Artificial Intelligence in Waste Electronic and Electrical Equipment Treatment: Opportunities and Challenges

Research on artificial intelligence safety prediction and intervention model based on ship driving habits

MATEC Web of Conferences ◽

10.1051/matecconf/202235503032 ◽

2022 ◽

Vol 355 ◽

pp. 03032

Author(s):

Runnan Liu ◽

Guangze Liu ◽

Pengfei He ◽

Xingzhi Lin

Keyword(s):

Artificial Intelligence ◽

Small World ◽

Electrical Equipment ◽

Intervention Model ◽

Safe Driving ◽

Safe Navigation ◽

Intelligent Analysis ◽

Development Direction ◽

Processing Platform ◽

Ship Safety

Based on the analysis of the causes of ship accidents, the development prospect and development direction of ship intelligent safe driving, the artificial intelligence safety prediction and intervention model is put forward. This model solves the problem of ship intelligent safety prediction by using intelligent analysis technology and network technology, and promotes the development of ship intelligence and ship safety navigation technology. Additionally, it expands the channels of obtaining information, connects the ship's mechanical and electrical equipment, collects, stores and analyzes the data reasonably, and constructs the intelligent analysis and processing platform of ship small-world data processing to implement intelligent intervention. What is impressive is that it makes ship navigation safer, more economical, more reasonable and optimized, and accelerates the development of ship artificial intelligence safe navigation.

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Modeling of hybrid active power filter using artificial intelligence controller: hardware and software prospective

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v12.i4.pp2545-2556 ◽

2021 ◽

Vol 12 (4) ◽

pp. 2545

Author(s):

Sandhya P. ◽

Nagaraj R.

Keyword(s):

Artificial Intelligence ◽

Power Systems ◽

Power Loss ◽

Reactive Power ◽

Active Power Filter ◽

Electrical Equipment ◽

Active Power ◽

Power Filter ◽

Software And Hardware ◽

Filtering Techniques

<span lang="EN-US">The power factor is a significant concern in power systems. The significant power loss occurred due to electronic and electrical equipment damages affected by the deviation of physical characteristics, including voltage, current, and frequency parameters.The power loss and quality issues were resolved by introducing filtering techniques in electronic and electrical equipment. Many filtering techniques include passive filtering (PF), Active power filter (APF), and many hybrid approaches are already available. Most of these methods use proper compensation controlling approaches and failed to minimize the total harmonic distortion (THD), and harmonic mitigation in power systems has its best. In this article, an efficient Hybrid-APF using Artificial-Neuro Fuzzy interface system (ANFIS) for software and hardware perspective is designed. The proposed approach uses hybrid controlling strategies which include PI with artificial intelligence (ANFIS) controller, to control the power losses for H-APF. Additionally, current compensation is achieved by PQ-theory, followed by Hysteresis-Current- Controller (HCC). The hardware architecture of ANFIS with HCC is designed to improve the chip-area for real-time power applications.The present work analyzed by simulating the voltage and current waveform. The proposed-H-APF using ANFIS controller, both software and hardware approaches, is compared with other control techniques like H-APF with PI and Fuzzy logic controller by concerning THD,Reactive power, and Different Harmonics and loads improvements.</span>

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Assessment of Energy Use Based on an Implementation of IoT, Cloud Systems, and Artificial Intelligence

Energies ◽

10.3390/en14113202 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3202

Author(s):

Ciprian Mihai Coman ◽

Adriana Florescu ◽

Constantin Daniel Oancea

Keyword(s):

Artificial Intelligence ◽

Data Acquisition ◽

Energy Use ◽

Electrical Equipment ◽

Data Sets ◽

Product Knowledge ◽

Cloud Systems ◽

Electrical Systems ◽

Rapid Pace ◽

Technology Product

Nowadays products are developed at a rapid pace, with shorter and shorter times between concept and go to market. With the advancement in technology, product designers and manufacturers can use new approaches to obtain information about their products and transform it into knowledge that they can use to improve the product. We developed the Poket Framework platform to facilitate the generation of product knowledge. In order to increase the reliability and safety in operation of electrical equipment, an evaluation is proposed, through tests and studies, using the original Poket Framework platform. Thus, several tests and studies were performed, which included testing and analyzing the correct integration in several use cases and remote data acquisition, and testing and analysis of the Poket Framework using literature established data sets of household appliances and electrical systems. Possible evolutions and Poket platform extensions are also considered.

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