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 intelligence is the subject of a new report. According to news reporting originating from Nanjing, People's Republic of China, by NewsRx correspondents, research stated, "To address the significant energy waste generated by axial flow pumps, this paper proposes an intelligent optimization method based on physics-considering machine learning." The news reporters obtained a quote from the research from Hohai University: "First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics (CFD) method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on BPNN and the Encoding Layers of RandLA-Net created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%."
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 are discussed in a new report. According to news originating from Vellore, India, by NewsRx correspondents, research stated, "The choice of a suitable collaborative robot (cobot) for a real-time industrial process is one of the obstacles to effective robot implementation in terms of energy and cost." Our news journalists obtained a quote from the research from School of Mechanical Engineering: "The cobot selection process for an application have become more complex due to increasing sophisticated features and capabilities in cobots offered by the manufacturers. The paper presents a hybrid Multi- Criteria Decision-Making (MCDM) technique based on Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches for selecting cobots for fuel filter assembly operation. The product design methodology, manufacturing method, and associated cost are directly influencing the decision on cobot selection. The most appropriate robot to accomplish the desired task at the lowest possible cost and capability can be selected by AHP with prospective criterion weight for subsequent processing. The TOPSIS approach orders alternatives based on the prominence of criteria. A diesel fuel filter assembly process case was considered for validating the proposed technique of cobot selection process."
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 Machine Learning is now available. According to news reporting out of Tallahassee, Florida, by NewsRx editors, research stated, "Computational prediction of phase stability of multi-principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network." Funders for this research include Startup funding from Florida State University, Florida State University, Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility, United States Department of Energy (DOE), Research Computing Center (RCC) at Florida State University, United States Department of Energy (DOE).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. According to news reporting out of Wuhan, People's Republic of China, by NewsRx editors, research stated, "In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed." Financial supporters for this research include Hubei Province Nature Science Foundation; Hubei Province Technology Innovation Key Research And Development Project; National Natural Science Foundation of China; Hubei Province Central Government Guide Local Science And Technology Development Project. The news reporters obtained a quote from the research from Wuhan Institute of Technology: "Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learning have been published. According to news originating from Kuala Lumpur, Malaysia, by NewsRx correspondents, research stated, "Blast-induced overbreak in tunnels can cause severe damage and has therefore been a main concern in tunnel blasting. Researchers have developed many machine learning-based models to predict overbreak." Our news journalists obtained a quote from the research from the University of Malaya, "Collecting overbreak data manually, however, can be challenging and might obtain insufficient or poorly structured data. Thus, this study aims to utilise a deep generative model, namely the Conditional Tabular Generative Adversarial Network (CTGAN), to establish an acceptable dataset for overbreak prediction. The CTGAN model was applied to overbreak data collected from paired tunnels: a left-line tunnel and a right-line tunnel. The overbreak dataset collected from the left-line tunnel-nominated as the true dataset-served to train the CTGAN model. Then the well-trained CTGAN model generated a synthetic overbreak dataset. Statisticalbased approaches verified the similarity between the true and synthetic datasets; machine learning-based approaches verified the feasibility of using the synthetic dataset to train overbreak prediction model. Lastly, this study clarified how to resolve the problem of data shortage and data imbalance by leveraging the CTGAN model. The results evidence that the CTGAN model can effectively generate a high-quality synthetic overbreak dataset. The synthetic overbreak dataset not only greatly retains the properties of the true dataset but also effectively enhances its diversity. The way, integrating the true and synthetic overbreak datasets, can dramatically resolve the problem of data shortage and data imbalance in overbreak prediction."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learning have been published. According to news reporting originating in Punjab, India, by NewsRx journalists, research stated, "Public highways are, in reality, the cornerstone of the country's transportation system. Accidents are unavoidable with this mode of transportation." The news reporters obtained a quote from the research from the Department of Computer Sciences and Engineering, "Collisions involving resting livestock on national highways occur in most countries around the world. It endangers both the drivers and the animals. This paper proposes a method for mitigating the risk of accidents caused by deceased animals, notably cattle that are generating traffic and congestion on national highways and may constitute a safety risk. We have proposed an Internet of Things (IoT) fog-based framework for reclining livestock identification techniques for roadways, data are collected using the IoT-enabled video recording surveillance cameras. We use feature extraction, characteristic expression, assessment criteria, and an unrestricted approach for detecting deceased livestock (such as cows or buffalos), as well as recommendations on whether their placement is harmful to highway traffic. In this study, you only look once (YOLO) image recognition algorithm is implemented for reclining cattle on roadways using the fog layer for training and evaluating datasets. The performance parameters of the proposed framework, such as accuracy, recall, precision, mean average precision (mAP), and interference time, have been measured, and a comparison with existing state-of-the-art techniques has been presented. The obtained findings indicate that the suggested framework surpasses the present approaches, with a higher accuracy of 98% and an interference time of 4.68 ms. Artificially intelligent surveillance system can spot reclined livestock utilizing surveillance videos on roadways."
