You've heard of robotic bees, but have you heard of robotic butterflies? Chinese researchers have published a study that focuses on their efforts to develop solar-powered wings that imitate the flapping motion of a butterfly. They were able to develop wings that can do this at a rapid rate using light-driven actuators, and a new video shows all of the different ways they can utilize what they've created. The study was published in the journal ACS Applied Materials & Interfaces on January 16th, and a video put out on Wednesday explains how the project came together. When the wing was exposed to the heat of a strong light source, much like the Sun, the polymer layer on the bottom expanded significantly more than the metallic layer on the top, which caused the wing curl.
Artificial intelligence is at the peak of its hype curve, and its applications in the solar energy sector are amid a surge in popularity. Once upon a time confined solely to the domains of science fiction, this technology is transforming the energy landscape, altering how solar assets are managed, operated, and maintained. Year after year, the cumulative global PV capacity is increasing by gigawatts, which are highly dependent on operating conditions that are inherently variable and hard to predict. Also, further consolidation of these solar assets is leading to these portfolios growing not only in size but also in dispersity. These factors have made managing solar assets considerably more challenging.
Solar power and advanced computing are a key cleantech intersection point. From renewables return on investment optimization to optimal rooftop commercial solar deployment, machine learning is helping us get more efficient and effective in our global transformation. Researchers in the US and China are using machine learning to discover new solar panel chemistries to increase the base efficiency and economic effectiveness of solar panels. They are trialing hundreds or thousands of combinations in virtual test beds before bringing them into the physical world, a key element of the machine-to-reality value proposition. Let's start in the United States with Jinxin Li, Basudev Pradhan, Surya Gaur, and Jayan Thomas from the sun-drenched campus of the University of Central Florida.
Deniz Kalaslioglu is the Co-Founder & CTO of Soar Robotics a cloud-connected Robotic Intelligence platform for drones. You have over 7 years of experience in operating AI-back autonomous drones. Could you share with us some of the highlights throughout your career? Back in 2012, drones were mostly perceived as military tools by the majority. On the other hand, the improvements in mobile processors, sensors and battery technology had already started creating opportunities for consumer drones to become mainstream.
The MyFood Family Smart Greenhouse got a lot of traction at CES 2020, so much that the unit exhibited at the FrenchTech pavilion was sold during the show! Given that the price tag for such a large model (Family model, 22 m² / 242 ft²) varies between 8,600 to 22,000 euros (VAT incl.), that sale was a great validation of the product and the technology powering it. Mickaël Gandecki, co-founder, CTO and Managing Partner, MyFood, told me that the Chef of the Tao restaurant at the Venitian Hotel was so impressed that he wanted his team to visit the smart solar aquaponic greenhouse during CES. Founded in 2015 by Mickaël Gandecki, Matthieu Urban and Johan Nazaraly, MyFood aims to fight the damages caused by industrial agriculture by bringing food production back home, off-grid, using 90% less water, and without pesticide. The company's mission statement reads: "Our ambition: to make it possible to produce at home a healthy, diverse and ultra-fresh diet all-year-round. Reconnect with nature and enjoy a sense of well-being."
Artificial intelligence, big data analytics, and machine learning are revolutionizing renewable energy sector and allowing the companies to improve their overall customer experience by means of automating work processes. Optimization and predictions are two major factors on which the energy sector heavily depends. The energy industry also produces vast amounts of data, and to turn this data into insights, major energy players are turning to AI. The historical data collected by power plants can now be combined with weather and satellite data through advancements in big data, AI, and machine learning. Consequently, solar and wind forecasting technology can predict weather conditions well in advance.
A model and expansion plan have been developed to optimally determine microgrid designs as they evolve to dynamically react to changing conditions and to exploit energy storage capabilities. In the wake of the highly electrified future ahead of us, the role of energy storage is crucial wherever distributed generation is abundant, such as microgrid settings. Given the variety of storage options that are recently becoming more economical, determining which type of storage technology to invest in, along with the appropriate timing and capacity becomes a critical research question. In problems where the investment timing is of high priority, like this one, developing analytical and systematic frameworks for rigorously considering these issues is indispensable. From a business perspective, these strategic frameworks will aim to optimize the process of investment planning, by leveraging novel approaches and by capturing all the problem details that traditional approaches are unable to. Reinforcement learning algorithms have recently proven to be successful in problems where sequential decision-making is inherent. In the operations planning area, these algorithms are already used but mostly in short-term problems with well-defined constraints and low levels of uncertainty modeling. On the contrary, in this work, we expand and tailor these techniques to long-term investment planning by utilizing model-free approaches, like the Q-learning algorithm, combined with simulation-based models. We find that specific types of energy storage units, including the vanadium-redox battery, can be expected to be at the core of the future microgrid applications, and therefore, require further attention. Another key finding is that the optimal storage capacity threshold for a system depends heavily on the price movements of the available storage units in the market.
Samsung has shown off an 8K QKED bezel-less TV that is 99 per cent screen and ultra-thin – only 15mm. Fellow South Korean rival LG has its own set of OLED TVs that double as'a piece of art' thanks to an outer edge that mimics a picture frame and the ability to display HD art pieces when not in use. Sony unveiled a concept connected car loaded with sensors and technology from its audio/visual business as part of its own push into mobility. Panasonic had as part of its CES showcase a miniature, battery-powered prototype fire engine that can transport the same level of equipment as a full-sized fire engine but at a fraction of the cost and energy. Lenovo has showcased its foldable PC with a 13.3-inch screen that it says is more durable than Samsung's Galaxy Fold.
First, in this post, we'll take a look at the year's top articles from Singularity Hub, and next week we'll post some of our favorite writing from around the web. The year was a bit of a rollercoaster. We got the Impossible Whopper, an advanced robot dog called Spot, a "word processor" for gene editing, and the first image of a black hole. We also marked the dubious anniversary of the first genetically modified babies, scientists called for a global moratorium on germline engineering, and big tech continued to face a backlash from within and without. Machine learning algorithms beat top players in multiplayer video games, and a former world champion in the game of Go retired, saying AI cannot be defeated.