To improve the safety and automation during the spanning construction of transmission lines with voltage levels of 110kv and below, an intelligent spanning frame monitoring system based on PLC and configuration software is developed. The system performs real-time monitoring and early warning of various parameters such as height, inclination, and wind speed at the construction site of the spanning frame during the construction process, and controls a series of actions such as the synchronous lifting and spanning of the crossing pole, and the insulating mesh distribution and net collection. The system adopts the distributed structure of a monitoring center and four substations to realize distributed hybrid networking by combining wireless communication, serial communication, and 4G communication.
Human mobility has been studied extensively in various biomedical contexts with applications in clinical rehabilitation, disease diagnosis, health risk prognosis, and general performance assessments. In this paper, we present ATOMHP (Analytical Technologies to Objectively Measure Human Performance) Kinect: a system to objectively quantify human performance using the Microsoft Kinect as a single camera sensor to capture human mobility. We explore the viability of this noninvasive performance assessment system by studying a cohort of cancer patients undergoing various therapy regimens who are assigned a performance score based on a qualitative clinical test. The ATOM-HP Kinect is a clinically usable system which consists of tools for Kinect, clinical data collection, data quality validation, and mobility feature extraction, which can be used for downstream analysis of performance. Preliminary results based on the clinical case study indicate that ATOM-HP Kinect can quantify changes in kinematic parameters, and that these features are correlated with clinically measured risk factors which could be used for early prediction of diseases, or making decision on treatment modification.
This paper discusses the construction of the Accumulate-Repeat-4-Jagged-Accumulate(AR4JA) codes which is suitable for the deep-space communication based on the protograph. Two-step construction method of designing the AR4JA codes whose minimum distance grows linearly with block size, is proposed. Two-step expansion improves the traditional PEG with adding the ACE algorithm for placing edges in a protograph-based code, which brings down the error floor. Compared to the traditional random expansion and the one step expansion like Progressive Edge-Growth(PEG), the two-step construction algorithm aims at constructing the AR4JA codes with high performance and low complexity. Verified by the simulation, the BER performance of the two-step expansion method is similar to the ideal AR4JA codes in the deep space channel condition. With the same Eb/N0, the two-step construction method shows the lower BER than random construction method and one-step extension method through the simulation results.
In this paper, we propose a novel algorithm to deal with the problem of visual tracking in some challenging situations, which is based on HOG feature and sparse representation.First of all, describe target according to the HOG feature; secondly, construct the appearance model of target with the sparse representation, and then predict the target position on the basis of the particle filter method.At last, apply Naive Bayes classifier to track target.The experiment results show that the proposed algorithm is superior in accuracy than the classical tracking algorithm and has better robustness in the scene that contains the target posture changes, illumination variations and occlusion.
The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and the conventional centralized cloud computing-based IoT solutions always lead to a heavy burden on the network bandwidth due to the large amount of sensor data collected frequently that has to be transmitted to the central server and this leads to poor response time for the monitoring system. To address this problem, this article develops an artificial intelligence-assisted distributed system for manufacturing plant-wide predictive maintenance applications. The developed distributed system relies on the feature selection technique to identity an optimal feature subset for each type of fault and is enabled by deploying each independent model built on the obtained feature subset into different edge nodes. The distributed approach enables the data to be processed near the sensors, requiring less data to be transmitted to the central cloud server reducing network delay and delivering more accurate results. In addition, our proposed feature selection approach is especially designed to accommodate the characteristics of IIoT data such as the lack of labels. The effectiveness of the proposed method is validated using the widely used public Tennessee Eastman dataset.
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