Cross-modal hashing has attracted much attention due to its low storage cost and high retrieval efficiency. Most of the existing cross-modal hashing methods require the inter-instance association information provided by additional manual labels. However, the deep features learned by pre-trained deep unsupervised cross-modal hashing methods can also provide similar information. In addition, the discrete constraints are relaxed in the learning process of Hash codes, resulting in a large quantization loss. To solve the above two issues, a Deep Unsupervised Discrete Cross-modal Hashing (DUDCH) method based on knowledge distillation was proposed. Firstly, combined with the idea of knowledge transfer in knowledge distillation, the latent association information of the pre-trained unsupervised teacher model was used to reconstruct the symmetric similarity matrix, so as to replace the manual labels to help the supervised student method training. Secondly, the Discrete Cyclic Coordinate descent (DCC) was adopted to update the discrete Hash codes iteratively, thereby reducing the quantization loss between the real-value Hash codes learned by neural network and the discrete Hash codes. Finally, with the end-to-end neural network adopted as teacher model and the asymmetric neural network constructed as student model, the time complexity of the combination model was reduced. Experimental results on two commonly used benchmark datasets MIRFLICKR-25K and NUS-WIDE show that compared with Deep Joint-Semantics Reconstructing Hashing (DJSRH), the proposed method has the mean Average Precision (mAP) in image-to-text/text-to-image tasks increased by 2.83 percentage points/0.70 percentage points and 6.53 percentage points/3.95 percentage points averagely and respectively, proving its effectiveness in large-scale cross-modal retrieval.

Cyber Physical System (CPS) involves the integration and collaboration of various computing models. Concerning the problems of inconsistent CPS design methods, poor plasticity, high complexity and difficulty in collaborative modeling and verification, a structured and descriptive heterogeneous component model was proposed. Firstly, the model was constructed by a unified component modeling method to solve the problem that the model was not open. Then, eXtensible Markup Language (XML) was used to realize the standard description of all kinds of components to resolve the inconsistency and non-extensibility of different computing model description languages. Finally, the collaborative simulation verification method of multi-level open component model was used to realize the simulation verification to solve the non-collaboration problem of verification. The medical thermostat was modeled, described and simulated by the general component modeling method, the XML component standard description language and the verification tool platform XModel. The case of medical thermostat shows that, the proposed model-driven process of building reconfigurable heterogeneous components and confirming their design correctness supports the collaborative design of cyber physics and the correction while constructing, avoiding repeated modifications when problems are found in the process of system implementation.

Aiming at the processes with dynamic and multimode characteristics, a fault detection strategy based on Local Neighbor Standardization (LNS) and Dynamic Principal Component Analysis (DPCA) was proposed. First, the K nearest neighbors set of each sample in training data set was found, then the mean and standard deviation of each variable were calculated. Next, the above mean and standard deviation were applied to standardize the current samples. At last, the traditional DPCA was applied in the new data set to determine the control limits of T ² and SPE statistics respectively for fault detection. LNS can eliminate the multimode characteristic of a process and make the new data set follow a multivariate Gaussian distribution; meanwhile, the feature of a outlier deviating from normal trajectory can also be maintained. LNS-DPCA can reduce the impact of multimode structure and improve the detectability of fault in processes with dynamic property. The efficiency of the proposed strategy was implemented in a simulated case and the penicillin fermentation process. The experimental results indicate that the proposed method outperforms the Principal Component Analysis (PCA), DPCA and Fault Detection based on K Nearest Neighbors (FD- KNN).

Aiming at the nonlinear and multi-mode features of batch processes, a fault detection method for batch process based on k Nearest Neighbors ( kNN) rule in Discriminated kernel Principle Component space, namely Dis-kPC kNN, was proposed. Firstly, in kernel Principal Component Analysis (kPCA), according to discriminating category labels, the kernel window width parameter was selected between within-class width and between-class width, thus the kernel matrix can effectively extract data correlation features and keep accurate category information. Then kNN rule was used to replace the conventional T ² statistical method in the kernel principal component space, which can deal with fault detection of process with nonlinear and multi-mode features. Finally, the proposed method was validated in the numerical simulation and the semiconductor etching process. The experimental results show that the kNN rule in discriminated kernel principle component space can effectively deal with the nonlinear and multi-mode conditions, improve the computational efficiency and reduce memory consumption, in addition, the fault detection rate is significantly better than the comparative methods.

