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In this work, we focus on the model-mismatch problem for model-based subspace channel tracking in the correlated underwater acoustic channel. A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model. Even though the data support the assumption that the model is slow-varying and uncorrelated to some degree, to improve the tracking performance further, we can not ignore the model-mismatch problem because most channel models encounter this problem in the underwater acoustic channel. Therefore, in this work, we provide a dynamic time-variant state-space model for underwater acoustic channel tracking. This model is tolerant to the slight correlation after decorrelation. Moreover, a forward-backward Kalman filter is combined to further improve the tracking performance. The performance of our proposed algorithm is demonstrated with the same at-sea data as that used for conventional channel tracking. Compared with the conventional algorithms, the proposed algorithm shows significant improvement, especially in rough sea conditions in which the channels are fast-varying., Comment: Submitted to IEEE Access

This paper mainly investigates the fixed-time attitude stabilization problem of a rigid spacecraft subject to inertia uncertainties and external disturbance. Firstly, a terminal sliding mode surface is constructed to guarantee the fixed time convergence of the attitude independent of initial states. Then, active disturbance rejection control is used to guarantee the states of the spacecraft system reach an arbitrary small vicinity of the terminal sliding mode surface in fixed time. Moreover, to solve the singularity problem in terminal sliding mode control design, an asynchronous switching law is proposed to avoid the singularity. Finally, numerical simulations illustrate the control performance of the proposed control method.

The self-locking nut is an advanced fastening part, which can be locked by friction. It has been widely used in the connection of aeroengine system nowadays. The performance and reliability of self-locking nut will directly affect the tightness of various aeroengine components, and play an important role in the operation of aeroengine. In this paper, based on the collected lifetime data of aeroengine selflocking nut, the survival analysis method is introduced to discover the influence of each individual variable on the life of self-locking nut at first. Then, after eliminating the irrelevant variables according to survival analysis, a novel Bayesian network (BN) based life prediction model is established to support the decision making of extending service life and improving system reliability. Finally, by comparing with the general BN model with all variables, the performance of the proposed model is verified, and the applications of the model are also given.

Microring resonator (MRR) is a versatile photonic device that finds a wide range of applications in communication, computing, and sensing. However, it is susceptible to fabrication, thermal, and laser wavelength variations. This paper presents a PWM wavelength locker for a MRR. The wavelength locker and the MRR have been fabricated in a monolithic photonic BiCMOS platform. The effectiveness of this design is verified by extensive computer simulations. To the best of our knowledge, this is the first closed-loop PWM wavelength locker for MRRs reported in the literature.

Aiming at the problem that traditional bearing fault diagnosis methods rely on artificial feature extraction and expert experience, this paper proposes an adaptive bearing fault diagnosis method based on two-dimensional convolutional neural network. In order to retain the features of the original fault data to the greatest extent, the original signal is directly used as the input, and the two-dimensional convolutional neural network fault diagnosis model is used to perform adaptive hierarchical feature extraction, and optimization algorithms are used to improve the performance of the test set. The experimental results show that this method can achieve a fault recognition rate of more than 99% on the bearing data set, and shows good generalization performance under different loads, which is feasible for practical applications.

Photovoltaic (PV) power generation is one of the most promising renewable energy power generation systems. PV monitoring system is of great significance to ensure the stability and performance of the photovoltaic grid connected system. Because when the temperature of photovoltaic panel is high, the whole photovoltaic module may be damaged, and the power generation of photovoltaic panel will also be affected. Therefore, it is necessary to monitor the temperature of the photovoltaic panel. In this paper, we design a wireless PV monitoring system based on LoRa (long range) technology, which can monitor the front and back temperature of the photovoltaic panel in real time. First, the application architecture of LoRa in PV monitoring system is studied. Then, the software and hardware design is introduced. Finally, by conducting a system test and analysis, the communication distance of LoRa in a complex environment can reach 515 m, the battery life of acquisition node using LoRa can reach 2.93 years, the collected data are accurate and the system runs stably. The long-term monitoring data obtained by the monitoring system can provide important data support for the subsequent performance analysis and evaluation of photovoltaic panels, as well as the early warning of degradation and fault location of photovoltaic panels.

