Application of machine learning in ocean data

Lou, Ranran; Lv, Zhihan; Dang, Shuping; Su, Tianyun; Li, Xinfang

doi:10.1007/s00530-020-00733-x

Cited by 83 publications

(46 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At acidic pH values, there is a competition between H + ions and Cr(VI) ions in the solution for placement on the nanocomposite pores, and due to the high concentration of H + ions in water, the nanocomposite pores are saturated with H + positive charges. As a result, the sorption efficiency decreases 33, 34. However, at alkaline pH values, chromium is in the form of Cr(OH) 3 , and at these pH values, the Cr(VI) ions compete with OH − ions.…”

Section: Resultsmentioning

confidence: 99%

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

et al. 2021

View full text Add to dashboard Cite

Activated carbon/bentonite/magnetite nanobiocomposite was used to remove Cr(VI) ions from water. The physical properties of the nanocomposite were characterized by various spectroscopic analyses. The specific surface area of the nanocomposite was 337.96 m2g−1, which is a significant value. The maximum uptake efficiency and maximum uptake capacity of the adsorbent were 98 % and 29.32 mg g−1, respectively, which are also considerable values. The maximum uptake efficiency was obtained at pH 9, after 80 min, at a nanocomposite dosage of 2 g L−1, an ion concentration of 5 mg L−1, and a temperature of 25 °C. The thermodynamic study indicated that the sorption process was spontaneous and exothermic. The nanocomposite reusability study after six cycles of reuse demonstrated that the bionanocomposite can be used for up to three cycles without significant change in its uptake efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The TV distortion is -13.6%, the relative brightness is greater than 47.4%, and the main beam angle is less than 31 ° [11][12][13]. So, after introducing the operating principles of UAVs, the algorithm and principles of UAV target prediction will be introduced next.…”

Section: Theoretical Analysis and Methodsmentioning

confidence: 99%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

show abstract

“…Virtual reality technology includes computers, electronic information, and simulation technology, and its basic realization is that a computer simulates a virtual environment to give people a sense of environmental immersion. rough the interaction between the participants and the simulated environment, with their own help, the perception ability and cognitive ability can stimulate the thinking of the participants and help to obtain various spatial and logical information contained in the virtual environment through various methods [4][5][6]. Users use various interactive devices such as headmounted displays (HMD) and data gloves to interact with entities in the virtual environment, giving an immersive feeling.…”

Section: Virtual Reality Technologymentioning

confidence: 99%

Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

Zhu

Han

2021

Mobile Information Systems

View full text Add to dashboard Cite

In modern society, with the rapid increase of population and the serious shortage of resources, the marine environment has been destroyed; there are also many people who go out to sea without permission, regardless of the legal constraints, fishing. This kind of behavior leads the marine environment to get worse and worse, so the real-time monitoring of the marine environment is very necessary. The main article marine environment monitoring, virtual reality technology, and fuzzy C-means clustering algorithm combine to improve the efficiency of monitoring and processing power of the data information. Through the application of virtual reality technology in the marine environment monitoring base and real-time simulation of the dynamics of the ocean, the monitoring personnel can understand the emergencies on the sea in time; the fuzzy C-means clustering algorithm is applied to the server receiving the data to classify the data. It is found in the experiment that when virtual reality technology and fuzzy C-means clustering algorithm are not used, the data of marine environment monitoring takes more than 1.3 s to return to the server, but, after applying two advanced technologies, the return efficiency is greatly improved, and the time consumed is less than 0.82 s. The results show that virtual reality technology and fuzzy C-means clustering algorithm can improve the efficiency of environmental monitoring, and through virtual reality technology, real-time monitoring of the marine environment can be achieved; in the absence of people out to sea, the actual situation on the sea can be clearly understood; and fuzzy C-means clustering algorithm can improve the speed of data processing, so that the monitoring personnel can solve the problem faster.

show abstract

Application of machine learning in ocean data

Cited by 83 publications

References 87 publications

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

Contact Info

Product

Resources

About

Application of machine learning in ocean data

Cited by 83 publications

References 87 publications

Activated Carbon/Bentonite/Fe3O4 as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

Activated Carbon/Bentonite/Fe3O4 as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

Contact Info

Product

Resources

About

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions

Activated Carbon/Bentonite/Fe₃O₄ as Novel Nanobiocomposite for High Removal of Cr(VI) Ions