JournalTOCs

Open Journal of Geology
Number of Followers: 13

This is an Open Access Journal

Open Access journal
ISSN (Print) 2161-7570 - ISSN (Online) 2161-7589
Published by Scientific Research Publishing

[243 journals]

Multimedia Concepts on Object Detection and Recognition with F1 Car
Simulation Using Convolutional Layers
- Abstract: This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.
  PubDate: Thu, 09 Dec 2021 14:35:01 +000
A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET
- Abstract: The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the proposed approach is tested on the open-source network and traffic simulation tools to verify the results. The three different traffic scenarios were deployed on Simulation of Urban Mobility (SUMO) and tested using NS3.2. After comparing them, the results were satisfactory and it is found that the GAACO algorithm has performed better in all three different traffic scenarios. The realistic traffic network scenarios are taken from Dehradun City with four performance metric parameters including the average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network. The experimental results conclude that the proposed GAACO algorithm outperforms particle swarm intelligence (PSO), ACO, and Ad-hoc on Demand Distance Vector Routing (AODV) routing protocols with an average significant value of 1.55%, 1.45%, and 1.23% in three different VANET network scenarios.
  PubDate: Thu, 09 Dec 2021 12:05:02 +000
Discussion on Teaching Method of Digital Image Processing Technology Based
on PBL
- Abstract: A required course for students majoring in digital media technology, computer science and technology, or artificial intelligence is “digital image processing technology.” Aviation, medical image processing, intelligent manufacturing, and many more fields may benefit from the knowledge and skills gained in this course. It contains the qualities of “many conceptions, numerous principles, and various formulae,” according to the curriculum. As a result, traditional teaching techniques only pay attention to the explanation of theoretical information, which may easily lead students to create uninteresting feelings; they have abandoned the in-depth investigation and learning of the course material. The PBL approach is used to provide an interest-driven and problem-solving-driven grounded teaching technique that naturally connects the theoretical foundation with real-world examples and problems. We utilize case teaching to assist students better comprehend theoretical information and to teach them how to apply theoretical knowledge to actual difficulties they encounter in their lives. During the course of many semesters of practice, we discovered that our teaching approaches are quite popular with students. The deployment of a teaching style focused on problem-based learning has resulted in significant improvements in students’ learning initiative, practical ability, and innovative ability.
  PubDate: Thu, 09 Dec 2021 11:50:01 +000
Image Analysis Technology in the Detection of Particle Size Distribution
and the Activity Effect of Low-Silicon Copper Tailings
- Abstract: To speed up the comprehensive utilization and treatment of copper tailings, the digital image processing technology is proposed in this study to detect the low-silicon copper tailings (LSCT) using a scanning electron microscope (SEM), and the particle size distribution (PSD) and the activity of LSCT are analysed under the action of mechanical force. Firstly, the current status and application of copper tailings are introduced, and the influence of the particle size of LSCT on its practical application performance is explained. Secondly, the LSCT SEM image target recognition model is designed based on the convolutional neural network (CNN), and the model parameters and the reference CNN are selected. Finally, the experimental process is designed, a SEM image data set of LSCT is prepared, the model is trained through the training set, and the image recognition test is performed on the produced data set. The experimental results show that when the number of iterations of the CNN is 10, the accuracy of model recognition can be guaranteed. After the action of mechanical force, the PSD of LSCT is mainly concentrated at 1 μm~100 μm; that around 1.4 μm~10 μm is the largest, and the PSD of LSCT around 1.4 μm increases with the increase of action time of mechanical force, but the PSD of the LSCT begins to increase when the grinding time exceeds 150 minutes, and the activity of LSCT reaches the maximum (75.545%) at 150 minutes. The average accuracy of SEM image detection of the model is 86.97%, and the model based on DenseNet shows better recognition accuracy than other models. This study provides a reference for analysing the PSD of LSCT.
  PubDate: Thu, 09 Dec 2021 06:20:01 +000
VarDefense: Variance-Based Defense against Poison Attack
- Abstract: The emergence of poison attack brings a serious risk to deep neural networks (DNNs). Specifically, an adversary can poison the training dataset to train a backdoor model, which behaves fine on clean data but induces targeted misclassification on arbitrary data with the crafted trigger. However, previous defense methods have to purify the backdoor model with the compromising degradation of performance. In this paper, to relieve the problem, a novel defense method VarDefense is proposed, which leverages an effective metric, i.e., variance, and purifying strategy. In detail, variance is adopted to distinguish the bad neurons that play a core role in poison attack and then purifying the bad neurons. Moreover, we find that the bad neurons are generally located in the later layers of the backdoor model because the earlier layers only extract general features. Based on it, we design a proper purifying strategy where only later layers of the backdoor model are purified and in this way, the degradation of performance is greatly reduced, compared to previous defense methods. Extensive experiments show that the performance of VarDefense significantly surpasses state-of-the-art defense methods.
