Measurement Sensors最新文献

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

The purpose of network forensics is forensic analysis of traces after hacker attacks, obtaining electronic evidence of Cyber Crime, and accusing hackers by electronic evidence. Both foreign and domestic, the research of network forensics is in the beginning stage, and the technology of network forensics is developed in this background. An analysis system of fuzzy decision tree based network forensics, network forensics personnel to assist in the network environment of computer crime forensics analysis. The experimental results of this method are given and compared with the existing methods of the analysis results. The experimental results show that this system can classify most kinds of events (the average correct classification rate. 91.16 %), can provide comprehensible information for network forensics personnel, to assist forensic personnel for rapid and efficient analysis of the evidence.

As a cross-perception and cognitive research field in video understanding, motion training feature recognition is a very challenging task to establish a good spatio-temporal modeling of human motion due to the uncertainty of human motion speed, start and end time, appearance and posture, as well as the interference of physical factors such as lighting, perspective and occlusion. The purpose of this study is to use artificial intelligence data mining technology to study the feature recognition application of iot voice devices in sports training. Install the sensor in the appropriate position according to the position and posture to be measured. Ensure that the sensor can accurately measure the relevant features and maintain a stable connection. Using iot voice devices for data acquisition, sensors collect data on relevant features in real time to transmit the data to a cloud platform or local processing device via a wireless connection. By analyzing and mining the data collected by iot voice devices, we hope to effectively identify the characteristics of sports training and provide accurate feedback and guidance for athletes and coaches. The experimental results show that the iot voice device based on artificial intelligence data mining has achieved good results in the feature recognition application of sports training. Through the analysis of sports training data, we can successfully identify the characteristic patterns of different movements, and accurately predict the athletic state and posture of athletes.

Traditional financial risk detection methods are mainly based on statistical models and market data, in which sensor network has a wide application prospect. The research can improve the accuracy and efficiency of financial risk detection by using the data and information of sensor network. By making full use of the data collection capabilities of sensor networks to obtain more comprehensive and accurate financial data, it helps to identify potential risk factors more accurately. This paper introduces DTW (Dynamic Time warping) algorithm as the main financial risk detection method, which can effectively capture the similarity between time series data and apply it to the financial data obtained by sensor network. Through regularization and matching of time series data, abnormal changes and abnormal patterns can be identified, so as to timely warn and control financial risks. By comparing the data of sensor network with that of traditional methods, we found that the financial risk detection method based on DTW algorithm and sensor network has higher accuracy and efficiency, and can identify potential risk factors more accurately.

With the rapid development of cloud computing technology, sensor networks are becoming more and more important in the application of economic statistics in enterprises. This paper aims to discuss the application prospect and effect of cloud computing detection based on sensor network in enterprise economic statistics. The research identifies the needs and objectives of enterprise economic statistics, and determines the appropriate scenarios for the application of sensor networks. The sensor nodes are arranged and distributed in the areas that need to be monitored, and the sensor nodes are connected to the cloud computing platform through wireless communication technology. Sensor nodes collect data on a regular basis and transmit it via wireless communication to the cloud computing platform. The cloud computing platform will store and process the received data, and can use machine learning and data mining algorithms to analyze and predict the data. The collected data are tested and analyzed, and the corresponding results are obtained. Results The advantages of cloud computing detection based on sensor network in the application of enterprise economic statistics are summarized, and further research directions and prospects are put forward.

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks.

Simulation results

The energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

Regarding the fault detection problem of the displacement sensor, an enhanced fireworks algorithm with information crossover and conversion factor (EFWA-IC) is proposed in this paper. In EFWA-IC, an information crossover strategy is proposed to maintain population diversity. This strategy is based on the predatory behavior of carnivores in nature. In addition, a conversion factor is set to control whether the explosion operator is executed or not to provide a more reasonable search. To fully evaluate the performance of EFWA-IC, a range of tests are carried out based on the CEC2017 and 23 classical test functions. The results show that the performance of EFWA-IC is better than other state-of-the-art optimization algorithms in terms of solution accuracy, convergence speed, and stability. Finally, EFWA-IC is utilized to optimize the particle filter (PF) to establish a fault detection model of displacement sensor in the continuous casting mold. The simulation experiment result of field data manifests that EFWA–IC–PF can accurately detect faults in the displacement sensor. For bias faults, the RMSE of the EFWA–IC–PF model is 0.14468, and the false alarm rate (FAR) and missed detection rate (MDR) are 1 % and 0.5 %, respectively. For stuck faults, the RMSE is 1.00148, and the FAR and MDR are 0.88 % and 1 %, respectively.

The combined system based on multiple MEMS sensors is a miniature measurement system used for dynamic output and display of 3D information about the user's posture. It is mainly used for various Tai Chi movement posture calculation simulation research, wearable devices, etc. This article explores MEMS sensor technology, focusing on MEMS sensor data processing, Tai Chi movement position calculation and fusion calculation positioning algorithm. Due to the high noise characteristics of MEMS sensor devices, time series analysis is used to model MIMU signals and Kalman filtering is optimized. As a research field, simulation of Tai Chi movement appears in the intersection of biomechanics, robotics and computer science. The purpose is to create a computer model to simulate the natural and real body movements of the human body under certain conditions. In addition to creating special effects, Tai Chi movement posture calculation simulation can also be used for operation training and research on body structure. This article first introduces the typical applications of several MEMS sensor combinations, and then introduces the key technology of studying Tai Chi movement simulation. The kinematics and mechanics data of Tai Chi are obtained using biomechanical measurement technology, while the individual simulation of Tai Chi dynamics is realized in a certain mode of the machine. By creating a kinematic model of the human upper limb, and finally creating a flexible machine that imitates the human upper limb, to analyze the kinematic characteristics of the human upper limb, and cleverly realize the imitation of active interaction, the simulation of human movement and the solution of Tai Chi movement posture Simulation.

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

Ensuring the accuracy and reliability of ventilation parameter monitoring is pivotal for the development of intelligent ventilation systems. To attain a visual representation of airflow, solving the challenge of airflow reconstruction necessitates the strategic use of a limited number of sensors. In addressing these concerns, this article introduces an optimization approach for ventilation airflow leveraging the Breadth-First Search (BFS) algorithm. Additionally, it proposes an optimization distribution method for mine ventilation sensors, grounded in the Independent Cut Set algorithm. Research has found that compared to the traditional PSO algorithm, the BFS algorithm produces a higher optimal air volume solution when optimizing the air volume; Comparatively, the proposed algorithm exhibits significantly shorter average running times than the Particle Swarm Optimization (PSO) algorithm. It boasts the highest average convergence rate, ensuring superior accuracy, and possesses a notable capability to escape local minima, facilitating the acquisition of optimal solutions. Leveraging the independent cut set algorithm optimizes the calculation process through matrix operations. Exploiting the properties of matrices allows for a more rapid and intuitive resolution of sensor localization problems.