Solar irradiance clustering can enhance solar power capacity planning and help improve forecasting models by identifying similar irradiance patterns influenced by seasonal and weather changes. In this study, we adopt an efficient two-level clustering approach to automatically identify seasons using the clear sky irradiance in first level and subsequently to identify daily cloud level as clear, cloudy and partly cloudy within each season in second level. In the second level of clustering, three methods are compared, namely, Daily Irradiance Index (DII or $\beta$), Euclidean Distance (ED), and Dynamic Time Warping (DTW) distance. The DII is computed as the ratio of time integral of measured irradiance to time integral of the clear sky irradiance. The identified clusters were compared quantitatively using established clustering metrics and qualitatively by comparing the mean irradiance profiles. The results clearly establish the superiority of the $\beta$-based clustering approach as the leader, setting a new benchmark for solar irradiance clustering studies. Moreover, $\beta$-based clustering remains effective even for annual data unlike the time-series methods which suffer significant performance degradation. Interestingly, contrary to expectations, ED-based clustering outperforms the more compute-intensive DTW distance-based clustering. The method has been rigorously validated using data from two distinct US locations, demonstrating robust scalability for larger datasets and potential applicability for other locations.

A compass that relies on polarised light can use the sun to tell where magnetic north is, even when it is cloudy. It is based on how some insects navigate and could be used when conventional magnetic compasses aren't reliable, such as on robotic drones. Insects like ants and bees can sense direction using sunlight's intensity and its polarisation, a measure of how light rays can appear twisted, through specially adapted receptors arranged in a ring in their…

Adversarial Examples (AEs) can deceive Deep Neural Networks (DNNs) and have received a lot of attention recently. However, majority of the research on AEs is in the digital domain and the adversarial patches are static, which is very different from many real-world DNN applications such as Traffic Sign Recognition (TSR) systems in autonomous vehicles. In TSR systems, object detectors use DNNs to process streaming video in real time. From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world. In this paper, we propose a systematic pipeline to generate robust physical AEs against real-world object detectors. Robustness is achieved in three ways. First, we simulate the in-vehicle cameras by extending the distribution of image transformations with the blur transformation and the resolution transformation. Second, we design the single and multiple bounding boxes filters to improve the efficiency of the perturbation training. Third, we consider four representative attack vectors, namely Hiding Attack, Appearance Attack, Non-Target Attack and Target Attack. We perform a comprehensive set of experiments under a variety of environmental conditions, and considering illuminations in sunny and cloudy weather as well as at night. The experimental results show that the physical AEs generated from our pipeline are effective and robust when attacking the YOLO v5 based TSR system. The attacks have good transferability and can deceive other state-of-the-art object detectors. We launched HA and NTA on a brand-new 2021 model vehicle. Both attacks are successful in fooling the TSR system, which could be a life-threatening case for autonomous vehicles. Finally, we discuss three defense mechanisms based on image preprocessing, AEs detection, and model enhancing.

Imagine that there were two possible states for weather: sunny or cloudy. You can always directly observe the current weather state, and it is guaranteed to always be one of the two aforementioned states.Now, you decide you want to be able to predict what the weather will be like tomorrow. Intuitively, you assume that there is an inherent transition in this process, in that the current weather has some bearing on what the next day's weather will be. So, being the dedicated person that you are, you collect weather data over several years, and calculate that the chance of a sunny day occurring after a cloudy day is 0.25. You also note that, by extension, the chance of a cloudy day occurring after a cloudy day must be 0.75, since there are only two possible states.You can now use this distribution to predict weather for days to come, based on what the current weather state is at the time.

Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling. A popular example is r/SubredditSimulator, which uses Markov chains to automate the creation of content for an entire subreddit. Overall, Markov Chains are conceptually quite intuitive, and are very accessible in that they can be implemented without the use of any advanced statistical or mathematical concepts. They are a great way to start learning about probabilistic modeling and data science techniques.

Earlier this month researchers from the Massachusetts Institute of Technology and Stanford University reported that they had found that three commercial facial-analysis programs from major tech companies showed bias in both skin-type and gender. The error rates for determining the gender of light-skinned men were 0.8% compared with much higher error rates for darker-skinned women, which in some cases was as much as 20% and 34%. This is not the first time an algorithm powering an AI application has delivered an erroneous -- to say nothing of embarrassing -- result. In 2015, Flickr, a photo-sharing site owned by Yahoo launched image-recognition software that automatically created tags for photos. Some of the tags being created were highly offensive -- such as "sport" and "jungle gym" for pictures of concentration camps and "ape" for pictures of humans including an African American man.

Let's consider a more sophisticated example, that of determining whether a tank is hiding in a photograph. A neural net can be configured so that each output value correlates to exactly one pixel. If the pixel is part of the image of a tank, the net should output a one; otherwise, the net should output a zero. The input information would most likely consist of the color of the pixel. The network would be trained by feeding it many pictures with and without tanks.

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. Wisely, the researchers had originally taken 200 photos, 100 photos of tanks and 100 photos of trees. They had used only 50 of each for the training set.

Hendrik Hamann is into cloud computing -- as in real clouds, those puffy things in the sky. Working at IBM alongside some of the computer giant's most advanced systems, Hamann and his team seek a breakthrough in cloud-cover forecasting. They're aiming to help ease the introduction of solar electricity into the nation's major power grids, as solar-generated power is increasingly being loaded onto the grid, propelled by government mandates and solar-technology price declines. There's a big problem with solar power that the IBM team is trying to solve: You can't pump out much electricity on a cloudy day. Another source of power has to take its place.

In this special guest feature, Victor Amin, Data Scientist at SendGrid, advises that businesses implementing machine learning systems focus on data quality first and worry about algorithms later in order to ensure accuracy and reliability in production. After graduating cum laude from Princeton University, Victor earned a PhD studying applications of machine learning to quantum chemistry at Northwestern University. At SendGrid, Victor builds machine learning models to predict engagement and detect abuse in a mailstream that handles over a billion emails per day. It's obvious that you need data before you can implement a machine learning system, but project planners often overlook questions regarding training set collection, cleaning, and maintenance. There are so many sources of big data in today's business systems that it seems like getting enough of the right data ought to be easy!