Query: artificial intelligence operating system

Candidate papers:

abstract: The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human to machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.
author_names: Abdul Waheed

H F Khan
title: Artificial Intelligence in Operating System
venue: CSAI
year: 2019

abstract: The paper proposes flexible system that is based on Robot Operating System framework for integration of 3D computer vision and artificial intelligence algorithms with industrial robots for automation of industrial tasks. The system provides flexibility of 3D computer vision hardware and industrial robot components, allowing to test different hardware with small software changes. The experimental system consisting of Kinect V2 RGB+Depth camera and Universal Robots UR5 robot was set up. In experimental setup the pick and place task was implemented where randomly organized two types of objects (tubes and cans) where picked from the container and sorted in two separate containers. Average full cycle time for the task was measured to be 19.675 s.
author_names: Janis Arents

Ricards Cacurs

Modris Greitans
title: Integration of Computervision and Artificial Intelligence Subsystems with Robot Operating System Based Motion Planning for Industrial Robots
venue: Automatic Control and Computer Sciences
year: 2018

abstract: Abstract Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasting, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on swarm intelligence optimization and data preprocessing, which includes two modules: wind energy potential analysis and wind speed forecasting. In the wind energy potential analysis module, the parameters of the Weibull distribution are optimized by a multiple swarm intelligence optimization algorithm, which can provide better wind energy assessment results. In the wind speed forecasting module, the data preprocessing method can effectively eliminate the noise of the original wind speed time series, maintain the characteristics of the wind speed data, and improve the accuracy of the forecasting model. The numerical results show that the wind energy decision system not only provides an effective wind energy assessment, but can also satisfactorily approximate the actual wind speed forecasting. Therefore, it can serve as an effective tool for wind farm management and decision making.
author_names: Xuejing Zhao

Chen Wang

Jinxia Su

Jianzhou Wang
title: Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system
venue: Renewable Energy
year: 2019

abstract: It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. 0.98% Res. 50% and minimum Vulnerability (Vul. 21.9 of demand) It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long term optimization problem in operation a reservoir system.
author_names: Mohammed Falah Allawi

Othman Jaafar

Mohammad Ehteram

Firdaus Mohamad Hamzah

Ahmed El-Shafie
title: Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
venue: Water Resources Management
year: 2018

abstract: In this work, four artificial intelligence (AI) techniques, based on Artificial Neural Networks, Support Vector Machine (SVM) and Regression Tree Ensembles, were used to estimate the operating temperature of photovoltaic (PV) modules (TPV) The models' input parameters correspond to experimental measurements of environmental (solar radiation, ambient temperature, relative humidity, wind speed, and wind direction) and operational (power output and tracking system) variables. Several AI models were trained and statistically compared with the measured data using a computational methodology that determines the performance and accuracy of the AI technique. Finally, a global sensitivity analysis was conducted to identify the ability of each technique to reflect the physical coherence of the phenomenon that is under study. It is reported that the four techniques can provide an estimate having a precision of about 93% On the other hand, the sensitivity analysis demonstrates that all the models cannot correctly
author_names: O May Tzuc

Ali Bassam

Paul Erick Mendez-Monroy

Israel Sanchez Dominguez
title: Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach
year: 2018

abstract: Scheduling is one of the classic problems in real time systems. In real time adaptive applications, the implementation of some sort of run time intelligence is required, in order to build real time intelligent systems capable of operating adequately in dynamic and complex environments. The incorporation of artificial intelligence planning techniques in a real time architecture allows the on line reaction to external and internal unexpected events. In this work a layered architecture integrating real time scheduling and artificial intelligence planning techniques has been designed, in order to implement a real time scheduler with capability to perform effectively in these scenarios. This multi level scheduler has been implemented and evaluated in a simulated information access system destined to broadcast information to mobile users. Results show that incorporation of artificial intelligence to the real time scheduler improves the performance, adaptiveness and responsiveness of the system.
author_names: Jesus Fernandez-Conde

