查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Milano, Italy, by NewsRx c orrespondents, research stated, “The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pi votal solution to address the challenges of energy efficiency, battery degradati on, and optimal power management.” Our news journalists obtained a quote from the research from Polytechnic Univers ity Milan: “The capability of such systems to differ from theoretical modeling e nhances their applicability across various domains. The vast amount of data avai lable today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable fu ture, the electrification of vehicles and integrating electric systems for energ y storage are becoming increasingly important and need to be addressed. The syne rgy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To th e best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The rese arch investigates the importance of AI advancements in energy storage systems fo r electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of -Health (SOH), and exploring the potential for integrating Renewable Energy Sour ces with EV charging needs and optimizing charging cycles.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics - Roboti cs and Automation are discussed in a new report. According to news originating f rom Beijing, People’s Republic of China, by NewsRx correspondents, research stat ed, “Visual localization, also known as camera pose estimation, is a crucial com ponent of many applications, such as robotics, autonomous driving, and augmented reality. Traditional visual localization algorithms typically run on point clou d maps generated by algorithms such as Structure-from- Motion (SfM) or Simultaneo us Localization and Mapping (SLAM).” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “However, point features are sensitive to weak textures and illumi nation changes. In addition, the generated 3D point cloud maps often contain mil lions of points, which puts higher demands on device storage and computing resou rces. To address these challenges, we propose a visual localization algorithm ba sed on lightweight structured line maps. Instead of extracting and matching poin t features in the images, we select line segments that represent structured scen e information as image features. These line segments are then used to construct a lightweight line map containing rich structured scene information. The camera pose is then estimated through a series of steps that include line extraction, m atching, initial pose estimation, and pose refinement.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Robotics. A ccording to news originating from Blida, Algeria, by NewsRx editors, the researc h stated, “In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard mul ti-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables.” Our news journalists obtained a quote from the research from Saad Dahlab Univers ity, “Then, in the second phase, a global search is performed using a population -based search metaheuristic in order to build a first approximation of the Paret o front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in orde r to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direc t search method to enhance the quality of compromise solutions and to converge t oward the true optimal PF. By combining different optimization techniques, we in tend not only to improve the overall search mechanism of the optimization strate gy but also the resulting hybrid algorithm should keep the robustness of the pop ulation-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flex ible, and it can be implemented in different ways according to the applied techn iques in the different phases.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating in Baton Rouge, Louisiana, by NewsRx journalists, research stated, “Pavement markings are essential traffic c ontrol devices that enhance safety for motorists during nighttime. Numerous stat istical learning models have been developed in prior studies to predict the retr oreflectivity of the markings, but the applicability of these models is question able in terms of accuracy.” Financial supporters for this research include National Cooperative Highway Rese arch Program (NCHRP), NCHRP IDEA project. The news reporters obtained a quote from the research from Louisiana State Unive rsity, “The key objective of this study was to develop a machine learning-based framework that can be used by US transportation agencies to reliably predict the retroreflectivity of their pavement markings over a period of 3 years utilizing the initially measured retroreflectivity and other key project conditions. The random forest (RF) algorithm was used in this study to develop the proposed fram ework considering seven types of marking materials in three different US climate zones. A total of 49,632 transverse skip retroreflectivity measurements were re trieved from the National Transportation Product Evaluation Program (NTPEP) and 11 RF models were developed to sequentially predict retroreflectivity at differe nt prediction horizons. The models were trained with randomly selected 80% of the total data points, and the remaining 20% data points were u tilized for testing the predictive performance of the developed models. The RF m odels predicted the retroreflectivity with a superior level of accuracy (R2 rang ing between 0.88 and 0.99) than the models proposed in prior studies.