Task allocation of multiple unmanned aerial vehicles (multi-UAVs) is a typical NP-hard problem. In this paper, according to practical battlefield needs, mathematical model is constructed based on complex constrains of task allocation, and objective function is constructed based on multi-UAVs’ global voyage and task time. An improved strategy of particle position based on basic Particle Swarm Optimization (PSO) algorithm is applied to the problem, and reasonable allocation schemes are obtained. The allocation schemes meet the complex constrains including task sequence, time window, UAVs’ capacities and flight path, and can be chosen and adjusted flexibly by the decision maker according to the practical battlefield needs. A large number of simulation experiments show that improved PSO algorithm is effective and provides a reference for multi-UAVs’ task allocation problem with complex constrains and multi-objectives.
Aiming at the inaccuracy of Non-Local Means (NLM) algorithm for measuring the similarity of neighborhood blocks, an improved Non-Local Means denoising algorithm based on Difference Hash (dHash) algorithm and Hamming distance is proposed. The traditional algorithm measures the similarity between neighborhood blocks by Euclidean distance, so the ability to preserve edges and details is weak, which leads to the blurred and distorted images after filtering. To this end, the Difference Hash algorithm containing the gradient information is introduced, the difference hash images are generated from neighborhood blocks, and the Hamming distance of the difference hash images is calculated to measure the similarity of the neighborhood blocks. Finally, the Euclidean distance is improved. Experiment results show that the proposed method can preserve edges and details while denoising the low-noise images. Compared with other improved algorithms, the running speed of the proposed algorithm is also greatly improved, which has a certain application value.
In the context of the strategic goal of building energy Internet, State Grid Corporation of China has established a crossdisciplinary joint construction and common Internet of Things management platform, which carries out standardized access and unified online control for equipment in transmission, power transformation, distribution and other fields. In order to improve the access debugging efficiency of the IoT management platform accessed by terminal devices, this paper designed a full-scene simulation detection system for the intelligent IoT system oriented to the energy Internet. By using the existing construction ideas of intelligent IoT system and following the complete architecture of "cloud, tube, edge, terminal ", the construction method of the simulation and coordination platform of intelligent IoT system of "information-physical coupling" is realized. Advanced technologies such as micro-service and distribution are adopted to build a lightweight simulation test system. A small amount of server resources can be used to simulate and test whether the communication protocol of the side device supports the communication protocol requirements of the intelligent IoT management platform. The functions of reporting equipment data collection and issuing control commands are tested, so as to complete the simulation, verification and testing functions of the whole electric power industry chain intelligent IoT system covering both internal and external businesses, and provide technical support for the construction and promotion of the IoT management platform of State Grid Corporation of China. Combined with the whole scene simulation and detection system of the intelligent Internet of Things system, the operating state sensing monitoring scene of the power distribution area is built to verify the functions of data reporting of the fusion terminal and command issuing of the simulation and detection system.
This paper proposes a multi-beamspace division multiple access (MB-SDMA) by utilizing a directional radiation communication beam of the hemispherical LED array, which can solve the problem of network forming for underwater nodes. Meanwhile, a multiple-input multiple-output non-orthogonal multiple access(MIMO-NOMA) technologies for MB-SDMA is constructed. In this system, a multiple beam selection strategy aiming at the maximum receiving beam energy for the different spatial position is proposed. Then, an optimization model is established to make the user nodes satisfy the fairness criterion of maximizing the minimum rate under the constraint of total transmit power, and the non-convex model is solved by using the bisection method and the KKT condition. Finally, the power allocation algorithm to ensure user fairness is proposed in the multi-beam MIMO-NOMA system.The simulation results show that the system reduces inter-beam interference by reducing the number of radio frequency(RF) links, and it can be seen that the throughput of the multi-beam MIMO-NOMA system is higher than that of the traditional OMA system.
