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To further improve the security of SHA-256, a compression function construction scheme based on chaotic S-box is proposed. Through the reasonable design of the iteration mode and using the nonlinearity, confusion, and anti-difference of chaotic S-box, the relationship between the plaintext and the hash value becomes more complicated and unpredictable, by the diffusion of the linear transformation P-box, the dependence between the plaintext and the hash value is further improved. The compression function is applied to the SHA-256 Hash Algorithm to test and analyze the distribution, sensitivity, confusion, diffusion, and collision resistance, and compared with the original SHA-256 and the mainstream Hash Algorithms. The results show that the proposed scheme can effectively improve the collision resistance of the SHA-256 Hash Algorithm and enhance the stability of confusion and diffusion with fewer cryptographic components and lower operational consumption to provide more reliable security guarantee for its application.

Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types.

An interval support vector deterministic optimization model (ISVD) is proposed for the fault classification problem of uncertainty samples in this paper. Firstly, based on the order relation theory, support vector machines using interval samples is transformed into ISVD, which is a two-objective optimization problem, with the midpoint and the radius optimized at the same time. Then, ISVD is converted into a single objective optimization model by the linear combination. The single objective optimization model includes Lagrange multiplier vector and interval sample vectors, both of which are nested. Thus, the nested particle swarm optimization (PSO) based on dynamic decreasing inertia weight is applied to select the optimal Lagrange multiplier vector of this model. Lastly, the effectiveness of the proposed method is proved by the data set of University of California Irvine (UCI) and the roller bearing fault experiments. The experimental results show: ISVD owns outstanding generalization ability with the help of the structured risk minimization and global optimization. The accuracy of the proposed ISVD is better than that of the native Bayes uncertain classification method 1 (NBU1), native Bayes uncertain classification method 2 (NBU2) and the formula-based Bayes classifier (FBC).

Hierarchical multi-label text classification is used to assign documents to multiple categories stored in a hierarchical structure. However, the existing methods pay more attention to the local semantic information of the text, and make insufficient use of the label level information. In this paper, we propose a new self-adaption semantic awareness network integrating text topic and label level information. Compared with the existing methods, our method has two contributions: (1) On the basis of the traditional text encoder, we add the global topic information of the text and the hierarchical information of the label, describe the text from different levels and effectively use the correlation between the labels. (2) We design an adaptive fusion strategy that adaptively retains information helpful for classification from three different levels of features and exploits their mutual influence. Experimental results on two benchmark datasets show that our model is better than the state-of-the-art methods on six metrics, especially in the prediction of low-frequency labels with sparse samples.

Embedding knowledge graph with graph attention network has become a novel research topic in the field of knowledge graph completion. However, the current graph attention network generates the same embeddings for different structures and different entities when generating entity embeddings for knowledge graph. The quality of embedding directly contributes the effective of completion. We analyze the reason why graph attention network cannot distinguish structure, because the aggregation based on attention GNN ignores the cardinality information, which is the mapping of diverse features, and helps to distinguish the contributions of different nodes in the domain. Therefore, we propose the graph attention preserving (KBCPA) model. Cardinality information is added into the attentional mechanism based aggregation to generate different representations for different entities, thus improving the discrimination ability of the model. Our experiments present that our model is effective and competitive, and it obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237.

Traditionally, gear faults can be classified with the ignorance of the sample uncertainty. In this paper, a novel approach is proposed for the problem diagnosis of uncertain gear interval faults. First, a statistical property interval feature vector composed of mean, standard deviation, skewness, kurtosis, etc. is proposed. Then, the native Bayes uncertain classification (NBU) is used for the diagnostics of these uncertain gear interval faults. Conventionally, the NBU utilizes all the attributes to distinguish fault types. However, each fault type has its own distinct classification accuracy for different feature vector attributes. Thus, the particle swarm optimization (PSO) is used to select the optimal feature vector attributes for each fault type in the NBU (NBU_PSO_EACH). The experimental results show: (1) the accuracy of the proposed method is better than that of NBU1, NBU2 or FBC; (2) in terms of accuracy, the proposed method is also more advanced than the method which selects the same optimal attributes for all fault types based on the PSO (NBU_PSO); (3) the proposed method can reduce the physical size of feature vectors.

