查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on computational intelligence is the subject of a new report. According to news originating from Beijing, People’s R epublic of China, by NewsRx editors, the research stated, “To accurately determi ne the leakage source location and strength during gas leakage accidents, this s tudy compares the concentration obtained from the diffusion model with that meas ured by the sensor and proposes an improved gray wolf optimization algorithm for leakage source location.” Financial supporters for this research include Social Science Foundation of Beij ing; Beijing Municipal Commission of Education; Natural Science Foundation of Be ijing Municipality.
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 artificial in telligence have been published. According to news reporting from Chengdu, People ’s Republic of China, by NewsRx journalists, research stated, “Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems.” Financial supporters for this research include Science And Technology Department of Sichuan Province; National Natural Science Foundation of China. The news reporters obtained a quote from the research from Xihua University: “Wh ile machine learning technology is widely employed for industrial equipment faul t diagnosis, its effective application relies on the availability of a large dat aset with annotated fault data for model training. However, in practice, the ava ilability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited in formation. To address this issue, this paper proposes a novel fewshot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation i njection and meta-confidence learning to improve detection accuracy.”
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 Learn ing - Pattern Recognition and Artificial Intelligence have been published. Accor ding to news reporting originating from Bengaluru, India, by NewsRx corresponden ts, research stated, “In the medical field, analyzing various bone structures is crucial due to the rigid nature of bones. X-ray imaging plays an essential role in medical procedures, including bone age evaluation, fracture detection, and i mplant creation.” Our news editors obtained a quote from the research from Bangalore University, “ However, operator involvement can introduce biases and increase processing time. Automating the process could reduce processing time and enhance diagnostic accu racy by minimizing biases and operator involvement. This paper introduces the Re fined DeepLab model, a lightweight encoder-decoder-based approach for multiclass segmentation of hand bones. The primary objective is to assist physicians in ta sks such as bone age analysis, fracture detection, hand movement analysis, and i mplant design. The research objectives are organized into three phases, with thi s work focusing on the first phase of our objectives, which is delineating bones from tissues, studying the bone structure, and multiclass segmentation of hand bones. The model utilizes DenseNet121 as its feature extractor and Sigmoid-weigh ted Linear Unit (SiLU) as its activation function. Experimental findings demonst rate promising performance in hand bone multiclass segmentation, with a Mean Int ersection over Union (mIoU) of 85.02% and a Dice score of 92.2% .”
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 Artificial Intelligence. According to news reporting from Urbana, Illinois, by N ewsRx journalists, research stated, “The quality evaluation of sweetpotatoes is of utmost importance during postharvest handling as it significantly impacts con sumer satisfaction, nutritional value, and market competitiveness. This study pr esents an innovative approach that integrates explainable artificial intelligenc e (AI) with hyperspectral imaging to enhance the assessment of three important q uality attributes in sweetpotatoes, i.e., dry matter content, soluble solid cont ent, and firmness.” Financial support for this research came from U.S. Department of Agriculture Agr icultural Marketing Service through the Specialty Crop Multistate Program grant.
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 androids is now availab le. According to news reporting originating from Soroti University by NewsRx cor respondents, research stated, “Humans use collaborative robots as tools for acco mplishing various tasks. The interaction between humans and robots happens in ti ght shared workspaces.” Financial supporters for this research include University of Genova; Centre Nati onal De La Recherche Scientifique; Lobbybot Project: Novel Encountered Type Hapt ic Devices.
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 Robotic s. According to news reporting originating in Dresden, Germany, by NewsRx journa lists, research stated, “Short-term automation solutions for industrial companie s have potential to compensate for volatile demand, employee turnover or shortag es.” The news reporters obtained a quote from the research from Fraunhofer Institute for Machine Tools and Forming Technology, “Current solutions are either not avai lable at short notice or economically unviable, as integration in existing contr ol systems and machine tools would be necessary. The paper proposes a cognitive robotic system that circumvents this problem by emulating the behaviour of opera tors through automated, cognitive skills.” According to the news reporters, the research concluded: “The approach uses a pe rception system, handling and skill modules and a skill-based control to automat e in an industrial brownfield use case without additional interfaces to the exis ting control systems to reduce integration effort to a minimum.”
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 i n a new report. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Local path planni ng is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance.” Funders for this research include National Natural Science Foundation of China; Zhejiang Provincial Natural Science Foundation. The news editors obtained a quote from the research from Zhejiang University of Technology: “In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is propo sed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle cl usters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated : we predict the obstacle position corresponding to the TEB pose through the det ected obstacle velocity and add this constraint to the corresponding TEB pose ve rtex.”
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 ar tificial intelligence. According to news reporting from Hengyang, People’s Repub lic of China, by NewsRx journalists, research stated, “Wire arc additive manufac turing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposi tion rate.” Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province. The news reporters obtained a quote from the research from University of South C hina: “One of the basic tasks in WAAM is to determine appropriate process parame ters, which will directly affect the morphology and quality of the weld bead. Ho wever, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expendi tures. This paper employed different machine learning models, including SVR, BPN N, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR mod el was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization ( PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing al gorithm as the training data. The K-fold cross-validation strategy was applied t o train and validate machine learning models.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on intelligent systems are presented i n a new report. According to news originating from Liaoning, People’s Republic o f China, by NewsRx correspondents, research stated, “This study provides a conci se overview of a grammatical error correction algorithm that is based on an enco der-decoder machine translation structure.” Our news journalists obtained a quote from the research from University of Scien ce and Technology Liaoning: “Additionally, it incorporates the attention mechani sm to enhance the algorithm’s performance. Subsequently, simulation experiments were conducted to compare the improved algorithm with an algorithm based on a cl assification model and an algorithm based on the traditional translation model u sing open corpus data and English translations from freshmen. The results demons trated that the optimized algorithm yielded superior intuitive error correction outcomes. When applied to both the open corpus and the English translations of c ollege freshmen, the optimized error correction algorithm outperformed the other s. The traditional translation model-based algorithm came in second, while the c lassification model-based algorithm showed the least favorable performance.”
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 ro botics. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Different patients have different re habilitation requirements. It is essential to ensure the safety and comfort of p atients at different recovery stages during rehabilitation training.” The news correspondents obtained a quote from the research from Beijing Jiaotong University: “This study proposes a multi-mode adaptive control method to achiev e a safe and compliant rehabilitation training strategy. First, patients’ motion intention and motor ability are evaluated based on the average human-robot inte raction force per task cycle. Second, three kinds of rehabilitation training mod es-robotdominant, patient-dominant, and safety-stop-are established, and the ad aptive controller can dexterously switch between the three training modes. In th e robot-dominant mode, based on the motion errors, the patient’s motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and enco urage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the co ntroller switches to the safety-stop mode to ensure patient safety.”
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