June 2020

JICE June 1, 2020 Publication

Showing all 11 results

  • Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network

    $15.00

    Abstract: In the terahertz time-domain spectroscopy technique, 16 common pathogens were experimentally studied and their characteristics absorption spectra in the frequency range of 0.1 to 2.2THz were obtained. The terahertz absorption spectra of 16 common pathogens were trained and identified by Siamese neural network method. First, the terahertz absorption spectra of the 16 pathogens were reduced by PCA to construct training data. Then, the constructed Siamese neural network model was trained by back propagation. Finally, the pathogen measured at different times was used as the target spectrum to evaluate the model, after comparing with the training data, the matching absorption spectrum was obtained, and the recognition rate reached 97.34%. The recognition results fully indicate that the identification of different kinds of pathogens can be recognized by Siamese neural network, which provides an effective method of the detection and identification of pathogens by terahertz spectroscopy.

    Keywords: THz spectroscopy; machine learning; Siamese neural network; similarity learning

  • Application of vertical switching signal prediction based on ship networking in heterogeneous networks

    $15.00

    Abstract: With the rise of ship networking, in view of the coexistence of many heterogeneous networks, the vertical switching technology of heterogeneous networks is studied, and a vertical signal prediction algorithm is proposed by using grey theory. Through the simulation analysis of MATLAB test, it is verified that the algorithm can effectively improve the accuracy of the prediction signal and is suitable for ship terminals.

    Keywords: Ship Networking, Heterogeneous Network, Vertical Switching, Prediction Algorithm.

  • Suspicious Bank Card Transaction Recognition Based on K-means Clustering and Random Forest Algorithm

    $15.00

    Abstract: Suspicious transactions are hidden in thousands of massive transaction data, causing incalculable losses and risks, but the detection is very difficult. In terms of how to effectively explore and identify suspicious transactions from massive transaction data accurately and quickly, this paper adopts the method based on the combination of k-means algorithm and random forest algorithm to solve the problem of data imbalance in the identification of suspicious transactions in bank accounts, and proposes an effective suspicious transaction detection model. At the same time, the AUC(Area Under Curve) and Recall indicators such as the unbalanced data classification standard of performance evaluation, and finally to Kaggle data platform for the bank account of suspicious transactions data set, the results show that the proposed detection model of performance evaluation index AUC increased by 5%, F1-measure increases by 1%, show that the method has some reference value to the suspicious transactions recognition, limited information utilization rate is higher, which makes all kinds of Banks prediction speed and accuracy of suspicious transactions events get improved, can to some extent, reduce the operating cost and risk of the banking sector.

    Keywords: Big data, K-means Algorithm, Random Forest, Suspicious Identification, Unbalanced Data

  • Association Analysis of primary Liver Cancer based on Apriori Algorithm

    $15.00

    Abstract: This paper briefly describes the Apriori algorithm for primary liver cancer data, and then performs data preprocessing based on the characteristics of primary liver cancer patients, including data import and extraction, and embedding the algorithm into primary liver cancer. The implementation of clinical warning. After that, the Apriori algorithm is used to realize the association of the data association rules of primary liver cancer, and the internal valuable association rules are obtained, which provides suggestions for improving the doctor’s remediation and prevention, so as to prevent the occurrence and reduction of primary liver cancer. The incidence of primary liver cancer has important practical significance.

    Keywords: Apriori algorithm; primary liver cancer; Association rules; association analysis

  • Development of Drone Detecting Free Parking Space for Car Parking Guidance

    $15.00

    Abstract: In the course of transportation of medical drugs, the requirements for temperature and humidity are high. We should strictly control it within a certain range, otherwise the deterioration of drugs will occur, which will lead to serious consequences. Therefore, a cold chain transport vehicle with temperature and humidity sensors should be used in medical transportation. This paper designs a wireless monitoring system based on LoRa technology, which can collect the data of temperature and humidity sensors on the transport vehicle in real time, and transmit them to the background through the LoRa gateway. Solve the technical problems of high power consumption and data upload failure. A wireless cold chain monitoring system with high data accuracy, strong expansibility and convenient networking is realized ensure the quality of drugs quantity safety.

    Keywords: Drone; ardrone 2.0; template matching; parking lot

  • Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization

    $15.00

    Abstract: This paper studied on the path planning problem in known environments. According to Dijkstra algorithm and ant colony optimization (ACO), a hybrid algorithm to search the path was designed. Based on the environment model, constructed by using visual graph method, Dijkstra algorithm was used for initial path planning. Then the ACO was improved and used to optimize the initial path to minimize the path of the robot. The simulation on MATLAB showed that the path planning algorithm based on Dijkstra-ACO has higher efficiency of path search and good effect of path planning, and the algorithm is effective and feasible.