Aiming at the problem that the traditional fault diagnosis methods cannot deal with the uncertain samples, this paper proposes an interval fault diagnosis method and its parameter optimization. First, the interval Gauss function is used to construct the kernel of the proposed interval fault diagnosis method. The weights and offsets of this interval method are calculated by solving the equality equation. Second, with the goal of maximizing the classification accuracy, the Glowworm swarm optimization algorithm is used to select the parameters of this method. Finally, the experimental results of the rotor crack interval fault samples and the University of California Irvine samples show that this method has higher classification accuracy than the traditional interval classification methods.

In this paper, the basic theory of Voltera series is briefly introduced. The kernel of Volterra is identified by the polyhedron search algorithm. Different from the traditional diagnostic method based on signal processing, this method applies the Volterra series based on the system model to the fault diagnosis of the roller bearing system. The change of the Volterra series kernel is used to distinguish the different fault state of the roller bearing. The experimental results verify the feasibility and effectiveness of the method.

Based on a brief introduction to the basic theory of Volterra series, PSO algorithm is applied to obtain the three order time domain of volterra. Experiments are constructed based on the Jeffet cracked rotor simulation model. The results prove this model can identify the different crack depth rotor crack fault.

Carrier-phase multipath effects are one of the most significant error sources in precise Global Positioning System (GPS) positioning applications. A new sidereal filtering algorithm based on single differences is developed to mitigate multipath effects for short-baseline high-rate GPS applications such as structural deformation monitoring. This method differs from traditional sidereal filtering in that our method operates on the single differences rather than the coordinates or double differences. A multipath model for the single differences on the reference day is established for each satellite and is used to remove multipath errors from observations of subsequent days by taking advantage of the sidereal repeatability of multipath signals. Using both simulated and real GPS observations, we demonstrate that this method is insensitive to different weighting strategies used in computing single differences from double differences. Applying the proposed method can reduce the root mean square (RMS) of positioning noises by 82% on average. Compared to sidereal filtering (in either coordinate or double differences domain) and aspect repeat time adjustment, this method can further reduce the RMS values by 13 and 7%, respectively. Wavelet spectra have shown that the proposed method is more effective in mitigating multipath errors of both long and short periods. This method is also more advantageous in that it is applicable when different GPS satellites are observed on different days.

Global Positioning System (GPS)-based vehicle navigation systems have been rapidly developed over recent years. In urban areas, due to the problems of satellite signal blockage and severe multipath effects, GPS may not be available for a long period of time. Integrated GPS/Dead Reckoning (DR) systems are commonly used to bridge GPS position gaps. Moreover, map-matching (MM) techniques can be implemented to improve positioning accuracy by locating an estimated vehicle position (from GPS/DR) on road; however, the reliability of map-matching methods is still a problem because vehicle position may be located to an incorrect road section (mismatch) due to large vehicle positioning errors from GPS/DR and complexity of road network, which is a typical environment in urban areas. In this article, we present a tightly integrated vehicle navigation system to overcome these problems. Performance of any MM algorithms is closely linked with position data quality. In our approach, we integrate MM results with GPS/DR ou...

Although GPS has been widely used for precision surveys, many survey environments, like in urban areas, deep open-pit mines and valleys, limit the number of visible GPS satellites, deteriorating the survey accuracy. In addition some engineering projects require higher positioning accuracy in a particular direction. A GPS-only system hardly satisfies the requirements. Pseudolite-augmented GPS survey technique can strengthen the positioning geometry. Pseudolite (PL) measurements, however, suffer from various sources of errors different from GPS observations. In order to employ them for precise surveys these sources of errors must be treated properly. This paper discusses of the major sources of errors in PL measurements and the methodologies for their mitigation. Some experimental tests are then presented.

This paper investigates single epoch ambiguity resolution performance using Galileo four frequency data. Two commonly used ambiguity resolution methods are used in the tests, including the Cascade Ambiguity Resolution (CAR) and the Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) methods. For CAR method, four optimal combinations are carefully selected according to their wavelength to noise ratios and success rate for ambiguity fixing. The test results show, in general, the LAMBDA method performs better than the CAR method. The speed of ambiguity resolution is closely related to the carrier phase measurement precision. With carrier phase measurement precision of 3 mm, single epoch ambiguity resolution can be achieved at every epoch with simulated 1-s interval 24-h Galileo data (total epochs 86,400). With the increase of carrier phase noise, ambiguity resolution performances become worse. When the noise level is increased to 12 mm, single epoch ambiguity resolution can only be achieved about 50% of epochs.

