Integrity monitoring for undifferenced and uncombined PPP under the local environment

Integrity monitoring of precise point positioning (PPP) can provide tightly guaranteed absolute position error bounds for safety-critical applications. However, complex local environment makes PPP integrity monitoring much more challenging, such as urban canyons. Significant prone multipaths and low observation redundancy are main difficulties to the accuracy and the reliability of PPP. Therefore, we proposed a solution separation-based integrity monitoring algorithm, which is based on a single and dual frequency-mixed undifferenced and uncombined PPP model considering compensation for the multipath error distortion by Gaussian overbounding. Both the static and the kinematic data are utilized to test the proposed algorithm. The results show that the proposed algorithm can produce adequate protection level in horizontal and vertical directions. Furthermore, the proposed algorithm can obtain smoother protection level and positioning error under the dynamic local environment, and effectively suppress the misleading information.

A Triple-threshold-based Load Event Detection Algorithm for Non-intrusive Load Monitoring

Event detection is an important foundation of non-intrusive load monitoring algorithm. In this paper, the common household appliance load events are classified, and a new triple-threshold event detection algorithm is proposed aimed at solving the problems of false detection and missing detection in the practical application. Firstly, a low power threshold is used to realize high-sensitive detection of the load events, and secondly the detected events are spliced according to the time threshold to get the complete events. Thirdly, the high threshold is used to discriminate the complete event set to filter out the disturbance caused by load fluctuation. Finally, the results are modified with a correction logic. The test results carried with static data show that, the algorithm proposed in this paper is more accurate for positioning the time of putting into and cutting off load, which is conducive to improve the accuracy of transient interval interception of load events, and has advantages in detecting slow rising load events. In addition, the algorithm proposed in this paper has a small amount of calculation, which can meet the requirements of application in the hardware of smart meter.

Wireless monitoring algorithm of intelligent greenhouse based on fuzzy control

The existing greenhouse monitoring algorithm has a long delay time, so it is unable to carry out effective remote greenhouse monitoring, therefore, a new wireless monitoring algorithm based on the fuzzy control technolog was put forward, which was able to remotely monitor the greenhouse temperature, humidity and illumination data in real time. Firstly, the overall framework of greenhouse monitoring algorithm was built, including fuzzy clustering algorithm and sensing layer devices. Secondly, the temperature-humidity sensors and light sensitivity sensors in the sensing layer devices were used to deeply mine and optimize the parameters of temperature, humidity and light intensity in current greenhouse, so as to ensure the stability of subsequent transmission. Meanwhile, the corresponding perceptual recognition layer and broadband access method were designed, and GPRS technology was used to feed back the data information to the monitoring data layer through temperature-humidity sensors and light sensitivity sensors. Moreover, UDP protocol was taken as the data core transmission protocol, and the adaptive protection design algorithm was proposed to ensure the most reasonable transmission of monitoring data, get the current monitoring data of temperature, humidity and illuminance. The experimental results show that the maximum delay time of the algorithm is 46 s, which is far lower than the traditional algorithm, and the delay time of temperature monitoring is also lower than the traditional algorithm. It is results show that the response delay of remote intelligent greenhouse monitoring algorithm is low and the overall monitoring effect is ideal. The purpose of monitoring temperature, humidity and illumination can be achieved.

High-Sensitivity Ultrasonic Guided Wave Monitoring of Pipe Defects Using Adaptive Principal Component Analysis

Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects.

Low-Power FPGA Architecture Based Monitoring Applications in Precision Agriculture

Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on a monitoring algorithm for agricultural fields, an electronic architecture based on a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. We focused our study on our own dataset of agricultural fields to validate the results. The fields studied in our case are in the Guelmin-Oued noun region in the south of Morocco. These fields are divided into two areas, with a total surface of 3.44 Ha2 for the first field and 3.73 Ha2 for the second. The images were collected using a DJI-type unmanned aerial vehicle and an RGB camera. Performance evaluation showed that the system could process up to 86 fps versus 12 fps or 20 fps in C/C++ and OpenMP implementations, respectively. Software optimizations have increased the performance to 107 fps, which meets real-time constraints.

Adaptive aerodynamic part feeding enabled by genetic algorithm

Aerodynamic feeding systems represent one possibility to meet the challenges of part feeding for automated production in terms of feeding performance and flexibility. The aerodynamic feeding system investigated in this article is already able to adapt itself to different workpieces using a genetic algorithm. However, due to the operating principle, the system is susceptible to changes in environmental conditions such as air pressure and pollution (e.g. dust). To minimise the effect of ambient influences, the system must be enabled to detect changes in the feeding rate and react autonomously by adapting the system’s adjustment parameters. In this work, based on pre-identified factors interfering with the aerodynamic orientation process, a new approach is developed to react to changes of the ambient conditions during operation. The presented approach makes us of an alternating sequence of monitoring and corrective algorithms. The monitoring algorithm measures the ratio of correctly oriented parts to the total number of fed parts of the process and triggers the corrective algorithm if necessary. Simulated and experimental results both show that an increased feeding rate can be achieved in varying conditions. Furthermore, it is shown that integrating both known process and parameter information can reduce the time for re-parametrisation of the feeding system.

Convergence Time Monitoring Algorithm in Hybrid Software Defined Networks

Network consummation especially dependent on traffic monitoring. Therefore, Software Defined Network (SDN) technology is submitted to support the flow control suitable monitoring by providing a global view of the network. Unfortunately, replacing the entire traditional network to SDN is complex, which leads to the need of SDN switches deployment to the current network. Thus, a hybrid network environment has emerged which consists of centralized controller, SDN switch and legacy routers. Hence, the advantage of the integration of traditional network and SDN will take place. The controller can collect SDN data instantly, while it waits for a long time to obtain the legacy network data. On the other hand, the rest of paths cannot be processed directly by the controller. Therefore, legacy path load data is estimated for the past time to support the controller for obtaining the current data. The convergence time of the proposed algorithm takes more convergence time than the full SDN by only 12%. Therefore, the proposed algorithm provides installing the minimum possible number of SDN switches that reduce the infrastructure cost.