  PubDate: Thu, 09 Dec 2021 05:05:01 +000
Current-Fed Bidirectional DC-DC Converter Topology for Wireless Charging
System Electrical Vehicle Applications
- Abstract: This paper compares the efficiency of a modified wireless power transfer (WPT) system with a current-fed dual-active half-bridge converter topology and a complete bridge converter topology for a current-fed resonate compensation network with current sharing and voltage doubler. Full-bridge topologies are widely used in current WPT structures. The C-C-L resonate compensation networks for dual-active half-bridge converter and full-bridge converter topologies are built in this paper on both the transmitter and receiver sides. Due to higher voltage stress around inverter switches, series-parallel (S-P) tanks are not recommended for current-fed topologies because they are not ideal for medium power applications. A series capacitor is connected to reduce the reactive power absorbed by the loosely coupled coil. As a consequence, the C-C-L network is used as a compensation network. Dual-active half-bridge topology is chosen over full-bridge topology due to the system’s component count and overall cost. Soft-switching of the devices is obtained for the load current. The entire system is modelled, and the effects are analysed using MATLAB simulation.
  PubDate: Wed, 08 Dec 2021 15:05:01 +000
Information Fusion and Its Intelligent Sensing for Learning Intervention
Model of Educational Big Data
- Abstract: With the continuous development of science and technology, a large number of devices containing high technology began to appear in people’s lives. With the popularity of big data, it not only drives the development of the whole information industry in society but also leads to different degrees of innovation and development in the reform industry worldwide. The purpose of this paper is to study how to use information fusion and its intelligent sensing technology to play an active role in the education industry, to help students identify problems in the learning process, to give timely intervention and guidance, and to help students complete their learning tasks with high quality. This paper proposes to use information fusion and its intelligent sensing technology to take advantage of learning analytics to collect, organize, analyze, and guide the learning data generated by students in the learning process and then to generate interventions that can have an impact on learning and improve learning methods for students. The experimental results of this paper show that after the learning intervention, the students’ frequency in discussion and communication was 72 in the first four weeks and reached 300 after the intervention, and the learning resources changed from 95 to 370 after the learning intervention, which is very significant progress.
  PubDate: Wed, 08 Dec 2021 15:05:01 +000
Service Partition Method Based on Particle Swarm Fuzzy Clustering
- Abstract: It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.
  PubDate: Wed, 08 Dec 2021 12:05:00 +000
Research on News Text Classification Based on Deep Learning Convolutional
Neural Network
- Abstract: Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.
  PubDate: Wed, 08 Dec 2021 12:05:00 +000
Construction of Quality Virtual Backbones with Link Fault Tolerance in
Wireless Sensor Networks
- Abstract: Wireless sensor networks (WSNs) are extensively utilized in various circumstances. For applications, the construction of the virtual backbones (VBs) of WSNs has attracted considerable attention in this field. Generally, a homogeneous WSN is formulated as a unit disk graph (UDG), and the VB of the corresponding WSN is modeled as a connected dominating set (CDS) in the UDG. In certain applications, communication between sensors in a network may fail for various reasons, such as sensor movement, signal interference, and the appearance of obstacles. Consequently, a CDS in a UDG should possess fault tolerance on the edges. In this paper, we introduce a new concept called the 2 edge-connected 2 edge-dominating set (-ECDS); then, we design an approximation algorithm for computing -ECDSs in UDGs, the performance ratio of which is 30.51. By means of simulations, we compare our algorithm and existing algorithms in terms of the CDS size, running time, success rate, and network lifetime. The simulation results indicate that our algorithm exhibits better performance and is more suitable for constructing a VB with edge fault tolerance in a WSN.
  PubDate: Wed, 08 Dec 2021 10:20:01 +000
A Novel Smart Healthcare Monitoring System Using Machine Learning and the
Internet of Things
- Abstract: The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age-related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long-term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real-time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signals. It was proposed to automatically remove outlier points generated by movement artefacts from the blood pressure signal. Next, eleven features taken from the oscillometric waveform envelope were utilised to analyse the relationship between diastolic blood pressure (SBP) and systolic blood pressure (DBP). This paper validates a proposed computational method for estimating blood pressure. The proposed architecture leverages sophisticated regression to predict systolic and diastolic blood pressure values from PPG signal characteristics.
