查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting originating in Weihai, People's Republic o f China, by NewsRx journalists, research stated, "Object goal navigation tasks a re critical for robots operating in unfamiliar environments, where they must loc ate specific objects using visual cues. The ability to leverage prior knowledge significantly enhances a robot's associative capabilities, leading to improved n avigation performance." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Shandong Province, China Postd octoral Science Foundation, Young Scholars Program of Shandong University, Weiha i. The news reporters obtained a quote from the research from Shandong University, "However, existing methods struggle with the generalization challenge when trans ferring navigation models to new environments, a key issue addressed in this pap er. To overcome this challenge, on the one hand, a time-varying knowledge graph is proposed to update the prior knowledge graph with context vectors derived fro m co-occurrence objects in the current observation. This approach prioritizes lo cal graphs centered around the target and co-occurring objects, allowing for eff icient and accurate target localization. Furthermore, the dynamic updating mecha nism facilitates efficient exploration in new scenarios. On the other hand, to e mbed prior knowledge more rationally in the reinforcement learning-based navigat ion strategy, a timevarying knowledge graph inference network (TVGN) is present ed. The TVGN utilizes context vectors and global spatial semantic information to perceive and understand the environment in real-time. It formulates navigation strategies based on the precise goal information encoded within the graph, there by enhancing the robot's efficiency in reaching the target. Based on the widely applied dataset AI2-THOR, extensive comparative experiments are conducted to ill ustrate the effectiveness of the proposed method."
查看更多>>摘要：Current study results on Robotics -Ro botics and Automation have been published. According to news reporting out of Si ngapore, Singapore, by NewsRx editors, research stated, "This letter details the design and development of a novel 3D-printed, lightweight and rapid-curing auto mated payload installation system for aerial robots, using a 3D printed resin-fi lled adhesive carrier tile (ACT). Its structure is designed to fracture and disp erse ultraviolet (UV) curable resin on impact, delivered with a lightweight spri ng-driven impactor that rams the tile against a target surface." Financial support for this research came from Ministry of Education, Singapore. Our news journalists obtained a quote from the research from the Singapore Unive rsity of Technology and Design, "The dispersed resin is then cured with UV light . Shear-testing experiments with 40 x 40 mm ACTs across common building material s, surface conditions and roughness demonstrate loading exceeding 900 N only aft er 10 seconds of curing, showcasing the strength, robustness and speed of the pr oposed system. Automated payload installation experiments show potential for app lications requiring strong and permanent bonds to wall structures, such as senso r payloads or tether points within urban environments."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsResearch findings on Machine Learning are discuss ed in a new report. According to news reporting out of Oxford, United Kingdom, b y NewsRx editors, research stated, "In-network machine learning inference provid es high throughput and low latency. It is ideally located within the network, po wer efficient, and improves applications' performance." Financial supporters for this research include VMware, European Union (EU), Inte l Corporation, Nvidia Corporation.Our news journalists obtained a quote from the research from the University of O xford, "Despite its advantages, the bar to in-network machine learning research is high, requiring significant expertise in programmable data planes, in additio n to knowledge of machine learning and the application area. Existing solutions are mostly one-time efforts, hard to reproduce, change, or port across platforms . In this paper, we present Planter: a modular and efficient open-source framewo rk for rapid prototyping of in-network machine learning models across a range of platforms and pipeline architectures. By identifying general mapping methodolog ies for machine learning algorithms, Planter introduces new machine learning map pings and improves existing ones. It provides users with several example use cas es and supports different datasets, and was already extended by users to new fie lds and applications. Our evaluation shows that Planter improves machine learnin g performance compared with previous model-tailored works, while significantly r educing resource consumption and co-existing with network functionality."
查看更多>>摘要：Current study results on robotics have been published. According to news reporting out of Zhengzhou, People's Republic of China, by NewsRx editors, research stated, "Research on animal robots utiliz ing neural electrical stimulation is a significant focus within the field of neu ro-control, though precise behavior control remains challenging." The news journalists obtained a quote from the research from Zhengzhou Universit y: "This study proposes a parameter-adaptive strategy to achieve accurate path t racking. First, the mapping relationship between neural electrical stimulation p arameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are es tablished to dynamically generate different stimulation patterns. A parameter-ad aptive path tracking control strategy (PAPTCS), based on fuzzy control principle s, is designed for the precise path tracking tasks of pigeon robots in open envi ronments. The results indicate that altering stimulation parameter levels signif icantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%."
查看更多>>摘要：Current study results on Machine Learn ing have been published. According to news originating from Zibo, People's Repub lic of China, by NewsRx correspondents, research stated, "The process optimizati on and control of nitrogen-rich biomass pyrolysis technology is crucial. This te chnology effectively converts agricultural waste into high-value energy products ." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Shandong Innovation Fund for Small and Medium-sized Technolo gy-based Firms.
