New Method of POS based on Artificial Intelligence and Cloud Computing

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

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IMPROVING INTERNET OF THINGS PARKING SYSTEMS

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr2003163b ◽

2021 ◽

Vol 19 (3) ◽

pp. 163

Author(s):

Dušan Bogićević

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cloud Computing ◽

Internet Of Things ◽

Information Needs ◽

Vehicle Classification ◽

The Internet ◽

Parking Space ◽

Automated Systems ◽

The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

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The Pros & Cons of Business Innovation at IT Organizations

Shanlax International Journal of Management ◽

10.34293/management.v7i1.541 ◽

2019 ◽

Vol 7 (1) ◽

pp. 82-85

Author(s):

Geetha Swaminathan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Information Technology ◽

Cloud Computing ◽

New Technologies ◽

Global Market ◽

Innovative Product ◽

Business Innovation ◽

It Organization ◽

Pros And Cons

In the 21st Century, the buzzword is often used in all fields is “Innovation". It is no wonder using Innovation in day to the conversation as well as striving for innovation execution at organisations in Information Technology (IT) sectors. When we need to talk about innovation in IT sectors in the fast-moving technology IT organisations, they are in a position in increasing its capability in its innovative product and services. There is a lot of benefits out of business innovations that are being reaped in IT companies; there are apparent disadvantages are also the outcome of them. It is quite common, despite all benefits and drawbacks, they are in apposition to survive in the global market. That becomes a great challenge to all IT organisations. In IT organisations which consist of departments such as Development, Testing, Consulting, Networking, Infrastructure, Process and having common platforms and legacy languages, Apart from that they are in the way of invading new technologies such as Digital, Mobile, IoT, Artificial Intelligence, Machine learning Cloud computing. In all the fields, as mentioned above and area, they need to do innovation to sustain their business. This paper will provide elaborate results on Pros and Cons of Business Innovation in IT Organization.

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Measuring Digital Transformation Maturity of Supply Chain

Tehnički glasnik ◽

10.31803/tg-20200414191933 ◽

2021 ◽

Vol 15 (2) ◽

pp. 199-204

Author(s):

Krešimir Buntak ◽

Matija Kovačić ◽

Maja Mutavdžija

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Supply Chain ◽

Industry 4.0 ◽

Digital Transformation ◽

Strategic Decision ◽

Traditional Practices ◽

Long Run ◽

Efficiency And Effectiveness ◽

Different Levels

Digital transformation signifies changes in all components and systems of the supply chain. It is also a strategic decision of the organization which, in the long run, can result in the creation of competitive advantage in the market. Digital transformation is affecting all organizations, regardless of their activity. Digital transformation of the supply chain involves the use of industry 4.0 based technologies as well as the replacement of traditional practices with new ones based on digital solutions. The implementation of digital solutions, such as artificial intelligence, IoT, cloud computing, etc., therefore, improve communication between stakeholders in the supply chain, as well as improve efficiency and effectiveness. When conducted, digital transformation must be measured by different levels of maturity. In this paper, authors research current models of measuring digital transformation maturity in supply chain and propose a new model based on identified theories and needs.

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Application Problems and Solutions of Intelligent Airport Service Robots

E3S Web of Conferences ◽

10.1051/e3sconf/202019804030 ◽

2020 ◽

Vol 198 ◽

pp. 04030

Author(s):

Dai Yanyan ◽

Chen Meng

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Big Data ◽

Large Scale ◽

New Technologies ◽

Effective Means ◽

Third Party ◽

Service Robots ◽

Service Robot ◽

Operation Conditions

With the development of new technologies such as artificial intelligence, big data, and cloud computing, the “intelligent airport” is considered to be an effective means to solve or alleviate the current industry problems such as large-scale airport business, the large number of operating entities, and the complicated operation conditions. This paper is about the collaboration between universities and enterprises based on the concept of service design. Relying on big data and cloud computing technology, this paper addresses the problems of airport service robots in inquiries, blind spots of security inspection, and full monomer smart navigation diffluence, combined with the basic technology of service robot artificial intelligence and the third-party interface to design solutions to effectively solve the problems of process.

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Design and Application of Innovative 3DVC in AI Server System

10.1115/ipack2021-69723 ◽

2021 ◽

Author(s):

Xianguang Tan ◽

Yongzhan He ◽

Bin Liu ◽

Jiang Yu ◽

Ahuja Nishi ◽

...

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Power Consumption ◽

Heat Dissipation ◽

Air Cooling ◽

Computing Power ◽

Advanced Phase ◽

Cooling Technology ◽

Saving Effect ◽

Server System

Abstract With the accelerated application of cloud computing and artificial intelligence, the computing power and power consumption of chips are greatly enhanced, which brings severe challenges to heat dissipation. Based on this, Baidu has adopted advanced phase change cooling technology and successfully developed an innovative 3dvc air cooling scheme for AI server system. This paper introduces the design, test and verification of the innovative scheme in detail. The results show that the scheme can reduce the GPU temperature by more than 5 °C compared with the traditional heat pipe cooling scheme, save 30%+ of the fan power consumption, and achieve good cooling and energy saving effect.

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The Intelligent Field Development Plan Through Integrated Cloud Computing and Artificial Intelligence AI Solutions

10.2118/208106-ms ◽

2021 ◽

Author(s):

Samat Ramatullayev ◽

Shi Su ◽

Coriolan Rat ◽

Alaa Maarouf ◽

Monica Mihai ◽

...

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

History Matching ◽

Cutting Edge ◽

Optimization Process ◽

Computing Technology ◽

History Match ◽

Field Development ◽

Uncertainty Parameters

Abstract Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.

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