Application of Mm-Wave Radar Detection Technology and Artificial Intelligence Learning to Implement the Real-Time and Predictive Design of Parking Spaces

2021 ◽  
Author(s):  
Yong-Ye Lin ◽  
Fu-Chun Chan ◽  
Min-Chi Wei ◽  
Chi-Chia Sun ◽  
Wen-Kai Kuo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Radar Detection ◽  
The Real ◽  
Detection Technology ◽  
Wave Radar ◽  
Predictive Design

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Study on the Online Detection Technology of the Band Saw Equipment

2013 ◽  
Vol 753-755 ◽  
pp. 2329-2333
Author(s):  
Guo Jin Chen ◽  
Jing Ni ◽  
Ting Ting Liu ◽  
Hui Peng Chen ◽  
Ming Xu
Keyword(s):  
Control System ◽  
Real Time ◽  
Digital Control ◽  
Surface Profile ◽  
Fine Tuning ◽  
Digital Control System ◽  
The Real ◽  
Detection Technology ◽  
Real Time Detection ◽  
Band Saw

Aiming at the lower automation, accuracy and efficiency of the domestic band sawing machine, this paper studies the real-time detection technology based on the sawing load, develops the digital control system of the constant power sawing with the micro-feed performance to improve the load imbalance of the band saw blades in the sawing process. The real-time detection technology based on the micro-deviation of the band saws trajectory is studied. The digitized deviation-correction control system of the band saws trajectory is developed with the fine-tuning performance of the saw stiffness to correct automatically the band saws trajectory. The weight-detection technology based on the scan reconstruction of the surface profile size is researched. The digital control system of the fixed weight sawing is developed to meet that the weight error of the sawed workpiece is fewer than 3%. That can improve the accuracy and efficiency of the band sawing machine and provide the foundation for the realization of the digital control of the band sawing machine.

scholarly journals Soundpainting Sign Language: Possibilities and Connections with Tactileology

Philosophies ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 69
Author(s):  
Arnau Millà
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Sign Language ◽  
The Real ◽  
Collective Creation

This article introduce and expose the language of Soundpainting (SP), its background, and how this artistic tool is being used as a language of communication and creation. It also presents the real-time composition and its peculiarities and the power of collective creation as a creative tool and interaction between artistic disciplines. As there are several cases of sensitive and creative languages, such as Soundpainting, that are used to communicate with artificial intelligence, finally, it expose two of them, which are both still in their embryonic state. Both are collaborations and research between SP sign language and Tactileology. Both can lead to creative results that contribute to new ways of perceiving living art, in a sensitive, social and inclusive way.

A Collision Detection Algorithm Based on Spatial Partitioning and Bounding Volume

2013 ◽  
Vol 433-435 ◽  
pp. 932-935
Author(s):  
Wei Zhao ◽  
Li Ming Ye
Keyword(s):  
Real Time ◽  
Collision Detection ◽  
Detection Algorithm ◽  
Spatial Partitioning ◽  
The Real ◽  
Bounding Volume ◽  
Detection Technology ◽  
Bounding Boxes ◽  
Intersection Test ◽  
Rule Out

In order to improve the real-time and accuracy in the collision detection technology, a collision detection algorithm based on spatial partitioning and bounding volume was proposed . This algorithm adopted different spatial division strategies for different locations of the spaces according to the details in the scenes to exclude objects which can not intersect.Thus defined the potential intersection areas. Then we used a dynamic S-AABB hierarchy bounding boxes to test whether the intersection happened between the objects in the same grids. We used the sphere boxes to rule out the disjoint objects quickly. Then constructed the dynamic AABB bounding boxes trees for the rest of objects for further intersection test. At last, we improved the traditional overlapping test between the primitives for accurate collision detection . Compared to the traditional collision detection algorithm based on spatial partitioning and AABB bounding volume. This algorithm effectively improves the real-time of the collision detection without affecting the accuracy of original collision detection.

scholarly journals Machine Learning for Healthcare Diagnostics

2019 ◽  
Vol 8 (6S2) ◽  
pp. 999-1001
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Genetic Disease ◽  
World Wide ◽  
Early Stage ◽  
Human Life ◽  
The Real ◽  
Initial Stage

Presently machine learning and artificial intelligence is playing one of the most important role in diagnose many genetic and non genetic disease. So that the rapid inventions in machine learning can save thousands of life’s as it can diagnose the early stage of many serious diseases. In this research the datasets for such diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life. The problem with such methodology is that it is not possible to get accurate results in the initial stage of research. The reason is every human have different immunity power and stamina. There are many diagnostics center who are fully dependent on the equipments which are fully based on machine learning. In order to boost this process it is necessary to collect the real time patient’s data from different hospitals, states and countries. So that it will be beneficial for world wide.

