Switching mechanism and optimisation research on a pressure-attitude adaptive adjusting coal seam water jet slotter

To investigate the attitude-switching mechanisms of existing jet slotters,which integrate drilling,punching and slotting operations,and to improve its fracture ability,we used the power bond diagram theory to analyse the dynamic flow pressure,and force of slotters.A mathematical model was developed for the dynamic characteristics of slotter systems.Furthermore,to study the effect of the main characteristic parameters on the ability of the nozzle to erode sandstone,multi-orthogonal experiments were carried out.And the optimised slots were applied in later practical operations.The research results show that the inlet fluid passed through the time-varying orifice to generate pressure differential thrust,which overcame the spring force,pushed the valve core to open the side nozzle,and closed the rear cavity channel thereby realising the switch of the slotter attitude.An optimal plan was established to balance the diameter,depth,and volume of punching,and a rock-breaking plan was developed for the slotter.Subsequently,the optimised water jet slotter was practically used in coal seam gas drainage.Compared with conventional dense drilling,water jet slotting technology significantly improves the ability,efficiency,and effect of increasing the permeability of the coal seam.

Adaptive linear active disturbance-rejection control strategy reduces the impulse current of compressed air energy storage connected to the grid

The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.

Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

Design and Research of Efficient Adaptive Spray Plant Protection UAV

When the plant protection UAV is spraying on plants, the operator usually performs visual inspection on the vertical distance between the UAV and the plant to adjust the spray height to complete the application. Visual inspection by human eyes is easy to cause the error of spray deposition and deposition density. In order to improve the full utilization of drugs and the spraying efficiency of plant protection UAV, an efficient adaptive spray plant protection UAV was designed, and the vertical distance from the UAV to the plant was collected by ultrasonic wave. Meantime, the plant density was detected in real time, and the flight attitude of plant protection UAV and application rate of spray nozzle were automatically adjusted. The experimental results showed that the designed highly efficient adaptive spray plant protection UAV had fast response speed, precise spray location and less drug loss.

Slip-draft embedded control system by adaptively adjusting the battery position for electric tractor

A slip-draft embedded control system was designed and developed for an independent developed 2WD(two-wheel drive)electric tractor,to improve the traction efficiency,operation performance and ploughing depth stability of the electric tractor.In this system,the battery of electric tractor was innovatively equivalent to the original counterweight of the fuel tractor.And through dynamic analysis of electric tractor during ploughing,the mathematical model of adjusting the center of gravity about draft force and slip rate was established.Then the automatic adjustment of the center of gravity for electric tractor was realized through the adaptive adjustment of battery position.Finally,the system was carried on electric tractor for performance evaluation under different ploughing conditions,the traction efficiency,slip rate and front wheel load of electric tractor were measured and controlled synchronously to make it close to the set range.And the comparative experiments of ploughing operation were carried out under the two modes of adaptive adjustment of center of gravity and fixed center of gravity.The test results showed that,based on the developed control system,the center of gravity of electric tractor can be adjusted in real time according to the complex changes of working conditions.During ploughing operation with adjusting adaptively battery position,the average values of traction efficiency,slip rate,front wheel load and relative error of tillage depth of electric tractor were 64.5%,22.2%,2045.0 N and 2.0%respectively.Which were optimized by 15.0%,29.5%,19.6%and 80.0%respectively,compared with electric tractor with fixed battery position.The slip-draft embedded control system can not only realize the adaptive adjustment of the center of gravity position in the ploughing process of electric tractor,but also improve the traction efficiency and the stability of ploughing depth,which can provide reference for the actual production operation of electric tractor.

Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks

Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals.The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking,but it is still hard to identify similar head states.To solve this problem,the fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks(FBA-CNN).Grid Region-based CNN(R-CNN),a convolution neural network(CNN),was optimized with the Squeeze-and-Excitation(SE)and Depthwise Over-parameterized Convolutional(DO-Conv)to detect layer heads from cages and to accurately cut them as single-head images.The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50.Finally,we returned to the original image to realize multi-target detection with coordinate mapping.The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947,the accuracy of classification with SE-Resnet50 was 0.749,the F1 score was 0.637,and the mAP@0.5 of FBA-CNN was 0.846.In summary,this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia.

Adaptive price adjustment method for used mobile phone based on dual deep fuzzy networks

Aiming at solving the problem that it is challenging to choose the appropriate price adjustment strategy according to the market fluctuations,an adaptive price adjustment method based on dual deep fuzzy networks(DDFN)is designed.First,a price adjustment model based on DDFN is established.Through interactively learning the recycling market environment,the description of the mapping relationship between the market environment information and the price adjustment action is realized.Second,based on a greedy strategy to calculate the optimal price adjustment action,it is possible to make small adjustments based on the preliminary estimated value of the waste mobile phone,and complete the judgment of the mobile phone recycling price.Third,based on the market feedback,the gradient descent algorithm is used to update parameters of the model to improve the performance.The proposed adaptive price adjustment method based on DDFN is applied to the actual transaction process,and the results show that the proposed method can ensure the accuracy and reliability of the adjustment results of the mobile phone recycling price.

Reverse Droop Control-based Smooth Transfer Strategy for Interface Converters in Hybrid AC/DC Distribution Networks

Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution networks.However,the networks possess drawbacks with AC voltage and frequency offsets when transferring from grid-tied to islanding modes.To address these problems,this paper proposes a simple but effective strategy based on the reverse droop method.Initially,the power balance equation of the distribution system is derived,which reveals that the cause of voltage and frequency offsets is the mismatch between the IFC output power and the rated load power.Then,the reverse droop control is introduced into the IFC controller.By using a voltage-active power/frequency-reactive power(U-P/f-Q)reverse droop loop,the IFC output power enables adaptive tracking of the rated load power.Therefore,the AC voltage offset and frequency offset are suppressed during the transfer process of operational modes.In addition,the universal parameter design method is discussed based on the stability limitations of the control system and the voltage quality requirements of AC critical loads.Finally,simulation and experimental results clearly validate the proposed control strategy and parameter design method.

Fringe contrast enhancement of digital off-axis hologram via sparse representation

This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.