查看更多>>摘要：Researchers detail new data in Robotic s. According to news originating from Shanghai, People's Republic of China, by N ewsRx correspondents, research stated, "Six-legged robots possess powerful terra in traversal capabilities. To achieve small mechanism dimensions that meet these capabilities is crucial for reducing weight and size." Financial support for this research came from Fund of the Shanghai Academy of Sp aceflight Technology. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, "Traditional tryand-verify design methods that repeat mechanism dime nsion design, simulation, and verification cannot rapidly ensure a suitable resu lt. Optimization methods can obtain an optimal result but cannot be visualized a nd utilize engineers' valuable experience. This paper proposes a novel mechanism dimension design method for six-legged robots that maximize terrain traversal c apabilities in four representative terrains: trenches, low spaces, obstacles, an d steps. Analytical conditions are established to model the relationship between robot's mechanism dimensions and terrain parameters, which are derived from rob ot-terrain non-interference conditions, static stabilities, and workspace limita tions. Performance charts of the terrain traversal capabilities are plotted to s how visualized regions for suitable mechanism dimensions. A suitable dimension i s then selected by engineers based on the charts."
查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news originating from Singapore, Singapore, by Ne wsRx correspondents, research stated, "Machine learning models play a crucial ro le in smart manufacturing by revolutionizing industrial automation so as to boos t productivity and product quality. However, the reliability of these models oft en faces challenges from factors such as data drift, concept drift, adversarial attacks, and increasing model complexity." Our news journalists obtained a quote from the research from Agency for Science Technology and Research (A*STAR), "In addressing these challenges, this paper pr oposes a novel approach called Reliability Improved Machine Learning (RIML), whi ch leverages on prior knowledge by incorporating it into the machine learning pi peline through a secondary output that is easily verifiable and assessable withi n the application domain. Built upon the Knowledge-embedded Machine Learning (KM L) framework, RIML differs from conventional strategies by modifying the model's architecture. In its implementation, additional layers were introduced, specifi cally designed to identify and discard misclassified cases to improve the model' s reliability. RIML's efficacy was successfully demonstrated through a simulated dataset and three real use-case studies, namely, a general walk/run scenario, a n industry-related case using metro railway dataset, and a smart manufacturing a pplication on gas detection."
查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting from Allschwil, Switzerland, by NewsRx j ournalists, research stated, "Statistical and machine learning models are common ly used to estimate spatial and temporal variability in exposure to environmenta l stressors, supporting epidemiological studies. We aimed to compare the perform ances, strengths and limitations of six different algorithms in the retrospectiv e spatiotemporal modeling of daily birch and grass pollen concentrations at a sp atial resolution of 1 km across Switzerland."
查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news originating from Madrid, Spain, by NewsRx correspondents, research stated, "Generative Artificial Intelligence ( AI) is a technological innovation with wide applicability in daily life, which c ould help elderly people. However, it raises potential conflicts, such as biases , omissions and errors." Our news journalists obtained a quote from the research from Hospital Universita rio, "Descriptive study through the negative stereotypes towards aging questionn aire (CENVE) conducted on chatbots ChatGPT, Gemini, Perplexity, YOUChat, and Cop ilot was conducted. Of the chatbots studied, three were above 50 % in responses with negative stereotypes, Copilot with high ageism level results, followed by Perplexity. In the health section, Copilot was the chatbot with the most negative connotations regarding old age (13 out of 20 points). In the perso nality section, Copilot scored 14 out of 20, followed by YOUChat. The Copilot ch atbot responded to the statements more ageistically than the other platforms."
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Zhejiang, People's Republic of Chin a, by NewsRx journalists, research stated, "The primary aim of this study is to predict the hardness of high entropy alloys and identify optimal alloy compositi ons with superior hardness through machine learning techniques. To enhance the a ccuracy of predictions, a dual-layer algorithmic machine learning model was empl oyed and augmented with Shapley Additive Explanations (SHAP) analysis to increas e the model's interpretability." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Wenzhou Universi ty, "During model development, multiple machine learning algorithms were evaluat ed, and innovatively, a combination of the three most optimal model outcomes was incorporated into the prediction process, thus improving the accuracy of hardne ss predictions. Furthermore, using the Al-Co-Cr-Fe-Ni system as an example, an H EA with a predicted hardness of 776HV was identified from 820,000 datasets."