© 2013 IEEE. Mobile service computing relies on efficient and secure data transfer. Geometric routing, which guarantees scalability, efficiency, and mobility, is a promising routing scheme for mobile services in the resource-constrained Internet of Things (IoT). However, the private data transmitted by geometric routing may be eavesdropped by the malicious node and the privacy-preserving becomes one of the important issues for the mobile service in IoTs. Due to the resource constrains, approaches using encryption or hashing are not suitable for geometric routing. In this paper, we propose a lightweight anonymous geometric routing (LAGER) to protect the node-related private data. Instead of using the encryption or hashing approach, LAGER adopts a coordinate confusion mechanism which provides each node an anonymous coordinate of virtual node, such that the private data is decoupled from the node coordinate. The malicious node cannot determine which node the data belongs to even if it obtains the private data. To support the data transmission with anonymous coordinates, LAGER adopts a hybrid routing scheme by combining the greedy routing and a source routing. The experiment results show that LAGER provides effective anonymity with acceptable costs of scalability and efficiency.

High-accuracy automatic recognition of facial expression is a challenging task because of the subtlety, complexity and diversity of facial expression. In this paper, we improve a traditional feature-based method, named the convolutional neural network (CNN). In order to increase the accuracy, we treat the tensor of the output layer as a multi-dimensional vector, and through matrix transformation, we can magnify its eigenvalues. Empirical evidence shows that this method makes the calculated difference between the probability values of each expression larger so that the computer obtains a smaller loss value when correctly recognized. Moreover, the training speed is faster than that before the optimization.

Stage separation of aircraft was a complex variation process of force. It was influenced by deviations of aircraft and environmental disturbance, which would result in the simulation inconsistent with the actual one, even lead to collisions occur between separators. In this paper, a method of aircraft design based on probability analysis is introduced that is applied to simulation and analysis of stage separation with guide pins scheme. Firstly, dynamic model was derived from the constraint force equation to analyst relative motion of separation bodies during stage separation processing. Then probability models of deviation factors were given. Inducing the probability model of deviation factors into the simulation process, using real boundary as design constraint, and according to the degree of reliability or robustness, quantitatively add design margin to design constraints, and relax the margin limitation by satisfying the reliability of constraints. Finally, Monte Carlo simulations of stage separation process were adopted under combined action of multiple random deviations. Distribution and relative motion of separated bodies in inertial frame was obtained. The statistical analysis of Monte Carlo simulation result was compared with reliability and failure domains to evaluate the effectiveness of separation schemes. Because the probabilistic design method above-mentioned has the characteristics of “deviation allowance and margin free”, it can improve the overall performance of aircraft and obtain a robust and reliable separation scheme.

In this paper, the robot system of dual-arm cooperative assembly is designed and analyzed in view of the scene of axle-hole assembly in industrial production. The d-h method is used to analyze the kinematics of the 6-dof robot, and the forward and inverse kinematics equations are derived. Then the image is processed by the algorithm to reduce noise and fluctuation interference, so as to obtain more effective information and facilitate the accurate positioning of the visual system. This paper discusses how to identify different color axes by deep learning in halcyon environment. Finally has carried on the preliminary assembly experiment, in the process of assembly, distinguish the color of the parts by using visual sensor characteristics, the main mechanical arm through visual identification, pick one color axis from the arm will be selected according to the main arm shaft select corresponding hole, through the preset trajectory placing it on the desktop, main arm also according to the hole center coordinate point, the position of the moving axis, which are assembled. During the experiment, the coordinate value of the robot arm passing the target position and the visual positioning coordinate value given by the visual system were recorded, and the two were analyzed and compared by Matlab.

The burgeoning technology of Mobile Edge Computing (MEC) is attracting the traditional Location-Based Service (LBS) and Location Service (LS) to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exist the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the Radio Access Network (RAN) access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the user's location precisely in both single-point and trajectory scenarios.

Auto disturbance-rejection controller method is applied to the design of autopilot for hypersonic rocket based combined cycle(RBCC) vehicle. At first, the characteristics and the main challenge of control system design for RBCC vehicle are analyzed, and the control system model with disturbance is established. Then, two close loops design method is proposed for the RBCC vehicle control system. The simulation results show that the designed system attains high dynamic performance and steady-state performance and the controller has excellent robustness performance.