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 robotics. According to news reporting from Ljubljana, Slovenia, by NewsRx journalists, research stated, "This paper focuses on the development of a novel climbing robot that is designed for autonomous maintenance of vertical gardens in urban environments." Financial supporters for this research include Slovenian Research Agency; European Commission's Directorate-general For Communications Networks, Content And Technology. Our news reporters obtained a quote from the research from Jozef Stefan Institute: "The robot, designed with a unique five-legged structure, is equipped with a range of electrical and mechanical components, enabling it to autonomously navigate and maintain a specially designed vertical garden wall facilitating interactive maintenance and growth monitoring. The motion planning and control of the robot were developed to ensure precise and adaptive movement across the vertical garden wall. Advanced algorithms were employed to manage the complex dynamics of the robot's movements, optimizing its efficiency and effectiveness in navigating and maintaining the garden structure. The operation of the robot in maintaining the vertical garden was evaluated during a two-week trial where the robot successfully performed nearly 8000 leg movements, with only 0.6% requiring human intervention."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Robotics. According to news reporting out of Harbin, People's Republic of China, by NewsRx editors, research stated, "This paper proposes an optimization-based adaptive local planner to address the aggressive motion planning problem of an autonomous mobile robot in realistic semi-structured scenarios. The goal is to enable the robot to perform autonomous navigation tasks at the limit of its speed capability to maximize the efficiency, while ensuring safety and feasibility." Our news journalists obtained a quote from the research from the Harbin Institute of Technology, "The proposed approach constitutes an integrated scheme that leverages real -time hierarchical collision avoidance constraints reformulation and handles the heterogeneous constraints using a unified nonlinear optimization. An aggressiveness adaption method is employed to cope with disturbances instead of relying on excessive obstacle inflation, resulting in reduced conservatism while enhancing safety. The efficacy of the proposed method is demonstrated through simulation and experimental results." According to the news editors, the research concluded: "Compared with previous work, the proposed approach provides significant improvements in navigation success rate without sacrificing the task efficiency."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented in a new report. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "Because of their low cost, large workspace, and high flexibility, industrial robots have recently received significant attention in large-scale part machining." Funders for this research include China Postdoctoral Science Foundation; National Key Research And Development Program of China For Robotics Serialized Harmonic Reducer Fatigue Performance Analysis And Prediction And Life Enhancement Technology Research. The news reporters obtained a quote from the research from Shanghai Jiao Tong University: "However, due to the stiffness limitations in robot joints and links, industrial robots are prone to vibration during milling processes, which leads to poor surface topography. In robotic milling processes, it remains challenging to simulate the surface topography accurately. This paper presents a mathematical model of surface topography combined with the effects of process parameters and tool vibrations in robotic milling. In this method, the kinematic trajectory of the cutting edge is first calculated by considering the cutter geometry, tool eccentricity, tool orientation, and redundancy angle. After that, the posture-dependent dynamic characteristics of the robotic milling system are predicted using an inverse distance-weighted approach. Then, a dynamic model of the robotic milling system is constructed for calculating tool vibration displacements."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cerebrovascular Diseases and Conditions-Stroke is the subject of a report. According to news reporting originating from Yunnan, People's Republic of China, by NewsRx correspondents, research stated, "Studies have shown that a series of molecular events caused by oxidative stress is associated with ferroptosis and oxidation after ischemic stroke (IS). Differential analysis was performed to identify differentially expressed mRNA (DEmRNAs) between IS and control groups." Funders for this research include Major Science and Technology Special Project of Yunnan Province, Nature Science Foundation of China, Yunnan Basic Research Projects, Applied Basic Research of Yunnan Neurological Disease Diagnosis and Treatment Center, Yunnan Applied Basic Research Project-Union Foundation of China.
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