scholarly journals Quality Monitoring and Analysis of Xinjiang ‘Korla’ Fragrant Pear in Cold Chain Logistics and Home Storage with Multi-Sensor Technology

2019 ◽  
Vol 9 (18) ◽  
pp. 3895 ◽  
Author(s):  
Jingjing Liu ◽  
Xu Zhang ◽  
Zhigang Li ◽  
Xiaoshuan Zhang ◽  
Tomislav Jemric ◽  
...  
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Soluble Solids ◽  
Sensor Data ◽  
Cold Chain ◽  
Sensor Technology ◽  
Index Measurement ◽  
Carbon Dioxide Co2 ◽  
Continuous Sensor

Korla fragrant pear is prestigious for its special texture and unique flavor but suffers storage and supply chain difficulties for its deterioration-prone properties. In order to improve the storage quality of Korla fragrant pears during the whole cold chain from the orchard to the customers, the paper deeply researches multiple influencing factors of cold chain logistics and home storage of Korla fragrant pears with multi-sensor technology (MST), such as the temperature, relative humidity, concentrations of oxygen (O2), carbon dioxide (CO2) and ethylene (C2H4). Cold chain logistics are assessed by sensory evaluation and physiological index measurement, and home storage environments are classified by using back propagation neural network (BPNN) in both refrigerators and ordinary rooms. Experimental results show that the MST-based detectors can improve the accuracy of continuous sensor data acquisition, such that the preservation quality of Korla fragrant pears is effectively enhanced by data analysis on gas contents, firmness, pH, and total soluble solids. These results indicate that Korla fragrant pears stored in refrigerators have a higher acceptance for customers.

scholarly journals Real Time Monitoring and Controlling of Cold Storage Truck Using IOT

2021 ◽  
pp. 673-674
Author(s):  
Mr. Bhushankumar N. Shinde ◽  
Mr. Madhur Sanjay Bothara ◽  
Mr. Nitin Ravindra Chaudhari ◽  
Mr. Rushikesh Waman Charaskar
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Cold Chain ◽  
Sensor Technology ◽  
New Class ◽  
Business Applications ◽  
Temperature And Humidity ◽  
The Status ◽  
Use Of Internet

Refrigerator transportation is an important part of cold chain. Aiming at monitoring the temperature and humidity inside the refrigerator trucks and managing information of the refrigerator trucks internal. In this system, there is a design of an intelligent monitoring system based on the Internet of thing, realized monitoring temperature and humidity inside the refrigerator trucks and the intelligent tracking the location of refrigerator trucks real-time in the entire transportation process by using advanced technologies like MQTT, Cloud Computing, sensor technology and the wireless communication technology. The paper explains the use of Internet of Things (IoT), where smart sensors collaborate directly without human involvement to deliver a new class of applications for remote monitoring of refrigerator truck. The IoT is expected to have significant home and business applications, to contribute to the quality of life and to grow the world's economy. The proposed system is designed to continuously monitor the temperature, humidity, location, status of the truck door, presence of the person inside the truck for best cold chain application. The new generating technology IoT collects and displays the sensor data on the cloud platform. The data This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an International Journal. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction collected on the database provides useful information to end user about the status of truck.

scholarly journals Dynamic Stress Measurement with Sensor Data Compensation

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 859
Author(s):  
Jingjing Gu ◽  
Zhiteng Dong ◽  
Cai Zhang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Dynamic Stress ◽  
Stress Measurement ◽  
Rapid Change ◽  
Back Propagation ◽  
Environmental Parameters ◽  
Back Propagation Neural Network ◽  
Sensor Data ◽  
Bee Colony ◽  
Stability Of Solution

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.

scholarly journals A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator-Driven Setup Using Back Propagation Neural Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Zhong ◽  
Xu Zhou ◽  
Minzhou Luo
Keyword(s):  
Back Propagation ◽  
Weight Ratio ◽  
Adaptive Controller ◽  
Back Propagation Neural Network ◽  
Sampled Data ◽  
Nonlinear Characteristics ◽  
New Approach ◽  
Pneumatic Muscle ◽  
Muscle Actuators

Pneumatic muscle actuators (PMAs) own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network (BPNN) is employed to identify the dynamics of the setup. An offline model is built up using sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

scholarly journals High Carbon Dioxide Treatment Modulates Sugar Metabolism and Maintains the Quality of Fresh-Cut Pear Fruit

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4261
Author(s):  
Di Wang ◽  
Quan Ma ◽  
Tarun Belwal ◽  
Dong Li ◽  
Wenxuan Li ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Fruit Quality ◽  
Soluble Sugars ◽  
Sugar Metabolism ◽  
Sucrose Phosphate Synthase ◽  
Soluble Solids ◽  
Related Enzyme ◽  
Fresh Cut ◽  
Carbon Dioxide Co2

The purpose of this study is to explore the effect of 10% carbon dioxide (CO2) on the fruit quality and sugar metabolism of fresh-cut pear during storage. The results indicated that carbon dioxide treatment maintained fruit quality by delaying the decline of firmness and promoting the accumulation of total soluble solids (TSS). Moreover, carbon dioxide enhanced activities of sucrose synthase (SS), and sucrose phosphate synthase (SPS). The activities of amylase, acid invertase (AI), neutral invertase (NI), SS-cleavage, fructokinase (FK), hexokinase (HK), sorbitol oxidase (SOX), NAD-dependent sorbitol dehydrogenase (NAD-SDH), and NADP-SDH in CO2-treated fruit were inhibited. Expression levels of key genes were found to correspond with the related enzyme activities. As a result, the accumulation of glucose, fructose, sorbitol, and sucrose were accelerated by CO2, which were 12.58%, 13.86%, 24.7%, and 13.9% higher than those of the control at the end of storage, respectively. The results showed that CO2 could maintain the quality of fresh-cut pears by regulating the conversion of various sugar components to enhance soluble sugars content.