  PubDate: Wed, 08 Dec 2021 10:20:01 +000
HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News
Detection
- Abstract: Fake news can cause widespread and tremendous political and social influence in the real world. The intentional misleading of fake news makes the automatic detection of fake news an important and challenging problem, which has not been well understood at present. Meanwhile, fake news can contain true evidence imitating the true news and present different degrees of falsity, which further aggravates the difficulty of detection. On the other hand, the fake news speaker himself provides rich social behavior information, which provides unprecedented opportunities for advanced fake news detection. In this study, we propose a new hybrid deep model based on behavior information (HMBI), which uses the social behavior information of the speaker to detect fake news more accurately. Specifically, we model news content and social behavior information simultaneously to detect the degrees of falsity of news. The experimental analysis on real-world data shows that the detection accuracy of HMBI is increased by 10.41% on average, which is the highest of the existing model. The detection accuracy of fake news exceeds 50% for the first time.
  PubDate: Wed, 08 Dec 2021 09:05:02 +000
The Choice of Multimodal Transport Mode of Agricultural By-Product
Logistics in Land-Sea New Corridor in Western China Based on Big Data
- Abstract: The “new land-sea corridor” has brought cross-border facilitation and increased trade financing channels. It not only has long been a road-sea transportation corridor but also has been upgraded to a trade corridor. As one of the most inclusive cities, Chongqing, with the help of this channel, can bring more dividends and international resources to the entire western region. Through the logistics base of Singaporean-Chongqing cooperation in multimodal transport, Chongqing can play an important role as a channel operation center and an important logistics hub. Some international shipping resources will be extended to Chongqing, letting the whole western region share the agricultural by-products brought by Southeast Asian countries. Multimodal transport is a common mode of transport in international trade; it combines various modes of transport organically, brings into play the advantages of various modes of transport, and can reduce costs to a large extent. At present, multimodal transport is mostly used for importing and exporting goods; multimodal transport is not widely used in agricultural by-product logistics transportation. Multimodal combination will be used in the transportation of agricultural by-product logistics; it can avoid the shortcomings of simply using road transportation and make the logistics transportation cost of agricultural by-products lower and management more convenient. Based on the large data, this paper considers factors such as route factors, transfer mode selection, and window meeting time in the transfer process; a mathematical model and advanced colony ant algorithm can be used to solve the transfer optimization problem of a very large fleet of agricultural by-product logistics. This solution can provide instructions and suggestions for companies that should increase relevant scientific research.
  PubDate: Wed, 08 Dec 2021 09:05:01 +000
LB-DDQN for Handover Decision in Satellite-Terrestrial Integrated Networks
- Abstract: The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.
  PubDate: Wed, 08 Dec 2021 07:35:00 +000
Research on Subway Pedestrian Detection Algorithm Based on Big Data
Cleaning Technology
- Abstract: The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images’ quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model’s performance.
  PubDate: Tue, 07 Dec 2021 15:50:01 +000
SPDNet: A Real-Time Passenger Detection Method Based on Attention
Mechanism in Subway Station Scenes
- Abstract: In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results.
  PubDate: Tue, 07 Dec 2021 13:35:01 +000
Enhancement of Predicting Students Performance Model Using Ensemble
Approaches and Educational Data Mining Techniques
- Abstract: Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.
  PubDate: Tue, 07 Dec 2021 07:35:01 +000
Applications of Deep Learning on Topographic Images to Improve the
Diagnosis for Dynamic Systems and Unconstrained Optimization
- Abstract: Studies carried out by researchers show that data growth can be exploited in such a way that the use of deep learning algorithms allow predictions with a high level of precision based on the data, which is why the latest studies are focused on the use of convolutional neural networks as the optimal algorithm for image classification. The present research work has focused on making the diagnosis of a disease that affects the cornea called keratoconus through the use of deep learning algorithms to detect patterns that will later be used to carry out preventive detections. The algorithm used to perform the classifications has been convolutional neural networks as well as image preprocessing to remove noise that can limit neural network learning, resulting in more than 1900 classified images out of a total of >2000 images distributed between normal eyes and those with keratoconus, which is equivalent to 92%.
