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Machine Learning for Robotics Applications
To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.
Concerning the external disturbance, nonlinear operation conditions and parametric uncertainties of wind conversion system based permanent magnet synchronous generator (PMSG), in this paper we propose a passivity-based control (PBC) associated to fuzzy logic controller for dynamic performance improvement. The studied renewable system is constituted by a wind turbine based PMSG connected to the electrical grid through PWM converter. The PBC is applied to the generator-side when a classical PI controller is applied to the grid-side. The controller aim of this work is to regulate the DC-link voltage and the reactive generated power to their desired value. The simulation results performed under MATLAB/Simulink show the effectiveness of the proposed strategy.
Insurance 4.0 platforms are strategic components to support organizational efficiency, effectiveness, profitability, and to create new business models, products, and services. The use of innovative digital platforms in insurance 4.0 can yield several benefits. They support the daily business and administrative tasks, aid to take complex decision-making, and help in managing processes. The real objective of the platforms is to free insurance organizations from operational tasks and support their focus on strategic decisions and activities. The leading platforms at the basis of insurance 4.0 are the internet of things, with black boxes and similar, the cognitive insurance, with its essential components of big data analytics, artificial intelligence, and RPA—robotic process automation. Blockchain, smart contracts, and mobileTechnologiesnetworkmobility, mobile can play essential roles in the insurance 4.0 transformation. This chapter confirms that to implement insurance 4.0, it is necessary to digitize insurance processes heavily. There might be challenges in this respect in current cultures, procedures, processes, capacities, and capabilities.
