Research on Remote Fault Detection System of Ceramic Kiln Based on 5G and IoT Technologies

In order to overcome the defects of the existing technology that the detection of ceramic electric kiln faults takes a long time and costs a lot,an electric kiln control and fault detection device was designed.The working process of the device includes detection module,control module,start⁃stop module and switch module.The detection module detects the resistance circuit and sends a fault signal to the control module.The control module generates stop signal and fault information according to the fault signal,and starts the electric kiln when the fault signal is not received within the preset time.The start⁃stop module can monitor the internal temperature of the electric kiln and control the closing status of the switch module.The switch module is used to control the connection status of AC power and each resistance circuit in the kiln.Based on the 5G DTU or 5G module,the control module could send the information to mobile terminal under the ultra⁃reliable and low⁃latency communication(uRLLC)technical characteristics of 5G communication.

Hyperspectral imaging and remote trace detection of cis-1,3,4,6-tetranitrooctahydroimidazo-[4,5 d]imidazole(BCHMX)compared with traditional explosives using laser induced fluorescence

cis-1,3,4,6-Tetranitrooctahydroimidazo-[4,5 d]imidazole(BCHMX)is an advanced energetic compound that expected to spread worldwide in the near future.Since,no approved remote detection methods were reported in current literature for this material,we performed hyper-spectral imaging and laser induced fluorescence(LIF)to a BCHMX sample under low laser fluence for determining the optimum laser wavelength used in any future BCHMX-LIF based remote detection systems.For this purpose,an experimental setup consisted of a sun spectrum lamp and hyper-spectral camera was built to illuminate and image white powder samples of BCHMX in comparison with the traditional explosives,HMX(1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane),RDX(1,3,5-trinitro-1,3,5-triazacyclohexane),PETN(2,2-Bis[(nitroxy)methyl]propane-1,3-diyldinitrate).The imaging reveals strong BCHMX sample absorption contrast among other samples at wavelength ranging from 400 to 410 nm.When light source was replaced by a 405 nm laser diode illuminator,a strong BCHMX sample LIF at the spectral range from 425 to 700 nm was observed under low laser fluence condition of 0.1 mJ/cm^(2).Finally,we demonstrated successfully the ability of the 405 nm LIF and the hyperspectral imaging technique to detect finger print traces of BCHMX on white cellulose fabric from a distance of 15 m and a detection limit of 1 mg/cm^(2).

THE REMOTE SENSING IN COAL GEOLOGY——A study on detection of coal spontanous burning by remote sensing in the coal fields in the north of China

The spontaneous burning has been lasting for thousands of years in the coal fields in the north of China. It spreads from the west (Tianshan coal field) to the east (Huolinhe coal field). Its E-W extension is up to 3750km, concentrating in N35°toN45°, its vertical depth up to 260m, and the surface temprature locally up to 270℃. Annually, it burns out 0, 250-300 million tones of coal, causing economic loss equivalent to 2-3 billion R.M.B. Yuan.It destroies coal resources and causes hazards in coal mines. In order to locate the extent and the direction in coal burning areas, the remote sensing technique has heen used and has produced an obvious benefit.

TEST STUDY OF DETECTING OIL AND GAS BY USING REMOTE SENSING TECHNIQUE

Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process

Laser Heating and Infrared Thermography of Building Materials

In this work, a 532 nm diode CW laser is used to heat samples used as building materials at a 1 meter standoff distance while using an FLIR (Forward-Looking Infrared) thermal camera to monitor and record the heating and then cooling of each sample after lasers are switched off. The data is then analyzed using FLIR proprietary software. Since the absorption spectra of materials are unique, using multiple lasers of different wavelengths to simultaneously shine onto the sample at different locations would give enough thermal data to successfully characterize the samples within a reasonable amount of time. The results are very promising for applications involving non-destructive detection and classification of materials.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

Remote weak-signal measurement via bound states in optomechanical systems

A scheme for remote weak-signal sensors is proposed,in which a coupled-resonator optical waveguide(CROW),as a transmitter,couples to a hybrid optomechanical cavity and an observing cavity at its two ends.Non-Markovian theory is employed to study the weak-force sensor by treating the CROW as a non-Markovian reservoir of cavity fields.The dissipationless bound states in the non-Markovian regime are conducive to remotely transmitting a signal in the CROW.Our results show that a sensor with ultrahigh sensitivity can be achieved with the assistance of bound states under certain parameter regimes.

Application status and challenges of machine vision in plant factory—A review

Plant factories have a great potential for mitigating the contradiction between the world’sgrowing population and food scarcity. During the process of its automatic production,machine vision plays a significant role. This technique almost covers every production linkfrom raising seedlings, transplanting, management, and harvesting to fruit grading. To provide references and a starting point for those who are committed to studying this issue. Inthis paper, the application prospects of machine vision in plant factories were analyzed,and the present researches were summarized from the fields of plant growth monitoring,robot operation assistance, and fruit grading. The results found that although the existingmethods have solved some practical problems at low cost, high efficiency and precision,some challenges still are faced by machine vision. Firstly, the changing lighting, complexbackgrounds, and color similarity within plant different parts cause the commonly usedimage segmentation algorithms to fail. The shortage of standard agricultural datasets alsokeeps deep learning and unsupervised classification algorithms from making progress.Secondly, there are some theoretical knowledge gaps for machine vision application in aparticular environment of plant factories, which seriously contains its application effect.Thirdly, the lack of special image acquisition devices and supporting facilities resulted inpoor image quality. All these factors hinder machine vision application in plant factories.Nevertheless, it is still a powerful tool and irreplaceable at present. We believed that thistechnique would promote plant factory development greatly with more robust, efficient,and reliable algorithms are developed in the future.

RS analysis of glaciers change in the Heihe River Basin, Northwest China, during the recent decades

The Heihe River Basin is the second largest inland river basin in Northwest China and it is also a hotspot in arid hydrology, water resources and other aspects of researches in cold regions. In addition, the Heihe River Basin has complete landscape, moderate watershed size, and typical social ecological environmental problems. So far, there has been no detailed assessment of glaciers change information of the whole river basin. 1：50,000 topographic map data, Landsat TM/ETM＋ remote sensing images and digital elevation model data were used in this research. Through integrated computer automatic interpretation and visual interpretation methods, the object-oriented image feature extraction method was applied to extract glacier outline information. Glaciers change data were derived from analysis, and the glacier variation and its response to climate change in the period 1956/1963–2007/ 2011 were also analyzed. The results show that：（1） In the period 1956/1963–2007/2011, the Heihe River Basin＇s glaciers had an evident retreat trend, the total area of glaciers decreased from 361.69 km2 to 231.17 km^2; shrinking at a rate of 36.08%, with average single glacier area decrease 0.14 km^2; the total number of the glaciers decreased from 967 to 800.（2） Glaciers in this basin are mainly distributed at elevations of 4300–4400 m, 4400–4500 m and 4500–4600 m; and there are significant regional differences in glaciers distribution and glaciers change.（3） Compared with other western mountain glaciers, glaciers retreat in the Heihe River Basin has a higher rate.（4） Analysis of the six meteorological stations＇ annual average temperature and precipitation data from 1960 to 2010 suggests that the mean annual temperature increased significantly and the annual precipitation also showed an increasing trend. It is concluded that glacier shrinkage is closely related with temperature rising, besides, glacier melting caused by rising temperatures greater than glacier mass supply by increased precipitation to some extent.