  PubDate: Tue, 07 Dec 2021 07:35:01 +000
Virtual Reality Interactive Method and Device Based on Wireless
Communication Tracking
- Abstract: Virtual reality is a computer system that creates a virtual world and then experiences through multiple senses. It is generated by a computer and stimulated by perception systems such as hearing, vision, touch, taste, and smell, providing users with a personal experience. Human-computer interaction is one of the core technologies of virtual reality. Wireless communication is the transmission of communications over long distances between multiple nodes without propagation through conductors or cables and can be carried out using radios, radios, etc. Wireless communication includes a variety of fixed, mobile, and portable applications such as two-way radios, mobile phones, personal digital assistants, and wireless networks. Other examples of radio wireless communication are GPS, garage door remotes, wireless mice, etc. Most wireless communication technologies use radio, including Wi-Fi with distances of just a few meters, but also deep space networks that communicate with Voyager 1 and distances of over millions of kilometers. With the continuous development of sensors and other supporting hardware facilities, the current development of human-computer interaction in virtual reality has made rapid progress. In the research to be conducted in this article, the virtual reality system used in this article cleverly integrates the three characteristics of immersion, interactivity, and conception, so that the experimenter can obtain more realistic data in comparison. To this end, this article first gives a general introduction to virtual reality technology and wireless communication tracking technology and then explains how to use wireless communication tracking technology to make the virtual reality interactive system smoother and smoother, as well as the introduction of its devices. This article explores and analyzes the possible or existing problems of wireless communication tracking technology in virtual reality interaction, hoping to contribute to the wider application of wireless communication tracking technology in virtual reality interaction. The positioning experiment on the wireless mobile signal identification points can be obtained. Among the 40 sensor nodes that are randomly deployed, when the interval of adjusting the mobile signal identification point to broadcast the current position information is 5 s, the average positioning error of the node is about 1.5 m; when the interval is 3 s, the average positioning error of the node is about 1.76 m. It can be seen that the positioning error of the node increases as the interval between the mobile signal identification points increases, which is consistent with the simulation detection result. When the node position of the target signal identification point is chosen to calculate does not just stay on the node communication circle, it introduces a certain localization distance difference, and the further the target signal identification point is from the position of the signal circle, the greater the error. Irregularity of RSS due to environmental changes analyzes the maximum error and provides the factors influencing the error and analyzing the maximum error and provide the factors that influence it.
  PubDate: Mon, 06 Dec 2021 15:35:01 +000
An Improved Simulated Annealing Particle Swarm Optimization Algorithm for
Path Planning of Mobile Robots Using Mutation Particles
- Abstract: Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.
  PubDate: Mon, 06 Dec 2021 15:35:01 +000
Hybrid Element Heuristic Algorithm Optimizing Neural Network-Based
Educational Courses
- Abstract: The rapid development of computer networks has enabled information technology to penetrate many fields, providing unprecedented opportunities for all aspects of our lives. In order to allow students to acquire necessary knowledge and skills through efficient learning, this article studies the design and development of educational technology courses based on hybrid metaheuristic algorithms to optimize neural networks. This paper proposes a metaheuristic algorithm and explains the simulated annealing algorithm and microregular annealing algorithm in detail. Using these algorithms, a mathematical model of the normal scheduling problem was also constructed, and the mathematical model was applied to the design and development of educational technology courses. In addition, the neuron model in the neural network and the activation function of the neural network are discussed from various aspects. In the experiment, according to the needs of students, a learning platform for educational technology courses was designed and developed. The experimental results of this article show that there are significant differences in the starting point of the ability level of learners for different majors. Education majors have a higher level of understanding of educational technology courses; 38.20% of students know well, while art majors have a low level of understanding of education technology courses, with 36.05% of students majoring in art.
  PubDate: Mon, 06 Dec 2021 15:35:01 +000
Performance Analysis of MEC Based on NOMA under Imperfect CSI with
Eavesdropper
- Abstract: Mobile edge computing (MEC) is becoming more and more popular because of improving computing power in virtual reality, augmented reality, unmanned driving, and other fields. This paper investigates a nonorthogonal multiple access- (NOMA-) based MEC system, which is under imperfect channel state information (ipCSI). In this system model, a pair of users offloads their tasks to the MEC server with the existence of an eavesdropper (Eve). To evaluate the impact of Eve on the performance of the NOMA-MEC system, the secrecy outage probability (SOP) expressions for two users with the conditions of imperfect CSI and perfect channel state information (pCSI) are derived. In addition, both throughput and energy efficiency are discussed in the delay-limited transmission mode. Simulation results reveal that (1) due to the influence of channel estimation errors, the secrecy outage behaviors of two users under ipCSI conditions are worse than those of users with pCSI; (2) the secrecy performance of NOMA-MEC is superior to orthogonal multiple access- (OMA-) aided MEC systems; and (3) the NOMA-MEC systems have the ability to attain better system throughput and energy efficiency compared with OMA-MEC.
