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This paper mainly proposes control laws to address trajectory tracking and vibration suppression problems for a two-link rigid-flexible wing system. Based on Hamilton’s principle, partial differential equations (PDEs) and ordinary differential equations (ODEs) are proposed to describe the dynamical model of the wing. The boundary controllers are designed to restrain the deformations both in bending and twisting, and to achieve the trajectory tracking. We can accurately regulate the motion of the wing to track a desired spatiotemporally varying trajectory with the implement of trajectory tracking control and vibration control. In addition, the stability of this wing system is proven by utilizing Lyapunov’s direct method. Finally, we can verify that the designed controllers are effective according to the numerical simulations.

Transmembrane proteins (TMPs) play important roles in diverse cellular processes, they are the most common drug targets. The interaction between TMPs and nontransmembrane proteins (nonTMPs) is an important part to form the pathway crossing biomembranes, which is directly related to TMPs associated signal transduction, substance transport, and drug metabolism. However, it is difficult for biologists to identify interactions between TMPs and nonTMPs by wet-lab experiments since TMPs are embedded in the phospholipid bilayer while most nonTMPs are soluble proteins. Predicting protein-protein interactions (PPIs) has always been a hot topic in bioinformatics. However, TMP related PPIs occupy a small proportion of those researches, and those methods designed for soluble protein PPI may have limitations to predict the interaction of TMPs and non-TMPs due to the difference between the aqueous and lipid senvironments. There still lack of deep-learning-based predictors using primary sequence information on TMP-nonTMP interactions. In this work, we constructed a benchmark dataset for TMP-nonTMP interactions, adopted the one-hot vector to encode protein sequence pairs and built a Convolutional Neural Network (CNN) based model, SeqTMPPI, predicting the TMP-nonTMP interactions. The experimental results indicated that our method achieved a good performance on an independent testing where the Matthews Correlation Coefficient (MCC) achieved at 0.7066. The predicted interactions were further analyzed in the scope of distribution of the protein family and species. Materials related are available in https://github.com/JulseJiang/SeqTMPPI

In highlight model, geometric highlights are often affected by elastic echoes. Based on “constrained blind source extraction” framework, an algorithm for LFM signal extraction in given frequency modulation slope (FMS) range is proposed. The cost function is constructed by combining negative entropy and interval constraint function, in addition, continuity and convergence are satisfied. The algorithm realizes extraction of the single source signal in specific FMS range. It is helpful to enhance the geometric highlights, which is of great significance to the subsequent target detection.

Anonymous authentication has significant contribution to privacy protection in safety message dissemination for Vehicular Ad Hoc Networks (VANETs); however, it suffers from heavy workload of vehicle revocation check and message signature verification, which leads to unsatisfactory timeliness and high message loss rate in heavy traffic situations. To manage these challenging problems, this paper proposes an efficient safety message authentication protocol for VANETs by combining batch group signature verification and our proposed Group Session Key (GSK). Specifically, our signature verification method for safety message can both achieve computation efficiency by reducing the number of computation operations of bilinear pairing and resist impersonation attack by using tracking key implement. GSK is associated with the forward and backward keys and is only shared among unrevoked vehicle within a group. Performance analysis and simulations demonstrate that our protocol can provide promising security against various common types of attackers and is more efficient than traditional group-signature-based authentication protocols, in terms of computation time cost, authentication delay and message loss rate.

Recently, drought is recognized as a crucial factor on controlling inter-annual variations of the terrestrial carbon cycle. Remote sensing of hydrological and ecological land surface variables offers a very important aid for identifying the impact of droughts on terrestrial ecosystems. GLEAM soil moisture (SM), reconstructed GRACE terrestrial total terrestrial water storage change (TWS), GIMMS normalized difference vegetation index (NDVI), reconstructed continuous solar-induced fluorescence (SIF) data from OCO-2, and SIF data from SCIACHAMY and GOME-2, in conjunction with multiple carbon flux data and CRUNCEP meteorological data, were employed to study the drought impacts on the terrestrial ecosystems in Europe during 2001–2015. We found GLEAM SM and GRACE TWS can effectively detect hydrological anomalies for four reported strongest drought years on the continental scale, in line with evident reductions of terrestrial carbon uptake indicated by models. Most vegetation products agree well on indicating drought impacts, but with some unexpected inconsistencies. Also, these land products are able to reasonably indicate the spatial patterns of drought impacts on ecosystems. We highlight the importance of ensemble satellite land products in deepening the interpretation of drought impacts on terrestrial ecosystems, and also on examining carbon cycle models and identifying potential gaps in satellite data products within the context of drought-carbon cycle studies.