Pedro Manuel Cuenca-Jimenez

Rafael Toledo-Moreo
title: Improving Scheduling Performance of a Real Time System by Incorporation of an Artificial Intelligence Planner
venue: IWINAC
year: 2019

author_names: Shreyovardhana Samethanahalli Kote
title: Additions to the operating system concepts animations library and artificial intelligence concepts animations library
year: 2015

abstract: This paper proposes a new methodology for automated design of power electronic systems realized through the use of artificial intelligence. Existing approaches do not consider the system's reliability as a performance metric or are limited to reliability evaluation for a certain fixed set of design parameters. The method proposed in this paper establishes a functional relationship between design parameters and reliability metrics, and uses them as the basis for optimal design. The first step in this new framework is to create a nonparametric surrogate model of the power converter that can quickly map the variables characterizing the operating conditions (e.g. ambient temperature and irradiation) and design parameters (e.g. switching frequency and dc link voltage) into variables characterizing the thermal stress of a converter (e.g. mean temperature and temperature variation of its devices) This step can be carried out by training a dedicated artificial neural network (ANN) either on experimental or simulation data. The resulting network is named as \text{ANN}_{1} and can be deployed as an accurate surrogate converter model. This model can then be used to quickly map the yearly mission profile into a thermal stress profile of any selected device for a large set of design parameter values. The resulting data is then used to train \text{ANN}_{2} which becomes an overall system representation that explicitly maps the design parameters into a yearly lifetime consumption. To verify the proposed methodology, \text{ANN}_{2} is deployed in conjunction with the standard converter design tools on an exemplary grid connected PV converter case study. This study showed how to find the optimal balance between the reliability and output filter size in the system with respect to several design constraints. This paper is also accompanied by a comprehensive dataset that was used for training the ANNs.
author_names: Tomislav Dragicevic

Patrick William Wheeler

Frede Blaabjerg
title: Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems
venue: IEEE Transactions on Power Electronics
year: 2019

abstract: The zoo is a local facility where some wild or exotic animals are placed in a fence. The main significance of the zoo is to provide educational and animal conservation functions, and secondly to provide public viewing and entertainment. Animal care and management in the zoo is almost open all year round. Its basic tasks include accommodation, breeding, health care, and medical care etc. Because there are nearly hundreds, thousands, or even ten thousands animals with different body shape and characteristics in the zoo that need to be cared for and managed, animal administrators must be skilled in various tools and real time control the condition of all animals, resulting in the heavy workload of the animal administrators and the huge operating expenses of the zoo. Therefore, it is necessary to find ways to reduce the workload of the animal administrators, but also to immediately control the current state of the animals, while saving animal care and management expenses. This study proposes a development architecture for the intelligent animal management system based on the Internet of Things (IoT) and artificial intelligence (AI) Its main purpose is to automate some tedious procedures for caring animals through the IoT and AI to help animal administrators to take care of them and manage them more systematically.
author_names: Yu-Huei Cheng
title: A Development Architecture for the Intelligent Animal Care and Management System Based on the Internet of Things and Artificial Intelligence
venue: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
year: 2019

abstract: Purpose To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and Methods A deep learning AI system was developed to identify suspicious soft tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC) reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies. Results Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI] 0.028, 0.087; P .01) from 0.795 without AI to 0.852 with AI. Reading time decreased 52.7% (95% CI: 41.8% 61.5% P .01) from 64.1 seconds without to 30.4 seconds with AI. Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0% 95% CI: 2.6% 13.4% P .01) specificity increased from 62.7% without to 69.6% with AI (6.9% 95% CI: 3.0% 10.8% noninferiority P .01) and recall rate for noncancers decreased from 38.0% without to 30.9% with AI (7.2% 95% CI: 3.1% 11.2% noninferiority P .01) Conclusion The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time.(c) RSNA, 2019See also the commentary by Hsu and Hoyt in this issue.
author_names: Emily F Conant

Alicia Y Toledano

Senthil Periaswamy

Sergei V Fotin

Jonathan Go

Justin E Boatsman

Jeffrey W Hoffmeister
title: Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.
venue: Radiology. Artificial intelligence
year: 2019