  PubDate: Mon, 06 Dec 2021 15:35:00 +000
Research on the Difficulties and Countermeasures of the Practical Teaching
of Ideological and Political Theory Courses in Colleges and Universities
Based on Wireless Communication and Artificial Intelligence Decision
Support
- Abstract: The rapid development of wireless communication technology has caused the computing speed and performance of information terminals to increase exponentially. Artificial intelligence technology emerged in this context. As an efficient work system, artificial intelligence has been introduced into many areas of social life. Especially in the field of education, it has made outstanding contributions. At this stage, the conflict between Eastern and Western civilizations is intensifying. As the most active ideological group, college students have not yet formed a scientific understanding of the pros and cons of Eastern and Western civilizations, and it is easy for them to fall into confusion in this kind of ideological turmoil. This requires that the ideological and political education in our country’s colleges and universities effectively play a role, strengthen ideological guidance, and create a new system of practical teaching of college ideological and political theory courses under the background of wireless communication. Ideological and political education, as one of the courses with outstanding characteristics in our country’s university education courses, has a guiding role that cannot be ignored for university students. This paper uses a questionnaire survey method to conduct an online survey of 500 college students from 6 universities in Shanghai to obtain first-hand information on the current ideological and political practice courses of Chinese universities and then sort out the emergence of artificial intelligence-based decision support systems in China. The practical class encountered difficulties and explored strategies to alleviate this dilemma, hoping to provide a useful reference for the development of ideological and political teaching in China in the future.
  PubDate: Mon, 06 Dec 2021 10:50:01 +000
Positioning Control Algorithm of Vehicle Navigation System Based on
Wireless Tracking Technology
- Abstract: To improve the accuracy and reliability of the on-board navigation system positioning, the positioning control algorithm of vehicle navigation system based on wireless tracking technology is proposed. By using modern information fusion technology, the accurate positioning of vehicle integrated navigation is realized, and the design goal of omnidirectional, all weather, and self-contained positioning function is realized. Finally, the test shows that the accuracy and reliability of the positioning control algorithm of vehicle navigation system based on wireless tracking technology are improved than existing point system, speed measurement accuracy can reach 0.02 m/s, and positioning accuracy is about 18 meters. The vehicle operation efficiency and safety are greatly improved, and the traffic capacity is improved. And the traffic congestion is effectively alleviated, which provides reliable guarantee for the realization of traffic management automation and intelligent vehicle driving.
  PubDate: Mon, 06 Dec 2021 09:05:01 +000
Multifeature Metric Learning Based on Enhanced Equidistance Embedding for
Electroencephalogram Recognition of Epilepsy
- Abstract: Mobile edge computing (MEC) has the ability of pattern recognition and intelligent processing of real-time data. Electroencephalogram (EEG) is a very important tool in the study of epilepsy. It provides rich information that can not be provided by other physiological methods. In the automatic classification of EEG signals by intelligent algorithms, feature extraction and the establishment of classifiers are both very important steps. Different feature extraction methods, such as time domain, frequency domain, and nonlinear dynamic feature methods, contain independent and diverse specific information. Using multiple forms of features at the same time can improve the accuracy of epilepsy recognition. In this paper, we apply metric learning to epileptic EEG signal recognition. Inspired by the equidistance constrained metric learning algorithm, we propose multifeature metric learning based on enhanced equidistance embedding (MMLE3) for EEG recognition of epilepsy. The MMLE3 algorithm makes use of various forms of EEG features, and the feature weights are adaptively weighted. It is a big advantage that the feature weight vector can be adjusted adaptively, without manual adjustment. The MMLE3 algorithm maximizes the distance between the samples constrained by the cannot-link, and the samples of different classes are transformed into equidistant; meanwhile, MMLE3 minimizes the distance between the data constrained by the must-link, and the samples of the same class are compressed to one point. Under the premise that the various feature classification tasks are consistent, MMLE3 can fully extract the associated and complementary information hidden between the features. The experimental results on the CHB-MIT dataset verify that the MMLE3 algorithm has good generalization performance.