Resolution including range resolution and azimuth resolution, is a key factor to evaluate the performance of synthetic aperture sonar(SAS). For bi-static SAS(bi-SAS), the physical positional separation of transmitter and receiver makes the resolution very different from mono-static SAS(mono-SAS). At first, there is little relevant research on the property of bi-SAS's two-resolution. As the existing ambiguity function method is not benefit for numerical calculation, this paper works this problem out utilizing a gradient vector method whose basis is to use the gradient of distance contour or Doppler frequency contour to calculate the two-dimensional resolution. Then this method is applied to a certain bi-static configuration and the mono-static configuration, respectively. The results prove the effectivity of the method and demonstrate the obvious space-variant property of two-dimensional resolution in bi-SAS. For a SAS system, it is a fact that there is more than one echo path with the same time-delay at any receipt time. This phenomenon can be regarded as a multipath problem, which may bring false target some times. Especially when the receiver is at a position able to receive the forward-scattering echo, multipath in range direction will occur to the system, which brings about ghost-point. This problem has hardly ever been theoretically analyzed, because it has little influence on final imaging result in practical application. This paper theoretically deduced the relationship between ghost-point position and configuration parameters of bi-SAS. Finally, we demonstrated that ghost-point position is related to height of sonar, and it basically can be inhibited by synthetic aperture processing under certain circumstances.

Unmanned underwater vehicles (UUVs) are a kind of marine power multipliers, with extensive and important applications for scientific research. It is out of question that UUVs will play an irreplaceable role in marine detection. In this paper, the issue of model design for UUVs with the purpose of marine detection is studied. A well designed architecture for UUV model is proposed first, which includes the motion control model, the decision model and the detection model. Secondly, the motion control model is described in detail, which satisfies the navigation control for UUVs in three-dimensional space. Thirdly, the detection model based on the active sonar equation is addressed in order to obtain the detected position. The influence factors related to the sonar equation are taken into consideration. Afterwards, a decision model based on the particle filter (PF) is presented in order to improve the accuracy of target detection and ensure the stable and effective tracking. In the end, the detection simulation experiments in typical sea areas are designed. Experimental results prove the reliability and effectiveness of the designed UUV model.

Utilizing MOD13Q1-NDVI data from 2000 to 2014, vegetation change in a 50 km buffer area around the Huitengliang wind farm, Inner Mongolia, was analyzed. Combining meteorological data in this analysis, the impact of wind direction on vegetation growth was discussed. Compared with the construction phase (2000–2008), the operation phase (2008–2014) has a more obvious impact on the growth of vegetation. However, the results showed that vegetation impact was uneven: (1) a zone 30–40 km from the center of the wind farm, on the windward side, is likely to be the area of greatest vegetation impact from the wind farm; (2) the proportion of vegetation restoration in the windward/leeward areas, especially in the windward area, is much higher than in the buffer or farm areas; (3) within the wind farm area, the wind farm is not conducive to vegetation growth, while it is helpful to vegetation growth in windward/leeward area.

Personalized consumption demand and global challenges such as energy shortage and population aging require flexible, efficient, and green production paradigm. Smart factory aims to address these issues by coupling emerging information technologies and artificial intelligence with automation to implement cyber-physical production system. Shop-floor entities can be interconnected with each other and with cloud and client terminals through industrial network and Internet. This framework incorporates self-organization of smart physical entities and big data based feedback and coordination to achieve both high flexibility and efficiency. In this paper, we propose a framework for smart factory. Especially, we focus on developing a solution, including negotiation mechanism for dynamic reconfiguration and strategy for deadlock prevention, for hybrid production of reduced-intelligence products. As hybrid production can process multi-type products concurrently in arbitrary order, and reduced-intelligence products are equipped with cheaper product memory such as bar codes, QR codes, or RFID tags instead of more expensive embedded controllers, this solution is flexible, efficient, and economic for producing customized products.

With the rapid development of Cloud computing, social computing, and Big Data, the service composition in support of increasing knowledge-intensive innovation activities across enterprises and organizations sets a new challenge of dealing with diversified users' needs and changeable structures in process. Automatic service composition mainly makes use of artificial intelligence planning techniques to address these challenges. But current research focuses on separate aspects of these challenges, and special effort should be made to put goals, domain knowledge and activities into a unified perspective. This paper proposes a goal-driven and content-oriented automatic service composition method based on Hierarchical Task Network. First a unified planning domain knowledge modeling approach is given regarding goals, contents, tasks and their relationships, then a planning framework and corresponding algorithms are designed by extending Simple Hierarchical Ordered Planner 2, finally a loosely-coupled, extensible and flexible planning system is implemented and the experiment proves its feasibility and features.