The concept of the formation and development of a digital intellectual ecosystem of blended university learning

2020 ◽  
pp. 15-23
Author(s):  
S. G. Grigoriev ◽  
R. A. Sabitov ◽  
G. S. Smirnova ◽  
Sh. R. Sabitov
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Rapid Development ◽  
Industrial Clusters ◽  
Real Sector ◽  
Integrated Network ◽  
The Real ◽  
Digital Twin ◽  
Network Cluster ◽  
Education Market

The article proposes the concept of the formation and development of an adaptive ecosystem of university learning. The concept can allow not only to eliminate the shortcomings inherent in the distance education system, but also to create the basis for building a full-fledged educational technology. The basis for constructing such an ecosystem, in addition to purely didactic developments, can be modern achievements in the field of systems theory, digitalization and artificial intelligence. The education market is seriously affected by advances in artificial intelligence and the rapid development of Industry 4.0. It is also necessary to consider rather unpredictable natural disasters and pandemics. Under these conditions, the only way to maintain and strengthen their positions in the education market, which will rapidly change in the coming decades, is the transformation of processes within the framework of new technological trends and integrated network cluster ecosystems. Decentralized training and outsourcing can become two key functions for the successful application of artificial intelligence in education. Modeling, optimization and analytics of big data make it possible to form a complete set of technologies for creating an outsourcing network and digital educational chains, which allows us to identify the state model of all processes in real time. At each moment in time, the digital twin displays the status of outsourcing processes and educational chains with actual data on planning, preparing the necessary equipment, directly preparing educational programs, loading teachers, accounting and monitoring learning outcomes, etc. The digital twin can be used both for making decisions in real-time, and for forecasting and planning outsourcing. In fact, the university and the companies providing outsourcing services within the framework of this approach are integrated into a single mechanism for solving tasks of flexible individual training. Within the framework of the proposed approach, it is possible to build an educational university environment integrated with real objects of the economy of the territory, which is a component of the educational ecosystem. The concept under consideration allows predicting and planning the training of required specialists, since the model of its work is closely connected with enterprises in the real sector. This becomes possible due to the fact that training takes place according to flexible programs that reflect the ever-changing requirements of enterprises to the competencies of their employees. In fact, a university or a group of universities is becoming an essential component of territorial industrial clusters, which makes it possible to increase the efficiency and quality of specialist training and to quickly develop new curricula and courses that will quickly develop competencies demanded by the real sector of the economy. The use of artificial intelligence technology in combination with the capabilities of the Internet of things and digitalization of the main business processes provides, in fact, the functioning and development of the university’s ecosystem by analogy with the ecosystems of large sectoral system-forming enterprises.

scholarly journals A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring

2021 ◽  
Author(s):  
Xin Yin ◽  
Quansheng Liu ◽  
Yucong Pan ◽  
Xing Huang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Clustering Algorithm ◽  
Microseismic Monitoring ◽  
Engineering Practice ◽  
Minority Class ◽  
The Real ◽  
Time Prediction ◽  
Microseismic Events ◽  
Validation Set

Abstract Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve the prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. In order to establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence.

Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM

2021 ◽  
pp. 1-18
Author(s):  
Xiangyu Zhang ◽  
Lilan Liu ◽  
Xiang Wan ◽  
Bowen Feng
Keyword(s):  
Artificial Intelligence ◽  
Tool Wear ◽  
Real Time ◽  
Online Monitoring ◽  
Machining Process ◽  
Time Behavior ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
The Real ◽  
Wear Monitoring

Abstract The real-time requirements of tool wear states monitoring are getting higher and higher, at the same time, tool wear monitoring lacks a modeling data comprehensive carrier, which hinders its application in the actual machining process. In order to solve this problem, combining the high fidelity real-time behavior simulation characteristics of digital twin(DT) and the powerful data mining capabilities of artificial intelligence, an online tool wear monitoring method based on DT and Stack Sparse Auto-Encoder-Parallel Hidden Markov Model(SSAE-PHMM) was proposed. Firstly, a DT which can reflect the real state of the tool was established, and the tool wear state was predicted by visual display and analysis in the virtual space; Secondly, a tool wear state recognition model based on SSAE-PHMM was established, which can adaptively complete time domain feature extraction. And for each tool wear state, multiple HMM models were combined into a PHMM model to realize accurate recognition of tool wear state. PHMM overcome the defects of poor convergence and long training time of artificial neural network, and greatly improved the performance of classifier. Through the deep integration of DT and artificial intelligence, real-time data-driven tool wear qualitative and quantitative online monitoring was realized, and the effectiveness of this method was verified by experiments.

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