    Keywords: Dijkstra Algorithm, Ant Colony Optimization (ACO), Path Planning

  • A Medicine Cold Chain Monitor System Based on LoRa Wireless Technology

    $15.00

    Abstract: In the course of transportation of medical drugs, the requirements for temperature and humidity are high. We should strictly control it within a certain range, otherwise the deterioration of drugs will occur, which will lead to serious consequences. Therefore, a cold chain transport vehicle with temperature and humidity sensors should be used in medical transportation. This paper designs a wireless monitoring system based on LoRa technology, which can collect the data of temperature and humidity sensors on the transport vehicle in real time, and transmit them to the background through the LoRa gateway. Solve the technical problems of high power consumption and data upload failure. A wireless cold chain monitoring system with high data accuracy, strong expansibility and convenient networking is realized ensure the quality of drugs quantity safety.

    Keywords: LoRa, Things of Internet, Cold Chain, Sensor, Temperature and Humidity

  • Research on Text Classification Method based on PTF-IDF and Cosine Similarity

    $15.00

    Abstract: Text classification is a foundational task in many NLP applications. The text classification task in the era of big data faces new challenges. We propose a Promoted TF-IDF (Promoted-TF-IDF) and cosine similarity method for text classification. In our model, with the pre-trained word segmentation tool, we apply PTF-IDF method to judge which words play key roles in text classification to capture the key components in category. We also apply Cosine Similarity algorithm to judge similarity between text and category. We conduct experiments on commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets.

    Keywords: Text classification, TF-IDF, Computer Application, Natural Language Processing

  • An attention mechanism-based multi-scale network crowd density estimation algorithm

    $15.00

    Abstract—It is becoming more and more important to calculate the people number in terms of the requirement for the safety management, because that the crowd gathering scenes are common whether or not it is daily urban traffic or some special gatherings. Calculating the people number in high-density crowd is a very difficult challenge due to the diversity of ways people appear in crowded scenes. This paper proposes a multi-branch network which combines the dilated convolution and attention mechanism. By combining dilated convolution, the context information of different scales of the crowd image are extracted. The attention mechanism is introduced to make the network pay more attention to the position of the head of the crowd and suppress the background noise, so as to obtain a higher quality density map. Then add all the pixels in the density map to get the total people number. Through a large number of experiments, this network can better provide effective crowd density estimation features and improve the dissimilarity of density map distribution, which has stronger robustness.

    Keywords: Attention mechanism; Crowd density estimation; Dilated convolutional

  • Vehicle Detection Method Based on ADE-YOLOV3 Algorithm

    $15.00

    Abstract: Aiming at the problem of repeated detection of YOLOV3 algorithm in vehicle detection, the ADE-YOLOV3 vehicle detection algorithm is proposed. The algorithm uses K-means clustering algorithm to determine the number of target candidate frames and aspect ratio according to the inherent width and height characteristics of the vehicle. Then, according to the results obtained by clustering, the anchor parameters are reset, which makes the ADE-YOLOV3 network have certain pertinence in vehicle detection. Finally, the migration learning method is used to improve the network structure, and the optimal weight model is obtained, which improves the training precision of the model. The experimental results show that compared with the original YOLOV3 method, the mAP is increased from 91.4% to 95%, the repeated detection rate is reduced from 5.6% to 2.1%,and the detection speed by 50fps. The detection accuracy is improved and the problem of repeated detection is effectively avoided.

    Keywords: Vehicle Detection; Deep Learning; YOLOv3; K-means; Migration Learning

  • Construction of Student model based on BP neural network

    $15.00

    Abstract: With the development of personalized learning, the construction of student models is becoming more and more important. At present, there are still problems in the student model that the characteristics are single and the indicators of each dimension are not clear. In this paper, learners will be analyzed from the perspective of student characteristics. And BP (Back Propagation) neural network algorithm will be used to establish a personalized student model. This paper first constructs the feature system of the student model from six dimensions. Secondly, the initial data is obtained through questionnaire survey, and the data is initialized to obtain 30 feature vectors as input to BP neural network. The output of the network is a learner type, which is divided into 36 categories. The construction of the student model will have certain practical significance for realizing the effectiveness of personalized education in distance education.

    Keywords: Student Model, Personalized Learning, BP Neural Network, Student Characteristics