According to the present works, it is more efficient by carrying out accelerated degradation test (ADT) with step-down stress than constant and step-up stress. However, maximum likelihood estimation (MLE) is currently the main analysis method for step-down stress accelerated degradation test (SDSADT), which is difficult to be applied into small sample size occasion. To solve this problem, the paper proposes a Bayesian analysis method for SDSADT based on Wiener process. Firstly, degradation path of product performance is described by Wiener process, and then data analysis of SDSADT is carried out by a segmented Bayesian method which can update the evaluation values of model parameters under different accelerated stresses. Lastly, recursive algorithm for parameters estimation is established for reliability assessment of SDSADT. Simulation studies show that the method is more accuracy by comparing with MLE in case of small sample size.

Accelerated life testing (ALT) can be utilized to shorten the test duration for demonstrating the high reliability target with a long mission time under the use condition in reliability demonstration test (RDT). In this paper, a reliability acceptance sampling plan (RASP) for Weibull distribution with a nonconstant shape parameter under the acceleration condition is designed to demonstrate the reliability target with the lifetime at the specified reliability as the reliability measure. An example is illustrated to validate the proposed test method. Finally, the effects of the model parameter on the RASPs are analyzed.

Accelerated degradation test can be used to demonstrate the reliability target of highly reliable products with fully utilizing the degradation data within feasible test duration. There are a few works dealing with reliability demonstration based on degradation data. Most existing methods are applications with respect to linear degradation models and Wiener process models, which cannot describe the monotonic degradation process, such as fatigue cracks development of alloy products, wear of bearings, erosion of pipes, while Gamma processes are suitable. A method for accelerated degradation demonstration test based on Gamma process models is proposed in this paper. Firstly, reliability target is translated into the requirement of performance degradation. Then, a demonstration test plan based on Gamma process models is designed according to the requirements of the consumer risk and producer risk. The method is illustrated and validated using a simulated example.

Particle piling process is a complex process of contact interaction between mechanical parts and particles which is widely found in modern industry and agriculture production. In this paper, the discrete element method (DEM) is adopted to analyze this process. The linear viscoelastic mechanical models is used to calculate the contact force in the piling process. The effects of particle friction coefficient and particle size on the magnitude of repose angle were studied. The numerical results show that, under identical conditions, increasing friction coefficient of particles leads to the rise of the repose angle.

Design is a job that must be finished before any complex task and activity, and curriculum teaching is also not to the exception. Before we carry out curriculum teaching, we must do well in curriculum teaching design. This article takes improving curriculum teaching quality as traction, takes systematic theories and modern education and teaching theory as reliance and states about essence, connotation, basic demands and main contents of curriculum teaching design.

In this paper, we focus on the need of space observation images registration, in order to detect dim space targets more effectively. The global motion parameters are calculated first, using centroids of high light stars, which are corresponding during the entire image sequence, as feature points. Then, an algorithm of star image sequence registration based on Star Centroid Coordinates Matrix (SCCM) is proposed. The registration algorithm includes 3 steps: Firstly, star image is converted into SCCM. Secondly, utilizing global motion parameters, SCCM is processed. And thirdly, the star image sequence is registered, and at the same time, stars in the image are filtered. This algorithm can overcome disturbance from stars effectively. During the registration process, converting star image into SCCM can leave out image re-sampling and transformation steps, and can decrease computation complexity. Because stars are all filtered in the registered image, dim space target detection can be easier.

Accelerated degradation testing (ADT) provides an effective way to assess the reliability of products. In engineering application, reliability of some complex products is often characterized by multiple performance parameters. However, current methods for ADT are mostly focused on the case which involves single performance parameter. This paper proposes an analysis method for ADT based on multiple performance parameters, by considering the correlation among performance parameters. From the results of application examples, the analysis procedure of the proposed method is more reasonable and the estimates are more exact.

The oxygen level, acidity and corrosivity of bio-oil were reduced by the extraction and separation of acetic and other carboxylic acids from bio-oil. The removal of carboxylic acid was verified by the increase in pH of the bio-oil from 2.52 to 5.47. The IR spectrum also shows that the carboxyl peak of bio-oil (1600 cm−1–1400cm−1) was much weaker after separation and upgrading. The aqueous phase separated from the bio-oil contains a large amount of carboxylic acid, which can be converted to calcium magnesium acetate (CMA), an environment-friendly deicer. The product's structure was confirmed by infrared (IR) spectroscopy and X-Ray Diffraction (XRD). Overall, the extraction and separation of carboxylic acid from bio-oil improves its quality and increases the utilization value of bio-oil.