Map-matching (MM) is a technique that attempts to locate an estimated vehicle position on road network. Many map-matching algorithms have been developed and widely incorporated into GPS/DR vehicle navigation systems for both commercial and experimental ITS applications. However, the reliability of these systems is still a problem because vehicle position may be located to an incorrect road section due to large vehicle positioning errors which occur frequently in urban areas. This incorrect locating is called a mismatch. To improve map matching techniques, it is necessary to enhance the ability of mismatch detection and to reduce the chance of mismatch, which are referred as integrity and reliability respectively. New techniques are developed in this paper to improve the integrity and reliability of map matching techniques. The new techniques have been integrated with a GPS/DR system and extensively tested in Hong Kong. Testing results demonstrate that the performance of the new integrated GPS/DR system is significantly improved in terms of its accuracy, coverage and reliability.

In this paper, the basic precise point positioning model has been reviewed. A recursive least square algorithm that separates the position coordinates and other parameters, such as ambiguities and tropospheric delays, is proposed for kinematic PPP applications. A test was carried out to test the method proposed in this paper, which made use of a GPS buoy equipped with a pressure and a tilt meter to monitor the sea level in Hong Kong. The initial results from kinematic PPP positioning compared with conventional kinematic positioning methods shows th

For both static and kinematic relative positioning with GPS, the success of baseline resolution depends closely on the reliable fixing of integer ambiguities of carrier phase measurements. Low noise code correlated dual-frequency GPS receivers offer accurate pseudo-range measurements on both carrier frequencies. With the help of a combination of code and carrier phase measurements, wide-lane ambiguities can be easily determined and used for the improvement of ambiguity resolutions on both L1 and L2 frequencies. A carrier smoothing technique is utilized to eliminate the noise of code measurements. Baselines ranging from 1.5 km to 48 km are used to test the proposed method.

A new mathematical approach, wavelet transformation, is introduced for GPS data analysis. Based on its good features on both time-domain and frequency-domain, one can obtain a true time-frequency representation of a signal. The so-called mutiresolution analysis (MRA) will be used for data analysis, such as for the removal of noises, and the detection and rejection of gross errors from signal. In this paper the authors present a wavelet-based algorithm for the modeling and prediction of SA effect by combining time series analysis methods. A new self-adaptive Kalman filtering algorithm is presented for anti-SA. Some preliminary test results from experimental data are also summarized.

The problem of navigation systems with uncertain noise is considered. A minimax robust filtering which can minimize the worst performance under noise uncertainties using the game theory is proposed. This new filter is applied to an integrated GPS/INS navigation system. A high dynamics aircraft trajectory is designed to test the new filter. The results show that minimax robust filtering performs better than standard Kalman filtering when noise parameters of an inertial measurement unit change their statistical properties.

The bridges over navigation waterways are exposed to ship collision and ship-bridge collision accidents have been widely reported worldwide. The measurement of ship impact force during a collision accident is of great importance for condition assessment of ship-collided bridges and subsequent strategic actions made by the bridge authority. The impact force of ship-bridge collision is usually evaluated by use of the measured structural responses during ship collision and an identification algorithm, but the accuracy is limited. In this paper, a method for direct impact force identification of ship-bridge collision using smart piezoelectric sensors is proposed. The feasibility and effectiveness of the proposed method is demonstrated by experimental study on a scale pier model of a cable-stayed bridge. The piezoelectric sensors are embedded into the scale pier model and the impact force is generated by a hammer. Various impact sceneries are taken into account to investigate the capacity of the piezoelectric sensors for impact force identification. Through acquiring the voltage signals from the piezoelectric sensors at different locations, the impact force is identified with the aid of the calibrated relationship between the impact force and the voltage output.

Lagrangian support vector machine(LSVM) is a kind of method with good generalization ability. But, LSVM is not suitable for classification online because the computation complexity. So in this paper, a fast LSVM is proposed. This method can deduce running time because it fully utilizes the historical training results and reduces memory and calculates time. Finally, an example is accomplished to demonstrate the effect of fast LSVM.