The kinematics of maneuvering multiple targets are generally unknown and time-varying. Using only a single frame of data, the conventional δ-Generalized Labeled Multi-Bernoulli (GLMB) filter has large tracking errors. Although the Multiple Hypothesis Tracking (MHT) algorithm utilizes multi-frame information, the amount of calculation will increase sharply with the number of targets and the complexity of environment. To address this issue, this paper incorporates the Multiple Model (MM) and MHT algorithm into δ-GLMB filter. MM is used to generate multiple hypothesis of δ-GLMB target components, and these hypotheses are enriched by association with measurements data on the level of multiple target component. In this way, multi- hypothesis information of each component is transmitted among successive multiple frames. Due to the use of multi-frame information, the performance of δ-GLMB filter for maneuvering targets in complex scenarios is effectively improved. At the same time, the computational complexity can be reduced by forming multiple hypothesis on the level of target components. Simulation results show that the proposed MM-MHT -δ-GLMB algorithm can effectively track targets in multi-target complex motion scenario and has better multi-target tracking accuracy than the conventional single model δ-GLMB filter.
Images taken in low-light conditions often have the problem of poor visibility. Besides inadequate lightings, different types of image quality degradation, such as a large amount of noise and color loss due to the limited quality of cameras and camera ISO setting, cause low quality of the captured image. However, directly amplifying the darkness of the lowlight image will inescapably bring into the pollution of the image. Therefore, the task of low-light image enhancement needs to kindle the dark regions and remove image degradation. To achieve this task, our work builds a Retinex theorybased neural network, which decomposes the input images into an illumination map and a reflectance map. Illumination map, representing the light information, is used for brightness adjustment, while reflectance map, representing the color information, is responsible for reconstructing low-light image into enhanced image with adjusted illumination map. However, there are few studies that notice the derivative of the image is used to solve the noise problem in Retinex decomposition and use spatial attention-based residual structures to increase the effect of light enhancement. For Decomposition sub-Network (Decom-Net), we purpose derivative features to alleviate the occurrence of noise in the reflectance map in the process of low-light image decomposition. For Illumination Enhancement sub-Network (Relight- Net), we use the Gaussian blur for reducing the problem of brightness enhancement degradation and build the Residual Spatial Attention Block (RSAB) to enlarge the volume and increase the capability of pixel-to-pixel mapping. Experiments are implemented to shows the effectiveness of our network, which improves the performance of previous methods on a large scale.
Ensuring information security is of great significance to a satellite communication system. In this paper, a chaotic encryption scheme is proposed for a satellite communication system. Firstly, five chaotic sequences are generated by a five-dimensional chaotic system. And then, XOR, scrambling and interpolation are performed on the data in turn. The simulation results show that the bit error rate of this method approaches 0 when the SNR is greater than 18, and the bit error rate of the illegal receiver is as high as 35.3% when the correct key is unknown. Therefore, secure communication can be achieved.
With the development of UAV technology, because of its advantages of low cost, easy operation and high flexibility, UAV has been applied to many fields such as transportation, patrol inspection, live broadcasting and so on. However, due to the problems of low rate, high delay and poor interaction, the task execution efficiency of UAV is not very satisfactory. In recent years, the rapid rise of 5g technology has brought another opportunity to the field of UAV. This paper proposes the application of UAV Based on 5g communication technology, which overcomes the current bottleneck of UAV. It provides a solution for the field application of UAV, and promotes the development of UAV.
This paper aims to repair missing regions which are corrupted along arbitrary directions. It presents a mixed image inpainting method based on Markov random field. By using Belief Propagation scheme in low gray-levels and alternately suggesting a directional Gaussian Graphical model (DGGM) for multiple references in a high-level range, it gains a balance between the model accuracy and the computation complexity in realization. On the basis of an existing method in [1], it improves the method in high level inpainting for the task under small train sets and large corrupted regions, by introducing these multi-head reference clues. Experimental results are given, the inpainting quality of different kinds of images with different sizes and contents under different parameter settings are compared in metrics of the peak signal noise ratio and the structural similarity index. The significance of parameter settings and the efficient computational cost could demonstrate the feasibility of this method.
Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has been considered a promising algorithm for tracking an unknown number of multiple extended targets (MET) with ellipsoidal shapes. However, when the MET are close to one another with irregularly varying shapes, the tracking accuracy will degrade seriously due to the incorrect measurement partition. To address the problem, we propose a new multiple extended target tracking and classification algorithm based on the shape driven strategy under the framework of PHD. First, the B-spline curve technique is employed to estimate the irregular MET shapes, and then the shape features are extracted for improving the measurement partition and state update for the closely spaced MET. Finally, the MET are classified according to the estimated shape information and the Gaussian mixture implementation of the proposed algorithm is derived and presented in this work. Experimental results show that the proposed technique has a better tracking performance than the existing GIW-PHD for the closely spaced MET with irregular shapes.
Using a natural conversation paradigm, this study investigated the acoustic characteristics of Mandarin utterances in drug addicts. Twenty-one native speakers of Mandarin, including four heroin addicts, two 3,4-methylamphetamine (MDMA, also known as ecstasy) addicts, and 15 healthy controls without any history of drug abuse, were recruited for the speech production experiment. In comparison with the healthy controls, heroin addicts exhibited a higher mean F0, a lower mean intensity, a higher variability in both F0 and intensity, and a lower H1-H2, while MDMA addicts exhibited a higher variability in both F0 and intensity. Discriminant analysis based on these acoustic parameters further showed a good accuracy of differentiating the three groups of speakers. These findings provide the basis for future research into identifying drug addicts on the basis of speech signals.
The traditional fault diagnosis method of ship power equipment has some problems, such as highly dependent on expert experience and low versatility. Therefore, an intelligent diagnosis method based on the deep convolutional neural network was proposed in this paper. In view of the interference from working environment of the ship equipment and the signal interference caused by variable load, the network depth and convolution kernel size are improved based on the one-dimensional CNN model, the FRN-DCNN model is proposed. To further improve the domain adaptive ability of convolutional neural network, the residual model was introduced and the Res14-DCNN diagnosis model based on the residual model was proposed. Experiments show that Res14-DCNN model has high noise resistance and variable load adaptability, which indicates that increasing the number of layers of neural network and introducing residual module can effectively improve the domain adaptability of deep convolution neural network.
This paper studies and presents the problem of scale effect of multistatic sonar. To study the detection performance of the multistatic sonobuoy system, the track-initiation probability is used as the detection probability of the target. The monostatic distributed and multistatic distributed detection modes are taken as examples. The influence of sonobuoy arrangements on sonar detection area is analyzed. The results of experiments show the method is available and compared with monostatic sonar system, multistatic system has scale effect. Moreover, the scale gain increases when the arrangement mode changes from triangular and square to hexagonal layout. Taking into account the flexible deployment of the multistatic sonobuoy system and the easy node expansion characteristics, we can make full use of the scale effect of multistatic detection, so as to effectively improve the capability for detecting submarine.
In hard disk drive (HDD) manufacturing processes, there are unrecovered serial number images about 0.01% from the standard optical character recognition (OCR) reading and deep learning approach. We found several failures from two main causes, i.e. manufacturing process and image capture process during standard OCR reading. We proposed classification model used for recognizing the serial number reading failures based on object detection You-Only-Look- Once (YOLO) algorithm and EfficientNet-B0 classification network as well as histogram analysis. The 1000 images captured by digital camera were used for training (600 images) and validation (400 images) the ROI detection model. The other 2100 captured images were used for training and testing classification OCR failure from manufacturing process model. The model testing was performed in 900 images contained 9 causes (classes) of failures. The proposed model reaches F1 score = 0.94.
Gait is one of important features to assess elderly physical condition which is directly relative to health status. The changes or abnormalities in gait may reflect risks in health. Different kinds of gait analysis methods have been proposed, such as pressure sensors based method and wearable equipment based method. Recently, with the development of computer vision technologies, more automatic, effective and non-intrusive ways are demanded to measure gait at either nursing facility or at-home in daily environment. In this paper, we propose an automatic approach for non-cooperative persons for gait analysis using 3D camera. This approach applies a tracking based recognition method to identify the targets on captured videos. Then 3D skeleton based behavior analysis is performed to select skeleton series of walking from daily behaviors. Finally gait characteristics are defined and calculated on selected skeleton series of each identified person for gait ability evaluation. Evaluations have been performed on a real-world environment where people do not stop in front of the camera. The result shows that the accuracy of recognition and behavior analysis method reaches above 90% for multiple persons, which is better efficiency and comparable accuracy than the previous methods and our approach is suitable for gait analysis in daily environment.