Accelerated testing (AT) is widely used to demonstrate and assess product reliability and is especially useful for products with long life and high reliability requirements. Currently, research is primarily focused on test planning, acceleration model and parameter estimation for AT of specific products, such as electronic and mechanical components, etc. In our application, for several components of the system undergoing AT we desire to obtain the system reliability at the lower limit confidence, which is a considerable issue. However little research on this issue is presently available. This paper proposes a feasible method, which converts component data from AT to equivalent binomial component data and then estimates the approximate lower limit confidence (ALLC) of system reliability using a Bayesian method. A numerical example is illustrated to verify that the proposed method is better than the alternative methods by Monte Carlo (MC) simulation. Eventually, the method is applied to the assessment of storage reliability for a safety valve.

Targets detection is important for the analysis of Synthetic Aperture Sonar (SAS) images. An automatic targets detection method from SAS images is presented by analyzing the statistical properties of the images. The method mainly has three procedures: preprocessing of SAS images, especially the mean-standard deviation segmentation method to binarize the images, extracting areas of connected components in the binary images as features, maximum area of connected components method detecting. At last, the targets detection method based on mean-standard deviation is applied to lake-trial datasets for validation.

This paper develops a remote distributed control system (DCS) for central air conditioner based on the Ethernet. The system architecture and function are described in detail in this paper. The DCS control system presents a control-platform that can realize the configuration of the controlled objects in different regions at the same time. In any place with access to the internet, we can perform the parameter setting, real-time monitoring and remote fault diagnosis quickly using the presented DCS control system in any time. The system has the function of auto power on and shut down, and can adjust the temperature settings of the entire terminals automatically. The long-term operation performance shows that the energy conservation effect is very significant by using the Ethernet-based remote distributed control system.

Three bacterial strains capable of transforming nitrate nitrogen designated as NCT-2, NCT-3 and NCT-4, were isolated from excessive nitrate nitrogen salinization soils. According to the sequence analysis of the partial 16s rRNA gene, it is found that strains NCT-2 and NCT-4 belong to Bacillus sp., and strain NCT-3 belongs to Stenotrophomonas sp. The effects of nitrate nitrogen concentration, pH and temperature on transformation were examined. At a concentration of 50.0 mg L-1, More than 93.3% of nitrate nitrogen was transformed by Bacillus sp. NCT-2 after 5d and 72.3% by Stenotrophomonas sp. NCT-3 after 5d. At a concentration of 400.0 mg L-1, the transformation rate was inhibited. Only 40.3% of nitrate nitrogen can be transformed by Bacillus sp. NCT-4 after 5d. More than 63.3% transformation rate was observed with Stenotrophomonas sp. NCT-3 after 5d. The growth of these three strains was inhibited at high pH buffers. The abiotic transformation was basically the same at different pH conditions. The transformation rate of nitrate nitrogen at 25°C was faster than those at 20°C and 35°C by these strains, except the highest degradation rate of Bacillus sp. NCT-4 was obtained at 35°C.

For computer-controlled systems (CCSs), actual sampling intervals inevitably deviate from its nominal sampling period, called sampling interval jitter (SIJ), which may degrade control performance and even unstabilize control systems. So it should be accounted for at design time. The model of CCSs with SIJ is presented and its effects on CCSs is illustrated by simulation. The model developed costs less computation resources and can be extended to deal with controller design.

Automatic detecting of rail profile based on computer vision usually uses the optical-section method. The light strip with some important geometry information of the rail surface image is achieved. However, when the sun is on the horizon(early morning and dusk), the noise will appear, and it is difficult to achieve and recognize the light strip correctly if the noise is not removed. This paper aims to remove the noise using algorithm of embranchment labeling for the next thinning and adjustment. After the image is binaried using fixed threshold and the connected components labeled, the parameter of each branch and the intersection table which is the base of collecting the labels can be obtained. According to the feature parameter of each connectivity computed, the light strip of rail profile is then extracted, and the noise removed. The algorithm can support parallel computation. A good effect is achieved on the scene testing of rail auto measurement.

Reliability is one of primarily considered problems in white LED application. In this presentation, main failure modes in white LEDs such as ohmic contact degradation, electrode bonding defect, thermal mismatch and degradation of the encapsulation material, as well as ESD damage were analyzed, also combination with many failure analysis practices. Finally, several reliability assurance measures were discussed, which is helpful to improve reliability of LEDs in the solid state lighting applications.

