This paper proposes a novel nature-inspired meta-heuristic algorithm called the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It mimics golden tortoise beetle's behavior of changing colors to attract opposite sex for mating and its protective strategy that uses a kind of anal fork to deter predators. The algorithm is modeled based on the beetle's dual attractiveness and survival strategy to generate new solutions for optimization problems. To measure its performance, the proposed GTBO is compared with five other nature-inspired evolutionary algorithms on 24 well-known benchmark functions investigating the trade-off between exploration and exploitation, local optima avoidance, and convergence towards the global optima is statistically significant. We particularly applied GTBO to two well-known engineering problems including the welded beam design problem and the gear train design problem. The results demonstrate that the new algorithm is more efficient than the five baseline algorithms for both problems. A sensitivity analysis is also performed to reveal different impacts of the algorithm's key control parameters and operators on GTBO's performance.

Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates. Results of the team's study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels. "Air quality is one of the major issues within an urban area that affects people's lives," said Manzhu Yu, assistant professor of geography at Penn State. "Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead." Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said.

In this maritime webinar, attendees will learn how to use ArcGIS automated workflows and machine learning techniques for coastline extraction. Due to anthropogenic activities and natural processes--for example, sea level changes, sedimentation, and wave energy--coastlines are changing worldwide. Traditionally, coastlines were manually digitized, which is a time and labor-intensive way. Remote sensing is an excellent alternative to extract coastlines, using satellite imagery. Satellite imagery of visible range can be used for interpretation and easily obtained.

Over the next 10-15 years, the growing adoption of electric vehicles, the electrification of heating systems, and the proliferation of distributed energy resources (DERs) like wind turbines and solar panels will require a delicate balancing act to match supply with demand without collapsing the grid. A similar scenario is playing out in many other parts of the world as businesses, government and residential consumers increasingly produce their own energy through solar panels, storing that energy in batteries and electric vehicles, or feeding it back to the grid. According to our forecasts (see figure below), approximately 36 million assets such as solar panels, electric vehicles and energy storage will be added to the grid in Europe in 2025, and 89 million by 2030. Soon, they will no longer be the sole source of energy, but will be needed to maintain a balanced grid, shifting electrons from disparate sources and storage systems to seamlessly deliver energy where it is needed second-by-second. With the help of AI software, decentralized energy sources can send any excess electricity they produce to the grid, while utilities direct that power to where it's needed.

To reach its full potential, AI at the edge will need to be self-adaptive. Enter tiny machine Continue Reading... When automated test equipment works for long periods during characterization testing, it's good to make periodic checks on its testing Continue Reading... Transfer function analysis isn't necessarily difficult, but there is a trigonometric booby trap to look out for, and correct for if Continue Reading... NPX combines the security subsystem on its iMX processors with Microsoft's Azure Sphere edge-to-cloud security Continue Reading... Functions in an SoC communicating with each other through an interconnect architecture is not so different from a Continue Reading... Power system engineers can use gravity to store energy from intermittent renewable sources and release grid-level Continue Reading... Large industrial systems need a whole different technique for radiated emissions pre-compliance Continue Reading... LDRA tool suite integration for automation of software analysis and verification helps developers accelerate functional safety Continue Reading... Ka-band ADCs and DACs offer the potential to extend software-defined radio to software-defined microwave for satellite Continue Reading... "Sensors in Automotive: Making Cars See and Think Ahead" canvases the vast landscape encompassing radars, LiDARs, vision cameras, and depth Continue Reading... What's behind the improved sonic performance in the third-generation Amazon Echo Continue Reading... With 16-bit resolution and SNR of 86 dB, STMicro's ISOSD61 sigma-delta modulator enhances performance and reliability in industrial motor Continue Reading... To make buildings smarter, one could place a tiny processor at every location and network them together using common Continue Reading... The Si823Hx isolated gate driver board from Silicon Labs simplifies the design of power systems that employ Wolfspeed SiC power Continue Reading...

CodeCarbon, a lightweight, open-source software package that integrates into a Python codebase, is one of the tools that can help organizations conduct these steps. By automatically fetching power and grid data, CodeCarbon can track the amount of carbon dioxide (CO2) produced by the cloud or by local computing resources used to execute an experiment such as training a machine-learning algorithm. It then provides developers with dashboards displaying the CO2 outcomes of the experiment or series of experiments. This visibility into the CO2 impact creates opportunities to reduce the resulting carbon footprints, by hosting the cloud infrastructure in geographical regions that use renewable energy sources, or by using more efficient hardware. CodeCarbon was jointly developed by Mila, a world-leading AI research institute in Montreal; BCG GAMMA, Boston Consulting Group's global data science and AI team; Haverford College in Pennsylvania; and Comet, a meta machine-learning platform.

Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%. The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment. In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.

Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented.

This paper presents an application of the energy shaping methodology to control a flexible, elastic Cosserat rod model of a single octopus arm. The novel contributions of this work are two-fold: (i) a control-oriented modeling of the anatomically realistic internal muscular architecture of an octopus arm; and (ii) the integration of these muscle models into the energy shaping control methodology. The control-oriented modeling takes inspiration in equal parts from theories of nonlinear elasticity and energy shaping control. By introducing a stored energy function for muscles, the difficulties associated with explicitly solving the matching conditions of the energy shaping methodology are avoided. The overall control design problem is posed as a bilevel optimization problem. Its solution is obtained through iterative algorithms. The methodology is numerically implemented and demonstrated in a full-scale dynamic simulation environment Elastica. Two bio-inspired numerical experiments involving the control of octopus arms are reported.

It is not hard to see that the need for clean water is growing by considering the decrease of the water sources day by day in the world. Potable fresh water is also used for irrigation, so it should be planned to decrease freshwater wastage. With the development of technology and the availability of cheaper and more effective solutions, the efficiency of irrigation increased and the water loss can be reduced. In particular, Internet of things (IoT) devices has begun to be used in all areas. We can easily and precisely collect temperature, humidity and mineral values from the irrigation field with the IoT devices and sensors. Most of the operations and decisions about irrigation are carried out by people. For people, it is hard to have all the real-time data such as temperature, moisture and mineral levels in the decision-making process and make decisions by considering them. People usually make decisions with their experience. In this study, a wide range of information from the irrigation field was obtained by using IoT devices and sensors. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB. With the help of Weka software, the data was normalized and the normalized data was used as a learning set. As a result of the examinations, a decision tree (J48) algorithm with the highest accuracy was chosen and an artificial intelligence model was created. Decisions are used to manage operations such as starting, maintaining and stopping the irrigation. The accuracy of the decisions was evaluated and the irrigation system was tested with the results. There are options to manage, view the system remotely and manually and also see the system s decisions with the created mobile application.