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgery - Nephrectomy is the subject of a report. According to news reporting originating from Florenc e, Italy, by NewsRx correspondents, research stated, “To compare two cohorts of patients submitted to robot-assisted partial nephrectomy (RAPN) for highly-compl ex renal masses (PADUA 10) with versus without the use of 3DVMs. We screened a p rospective consecutive cohort of 152 patients submitted to RAPN with 3DVM and 12 64 patients submitted to RAPN without 3DVM between 2019 and 2022.” Our news editors obtained a quote from the research from the University of Flore nce, “Only PADUA 10 cases were considered eligible for analysis. Propensity scor e matching (PSM) analysis was applied. Primary endpoint was to evaluate whereas RAPNs with 3DVM were superior in terms of functional outcomes at 12-month. Secon dary outcomes were to compare perioperative and oncological outcomes. Multivaria ble logistic regression analyses (MVA) tested the associations of clinically sig nificant eGFR drop and 3DVMs. Subgroups analysis was performed for PAUDA-risk ca tegories. Thirty seven patients for each group were analyzed after PSM. RAPN wit h 3DVM presented a higher rate of selective/no clamping procedure (32.5 % vs 16.2%, = 0.03) and a higher enucleation rate (43.2% vs 29.8%, = 0.04). Twelve-month functional preservation performed b etter within 3DVM group in terms of creatinine serum level (median 1.2 [IQR 1.1-1.4] vs 1.6 [IQR 1.1-1.8] , = 0.03) and eGFR (median 64.6 [IQR 56.2-74.1] vs 52.3 [IQR 49.2-74.1], = 0.03). MVA conf irmed 3DVM as a protective factor for clinically significant eGFR drop in this s ubgroup of patients. RAPN performed with the use of 3DVM assistance for PADUA 10 cases resulted in lower incidence of global ischemia and higher rate of enuclea tions.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics - Robotics an d Automation is the subject of a report. According to news reporting out of Buda pest, Hungary, by NewsRx editors, research stated, “This work addresses the chal lenge of reinforcement learning with reward functions that feature highly imbala nced components in terms of importance and scale. Reinforcement learning algorit hms generally struggle to handle such imbalanced reward functions effectively.” Financial support for this research came from Ministry of Culture and Innovation of Hungary from the National Research, Development, and Innovation Fund. Our news journalists obtained a quote from the research from the Budapest Univer sity of Technology and Economics, “Consequently, they often converge to suboptim al policies that favor only the dominant reward component. For example, agents m ight adopt passive strategies, avoiding any action to evade potentially unsafe o utcomes entirely. To mitigate the adverse effects of imbalanced reward functions , we introduce a curriculum learning approach based on the successor features re presentation.” According to the news editors, the research concluded: “This novel approach enab les our learning system to acquire policies that take into account all reward co mponents, allowing for a more balanced and versatile decision-making process.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Guang zhou, People’s Republic of China, by NewsRx correspondents, research stated, “Ce rvical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This r esearch aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology.” Our news editors obtained a quote from the research from Sun Yat-sen University, “The AICCS system was trained and validated using various datasets, including r etrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) model s: one for detecting cells at the patch-level and another for classifying whole- slide image (WSIs). The AICCS consistently showed high accuracy in predicting cy tology grades across different datasets. In the prospective assessment, it achie ved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AU C, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news reporting originating in Sichuan, People’s Republic of China, by NewsRx journalists, research stated, “Effective three-dimensional ( 3D) scene representation for grasping is significant in smart manufacturing and industrial applications. Serving as a foundational element for robot manipulatio n, the desired 3D scene representation should encapsulate critical high-level pr operties.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Sichuan Science and Technology Support Program, Sichuan Prov ince Information Application Support Software Engineering Technology Research Ce nter Open Project, Key Research and Development Program of Sichuan Province.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Artificial Intelligence is now available. According to news originating from Ottawa, Canada, by NewsRx correspondents, research stated, “Implementation of the state-of-the-art underst anding of the mechanics of unsaturated soils into geotechnical engineering pract ice is partly limited due to the lack of quick, reliable, and economical techniq ues for matric suction measurement. Matric suction is one of the key stress stat e variables that significantly influences the hydro-mechanical behavior of unsat urated soils.” Financial supporters for this research include China Scholarship Council, Univer sity of Ottawa, Canada, Natural Sciences and Engineering Research Council of Can ada (NSERC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of Macau, People’s Republic of China, by NewsRx editors, research stated, “In this paper, we propose a fully sy mmetrical obfuscated-interconnection PUF (SOI PUF), which contains n delay stage s with each stage having 4k obfuscated interconnections for resisting machine le arning (ML)-based modeling attacks. All the delay stages contribute to k PUF pri mitives while achieving a 20x increase in the number of possible interconnection s with the same hardware resources over similar prior arts.” Financial support for this research came from Macau Science and Technology Devel opment Fund.
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