A Regularized Ensemble of Classifiers for Sensor Drift Compensation

Verma, Mridula; Asmita, Shruti; Shukla, K. K.

doi:10.1109/jsen.2015.2497277

Cited by 26 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming the output value of the input layer mode is vf, b o is the threshold of the input layer [13], we have:…”

Section: Dc-bpnn Model Establishmentmentioning

confidence: 99%

“…Thus, how to achieve acceptable measurement accuracy with ability-limited sensor networks in such rapid changing scenarios, which could be referred as dynamic measurement and data compensation problem in sensor networks, is still an open problem. Recently, various methods for sensor compensation have gradually attracted researchers' attention because of its ability to reduce or eliminate the deficiency caused by error data on the systems [5][6][7][8][9][10][11][12][13]. For the compensation method of sensor data, most researches focus on hardware compensation based on physical or circuit characteristics of electronic components, such as magnetic field pulses, charge-coupled device, micro-electro-mechanical system, even material modification [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…There are also a few high precision sensor methods with hardware and software compensation for temperature based on physical or circuit characteristics of the electronic elements [10,11]. Only a few works have focused on software compensation based on models or algorithms [12,13]. Compared to the latter one, the hardware method suffers from the drawbacks such as complex circuits, high cost, poor versatility, circuit drift and insufficient flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we adopt BPNN to minimize Mean Square Error (MSE) of the network through reversing propagation to adjust weights and thresholds [14,15]. Although there are many researches on BP-NN published, its learning algorithm has drawbacks like low efficiency, meanwhile, its solution is easy to fall into local optimal because of its non-convex hyper-surface of error surface [13]. All these limitations greatly affect the practical applications of BP-NN, especially under the complex environment.…”

Section: Introductionmentioning

confidence: 99%

“…is the threshold of the hidden layer. From the Sigmoid function, the output hg(j) of the jth hidden layer node[12] is:Assuming the output value of the input layer mode is vf, b is the threshold of the input layer[13], we have:…”

mentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Stress Measurement with Sensor Data Compensation

Dong

Zhang

et al. 2019

Electronics

View full text Add to dashboard Cite

Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the high-altitude space is a promising solution for its effectiveness and cost. However, both the high deviation of data and the rapid change of various environment factors (air pressure, temperature, wind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation in dynamic stress measurements of parachutes during the working stage. Specifically, we construct a data compensation model to correct the deviation based on neural network by taking into account a variety of environmental parameters, and name it as Data Compensation based on Back Propagation Neural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN, we propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of solution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real implemented experiments of airdropped WSN.

show abstract

“…Assuming the output value of the input layer mode is vf, b o is the threshold of the input layer [13], we have:…”

Section: Dc-bpnn Model Establishmentmentioning

confidence: 99%