Accelerated degradation test (ADT) is widely used to evaluate the reliability of highly reliable products with long life if there exists a product quality characteristic whose degradation over time can be related to reliability. It is still a main challenge that how to design optimal plan, including sample size, measurement frequency, and termination time, for ADT which can result in the most accurate estimation within predetermined budget. This paper presents a novel approach of optimal design for ADT based on Monte Carlo simulation. There are three key steps in the approach: simulating; modeling; optimizing, by which it is convenient to set up a framework for applications. In this paper, we first apply a widely-used mix-effect model to describe a typical ADT problem. Next, the optimal test plans are obtained by minimizing the estimate of the mean-squared error of the estimated 100pth percentile of the product's lifetime distribution by statistical analysis of simulating degradation data of test units under the constraint that the total experimental cost does not exceed a predetermined budget. Finally, an example is provided to illustrate the proposed method and evaluations are made to assess the sensitivity of optimal plans to misspecifications of values of the parameters.

In order to evaluate lifetime of electron multiplier(EM), which is a key component of a cesium atomic frequency standard system, step stress accelerated degradation test in a two-way design is presented. The investigation of test design, establishment of lifetime evaluation model and model analyses is made. Then lifetime evaluation of EM is carried out.

A Bayesian method is proposed for analyzing incomplete data obtained from constant stress accelerated life testing when there are two or more failure modes, or competing failure modes. The incompleteness is mainly due to censoring, as well as masking which might be the case that the failure time is observed, but its corresponding failure mode is not identified. Furthermore, sensitivity analysis is performed to check model sensitivity. The method is illustrated by an example for validation, which also shows that the method provides better estimators than MLE by incorporating available prior information in the case of small sample sizes.

An equalizer is always adopted in an underwater acoustic communication system to reduce the intersymbol interference (ISI) due to the multipath effect. This paper introduces the basic structure and principle of a blind equalizer at first. Then the constant modulus algorithm (CMA) is derived and applied to an underwater communication system affected by multipath channel severely. The simulation results show that the CMA eliminates the ISI significantly. CMA is valuable practically because the training sequence is unnecessary for it.

In this paper, we report our work on spam filtering with three novel Bayesian classification methods: aggregating one-dependence estimators (AODE), hidden Naive Bayes (HNB), locally weighted learning with Naive Bayes (LWNB). Other four traditional classifiers: Naive Bayes, k nearest neighbor (kNN), support vector machine (SVM), C4.5 are also performed for comparison. Four feature selection methods: gain ratio, information gain, symmetrical uncertainty and ReliefF, are used to select relevant words for spam filtering. Results of experiments on two corpora show the promising capabilities of Bayesian classifiers for spam filtering, especial for that of AODE.

Software metrics are collected at various phases of the whole software development process, in order to assist in monitoring and controlling the software quality. However, software quality control is complicated, because of the complex relationship between these metrics and the attributes of a software development process. To solve this problem, many excellent techniques have been introduced into software maintainability domain. In this paper, we propose a novel classification method--Aggregating One-Dependence Estimators (AODE) to support and enhance our understanding of software metrics and their relationship to software quality. Experiments show that performance of AODE is much better than eight traditional classification methods and it is a promising method for software quality prediction. Furthermore, we present a Symmetrical Uncertainty (SU) based feature selection method to reduce source code metrics taking part in classification, make these classifiers more efficient and keep their performances not undermined meanwhile. Our empirical study shows the promising capability of SU for selecting relevant metrics and preserving original performances of the classifiers.

An identity-based broadcast encryption cryptosystem enable senders to efficiently broadcast cipher texts to a large set of receivers, which can use arbitrary strings as public keys. In this kind of cryptosystem, if the Private Key Generator (PKG) is untrusted, you may suddenly learn that a private key corresponding to your Identity is used to decrypt some ciphertexts. What causes it happens? Except a key compromise, perhaps it must be the PKG who is acting dishonestly and trying to make malicious activities by selling your key, which is a key escrow problem. In order to solve it, we introduce the concept of Accountable Authority Identity Based Broadcast Encryption (AA-IBBE) and design a concrete scheme, which is a new approach to mitigate the key escrow problem existing by restricting the ways in which the PKG can misbehave in identity based broadcast encryption schemes. Lastly we also give its correctness proof and security analysis.

In this paper, a new technique of null-steering mode filtering for a synthetic aperture array is applied for the passive source localization on an AUV mounted array. The proposed method utilizes modal spatiotemporal consistency on a moving array in the stratified waveguide, and overcomes physical aperture limitation for source localization by the AUV mounted short horizontal array. The simulated results indicate that performance of source localization is improved and with faster moving speed, the beam side-lobe level of bearing estimation can be reduced.