查看更多>>摘要：A new study on Artificial Intelligence is now available. According to news reporting out of Las Palmas de Gran Canaria , Spain, by NewsRx editors, research stated, "The integration of generative arti ficial intelligence technology into research environments has become increasingl y common in recent years, representing a significant shift in the way researcher s approach their work. This paper seeks to explore the factors underlying the fr equency of use of generative AI amongst researchers in their professional enviro nments." Our news journalists obtained a quote from the research from the University of L as Palmas de Gran Canaria, "As survey data may be influenced by a bias towards s cientists interested in AI, potentially skewing the results towards the perspect ives of these researchers, this study uses a regression model to isolate the imp act of specific factors such as gender, career stage, type of workplace, and per ceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, a nd scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decreas e. Female researchers experience a 7 % decrease in AI tool usage c ompared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researche rs in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively."
查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Numerical weather models often face significan t challenges in achieving high prediction accuracy. To enhance the predictive pe rformance of these models, a solution involving the integration of deep learning algorithms has been proposed." 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 North China Electri c Power University, "This paper introduces a machine learning approach for corre cting the numerical weather forecast results from the Weather Research and Forec asting (WRF) model. Initially, the WRF model is used to simulate summer precipit ation in the Jinsha River Basin. Subsequently, the adaptive noise-robust empiric al mode decomposition (CEEMDAN) method is employed to decompose WRF simulation e rrors. These decomposed subsequences are then input into four machine learning a lgorithms and two metaheuristic optimization algorithms to predict the error seq uences. Finally, the predicted error subsequences are merged and superimposed on the WRF simulation values to obtain the corrected precipitation. Research findi ngs demonstrate that the integration of machine learning algorithms with WRF sig nificantly improves prediction accuracy. The correlation coefficient of the opti mal model increases by 158%, and Nash-Sutcliffe Efficiency (NSE) in creases by 149% compared to before correction."
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Selangor, Malaysia, by NewsRx journa lists, research stated, "Anticipatory Multi-Unmanned Aerial Vehicles (UAVs) Netw ork is the key to the realization of high-bandwidth and demanding multi-UAV appl ications in the future. An accurate and robust Channel Quality Prediction (CQP) model is essential in such anticipatory networks to facilitate the eventual opti mization step." Funders for this research include Collaborative Research in Engineering, Science and Technology Center (CREST), Intel Microelectronics (M) Sdn Bhd, Department o f Electrical and Robotics Engineering, School of Engineering, Monash University Malaysia.
查看更多>>摘要：Current study results on Machine Learn ing have been published. According to news reporting out of Roorkee, India, by N ewsRx editors, research stated, "Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydr o -climatic factors, groundwater extraction, and the flow of surface water exhib it an indirect interdependence." Our news journalists obtained a quote from the research from the National Instit ute of Hydrology, "This study primarily aims to anticipate GWL in such highly ir rigated zones using the Machine Learning (ML) approach. To achieve this, the wid ely employed Random Forest (RF), Bagging -Reduce Error Pruning Tree (Bagging-REP Tree), and Bagging -Decision Stump Tree (Bagging-DSTree) models have been employ ed for the accurate forecasting of groundwater levels. The long-term pre -monsoo n and post -monsoon (fourteen locations) data set of South -Central Punjab state has been applied for the model calibration/training and validation/testing. Sev en statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSEobservation standa rd deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency ( NSE) and correlation coefficient (CC) for the model performance analysis. The re sults revealed that the RF model outperformed in pre -monsoon (testing phase) (R MSE = 0.682, NSE = 0.958) as well as the post -monsoon (testing phase) (RMSE = 0 .150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations."
查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Artificial Intelligence is the su bject of a report. According to news reporting originating in Nijmegen, Netherla nds, by NewsRx journalists, research stated, "This study investigates the feasib ility of creating an AI algorithm to enhance prosthetic socket shapes for transt ibial prostheses, aiming for a less operator-dependent, standardized approach. T he study comprised two phases: first, developing an AI algorithm in a cross-sect ional study to predict prosthetic socket shapes."
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