Multiple Profiles Sensor-based Engineering Data Processing

Nowadays big data is widely adopted in industry field.In an advanced manufacturing system hundreds of sensors are deployed to collect key variables for system performance and the real-time data would be used for further monitoring and anomaly detection.However,there are many challenges for applying the sensor-based data directly,including the profile data has unsynchronized different length for different samples,the existence of obvious longterm drift,strong correlation of sensor clusters and the particular feature extraction.To solve these problems this invention presents a multiple profiles sensorbased engineering data processing system,including(1)preprocessing the signals to align the data and remove long-term drift,(2)clustering the sensors which have strong correlations,and(3)extracting particular features from different sensor clusters.

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Optimizing air purification for household particulate matters using sensor-based and time-based intervention strategies

Filtration efficiency of portable air cleaner(PAC)is affected by resident perceptions and adherences to when and how to operate the PAC.Incorporating PAC with smart control and sensor technology holds the promise to effectively reduce indoor air pollutants.This study aims to evaluate the efficiency of a PAC at removing indoor fine particulate matters(PM_(2.5))exposure under two automated operation settings:(1)a time-based mode in which the operation time is determined based on perceived time periods of indoor pollution by residents;(2)a sensor-based mode in which an air sensor monitor is used to determine the PAC based on the actual PM_(2.5) level against the indoor air quality guideline.The study was conducted in a residential room for 55 days with a rolling setting on PAC(no filtration,sensor-based,time-based fil-trations)and a continuous measurement of PM_(2.5).We found that the PAC operated with sensor-based mode removed PM_(2.5) concentrations by 47%and prolonged clean air(<35 μg/m^(3))period by 23%compared to the purifications with time-based mode which reduced PM_(2.5) by 29%and increased clean air period by 13%.The sensor-based filtration identified indoor pollution episodes that are hardly detected by personal perceptions.Our study findings support an automated sensor-based approach to optimize the use of PAC for effectively reducing indoor PM_(2.5) exposure.

Measurement of the peripheral aberrations of human eyes:A comprehensive review

It has been nearly 50 years since the first glimpse of the relationship between myopia and peripheral refractive errors. According to experiments on both animals and humans, the eyes with hyperopic peripheral vision appear to be at higher risk of developing myopia than those with myopic peripheral refractive errors. Despite the first measurement of peripheral refraction being achieved by a modified manual optometer, the concept of emmetropization triggered a rapidly increasing number of studies on peripheral aberrations. Not only the horizontal off-axis aberrations but also the meridional aberrations at different angles are measured by researchers during the development of peripheral aberrations measuring techniques. According to the differences among the working principles, a variety of techniques have been adopted for performing such measurements. The methods developed to realize the high-performance measurement involve the subject cooperating actively by rotating the head or eyes, the rotation of the whole optical path, and the combination of measurements of many light paths. This paper provides a review of the peripheral aberrations measuring techniques and their current status. This article also highlights the development trend of the measuring techniques of peripheral aberrations and practical applications of peripheral aberration measurements, such as the control of the accommodation, the measuring time, and the dynamic range problem of the wavefront sensor. Although wavefront sensing peripheral measurement is widely recognized for its capability to reveal both lower-order aberrations and higher-order aberrations, the efficiency of an autorefractometer is incomparable. The current study reveals that the most widely used peripheral aberration measurement methods are the use of an open field autorefractometer and Hartman-shack wavefront sensor-based techniques.

ARDCH:适于多媒体传感器网络的自适应周期分簇机制

分析了多媒体传感器网络节点部署特点以及多媒体信息位置相关性特点,提出了一种基于剩余能量与传输距离的自适应周期簇头竞争机制(ARDCH,adaptive round distributed cluster head),综合考虑通信代价及剩余能量选取簇头,同时根据剩余能量来动态调节簇头的工作周期,以此均衡网络中节点的能量消耗。此外采用多信道簇间通信,避免簇间干扰,与已有的几个分簇协议相比,显著地延长了网络的生存周期,更适于多媒体传感器网络。

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.