This work is set to design pose-free visual feedback control laws. Our contribution is linked to improving 3D pose-free visual servoing strategy using 2D predictions of visual features’ trajectories. Given a set rigid body characterized by a set of 3D point features, our proposed visual servoing strategy enhanced with predictive elements is based on computing the following sample time positions of the point features given the known camera velocity from the previous time moment. In this way, the architecture decides to discard the point feature before exiting the field of view. Thus, any unnecessary computations are removed, and the camera’s motion is ensured to be more natural. The simulation results emphasize the potential of the new visual control architecture.
Smart homes use a variety of technology that automate tasks and increase convenience in our daily lives. Functions performed by the devices can be improving security access, controlling lighting and adjusting temperature. Given the importance of convenience and cost efficiency in such settings, as well as the large number of devices involved, it is necessary to monitor power usage in smart homes. Furthermore, rising energy consumption increases the carbon footprint, exacerbates climate threats and puts a strain on energy supplies. As a result, monitoring energy use becomes critical for guaranteeing sustainability and efficiency. In this paper, we analyzed the energy consumption and usage in a home with multiple smart devices that are connected to a smart meter and we tried to offer optimization. This research focuses on optimizing energy usage while balancing user convenience, incorporating factors such as air pressure, dew point, and wind speed. To evaluate the optimization process, a hybrid approach using Linear Regression, Random Forest Regression and Gradient Boosting was developed. The study aimed to optimize energy consumption by predicting net energy usage. By utilizing predictions and optimized control strategies, smart home appliances can be managed proactively and efficiently.
Logical dependencies, extracted from co-changes from the versioning system, have multiple applications across numerous fields, including fault detection, software reconstruction, key class identification, among others. This paper will focus on the influence of code co-changes on software clustering for architectural reconstruction. Specifically, we will analyze their impact on the clustering solution of Apache Ant in order to assess whether co-changes usage enhances the quality of the obtained solution.
This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.
This paper presents a controller based on input-output linearisation (IOL) for a vapor compression cycle leveraging a moving-boundary model of the evaporator. After a presentation of the test rig and adaptations of a previously published high-order simulation model, the control-oriented moving-boundary model is derived. Special emphasis is laid on steady-state solutions of the governing partial differential equations. The corresponding ansatz functions enable a high model accuracy while keeping the model order low. The IOL based on the moving-boundary model is compared by simulations with a previously published IOL based on a lumped-parameter approach. Here, the IOL based on the moving-boundary model shows a high performance in a much larger operation range. These results can be confirmed through the comparative evaluation with experimental data. Finally, we show the general applicability of the controller with an implementation on a dedicated test rig at the Chair of Mechatronics, University of Rostock.
