Formaldehyde Gas Sensor with Extremely High Response Employing Cobalt-Doped SnO2 Ultrafine Nanoparticles

Formaldehyde is a common carcinogen in daily life and harmful to health. The detection of formaldehyde by a metal oxide semiconductor gas sensor is an important research direction. In this...

Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation

In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.

SPIN-CROSSOVER IRON(II) COORDINATION COM­POUNDS: FABRICATION OF FUNCTIONAL MATERIALS AND THEIR INTEGRATION INTO MICRO- AND NANOCONSTRUCTIONS

Development of micro- and nanosized spin-crossover (SCO) materials has become an important research direction within the past decade. Such an interest is associated with high perceptive of practical application of these materials in nanoelectronic devices. Therefore, researches working in the field of SCO put considerable efforts to obtain SCO complexes in various functional forms, such as nanoparticles, thin films, etc. Fabrication of these materials is realized through different chemical and/or lithographical approaches, which allow to adjust size, shape and even organization of nanoobjects. In this review theoretical background of SCO phenomenon is described, additionally different classes of coordination compounds exhibiting spin crossover are covered. It is demonstrated that electric field, temperature and light irradiation can be effectively used for switching and control of spin state in nanosized SCO systems. Cooperative SCO with transition close to room temperature, wide hysteresis loop and distinct thermochromic effect is most often observed for Fe(II) coordination complexes. Therefore, Fe(II) SCO compounds form one of the most perspective classes of compounds for obtaining functional materials. It is shown that integration of Fe(II) compounds into micro- and nanohybrid devi­ces allows to combine unique functional pro­perties in one material due to synergy between SCO and physical properties (luminescent, electrical, etc.) of the other component. As a result, SCO compounds are interesting not only from the fundamental point of view, but also from practical, thanks to the possibility of integration of SCO Fe(II) complexes as active materials in devices of different configurations. It is expected that obtaining of new Fe(II) coordination polymers with unique SCO cha­racteristics will favor the development of new functional materials and devices on their basis in the nearest future.

Image recognition algorithms based on deep learning

Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

Evaluation and Analysis of Animation Multimedia 3D Lip Synchronization considering the Comprehensive Weighted Algorithm

The 3D lip synchronization is one of the hot topics and difficulties in the field of computer graphics. How to carry out 3D lip synchronization effectively and accurately is an important research direction in the field of multimedia. On this basis, a comprehensive weighted algorithm is introduced in this paper to sort out the related laws and the time of lip pronunciation in animation multimedia, carry out the vector weight analysis on the texts in the animation multimedia, and synthesize a matching evaluation model for 3D lip synchronization. At the same time, the goal of simultaneous evaluation can be achieved by synthesizing the transitional mouth pattern sequence between consecutive mouth patterns. The results of the simulation experiment indicate that the comprehensive weighted algorithm is effective and can support the evaluation and analysis of animation multimedia 3D lip synchronization.

SA-HAVE: A Self-Attention based Hierarchical VAEs Network for Abstractive Summarization

The abstractive automatic summarization task is to summarize the main content of the article with short sentences, which is an important research direction in natural language generation. Most abstractive summarization models are based on sequence-to-sequence neural networks. Specifically, they encode input text sequences by Bi-directional Long Short-Term Memory (bi-LSTM), and decode summaries word-by-word by LSTM. However, existing models usually did not consider both the self-attention dependence during the encoding process using bi-LSTM, and deep potential sentence structure information for the decoding process. To tackle these limitations, we propose a Self-Attention based word embedding and Hierarchical Variational AutoEncoders (SA-HVAE) model. The model first introduces self-attention into LSTM to alleviate information decay of encoding, and accomplish summarization with deep structure information inference through hierarchical VAEs. The experimental results on the Gigaword and CNN/Daily Mail datasets validate the superior performance of SA-HVAE, and our model has a significant improvement over the baseline model.

Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500~700 m less than the KFAT method.

Parameter estimation in multifactor uncertain differential equation

In uncertainty theory, parameter estimation of uncertain differential equation is a very important research direction. The parameter estimation of multifactor uncertain differential equation needs to be solved. Multifactor uncertain differential equation is a differential equation driven by multiple Liu processes. The paper introduces two methods to solve the unknown parameters of the multifactor uncertain differential equation, they are the method of moment estimation and the method of least squares estimation. Several numerical examples are used to illustrate the proposed parameter estimation methods.

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

RGB-D Human Action Recognition of Deep Feature Enhancement and Fusion Using Two-Stream ConvNet

Action recognition is an important research direction of computer vision, whose performance based on video images is easily affected by factors such as background and light, while deep video images can better reduce interference and improve recognition accuracy. Therefore, this paper makes full use of video and deep skeleton data and proposes an RGB-D action recognition based two-stream network (SV-GCN), which can be described as a two-stream architecture that works with two different data. Proposed Nonlocal-stgcn (S-Stream) based on skeleton data, by adding nonlocal to obtain dependency relationship between a wider range of joints, to provide more rich skeleton point features for the model, proposed a video based Dilated-slowfastnet (V-Stream), which replaces traditional random sampling layer with dilated convolutional layers, which can make better use of depth the feature; finally, two stream information is fused to realize action recognition. The experimental results on NTU-RGB+D dataset show that proposed method significantly improves recognition accuracy and is superior to st-gcn and Slowfastnet in both CS and CV.