Concerning the problem that multistage process data has the characteristics of multi-center and different process structure, a fault detection based on Improved Local Neighborhood Standardization and k Nearest Neighbors (ILNS- kNN) method was proposed. Firstly, K local neighbor set of k neighbors of the sample was found. Secondly, the sample was standardized to obtain the standard sample by using mean and standard deviation of K local neighbor set. Finally, fault detection was carried out by calculating the cumulative neighbor distance of samples in the standard sample set. The center of each stage data was shifted to the origin by Improved Local Neighborhood Standardization (ILNS), and dispersion degree of each stage data was adjusted approximately to the same, then the multistage process data was fused to single stage data obeying multivariate Gauss distribution. The fault detection of penicillin fermentation process experiment was carried out. The experimental results show that the ILNS- kNN method has more than 97% detection rate for six types of faults. The ILNS- kNN method can detect faults not only in general multistage process, but also in multistage process with significant different variances. It is better to ensure the safety of multistage process and the high quality of product.

A Local Outlier Factor fault detection method based on Statistics Pattern and Local Nearest neighborhood Standardization (SP-LNS-LOF) was proposed to deal with the problem of unequal batch length, mean drift and different batch structure of multi-process data. Firstly, the statistical pattern of each training sample was calculated; secondly, each statistical modulus was standardized as standard sample by using the set of local neighbor samples; finally the local outlier factor of standard sample was calculated and used as a detection index. The quintile of the local outlier factor was used as the detection control limit, when the local outlier factor of the online sample was greater than the detection control limit, the online sample was identified as a fault sample, otherwise it was a normal sample. The statistical pattern was used to extract the main information of the process and eliminate the impact of unequal length of batches; the local neighborhood normalization was used to overcome the difficulties of mean shift and different batch structure of process data; the local outlier factor was used to measure the similarity of samples and separate the fault samples from the normal samples. The simulation experiment of semiconductor etching process was carried out. The experimental results show that SP-LNS-LOF detects all 21 faults, and has higher detection rate than that of Principal Component Analysis (PCA), kernel PCA (kPCA), Fault Detection using k Nearest Neighbor rule (FD-kNN) and Local Outlier Factor (LOF) methods. The theoretical analysis and simulation result show that SP-LNS-LOF is suitable for fault detection of multimode process, and has high fault detection efficiency and ensures the safety of the production process.

Partial Least Squares (PLS) as the traditional data-driven method has the problem of poor performance of multi-modal data fault detection. In order to solve the problem, a new fault detection method was proposed, which called PLS based on Local Neighborhood Standardization (LNS) (LNS-PLS). Firstly, the original data was Gaussized by LNS method. On this basis, the monitoring model of PLS was established, and the control limits of T ² and Squared Prediction Error (SPE) were determined. Secondly, the test data was also standardized by the LNS, and then the PLS monitoring indicators of test data were calculated for process monitoring and fault detection, which solved the problem of unable to deal with multi-modal by PLS. The proposed method was applied to numerical examples and penicillin production process, and its test results were compared with those of Principal Component Analysis (PCA), K Nearest Neighbors ( KNN) and PLS. The experimental results show that, the proposed method is superior to PLS, KNN and PCA in fault detection. The proposed method has high accuracy in classification and multi-modal process fault detection.

For such difficulties as low mining efficiency and large amount of data that the data mining of residential electricity data has to be faced with, the analysis based on improved K-means algorithm and cloud computing on massive data of power utilization was researched. As the initial cluster center and the value K are difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density was proposed. Firstly, the product of sample density, the reciprocal of the average distance between the samples in the cluster, and the distance between the clusters were defined as weight product, the initial center was determined successively according to the maximum weight product method and the accuracy of the clustering was improved. Secondly, the parallelization of improved K-means algorithm was realized based on MapReduce model and the efficiency of clustering was improved. Finally, the mining experiment of massive power utilization data was carried out on the basis of 400 households' electricity data. Taking a family as a unit, such features as electricity consumption rate during peak hour, load rate, valley load coefficient and the percentage of power utilization during normal hour were calculated, and the feature vector of data dimension was established to complete the clustering of similar user types, at the same time, the behavioral characteristics of each type of users were analyzed. The experimental results on Hadoop cluster show that the improved K-means algorithm operates stably and efficiently and it can achieve better clustering effect.

The traditional traffic flow detection system based on sensor device has complex hardware equipment and the universal traffic flow detection algorithm cannot distinguish the directions of vehicles. To resolve the above problems, a real-time vehicle monitoring algorithm for single-lane based on Digital Signal Processor (DSP) was proposed and applied to parking lot. Firstly, the background differential algorithm was used to detect vehicles on virtual detection zone and the method of mean background modeling was improved. Then, an adjacent frame two-value classification algorithm was proposed to distinguish the directions of vehicles. Finally, virtual detection zone was used for vehicle counting and the number of empty parking spots was real-time displayed on a Light Emitting Diode (LED) screen. The feasibility of the proposed algorithm was verified by the simulation experiment. The actual test results showed that the accuracy rate of the adjacent frame two-value classification algorithm for direction detection was 96.5% and the accuracy rate of parking spot monitoring algorithm was 92.2%. The proposed real-time vehicle monitoring algorithm for single-lane has high accuracy and needs less detection equipment, so it can be applied to single-lane parking lot for real-time vehicle monitoring.