In order to improve the control performance of the aircraft and achieve on-line control, it is necessary to obtain the stability margin of the system in real time and design online attitude controller. In this paper, the stability margin online identification method is studied. Based on the Chirp-Z transform and frequency response estimation method, excitation signals are added to obtain the system’s stability margin online. Simulation results show that the method proposed has high recognition accuracy and can be applied to real aircraft and used in online controller design.

Location-Based Service(LBS) plays an important role in daily life. While LBS provides convenience, it also brings the threat of leaking location privacy. An attacker can even infer users' interests and hobbies based on the location information exposed by them. Although many location privacy protection approaches have been proposed, most of them focus on the stage of providing location to LBS server. In the paper, we propose an Optimal Fingerprints Construction(OFC) algorithm to achieve k-anonymity for users. Different from existing studies, the OFC algorithm focuses on the stage of getting location from Location Provider(LP) and generates dummy user fingerprints to resist adversaries. We first analyze the privacy threats in the positioning process. Considering LP might be potential adversary, we then design the anonymous knowledge base and the location privacy protection algorithm. We evaluate the efficiency of our proposed solution on the smart phone. Simulation results show that our proposed solution is promising.

The kNN query is one of the dominating function in various of location-based services. In real-time scenario, two phrases are actually involved in the kNN POI query process: the positioning process and the searching process, in the former process, the user obtains his location and then send it to the POI provider to retrieve the result in the later process. Location privacy concerns are exist in both two processes and have been studied separately in the past researches. We argue that this division is lack of globality. In this paper, we propose a real-time one phrase location privacy-aware method for the POI searching. By performing the projection method on the POI data and the positioning infrastructure, we design a one phrase POI searching scheme which can cloak the location information. The evaluation result shows the practicability of our method.

Aurora is the only geophysical phenomenon observed directly with the naked eye in the high latitude regions. The systematic observation of aurora plays an important role in the magnetosphere and solar-terrestrial electromagnetic activity. With the observed aurora data growing quickly, aurora morphology research becomes data intensive. This paper presents a big aurora data management framework toward aurora classification analysis considering the scalability and performance. The framework integrates aurora data acquisition and management, aurora data preprocessing, data query and explore, and aurora classification experiment management. The NoSQL database is employed to manage the aurora data and the classification experiment data. The in-memory index is constructed to support the in-situ analysis.

Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks(FCNs). To address this problem, we propose a Densely Connected Deconvolutional Network (DCDN) for semantic segmentation. In DCDN, multiple shallow deconvolutional networks, which are called as DCDN units, are stacked one by one to make the structure deeper and guarantee the fine recovery of localization information, meanwhile, the inter-unit and intra-unit dense connections are designed to make the network easy to train since the connections improve the flow of information and gradients throughout the network. Besides, the intermediate supervisions are applied to each DCDN unit to ensure the fast convergence. Extensive experiments on two urban scene datasets, i.e., CamVid and GATECH, demonstrate that the proposed model achieves better performance than some state-of-the-art methods without using any post-processing, pretrained model, nor temporal information, whilst requiring less parameters.

The test results show the moisture equilibrium between transformer oil and pressboard has changed in aged condition. The mechanism of the phenomenon is not clear. The increase of small molecule acid in pressboard is one of the possible causes. Research the effect of small molecule acid on moisture equilibrium for oil-pressboard helps to explain the phenomenon. In this paper, various aged transformer pressboard samples from un-aged to 40 years old were prepared by accelerated thermal aging method at 130 centigrade degree. Moisture equilibrium tests of paper samples with and without small molecule acid immersed in un-aged oil were carried out under the conditions of 80 centigrade degree, 60% relative humidity of air atmosphere. The results showed that the effect of small molecule acids increases with the deepening of aging. The effect of small molecule acid on moisture adsorption of 40-year-aged paper was 16.6%, 9 times of un-aged paper.

To satisfy the timely estimation requirement of aircraft fall point deviation, the nonlinear function approximation function of back propagation neural network was adopted to estimate inertial instrument bias effect on aircraft accumulative guidance system error. According to coefficients of inertial instrument model and trajectory parameters, the dominant coefficients and corresponding guidance instruments error are selected as neutral network training input-output sample. Once appropriate network weights and threshold values are fixed, the complicated aircraft fall point precision analysis procedure can be substituted.