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting out of Regina, Canada, by NewsRx editors, research stated, "This comprehensive study looks at how operational con ditions affect the performance of a novel seven-channel titania ceramic ultrafil tration membrane for the treatment of produced water." Funders for this research include Natural Sciences And Engineering Research Coun cil of Canada.
查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Floria nopolis, Brazil, by NewsRx editors, the research stated, "This paper presents th e modeling and implementation of a customized Machine Learning (ML) model design ed to take advantage of synchrophasor data to enhance the arming procedure of a critical System Integrity Protection Scheme (SIPS) of the Brazilian Interconnect ed Power System (BIPS)." Funders for this research include State Grid Brazil Holding S.A., Brazilian Inde pendent System Operator. The news reporters obtained a quote from the research from Federal University Sa nta Catarina, "This model allows risk-averse decision-making, mitigating loss of selectivity conditions. Implementation has been achieved using applications dev eloped in the Open and Extensible Control and Analytics (openECA) software envir onment."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsResearch findings on robotics are discussed in a new report. According to news reporting originating from Gumi, South Korea, by N ewsRx correspondents, research stated, "This paper deals with the problem of mul ti-robot task scheduling in the Antarctic environments with crevasses." Financial supporters for this research include Korea Institute of Marine Science & Technology Promotion; Institute of Information & Communications Technology Planning & Evaluation. The news journalists obtained a quote from the research from Kumoh National Inst itute of Technology: "Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be pl anned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, th e asymmetry of sensor data, and the possibility of crevasses drifting irregularl y as time passes."
查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news reporting out of Roorkee, India, by NewsRx edi tors, research stated, "Spatial prediction of debris flows in terms of susceptib ility mapping is the first and foremost requirement for disaster mitigation. In the present study, a comparative evaluation of machine learning and statistical approaches for debris flow susceptibility zonation (DFSZ) mapping has been attem pted using 10 causative thematic layers (slope, aspect, elevation, plan curvatur e, profile curvature, topographic wetness index, stream power index, geology, pr oximity to streams, normalized difference vegetation index) and a debris flow in ventory containing 85 debris flow locations." Our news journalists obtained a quote from the research from CSIR -Central Buil ding Research Institute, "The employed machine learning (ML) approaches include random forest (RF), na & iuml;ve Bayes (NB), and extreme gradient boosting (XGBoost) models whereas statistical models include the weight of evide nce (WoE) and index of entropy (IoE). The results indicated that in all 5 DFSZ m aps, about 21.20-47.98% of the area is very highly and highly susc eptible to debris flows. It is observed that the major debris flows as well as h igh susceptible zones are distributed along the river Alakananda and its tributa ries and at the vicinity of the NH-58. Among the statistical models, the DFSZ ma p prepared using the weight of evidence (WoE) model provides higher accuracy in terms of the success rate and the prediction rate compared to that prepared usin g the index of entropy model (IoE). Among the machine learning-based models, bot h the extreme gradient boosting (XGBoost) and random forest (RF) models give bet ter accuracy and are more efficient than the Na & iuml;ve Bayes (N B) model. It is also observed that the ML models perform better than the statist ical models for a part of Chamoli district, Uttarakhand state (India). The novel ty of the present study lies in the spatial prediction of one of the most destru ctive forms of mass movement (debris flow) in the Indian Himalayas using statist ical and ML models and establishing the superiority of the ML Random Forest & XGBoost model over other ML and statistical models for the present case. This st udy will help make decisions on the suitability of developmental activities and human settlement in the area under consideration."
查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news reporting originating in Wurzburg, Germany, by NewsRx journalists, research stated, "The Food and Drug Administration's recent decision to eliminate mandatory animal testing for drug approval marks a signifi cant shift to alternative methods. Similarly, the European Parliament is advocat ing for a faster transition, reflecting public preference for animal-free resear ch practices." Financial support for this research came from Deutsche Forschungsgemeinschaft. The news reporters obtained a quote from the research from Fraunhofer Institute for Silicate Research ISC, "In vitro tissue models are increasingly recognized a s valuable tools for regulatory assessments before clinical trials, in line with the 3R principles (Replace, Reduce, Refine). Despite their potential, barriers such as the need for standardization, availability, and cost hinder their widesp read adoption. To address these challenges, the Robotic Enabled Biological Autom ation (ReBiA) system is developed. This system uses a dual-arm robot capable of standardizing laboratory processes within a closed automated environment, transl ating manual processes into automated ones. This reduces the need for process-sp ecific developments, making in vitro tissue models more consistent and cost-effe ctive. ReBiA's performance is demonstrated through producing human reconstructed epidermis, human airway epithelial models, and human intestinal organoids. Anal yses confirm that these models match the morphology and protein expression of ma nually prepared and native tissues, with similar cell viability."
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