Longdom Conferences

Speaker

Nov 12-13, 2021    Las Vegas, USA
International Conference on

Robotics and Artificial Intelligence

Isham Alzoubi
10:10 PM-10:40 PM

Isham Alzoubi

Tehran University Israel

Title: Prediction of environmental indicators in land levelling using artificial intelligence techniques

Abstract:

This work aimed to determine the best linear model Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Analysis to predict the energy consumption for land leveling. In this research effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption were investigated. The study consisted of 90 samples were collected from 3 different regions. The grid size was set at 20 m in 20 m (20*20) from farmland in the Karaj province of Iran. The values of RMSE and R2 derived by the ICA-ANN model were, to Labor Energy (0.0146 and 0.9987), Fuel energy (0.0322 and 0.9975), Total Machinery Cost (0.0248 and 0.9963), Total Machinery Energy (0.0161 and 0.9987) respectively, while these parameters for multivariate regression model were, to Labor Energy (0.1394 and 0.9008), Fuel energy (0.1514 and 0.8913), Total Machinery Cost (TMC) (0.1492 and 0.9128), Total Machinery Energy (0.1378 and 0.9103). Respectively, while these parameters for the ANN model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, while these parameters for Sensitivity analysis model were, to Labor Energy (0.1899 and 0.8631), Fuel energy (0.8562 and 0.0206), Total Machinery Cost (0.1946 and 0.8581), Total Machinery Energy (0.1892 and 0.8437) respectively, respectively, while these parameters for ANFIS model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990), Results showed that ICA_ANN with seven neurons in hidden layer had better. According to the results of Sensitivity Analysis, only three parameters; Density, Soil Compressibility Factor and, Embankment Volume Index had a significant effect on fuel consumption. According to the results of regression, only three parameters; Slope, Cut-Fill Volume (V) and, Soil Swelling Index (SSI) had a significant effect on energy consumption. Using an adaptive neuro-fuzzy inference system for the prediction of labor energy, fuel energy, total machinery cost, and total machinery energy can be successfully demonstrated.

Biography:

Alzoubi has completed his Ph.D. at the age of 40 years at Tehran University and postdoctoral studies from Tehran University School of Surveying Geospatial Engineering-Department of Surveying and Geomatics Engineering. He is the director at the Directorate of Engineering and Transportation, a premier service organization. He has published more than 15 papers in reputed journals and has been serving as an editorial board member of repute. He Opening and studying the financial offers and the organization of the fundamental record, supervising the efficiency of electrical generators at the Nseeb border center, and Supervising the efficiency of agricultural machinery at the ministry of agriculture.

Useful Links

News

Artificial Intelligence and Machine Learning: Policy Paper

2020-08-13

Artificial intelligence is a technology that is already impacting how users interact with, and are affected by the Internet. In the near future, its impact is likely to only continue to grow. AI has the potential to vastly change the way that humans interact, not only with the digital world, but also with each other, through their work and through other socioeconomic institutions – for better or for worse. If we are to ensure that the impact of artificial intelligence will be positive, it will be essential that all stakeholders participate in the debates surrounding AI. In this paper, we seek to provide an introduction to AI to policymakers and other stakeholders in the wider Internet ecosystem.

This robot doesn't need any electronics

2020-12-31

Engineers have created a four-legged soft robot that doesn't need any electronics to work. The robot only needs a constant source of pressurized air for all its functions, including its controls and locomotion systems. The team, led by Michael T. Tolley, a professor of mechanical engineering at the Jacobs School of Engineering at UC San Diego, details its findings in the Feb. 17, 2021 issue of the journal Science Robotics. "This work represents a fundamental yet significant step towards fully-autonomous, electronics-free walking robots," said Dylan Trotman, a Ph.D. student in Tolley's research group and the paper's first author. Applications include low-cost robotics for entertainment, such as toys, and robots that can operate in environments where electronics cannot function, such as MRI machines or mine shafts. Soft robots are of particular interest because they easily adapt to their environment and operate safely near humans. Most soft robots are powered by pressurized air and are controlled by electronic circuits. But this approach requires complex components like circuit boards, valves, and pumps -- often outside the robot's body. These components, which constitute the robot's brains and nervous system, are typically bulky and expensive. By contrast, the UC San Diego robot is controlled by a light-weight, low-cost system of pneumatic circuits, made up of tubes and soft valves, onboard the robot itself. The robot can walk on command or in response to signals it senses from the environment. "With our approach, you could make a very complex robotic brain," said Tolley, the study's senior author. "Our focus here was to make the simplest air-powered nervous system needed to control walking." The robot's computational power roughly mimics mammalian reflexes that are driven by a neural response from the spine rather than the brain. The team was inspired by neural circuits found in animals, called central pattern generators, made of very simple elements that can generate rhythmic patterns to control motions like walking and running. To mimic the generator's functions, engineers built a system of valves that act as oscillators, controlling the order in which pressurized air enters air-powered muscles in the robot's four limbs. Researchers built an innovative component that coordinates the robot's gait by delaying the injection of air into the robot's legs. The robot's gait was inspired by side neck turtles. The robot is also equipped with simple mechanical sensors -- little soft bubbles filled with fluid placed at the end of booms protruding from the robot's body. When the bubbles are depressed, the fluid flips a valve in the robot that causes it to reverse direction. The Science Robotics paper builds on previous work by other research groups that developed oscillators and sensors based on pneumatic valves and add the components necessary to achieve high-level functions like walking.

New machine learning theory raises questions about nature of science

2021-01-31

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars. The algorithm, devised by a scientist at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop predictions. "Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations," said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. "What I'm doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law." Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a "serving algorithm," then made accurate predictions of the orbits of other planets in the solar system without using Newton's laws of motion and gravitation. "Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data," Qin said. "There is no law of physics in the middle." The program does not happen upon accurate predictions by accident. "Hong taught the program the underlying principle used by nature to determine the dynamics of any physical system," said Joshua Burby, a physicist at the DOE's Los Alamos National Laboratory who earned his Ph.D. at Princeton under Qin's mentorship. "The payoff is that the network learns the laws of planetary motion after witnessing very few training examples. In other words, his code really 'learns' the laws of physics." Machine learning is what makes computer programs like Google Translate possible. Google Translate sifts through a vast amount of information to determine how frequently one word in one language has been translated into a word in the other language. In this way, the program can make an accurate translation without actually learning either language. The process also appears in philosophical thought experiments like John Searle's Chinese Room. In that scenario, a person who did not know Chinese could nevertheless "translate" a Chinese sentence into English or any other language by using a set of instructions, or rules, that would substitute for understanding. The thought experiment raises questions about what, at root, it means to understand anything at all, and whether understanding implies that something else is happening in the mind besides following rules.

Emerging robotics technology may lead to better buildings in less time

2021-02-22

Emerging robotics technology may soon help construction companies and contractors create buildings in less time at higher quality and at lower costs. Purdue University innovators developed and are testing a novel construction robotic system that uses an innovative mechanical design with advances in computer vision sensing technology to work in a construction setting. The technology was developed with support from the National Science Foundation. "Our work helps to address workforce shortages in the construction industry by automating key construction operations," said Jiansong Zhang, an assistant professor of construction management technology in the Purdue Polytechnic Institute. "On a construction site, there are many unknown factors that a construction robot must be able to account for effectively. This requires much more advanced sensing and reasoning technologies than those commonly used in a manufacturing environment."

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

2021-07-16 - 2021-07

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

Newsletter

© 2024 All Rights Reserved by Longdom