Note: SteerMouse does not support the Apple Magic Mouse, Magic. Both USB and Bluetooth mice are supported. The influence of the model hyperparameters is evaluated. SteerMouse is a utility that lets you freely customize buttons, wheels and cursor speed.Easy to implement and can be applied for efficient charging infrastructure management.Outperform benchmark time series, machine learning, and deep learning approaches.New mixed LSTM method to predict discrete EV charging occupancy sequence.A sensitivity analysis is conducted to evaluate the impact of the model parameters on prediction accuracy. Macro Recorder: Mouse clicks can be automated in a sequence and recorded. I changed the amount of lines per scroll but it felt like it was super buggy and didn't understand what I was trying to do. Click on the button Start to start the Macro to do Mouse Clicking. On a last note, it should be mentioned 19.99USD only gets you one major release of SteerMouse, you'll need to pay 12.99USD to upgrade to the next major release. SteerMouse can customize up to eight mouse buttons, which can each be assigned. The scrolling was not smooth at all, barely even scrolled when I started scrolling. If you're after consistent, stable, up-to-date releases of a bug-free program designed to power the user, SteerMouse is definitely the way to go. You can also create your own keyboard shortcuts and record key sequences. The results show that the proposed method produces very accurate predictions (99.99% and 81.87% for 1 step (10 min) and 6 steps (1 h) ahead, respectively, and outperforms the benchmark approaches significantly (+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). Tried SteerMouse, and it worked great for getting rid of the acceleration, but the scroll wheel was basically useless. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of the city of Dundee, UK. Unlike the existing LSTM networks, the proposed model separates different types of features and handles them differently with mixed neural network architecture. You cant even set the buttons to act as any other key or key combination, much less program a menu item or sequence of keys. We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for multistep discrete charging occupancy state prediction. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. A key sequence can be cast to a QString to obtain a human readable translated version of the sequence. Key sequences can be constructed either from an integer key code, or from a human readable translatable string such as 'Ctrl+X,Alt+Space'. ![]() Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. For example, UNICODEACCEL + 'A' gives the same key sequence as KeyA.
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