Showing 61–72 of 180 results
A Medicine Cold Chain Monitor System Based on LoRa Wireless TechnologyOpen Access
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 SimilarityOpen Access
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 algorithmOpen Access
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 AlgorithmOpen Access
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 networkOpen Access
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
A Game-based Upper Limb AROM Measurement System for Older AdultsOpen Access
Abstract: At present, the aging problem is becoming more and more serious, which undoubtedly causes people’s attention to the life quality of the elderly. Active range of motion (AROM) is an important index to judge the ability of daily functional activities of the elderly. Therefore, it is very important to obtain the AROM data of the elderly quickly and accurately. The traditional method of measuring the range of motion (ROM) is to measure the passive range of motion (PROM) with a goniometer by nurses, which cannot accurately reflect the elderly’s active ability in the daily life. In order to overcome the shortcomings of traditional methods, the AROM measurement system has been developed. However, in the current AROM measurement system, the elderly is guided to several specific postures, ignoring the positive role of interest in stimulating the elderly to participate in ROM measurement. In this paper, we propose a game-based upper limb AROM measurement system. In the system, the joint coordinates of the player are measured by the depth image sensor, and the AROM is calculated automatically and objectively by using the coordinates. From the experimental results, the average flection value measured by our system is 21.6 degrees larger than that measured by the goniometer. The average abduction value measured by our system is 24.1 degrees larger than that measured by goniometer. This result means that the elderly can stretch better through a game in our measurement system. In order to encourage the elderly to participate more in the game, a questionnaire survey was conducted on the elderly’s views of the game. From the analysis results, we find that the merit factor and design factor of the game have a great impact on the players’ experience in the game. The research results provide us with ideas for the improvement of the measurement system in the
The Best Teaching Method to Improve Students Score in MathematicsOpen Access
Abstract: Mathematics the foundation of disciplines involved in the development of technologies that change the way of our living style is not an easy subject for many students. Some students cannot acquire the minimum credits require to pass to the next grade. As a result they are forced to repeat the same class again and consequently will have a negative impact on their curriculum vitae. In order to help students obtain not only the require credits to pass on to the next grade but also to gain mathematics skills, understand the mathematics concepts for their future career. Many teachers have devoted their efforts in the development of different methods of teaching mathematics where some of them have been tested with promising results while other not and the challenge is still going on. In order to help mathematics teachers having difficulties helping students increase their mathematics skills or scores, we propose the Best Teaching Method to Improve Students Score in Mathematics. This method proposes: 1) how an effective syllabus can be done to help both students and teachers to enjoy their mathematics lesson. This will eventually involve teacher to do students hearing to get an idea of their background in mathematics for the pasted academic year. 2) the importance of learning environment preparation which will help students to ask any questions with no fear and eventually will encourage passive students to participate in the class. 3) Introduction of Sudden Short Text (SST) to force students to revise their mathematics lesson each time before coming to a class. 4) Introduction of Group Assessment Test (GAT) to help each students to assess themselves in the working group and acquire communication skills as well. This method has been proved promising with an increasing students’ scores of 10% for SST and 4% for the GAT as shown in the results section.
A novel neural interfacing electrode array for electrical stimulation and simultaneous recording of EEG/EMG/ENGOpen Access
Abstract: Neural interface is man-made information pathway through which biological nerve system could communicate directly with electromechanical devices including computer, robot and even cyborg. This paper introduces a novel neural interfacing electrode array capable of bidirectional information transmission and multi-signals recording. The novel flexible electrode array with 32 channels enables close-looped control and feedback neural interfacing researches with the capability of both electrical stimulation and simultaneous recording of electroencephalogram (EEG), electromyogram (EMG), electroneurogram (ENG) signals. The electrochemical impedance spectroscopy (EIS) measurement of the electrode array was carried out to evaluate the electrode array electrochemical performance in the electrophysiology frequency range. Electrical stimulation to peripheral nerve was performed with various stimulation configuration of ENG electrode site pairs to produce distinct activation patterns. Muscle action potentials of the gastrocnemius of the hind limb indicated different configuration of electrode site pairs could generate distinct stimulating effect. In addition, three groups of in vivo experiments were conducted to demonstrate the recording ability of the electrode array for nerve signals. The data from 32 channels verified the effectiveness of this flexible electrode array in simultaneous recording of EEG/EMG/ENG in vivo.
Construction of a focused ultrasound neuromodulation system for the treatment of epileptic seizureOpen Access
Abstract: Thus far, Ultrasound has been proved to be useful for noninvasively stimulating brain activity and has hope to play a positive role in the treatment of neurological diseases. Epilepsy is a neurological disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, and sometimes loss of awareness. In this paper, we established a focused ultrasound (FUS) neuromodulation system for the treatment of epilepsy. We used the chemically-induced rat epilepsy model to explore the effect of pulsed focused ultrasound on epilepsy, and obtained preliminary experimental results. We have proved the feasibility of this system through experiments, which can be used in the treatment of epilepsy by ultrasound neuromodulation.
Model for Non-contact Blood Pressure Measurement Using the Facial Feature Amount based on Amplitude and Phase AnalysisOpen Access
Abstract: To monitor the daily blood pressure, developing a non-contact method for measuring blood pressure is necessary. In a previous study, we proposed a novel method that described the vascular structure of an entire human face as an electric circuit based on amplitude and phase analyses using visible and thermal images of the face. However, the model developed by that method did not consider the order of blood flow because the model applied at random the extracted features. In the present study, it considers a model that incorporates the order of blood flow utilizing amplitude and phase analyses. As a result, the estimated accuracy is improved by considering the order of blood flow. Higher accuracy requires more detailed vascular structure information of the face. It concluded that the facial feature, which is related to the blood pressure, can be obtained by the CEOF analysis.
Construction of an individual model for estimating blood pressure using independent components of facial skin temperature considering time variationOpen Access
Abstract: The objective of this study was to construct an individual model for estimating blood pressure (BP) using independent components of facial skin temperature considering time variation. In our previous study, an individual model was constructed for estimating BP by applying independent component analysis to facial skin temperature of each subject. However, in this previous study, time variations in facial skin temperature were not considered. Facial skin temperature is assumed to be related to blood flow over time as blood helps in transporting heat in the body. Therefore, the accuracy of the BP estimation model can be expected to improve if the variation of facial skin temperature caused by blood flow over time considered in addition to only the facial skin temperature. Consequently, the accuracy of the proposed model with the aforementioned considerations was found to be better than that of without these considerations. Therefore, the BP was accurately estimated using the proposed approach.
Research on Gesture Based on GA – SVMOpen Access
Abstract: Surface electromyography (sEMG) is a kind of weak electrical signal generated by muscle activity, which contains information of gesture and is widely used in prosthetic control, rehabilitation and medical treatment. The difference between different motion patterns can be reflected by the different sEMG characteristics, so the recognition of human motion can be studied. Four time-domain features including absolute mean value, waveform length, zero-crossing number and root mean square value were extracted from the double Myo arm-ring data set in Ninapro benchmark database. Classification and identification were performed by using the Support Vector Machine (SVM) optimized by Genetic Algorithms (GA). Experimental results showed that the optimized SVM classification had a better effect.