  PubDate: Mon, 06 Dec 2021 07:50:01 +000
Wireless Network Sensing of Urban Surface Water Environment Based on
Clustering Algorithm
- Abstract: To improve the wireless sensing image extraction technology of urban surface water environment, a regional FCM clustering method combined with water index was proposed in this paper. The normalized water index (NDWI) was obtained by calculating the fusion multispectral wireless sensing image. Through the combination with normalized water index, fuzzy clustering results were obtained by RFCM algorithm proposed in this paper. The optimal threshold was selected to defuzzify the fuzzy clustering results, and finally, the extraction results of urban surface water were obtained. The accuracy of the proposed algorithm was compared with that of the traditional surface water extraction algorithm. The experimental results showed that the size of different neighborhood regions affected the water extraction accuracy. In W city, the kappa coefficient of MFCM16 was 0.41% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.33% higher than that of MFCM. In G city area, the kappa coefficient of MFCM16 was 1.81% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.7% higher than that of MFCM. Comparing the RFCM algorithm with other algorithms, the RFCM algorithm obtained the best experimental results, to reduce the “salt-and-pepper phenomenon” effect.
  PubDate: Mon, 06 Dec 2021 06:50:00 +000
A Collaborative Cache Strategy in Satellite-Ground Integrated Network
Based on Multiaccess Edge Computing
- Abstract: Multiaccess edge computing (MEC) provides users with a network environment and computing storage capacity at the edge of the network, ensuring a deterministic service with low delivery delay. This paper introduces a new satellite-ground integrated collaborative caching network architecture based on MEC and studies the caching strategy. On the ground side, the edge nodes (ENs) are deployed to the user side to form a hierarchical collaborative cache mode centered on the base station. On the satellite side, we utilize intelligent satellite ENs to precache and multicast the highly popular contents, reducing the initial content delivery delay. Under the constraints of the user demand and storage capacity, we study the deployment and cache scheme of ENs and establish the delivery delay minimization problem. To solve the problem, we propose a content update decision parameter for content cache update and transform the problem into improving the hit rate of ENs. Simulation results show that the proposed MEC network architecture and content caching scheme can increase the caching system hit rate to 64% and reduce the average delay by 32.96% at most.
  PubDate: Fri, 03 Dec 2021 11:35:01 +000
“Avatar to Person” (ATP) Virtual Human Social Ability Enhanced System
for Disabled People
- Abstract: How to make communication more effective has been underlined unprecedentedly in the artificial intelligence (AI) era. Nowadays, with the improvement of affective computing and big data, people have generally adapted to construct social networks relying on social robots and smartphones. Although the technologies above have been widely discussed and used, researches on disabled people in the social field are still very limited. In particular, facial disabled people, deaf-mutes, and autistic patients are still meeting great difficulty when interacting with strangers using online video technology. This project creates a virtual human social system called “Avatar to Person” (ATP) based on artificial intelligence and three-dimensional (3D) simulation technology, with which disabled people can complete tasks such as “virtual face repair” and “simulated voice generation,” in order to conduct face-to-face video communication freely and confidently. The system has been proven effective in the enhancement of the sense of online social participation for people with disabilities through user tests. ATP is certain to be a unique area of inquiry and design for disabled people that is categorically different from other types of human-robot interaction.
  PubDate: Fri, 03 Dec 2021 11:35:01 +000
Stock Trading System Based on Machine Learning and Kelly Criterion in
Internet of Things
- Abstract: The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.
  PubDate: Fri, 03 Dec 2021 10:35:01 +000
Application of E-Commerce Interactive Marketing Model Based on Distributed
Algorithm of Mobile Ad Hoc Network
- Abstract: With the development of the mobile Internet, e-commerce has become one of the important ways of daily consumption, but how to effectively use e-commerce for interactive marketing and increase sales is an important research direction. Mobile ad hoc distributed algorithms are introduced in this paper. Through sorting out the mode of e-commerce interaction influence, process marketing is performed from two-dimensional code, short message, business district, mobile search, Bluetooth, wireless network, and other methods, and interactive marketing is tried in various industries such as education, tourism, agriculture, catering, finance, and publishing, and simulation experiments are used to verify them. The simulation experiment results show that the mobile ad hoc distributed algorithms are effective and can support the e-commerce interactive marketing model.
  PubDate: Thu, 02 Dec 2021 11:20:01 +000

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