In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The... Show More

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data; preprocessing all the collected data to obtain an initial sample set; constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The Gene Expression Programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data.

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the... Show More

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.

Semantic similarity is used extensively for understanding the context and meaning of the text data. In this paper, use of the semantic similarity in an automatic essay evaluation system is proposed.... Show More

Semantic similarity is used extensively for understanding the context and meaning of the text data. In this paper, use of the semantic similarity in an automatic essay evaluation system is proposed. Different text embedding methods are used to compute the semantic similarity. Recent neural embedding methods including Google sentence encoder (GSE), embeddings for language models (ELMo), and global vectors (GloVe) are employed for computing the semantic similarity. Traditional methods of textual data representation such as TF-IDF and Jaccard index are also used in finding the semantic similarity. Experimental analysis of an intra-class and inter-class semantic similarity score distributions shows that the GSE outperforms other methods by accurately distinguishing essays from the same or different set/topic. Semantic similarity calculated using the GSE method is further used for finding the correlation with human rated essay scores, which shows high correlation with the human-rated scores on various essay traits.

Smoking in public places not only causes potential harm to the health of oneself and others, but also causes hidden dangers such as fires. Therefore, for health and safety considerations, a... Show More

Smoking in public places not only causes potential harm to the health of oneself and others, but also causes hidden dangers such as fires. Therefore, for health and safety considerations, a detection model is designed based on deep learning for places where smoking is prohibited, such as airports, gas stations, and chemical warehouses, that can quickly detect and warn smoking behavior. In the model, a convolutional neural network is used to process the input frames of the video stream which are captured by the camera. After image feature extraction, feature fusion, target classification and target positioning, the position of the cigarette butt is located, and smoking behavior is determined. Common target detection algorithms are not ideal for small target objects, and the detection speed needs to be improved. A series of designed convolutional neural network modules not only reduce the amount of model calculations, speed up the deduction, and meet real-time requirements, but also improve the detection accuracy of small target objects (cigarette butts).

With the rapid development of social economy, people's demand for diversified and precise goals is increasingly prominent. In the face of a specific engineering application practice, how to find a... Show More

With the rapid development of social economy, people's demand for diversified and precise goals is increasingly prominent. In the face of a specific engineering application practice, how to find a satisfactory equilibrium solution among multiple objectives has been the focus of researchers at home and abroad. Aiming at the convergence and diversity imbalance in the current high-dimensional multi-objective evolutionary algorithm based on reference points, this article suggests a constrained evolutionary algorithm based on spatial division, angle culling, and hybrid matching selection strategy. Experimental practices show that the proposed algorithm has better performance compared with other related variants on DTLZ/WFG benchmark functions and in solving the problem of electricity market price.

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape... Show More

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy flight is used to generate the proposed random walk algorithm's variable step size and direction. Some tests show that the proposed LRW algorithm performs better in the uniformity of sampling points. Besides, the authors analyze the fitness landscape of the CEC2017 benchmark functions using the proposed LRW algorithm. The experimental results indicate that the proposed LRW algorithm can better obtain the structural features of the landscape and has better stability than several other RW algorithms.

A new localization algorithm based on large scale unmanned aerial vehicle swarm (UAVs) is proposed in the paper. The localization algorithm is based on a spring particle model (LASPM). It simulates... Show More

A new localization algorithm based on large scale unmanned aerial vehicle swarm (UAVs) is proposed in the paper. The localization algorithm is based on a spring particle model (LASPM). It simulates the dynamic process of physical spring particle system. The UAVs form a special mobile wireless sensor network. Each UAV works as a highly-dynamic mobile sensor node. Only a few mobile sensor nodes are equipped with GPS localization devices, which are anchor nodes, and the other nodes are blind nodes. The mobile sensor nodes are set as particles with masses and connected with neighbor nodes by virtual springs. The virtual springs will force the particles to move to the original positions. The blind nodes' position can be inferred with the LASPM algorithm. The computational and communication complexity doesn't increase with the network scale size. The proposed algorithm can not only reduce the computational complexity, but also maintain the localization accuracy. The simulation results show the algorithm is effective.

It is necessary for English teachers to grasp the causes of students' language anxiety and explore ways to avoid, reduce, and eliminate students' anxiety. This paper discusses the foreign language... Show More

It is necessary for English teachers to grasp the causes of students' language anxiety and explore ways to avoid, reduce, and eliminate students' anxiety. This paper discusses the foreign language anxiety of college English teachers in classroom teaching, its possible causes, teachers' awareness of anxiety, and countermeasures. This paper introduces the composition of student affairs analysis system from data layer, analysis layer, application layer, and display layer and combines data warehouse and data mining technology to improve the functions of student information, teacher information, achievement information, course selection information, and course evaluation. On the premise of data mining and data information management, it realizes the construction and application of teaching management data analysis system, using classification model. Apriori algorithm improves the algorithm, uses big data technology to analyze data and design courses, and analyzes the inherent relationship between mental health problems and attributes.

At the oriented object detection in aerial remote sensing images, the perceptual field boundaries of ordinary convolutional kernels are often not parallel to the boundaries of the objects to be... Show More

At the oriented object detection in aerial remote sensing images, the perceptual field boundaries of ordinary convolutional kernels are often not parallel to the boundaries of the objects to be detected, affecting the model precision. Therefore, an object detection model (DCN-BBAV) that fuses deformable convolution networks (DCNs) and box boundary-aware vectors (BBAVs) is proposed. Firstly, a BBAV is used as the baseline, replacing the normal convolution kernels in the backbone network with deformable convolution kernels. Then, the spatial attention module (SAM) and channel attention mechanism (CAM) are used to enhance the feature extraction ability for a DCN. Finally, the dot product of the included angles of four adjacent vectors are added to the loss function of the rotation frame parameter, improving the regression precision of the boundary vector. The DCN-BBAV model demonstrates notable performance with a 77.30% mean average precision (mAP) on the DOTA dataset. Additionally, it outperforms other advanced rotating frame object detection methods, achieving impressive results of 90.52% mAP on VOC07 and 96.67% mAP on VOC12 for HRSC2016.