Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the wasserstein GAN model. Four data sets of lithium-ion battery provided by the NASA Ames Research Center are used to prove the feasibility and accuracy of the proposed method.

In the traditional method, the software quality is measured by various metrics of the software, such as decoupling level (DL), which can be used to predict software defect. However, DL, which treats all the ?les equally, has not taken file importance into consideration. Therefore, a novel software quality metric, named as improved decoupling level (IDL), based on the importance of documents was proposed. First, the PageRank algorithm was used to calculate the importance of ?les to obtain the weights of the dependencies, and then defect prediction models was established by combining the software scale, dependencies, scores and software defects to assess the software quality. Compared to most existing module-based software quality evaluation methods, IDL has similar or even superior performance in the prediction of software quality. The results indicate that IDL measures the importance of each ?le in the software more accurately by combining the PageRank algorithm in DL, which indirectly re?ects the quality of software by predicting the bug information in software and improves the accuracy of prediction result of software bug information.

In recent years, with the development of smart devices, mobile users can use them to sense the environment. In order to improve the data quality and achieve maximum profits, incentive mechanism is needed to motivate users to participate. In this paper, reputation mechanism, participant selection, task allocation and joint pricing in mobile crowdsourcing system are studied. A user reputation evaluation method is proposed, and a participant selection algorithm (PSA) based on user reputation is proposed. Besides, a social welfare maximization algorithm (SWMA) is proposed, which achieves task pricing with maximizing the interests of all parties, including both task publishers and mobile users. The social welfare maximization problem is divided into local optimization sub-problems which can be solved by double decomposition. It is proved that the algorithm converges to the optimal solution. Results of simulations verify that algorithms PSA and SWMA are effective.

Test case prioritization (TCP) technique is an efficient approach to improve regression testing activities. With the continuous improvement of industrial testing requirements, traditional single-objective TCP is limited greatly, and multi-objective test case prioritization (MOTCP) technique becomes one of the hot topics in the field of software testing in recent years. Considering the problems of traditional genetic algorithm (GA) and swarm intelligence algorithm in solving MOTCP problems, such as falling into local optimum quickly and weak stability of the algorithm, a MOTCP algorithm based on multi-population cooperative particle swarm optimization (MPPSO) was proposed in this paper. Empirical studies were conducted to study the influence of iteration times on the proposed MOTCP algorithm, and compare the performances of MOTCP based on single-population particle swarm optimization (PSO) and MOTCP based on non-dominated sorting genetic algorithm II (NSGA-II) with the MOTCP algorithm proposed in this paper. The results of experiments show that the test case prioritization algorithm based on MPPSO has stronger global optimization ability, is not easy to fall into local optimum, and can solve the MOTCP problem better than test case prioritization algorithm based on the single-population PSO and NSGA-II.

As a special type of distributed denial of service (DDoS) attacks, the low-rate DDoS (LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.

To solve the problem of security and efficiency of anonymous authentication in the vehicle Ad-hoc network（VANET）, a conditional privacy protection authentication scheme for vehicular networks is proposed based on bilinear pairings. In this scheme, the tamper-proof device in the roadside unit (RSU) is used to complete the message signature and authentication process together with the vehicle, which makes it more secure to communicate between RSU and trusted authority (TA) and faster to update system parameters and revoke the vehicle. And this is also cheaper than installing tamper-proof devices in each vehicle unit. Moreover, the scheme provide provable security proof under random oracle model (ROM), which shows that the proposed scheme can meet the security requirements such as conditional privacy, unforgeability, traceability etc. And the results of simulation experiment demonstrate that this scheme not only of achieves high efficiency, but also has low message loss rate.

A hybrid model for broadband multiple-input multiple-output (MIMO) relay-aided indoor power line communications (PLC) system was proposed in this paper. The proposed model combines the top-down and bottom-up approaches and extends to a two-hop relay-aided cooperative system with variable gain relay in amplify-and-forward (AF) mode. Based on the proposed PLC model and generated channel, the channel statistical characteristics are further investigated in 2MHz - 100MHz bandwidth. Simulated results show that the proposed model overcomes the difficulties that the existing models need a lot of topological information of the network or measurements information. It provides a practical simulation analysis method for cooperative relay MIMO-PLC system. The results also show that cooperative MIMO relaying communications can improve the indoor PLC performances and communication reliability.

Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI). To address these limitations, a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.

The number of short videos on the Internet is huge, but most of them are unlabeled. In this paper, a rough labelling method of short video based on the neural network of image classification is proposed. Convolutional auto-encoder is applied to train and learn unlabeled video frames, in order to obtain the feature in certain level of the network. Using these features, we extract key-frames of the video by our method of feature clustering. We put these key-frames which represent the video content into the image classification network, so that we can get the labels for every video clip. We also compare the different architectures of convolutional auto-encoder, while optimizing and selecting the better performance architecture through our experiment result. In addition, the video frame feature from the convolutional auto-encoder is compared with those features from other extraction methods. On the whole, this paper propose a method of image labels transferring for the realization of short video rough labelling, which can be applied to the video classes with few labeled samples.

Modeling and Matching texts is a critical issue in natural language processing (NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features (MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network (CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.