New method for calibrating optical dissolved oxygen sensors in seawater based on an intelligent learning algorithm

A new method about renewable energy cooperation among small base stations (SBSs) is proposed, which is for maximizing the energy efficiency in ultradense network (UDN). In UDN each SBS is equipped with energy harvesting (EH) unit, and the energy arrival times are modeled as a Poisson counting process. Firstly, SBSs of large traffic demands are selected as the clustering centers, and then all SBSs are clustered using dynamic k-means algorithm. Secondly, SBSs coordinate their renewable energy within each formed cluster. The process of energy cooperation among SBSs is considered as Markov decision process. Q-learning algorithm is utilized to optimize energy cooperation. In the algorithm there are four different actions and their corresponding reward functions. Q-learning explores the action as much as possible and predicts better action by calculating reward. In addition, ε greedy policy is used to ensure the algorithm convergence. Finally, simulation results show that the new method reduces data dimension and improves calculation speed, which furthermore improves the utilization of renewable energy and promotes the performance of UDN. Through online optimization, the proposed method can significantly improve the energy utilization rate and data transmission rate.

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Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection

Sensors ◽

10.3390/s19183995 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3995 ◽

Cited By ~ 6

Author(s):

Yaoguang Wei ◽

Yisha Jiao ◽

Dong An ◽

Daoliang Li ◽

Wenshu Li ◽

...

Keyword(s):

Dissolved Oxygen ◽

Real Time ◽

Optical Detection ◽

Oxygen Sensors ◽

Detection Methods ◽

Future Research ◽

Advantages And Disadvantages ◽

Oxygen Detection ◽

Detection Technology ◽

Intelligent Technology

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

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Research on Dissolve Oxygen Modeling Based on Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.287-290.2640 ◽

2011 ◽

Vol 287-290 ◽

pp. 2640-2643

Author(s):

Guo Dong Gao ◽

Wen Xiao Zhang ◽

Gong Zhi Yu ◽

Jiang Hua Sui

Keyword(s):

Neural Network ◽

Dissolved Oxygen ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Structure Learning ◽

Learning Algorithm ◽

Impact Factors ◽

Function Learning ◽

Bp Neural Network Model

The structure, characteristics and principles of BP neural network model are described in this paper. First, three impact factors of the dissolved oxygen are selected as the sample input of network, and then the parameters of BP neural network are selected, such as network structure, learning algorithm, output layer transfer function, learning rate and so on. Finally, the BP neural network model is established and trained, in order to approach compensate the effects of improves non-linearity. The simulation results show that BP neural network is practical and dependable in the field of dissolved oxygen modeling and has nice applied prospect.

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New Method to Fabricate Limiting-Current-Type Oxygen Sensors

Journal of the American Ceramic Society ◽

10.1111/j.1151-2916.1999.tb02175.x ◽

2004 ◽

Vol 82 (10) ◽

pp. 2906-2908 ◽

Cited By ~ 3

Author(s):

Do-Hyeong Kim ◽

Masaru Miyayama

Keyword(s):

Oxygen Sensors ◽

New Method ◽

Limiting Current

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Voice Recognition System Through Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1072.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 4478-4483

Keyword(s):

Machine Learning ◽

Supervised Learning ◽

Learning Algorithm ◽

Voice Recognition ◽

Recognition System ◽

New Method ◽

Human Voice ◽

Developing Area ◽

Voice Processing ◽

The Voice

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

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