MSR Image Recognition Challenge (IRC) 2016 tries to deal with designing a dog breeds recognition system based on Clickture-Dog dataset. To address the task, we proposed an effective method with systematic strategies as follows. We presented a data cleaning method using faster-rcnn to learn a dog detector. Besides, we ensembeled a series of CNN models to enhance the robustness. A dense evaluation strategy was carried out to boost the test accuracy. Finally, we achieved the first prize of this challenge with 89.65% top-5 accuracy.

UHF is one of the effective methods which could detect partial discharge in GIS. However, more and more GIS have insulator of metal enclosed structure which leads UHF electromagnetic wave caused by partial discharge could not pass through the insulator. Fortunately, there are little slits between the metal band and the GIS shell which maybe lets higher frequency electromagnetic wave pass. It is possible to detect the partial discharge for GIS with metal enclosed insulator structure by higher frequency. In this paper, we measured the GIS equipment in the laboratory, set up the 1:1 model of GIS equipment based on the measured data. By the simulation software, we simulated the GIS partial discharge by giving a Gaussian pulse signal which is similar to the partial discharge, got the electromagnetic wave spectrum which can spread through the slits between the metal band and the GIS shell. On the basis of the data of the electromagnetic wave spectrum which can be detected, we designed an antenna which could be used for the detection of GIS with metal enclosed insulator structure. With usage of HFSS electromagnetic simulation software, we built up models of two kinds of sensors and repeatedly changed the size of the antenna to the optimal structure according to the simulation optimization data. At last, we got the standing wave ratio direction, radiation pattern, and 3D polar plot of the two sensors. Considering factors such as structural features and portable use, we compared the two kinds of antenna performance difference. The results show that the electromagnetic wave spectrum which can be detected of GIS partial discharge is 0.5–4 GHz, and in this band, the micro-strip antenna is more suitable than equiangular spiral antenna in the detection of partial discharge in GIS.

In this paper, we propose a two-stage CNN-based framework to learn color names from web images, aiming to predict color names for tiny image patches. To deal with the noisy labels widespread in web images, we propose a self-supervised CNN (SS-CNN) model in the first stage. The SS-CNN model is trained on image patches with their own color histograms as supervision information. Thus its outputs are able to reflect the color characteristics of images without the influence of the noisy labels. In the second stage, we finetune the SS-CNN model to learn the mapping from image patches to color names, where the patch labels are inherited from its father images. Besides, sample selection is imported iteratively in turns with the finetuning process, which helps filtering out some noisy samples and further improves the model accuracy. Our model shows high representation ability to colors and achieves better performance of color naming compared with the state-of-the-art methods.

In this paper, we propose a novel resource sharing strategy to improve the overall system throughput of a device-to-device (D2D) communication system underlaying a cellular network. Aiming to optimize the overall system throughput, the constraint of the leakage data from the cellular user is illustrated, considering the D2D receiver as a malicious partner sharing the spectrum with it. The optimality is also discussed under the requirement of the quality of service (QoS) of the cellular user (CU). In particular, for a given D2D receiver, we propose an admission region of the D2D transmitter, where the leakage rate to the D2D receiver should be minimized and the QoS of the CU can also be satisfied. It does not allow the resource sharing between the cellular link and the D2D link if the D2D transmitter fails to belong to the admission region. Furthermore, an optimal power control algorithm and the D2D pair selection policy are developed to maximize the overall system throughput. Numerical results are given to show that the proposed scheme can improve the system performance effectively.

Photoelectric colorimetry is often used in the monitoring instrumentation in power plants. It has major shortcomings as follows: the high requirement for the stability of the light source; the impact of long-running light window scaling, corrosion and components aging on the measurement accuracy; the affects of dosing accuracy, temperature fluctuations and cross-contamination on measurement results. This article describes a new type of photoelectric colorimeter measuring system. Its adaptive light control method can solve the above problems and improve the measurement accuracy.

This paper analyzes database requirement of embedded development and embedded Web server, combines with the database design on embedded home gateway using SQLite architecture, and implements the SQLite embedded database on home gateway. Web server is successfully ported, and embedded Linux operation system is ported to home gateway platform.