As low-resolution radar is still the main radar in service in China, the ground target classification and recognition technology of low-resolution radar has a wide application prospect in modern military and civil fields. This paper mainly studies and compares two main types of automatic target recognition and classification method for low-resolution ground radar: conventional recognition based on feature extraction and neural networks, and the conclusion is that the latter has better performance and needs less time to train. The former model in this paper fuses the time domain and frequency domain features of ground target echo, then simulates, compares and analyzes the performance of different classifiers. The classifiers studied include: naive bayes classifier (NBC), decision tree classifier (DT), linear discriminant analysis (LDA) classifier, k nearest neighbors (KNN) classifier and support vector machine (SVM) classifier. Five-fold cross validation is adopted in the experiment to effectively avoid the impact of arbitrariness on the results caused by the random division of the sample set into training sample set and test sample set. Besides, based on conventional convolutional neural networks, a new neural network structure named multi-scale residual neural network (Multi-scale ResNet) is proposed for one-dimensional feature target recognition, which effectively reduces the data dimension through auto-encoder and solves the problem of performance degradation caused by the difficulty in training too many levels of traditional convolutional neural network. The bayesian hyper-parameter optimization method is utilized to optimize the hyper-parameters of different classifiersl. Finally, compared the accuracy of the two types of target recognition, the best performance of the pattern recognition is the support vector machine, which recognition rate is 91.2%, while multi-scale residual neural network recognition rate is up to 99.6%.
The concentration prediction of mixed gases is crucial to the pattern recognition research of electronic nose (E-nose) systems. The response experiments of the E-nose towards the ethanol and propanol mixture with different concentrations are carried out. Five kinds of machine learning algorithms, including linear regression, support vector machine, K-nearest neighbor, random forest, and decision tree, are used for training the multiple output regressors to predict the content of each component simultaneously. The R2 score, root mean squared error, and mean absolute error are used to evaluate the performance of these models. The relationship between prediction accuracy and concentration distribution has also been studied. The results show that the model based on the random forest has superior performance for forecasting the concentration of ethanol and propanol, with the R2 score more than 0.98 in the 5-fold cross-validation. This study provides a significant inspiration for designing a multi-output regression model to realize the quantitative prediction of mixed gases by the E-nose.
Traditional adaptive beamforming algorithms require high accuracy for steering vectors(SV), array models and desired signal(DS). However, the performance of the beamformer will seriously degrade when the DS is present in training snapshots. For the purpose of improving output performance of adaptive beamformer, a novel adaptive beamforming algorithm is proposed. This approach estimates the desired signal SV and reconstructs the sampling covariance matrix (CM) based on integrating over a undesired signal region. Furthermore, only a little prior knowledge is required, such as the approximate incident angle of the DS. The proposed algorithm remove not only the influence of the DS in the sampling covariance matrix, but also the effect of background noise perturbation, which is significantly improved compared with other methods. The results of data simulation experiments confirms that the beamformer has a excellent performance in output performance.
Estimating the noise power spectral density (PSD) from the corrupted signal is an essential part of the signal enhancement algorithms. In this paper, a combined noise PSD estimation algorithm based on the minimum statistics (MS) and minimum mean-square error (MMSE) is proposed for the noisy multi-component underwater acoustic pulse signals. And in particular, the pulse signals are composed of continuous wave (CW) signals and linear frequency modulation (LFM) signals. Theoretical analysis and simulation results show that the proposed algorithm can effectively track the spectral noise power of the multi-component signal under non-cooperative conditions without prior information. Experimental result based on sea-truth data shows that the proposed method can efficiently enhance the noisy multi-component signal in terms of improving the relative signal-to-noise ratio (SNR).