For the extraction of land degradation information we should use not only information on climate, soil, vegetation, physiognomy, land use and its productivities, but also the knowledge and methodologies of geosciences. It is of importance to study some conceptual issues about geographic image cognition (GEOIC) on studying land degradation. The study is to discuss some conceptual issues and the theoretical background of the approach of geographic image cognition (GEOIC) on studying land degradation for building its methodological framework. Some issues concerning the approach of GEOIC on studying land degradation, especially the factors of impacting human's visual cognition, were discussed. The results indicated that the GEOIC is the objectification cognition on remote sensing images and multi-source information using geo-knowledge. As an integrated approach, it is the extension of the methodology of OBIA. The key objective of the GEOIC on studying land degradation is to simulate the function and process of the visual interpretation by experts, and extract spatial features, spatial object and spatial pattern of land degradation under the cognition mode of feature-object-pattern from remote sensing images and multi-source information. The methodology of the GEOIC is realized through the segmentation of geo-objects or meaningful image objects using remote sensing information, geographic information, vegetation, soil, and other ancillary information with geosciences knowledge and intelligence.

For estimating the fault signals of the discrete flight control system with unknown senor faults and actuator faults simultaneously, a discrete proportional integral observer (PIO) is presented. The proposed PIO uses the augment states to estimate the sensor faults of the flight control system. Moreover, this observer uses an additionally introduced integral term of the output error to obtain the estimation of actuator faults. The convergence of the PIO is proved. The proposed fault observer is applied in flight control system. Simulation results are given to demonstrate the effectiveness of the proposed fault observer.

For nonlinear time series fault prediction online, an incremental learning least squares support machine (LSSVM) is presented to replace LS-SVM which is as a kind of regression method with good generalization ability and trained offline in batch way. The incremented learning LS-SVM fully utilizes historical training results and reduces memory and computation time, which guarantee to predict time series online. Two simulations results show that the incremental learning LSSVM has good performance for predicting nonlinear series fault prediction online.

Tikhonov regularized SVM is a kind of new SVM which can convert multi-class problems to be single optimized problems. Since SVM has some limitations in disposition of big data collection, this paper puts forward a new reduction Tikhonov regularized SVM by utilizing pruning algorithm to gain reduction data collection. Meanwhile, the paper applies genetic algorithm to make automatic selection from the balance parameter and kernel function parameter of Tikhonov regularized SVM. The experiment proves this newly improved Tikhonov Regularized SVM is more advantageous for classifying precision and train rate.

To select parameters is important in the research area of support vector machine. Based on particle swarm optimization, this paper proposes automatic parameters selection for least squares support vector machine (LSSVM). The effect of this proposed method is demonstrated by function regression problem. Besides, an equipment fault classification further illustrates that LSSVM based on particle swarm optimization has better classification ability than LSSVM based genetic algorithm under the same condition.

A new method of using GPS for monitoring local area deformations such as landslides is presented. Unlike the standard method of using GPS for deformation monitoring where a GPS receiver is required for each point to be monitored, the new method allows multiple points to be monitored with one receiver. A system that implements the concept has been developed. It uses a specially designed electronic component that allows a number of GPS antennas to be linked to a single GPS receiver. The receiver takes data sequentially from each of the antennas attached to the receiver. A distinctive advantage of the approach is that one GPS receiver can be used to monitor more than one point. The cost per monitored point is therefore significantly reduced. The design of the system, as well as the data management and processing strategies will be introduced in detail. Results from some preliminary tests will also be given.

This paper describes GPS multi-antenna device (one GPS receiver links multiple antennas) developed by authors, and the experimental results are presented. GPS has already proven to be an efficient tool for monitoring dam deformations and stability of high-risk slopes. It offers greater accuracy than other surveying techniques. However, GPS has its disadvantages when employing for slope and dam monitoring. The major drawback has been high cost due to large-scale GPS deployments are required in monitoring sites. The conventional GPS monitoring methods, where a permanent GPS receiver must be located at each point, have significant limitations of the cost. A new approach that a single GPS receiver links multiple antennas mounted at the monitoring points, has been employed to solve these problems in this paper. A dedicated switching device has been developed by authors for this approach. Field testing results show that the dedicated switching device for GPS multi-antenna system has excellent performances. Post-processing positioning accuracy is around 1–2 mm for the deformation monitoring of the Xiaolangdi dam on the Yellow River.