to the type recognition of microburst, low-level jet stream, side wind shear and tailwind-or-headwind shear. In the improved algorithm, the adaptive crossover probability only considered the number of generation and mutation probability just emphasized the fitness valve of individuals and group, so that it could control the evolution direction uniformly, and greatly maintain the population diversity simultaneously. Lastly, the best feature subset chosen by the improved genetic algorithm was fed into 3-nearest neighbor classifier to classify. The experimental results show that it has a good direction and be able to rapidly converge to the global optimal solution, and then steadily chooses the critical feature subset in order to obtain a better performance of wind shear recognition that the mean recognition rate can reach more than 97% at last.

A static priority scheduling algorithm for periodic priority exchange was proposed to resolve the low-priority task latency problem in real-time multi-task system. In this method, a fixed period of timeslice was defined, and the two independent tasks of different priorities in the multi-task system exchanged their priority levels periodically. Under the precondition that the execution time of the task with higher priority could be guaranteed, the task with lower priority would have more opportunities to perform as soon as possible to shorten its execution delay time. The proposed method can effectively solve the bad real-time performance of low-priority task and improve the whole control capability of real-time multi-task system.

An improved Principal Component Analysis (PCA) method was proposed with the synchronous multi-dimensional data stream anomaly analysis techniques. In this method, the problem of the original data stream variation tendency was mapped to the eigenvector space, and the steady-state eigenvector was solved, then the abnormal changes of the synchronous multi-dimensional data stream could be diagnosed by the relationship between the instantaneous eigenvector and the steady-state eigenvector. This method was applied to the abnormality diagnosis of the tunnel strain monitoring data stream, and the real-time monitoring and warning system for the tunnel strain was realized by using VC++. The experimental results show that the proposed method can reflect the changes of the aperiodic variables timely and realize the anomaly monitoring and early warning for multi-dimensional data stream effectively.

Collaborative filtering is the most successful technology for building recommendation systems. But with a large number of users and items, this method faces serious problems such as sparsity which makes the recommendation efficiency decline linearly. In this paper a concept hierarchy methodology ameliorating user-item matrix was suggested. By using buy-data and click-through-data and integrating items of similar users and those of multi-level association, this method showed good performance on sparsity set.