Evaluating Artistic Voice of Singing Objectivly Using BPNN

2012 ◽  
Vol 546-547 ◽  
pp. 1240-1244 ◽  
Author(s):  
Xiao Wu Li ◽  
Hong Bo Ouyang
Keyword(s):  
Neural Network ◽  
Subjective Evaluation ◽  
Back Propagation ◽  
Average Energy ◽  
Back Propagation Neural Network ◽  
Fuzzy Synthetic Evaluation ◽  
Bp Network ◽  
Artistic Voice ◽  
And Training

In this paper, an objective way to evaluate artistic voice of singing is discussed. The model transforms artistic voice evaluation indicators into qualified data as BP network input and takes fuzzy synthetic evaluation results as output. The authors take F1(the first Formant), F3(the third Formant), vocal range, perturbation of F1, perturbation of F3 and average energy as the evaluating parameters and assess the quality of singing voices with BPNN(back propagation neural network). The results are then compared with the subjective evaluation of experienced professionals. Experiments show that BP neural network is effective to evaluate the singing voices, thus to be helpful to scientific guidance of selecting and training the talent of artistic voice.

Prediction for Air-Bending Springback Radius of Sheet Metal Using Back Propagation Neural Network and Micro Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1174-1177
Author(s):  
Ze Min Fu ◽  
Guang Ming Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sheet Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Accurate Prediction ◽  
Air Bending ◽  
Micro Genetic Algorithm ◽  
Bpnn Model

Springback radius is a very important factor to influence the quality of sheet metal air-bending forming. Accurate prediction of springback radius is essential for the design of air-bending tools. In this paper, a three-layer back propagation neural network (BPNN), integrated with micro genetic algorithm (MGA), is proposed to solve the problem of springback radius. A micro genetic algorithm is used for minimizing the error between the predictive value and the experimental one. Based on air-bending experiment, the prediction model of springback radius is developed by using the integrated neural network. The results show that more accurate prediction of springback radius can be obtained with the MGA-BPNN model. It can be taken as a valuable tool for air-bending forming of sheet metal.

Application of Neural Network in Free Air Ball Forming for Wire Bonder

1999 ◽  
Author(s):  
Jau-Liang Chen ◽  
Yeh-Chao Lin ◽  
Chun-Hsien Liu ◽  
Wen-Chang Kuo ◽  
Tzung-Ching Lee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Tail Length ◽  
Back Propagation Neural Network ◽  
Ball Bond ◽  
Error Back Propagation ◽  
Fine Pitch ◽  
Free Air ◽  
Network Method

Abstract The shape and size of free-air-ball formation deeply affect the quality of wire bonding. It not only affects the bondability of first bond (ball bond), but also affects the possibility of processing low loop height bonding for thin form packages and high I/O fine pitch packages. Several parameters, such as tail length, spark gap, supplied voltage, current and time of electrical flame-off unit etc., will affect the free-air-ball formation. This paper represents a study of using error-back-propagation neural network method to analyze the effect of each parameter and to predict the final result of the ball forming. From the experiment, it is shown that neural network can not only be used to precisely predict the size of ball formation, but also saves sampling time.

scholarly journals Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 59
Author(s):  
Lesong Wu ◽  
Lan Chen ◽  
Xiaoran Hao
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Standard Test ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Trend Extraction ◽  
Fire Scenarios

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

scholarly journals Tomato quality as influenced by different packaging materials and practices

2017 ◽  
Vol 1 ◽  
pp. 91
Author(s):  
Tsegahun Mekonnen Zewdie
Keyword(s):  
Storage Temperature ◽  
Co2 Concentration ◽  
Packaging Material ◽  
Soluble Solids ◽  
Packaging Materials ◽  
Open Market ◽  
Tomato Fruits ◽  
Tomato Quality ◽  
Carbon Dioxide Co2

In this study, the effect of retailing packaging material on tomato quality was investigated. Specifically, non-defective tomato fruits were selected after harvest and packed in four different packaging materials; open market bag, open box, sealed box and Xtend bag. A total of six treatments were prepared by storing the packaged fruits at 4 or 17°C for 10 days. Quality attributes of tomatoes such as calyx freshness, weight loss, fruit firmness, total soluble solids (TSS), colour and physiological damage were assessed. Generally, both packaging material and storage temperature affected the quality of the tomato fruits. The quality of tomato fruits stored at 4oC was generally superior to those stored at 17°C.Calyx of tomato fruits stored in open market bag (stored at 17°C) and open box (stored at 17°C) were very dry after storage compared to the tomato fruits stored at 4°C. Tomato fruits packed in Xtend bag and sealed box were firmer than those packed in open box and open market bag. The carbon dioxide (CO2) concentration in sealed box was substantially higher (8.25%) than that in Xtend bag (2.07%). In contrast, the oxygen (O2) concentration in the Xtend bag was higher (18.90%) than that in the sealed box (14.75%). Tomatoes packed in Xtend bag and sealed box had minimal changes in colour intensity (C*), showed lower TSS values compared to tomato fruits packed in other packaging materials. Xtend bag and sealed box seems to be better packaging material for storing tomato fruits for a period of 10 days.

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