Wolfberry tree dual-model detection method and orchard target-oriented fertilization system based on photoelectric sensors

Orchard target-oriented fertilizing with on-board sensor technology can improve fertilizer efficiency and reduce environmental pollution.Photoelectric sensors are widely used for object detection because of their low cost and fast response time.This paper presents a Wolfberry tree dual-model detection method and the design of an orchard target-oriented variable-rate fertilization system based on photoelectric sensors.The dual-model detection method includes the Trunk Detection Model(TDM)and Canopy Detection Model(CDM),which can be applied for Wolfberry orchards at the green cluster and mature stages,respectively.A target-oriented fertilization system was designed using the dual-model method,and tested in the lab and Chinese Wolfberry orchard.The laboratory test results showed that the average center offset distances on the condition of detecting trunks,continuous canopies,and discontinuous canopies were 4.1 cm,9.1 cm and 13.1 cm,respectively.The system could ignore the signals from canes when their diameters were less than 16 mm,and also could determine the gaps within a tree when they were less than 21 cm.The orchard test results showed that the system accomplished target-oriented fertilization 95 times for 92 trees at the mature stage.The results indicated that the dual-model detection method could be used for Wolfberry trees or other trees with similar canopy changes at different growth stages.

Multi-functional Photoelectric Sensor Based on a Three-fold Interpenetrated Cd(II)Coordination Polymer for Sensitive Detecting Different Ions

Athree-fold interpenetrated cadmium coordination polymer[Cd_(3)(BTC)_(2)(H_(2)O)_(9)]·2H_(2)O(CP 1)(H_(3)BTC=1,3,5-benzenetricarboxylic acid)was selected and synthesized to investigate its photoelectric properties.CP 1 showed excellent sensitivity for Cr_(2)O_(7)^(2−)and Fe^(3+),low limit of detection(LOD:0.39µmol/L for Cr_(2)O_(7)^(2−)and 1.72µmol/L for Fe^(3+))and stability as electrochemical sensor.More importantly,fluorescence sensing studies indicated that CP 1 exhibits sensing activity for Fe^(3+),Fe^(2+),and MnO_(4)−with good cyclic stability and selectivity.Low LOD and high-sensitivity capability of CP 1 make it a potential multifunctional photoelectric sensor.

Nonlinear correction of photoelectric displacement sensor based on least square support vector machine

A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.

A novel launch dynamics measurement system for multiple launch rocket system and comparative analysis with numerical simulations

In this paper,a novel launch dynamics measurement system based on the photoelectric sensor pair is built.The actual muzzle time(i.e.a time duration that originates from the initial movement to the rocket’s departure from the muzzle)and the muzzle velocity are measured.Compared with the classical methods,the actual muzzle time is obtained by eliminating the ignition delay.The comparative analysis method is proposed with numerical simulations established by the transfer matrix method for multibody systems.The experiment results indicate that the proposed measurement system can effectively measure the actual muzzle time and reduce the error of classical methods,which match well with the simulation results showing the launch dynamics model is reliable and helpful for further analysis and design of the MLRS.

Technology of signal de-noising and singularity elimination based on wavelet transform

Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.

Synchronous line-tracking robots based on STM 32

A pair of synchronous line-tracking robots based on STM32 are designed. Each robot is actually a small intelligent car with seven reflective infrared photoelectric sensors ST188 installed in the front to track the line. Two rear wheels each driven by a moter are the driving wheels, while each rooter is driven by an H-bridge circuit. The running direction is con- trolled by the turning of a servo fastened to the front wheel and the adjustment of speed difference between the rear wheels. Besides, the light-adaptive line-tracking can be performed. The speeds of the motors are controlled by adjusting pulse-width modulation （PWM） values and an angular displacement transducer is used to detect the relative position of the cars in real time. Thus, the speeds of the cars can be adjusted in time so that the synchronism of the cars can be achieved. Through ex-periments, the fast and accurate synchronous tracking can be well realized.

Apple Grading System Based on Weight and Colour

This paper involves the systematic,integrated and unified design of an automatic apple grading system.Apples are placed on a constant-speed conveyor system and graded based on 2 parameters:weight and colour.For weight detection,a piezo-resistive load cell(ELAF-T1-M-10 L)is used.For colour detection,an RGB colour sensor(46 CLR-D5 LAC3-D5)is used.If the apples are equal to or above a pre-set weight threshold of 0.08 kg and if the colour of the apple is red,green or yellow,they are graded in to three bins(bin 1:red apples,bin 2:green apples,bin 3:yellow apples).If the weight is below the threshold and/or if the colour of the apple is not red,green or yellow,they are sent to a reject bin.Both weight and colour requirements have to be met to be successfully graded,otherwise the apple is rejected.Electric linear actuators(PA-15),actuated by combined signals from load cell,colour sensor and photoelectric sensors(OBR2000-R2)positioned very close to the actuators,are used to eject the apples to the correct bins.A digital magnetic speed sensor(DIGISPEC 0090/0091)is used to monitor the speed of the motor and maintain the required constant conveyor speed.Food grade conveyor belt(NS20 UFMT)is used to design the conveyor and direct drive DC motor(DMS07 G)is used to move the conveyor belt.In this paper,the functionality,operation,important parameters and justifications for choosing individual components have been provided as well as detailed explanations of the overall system operation,along with potential drawbacks of proposed system.Relevant calculations necessary for system design are also provided.

Enhancing the Linearity Characteristics of Photoelectric Displacement Sensor Based on Extreme Learning Machine Method

Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine （ELM） method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error （MSE） value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.