Smart Wireless Water Meter Reading System (SWWMRS) built on the conventional Wireless Sensor Network (WSN) platform can not meet the requirements of low cost, low power consumption, high efficiency and high reliability in practice. In this work, a novel SWWMRS for typical multi-story buildings was proposed. Based on the feature of the SWWMRS and deployment environment as well as the business logic, an improved algorithm for all neighbor discoveries was proposed to achieve automatic networking and centralized routing management. At the meter reading stage, a minimum global forward strategy with a minimum residual energy nodes avoidance strategy were adopted to balance the energy consumption between nodes. Additionally, the mechanism to avoid confliction in Media Access Control (MAC) layer and the low power idle listening strategy were optimized. The testing results for the proposed system in a 24-story residential building show that the system performance of communication distance, power consumption and reliability can meet the needs of the practical applications. Meanwhile, compared with CC2530 scheme, better performance in communication distance, meter reading success rate, efficiency and power consumption can be achieved.

In view of the problems of traditional prepaid meters such as inefficiency and inconvenience, a Near Field Communication (NFC)-enabled water meter system that has the functions of mobile payment and data query was proposed. Firstly, according to the business requirements of the prepaid water meter, the overall architecture of the water meter system was developed based on NFC technology, and the software and hardware were designed. Secondly, a low-power mechanism which was used to wake up the water meter by detecting the external magnetic field changes was proposed. Finally, the security performance in mobile payment of the water meter system was analyzed based on NFC security protocols. The experimental results show that users can dynamically awake the water meter system, and utilize the functions of mobile payment, data querying and data uploading, by using the NFC mobile phones or other mobile terminals with NFC module.

Aiming at that high-dimensional data is hard to be understood intuitively, and cannot be effectively processed by traditional machine learning and data mining techniques, a new method for nonlinear dimensionality reduction called Discriminant Diffusion Maps Analysis (DDMA) was proposed. It was implemented by applying a discriminant kernel scheme to the framework of the diffusion maps. The Gaussian kernel window width was selected from the within-class width and the between-class width according to discriminating sample category labels, it made kernel function effectively extract data correlation features and exactly describe the structure characteristics of data space. The DDMA was used in artificial Swiss-roll test and penicillin fermentation process, with comparisons with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principle Components Analysis (KPCA), Laplacian Eigenmaps (LE) and Diffusion Maps (DM). The results show that DDMA represents the high-dimensional data in a low-dimensional space while successfully retaining original characteristics of the data; in addition, the data structure features in low-dimensional space generated by DDMA are superior to those generated by the comparison methods, the performance of data dimension reduction and feature extraction verifies effectiveness of the proposed scheme.

In this paper, aiming at the priority selection of the Gaussian kernel parameter (β) in the Kernel Principal Component Analysis (KPCA), a kernel parameter discriminant method was proposed for the KPCA. It calculated the kernel window widths in the classes and between two classes for the training samples.The kernel parameter was determined with the discriminant method for the kernel window widths. The determined kernel matrix based on the discriminant selected kernel parameter could exactly describe the structure characteristics of the training space. In the end, it used Principal Component Analysis (PCA) to the decomposition for the feature space, and obtained the principal component to realize dimensionality reduction and feature extraction. The method of discriminant kernel window width chose smaller window width in the dense regions of classification, and larger window width in the sparse ones. The simulation of the numerical process and Tennessee Eastman Process (TEP) using the Discriminated Kernel Principle Component Analysis (Dis-KPCA) method, by comparing with KPCA and PCA, show that Dis-KPCA method is effective to the sample data dimension reduction and separates three classes of data by 100%,therefore, the proposed method has higher precision of dimension reduction.

A two-stage approach was proposed which can solve the optimal packing of single size rectangles effectively. The best cutting patterns of standard sub-segment were solved and the problem was transformed into one-dimensional cutting stock problems in the first stage. In the second stage the best ideal solution was found with different methods for the one-dimensional cutting stock problems. With this method, an optimal packing of single size rectangles system was developed. The system not only can solve the segment layout of single size rectangles but also can solve other kinds of optimal packing of single size rectangles. Enterprise applications show that this method is an effective solution to the problem of single size rectangles packing.

The partially blind signature scheme without trusted Private Key Generator (PKG) proposed by Feng Tao et al. was analyzed, and it was found that this scheme did not satisfy unforgeability. A dishonest PKG could forge a valid partially blind signature. By using gap Diffie-Hellman group and bilinear pairings, a new partially blind signature scheme without trusted PKG was proposed. The analysis shows that the proposed scheme can overcome the defect of the original scheme and it possesses unforgeability, correctness, partially and tracility. Compared with the original scheme, the proposed scheme performs more efficiently in computation because it reduces two pairing operations.