Counterfactual prediction and selection bias are major challenges in causal effect estimation. To effectively represent the complex mixed distribution of potential covariant and enhance the generalization ability of counterfactual prediction， a Re-weighted adversarial Variational AutoEncoder Network （RVAENet） model was proposed for industrial causal effect estimation. To address bias problem in mixed distribution， the idea of domain adaptation was adopted， and an adversarial learning mechanism was used to balance the representation learning distribution of the latent variables obtained by the Variational AutoEncoder （VAE）. Furthermore， the sample propensity weights were learned to re-weight the samples， reducing the distribution difference between the treatment group and the control group. The experimental results show that， in two scenarios of the industrial real-world datasets， the Areas Under Uplift Curve （AUUC） of the proposed model are improved by 15.02% and 16.02% compared to TEDVAE （Treatment Effect with Disentangled VAE）. On the public datasets， the proposed model generally achieves optimal results for Average Treatment Effect （ATE） and Precision in Estimation of Heterogeneous Effect （PEHE）.

The Programmable Logic Controller (PLC) program needs modeling the program manually in the NuSMV model testing, which is not only a waste of manpower but also an error-prone procedure. In order to solve the problems, an automatic construction method of PLC program model based on state transition was proposed. Firstly, the Structured Text (ST) language features were analyzed and the ST program was parsed as an abstract grammar tree. Secondly, according to the abstract grammar tree, the control flow graph was generated based on different grammatical structure by control flow analysis. And then the program dependency graph was obtained by data flow analysis. Finally, the NuSMV input model was generated according to the program dependency graph. The experimental results shows that, the proposed method achieves the automatic construction from ST program to NuSMV input model, and the constructed NuSMV input model not only retains the original characteristics of ST program but also conforms to the input standard of NuSMV model detection tool. Compared with the traditional manual model construction method, the proposed method improves the efficiency and accuracy of model generation.

In order to further improve the precision of navigation signal, band efficiency and the reliability performance for navigation systems, a new signal system adopted Minimum Shift Keying (MSK) with Binary Offset Carrier (BOC) based on Low Density Party Check (LDPC) codes was presented, which was called LDPC-MSK-BOC signal system. The navigation performances of the BOC and MSK-BOC were evaluated based on the parameters of Compass and GPS typical signals, which were scaled with power spectral density, code tracking error, multipath error envelope, bit error rate, anti-narrowband jamming merit factor and anti-matched spectrum jamming merit factor for demodulation processing, anti-narrowband jamming merit factor and anti-matched spectrum jamming merit factor for code tracking processing, and spectral separation coefficient. The theoretical analysis and simulation show that the proposed system has better performance in the field of code tracking precision and anti-multipath compared with BOC signal system under the limited spectrum resource condition. Meanwhile, the signal structure can further improve system reliability and band efficiency.

To resolve that the malware (malicious software) usually avoids security detection and flow analysis through encryption function, this paper proposed a scheme which can identify the encrypted function in malware. The scheme generated the dynamic loop data flow graph by identifying the loop and input/output of the loop in the dynamic trace. Then the sets of input/output were abstracted according to the loop data flow graph, the reference of known encrypted function was designed and the reference whose parameters were elements of the input sets was computed. If the result could match any element of the output sets, then the scheme could conclude the malware encrypts information by the known encrypted function. The experimental results prove that the proposed scheme can analyze the encrypted function of payload in the obfuscated malware.

A transmission rate aware candidate forwarder selection and ordering algorithm based on expected transmission delay of nodes was proposed. It first separated opportunistic route forwarding into two components: the anycast forwarding from source node to its candidate forwarders set, and the remaining forwarding from that candidate forwarders set to destination, and then the shortest expected transmission delay of opportunistic routing was computed iteratively. Finally, candidate forwarders were selected and ordered according to the shortest expected transmission delay of nodes. The simulation results indicate that the proposed algorithm can improve the performance of opportunistic routing obviously.

Concerning the clock skew and clock drift problem in wireless sensor networks, some different methods of synchronization time on synchronization accuracy were studied. With the principle of clock synchronization of cluster-shaped network structure, an optimized clock basis algorithm was put forward. And the Kalman filtering method was applied to adjust the nodes’ clock deviation in optimized recursive way. Compared with the general synchronization algorithm of cluster-shaped, the proposed algorithm can not only improve the synchronization accuracy, but also reduce the energy consumption of synchronous nodes. The simulation results show that the algorithm can accurately describe the synchronization precision and is more efficient for clock synchronization.