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 originating from Hefei, People's Republi c of China, by NewsRx correspondents, research stated, "The time-interleaved ana log-to-digital conversion (TIADC) technique provides an effective way to achieve high sampling speed. However, a critical challenge in TIADC design arises from the presence of mismatches among parallel sub-analog-to-digital converters (ADCs ), which detrimentally affect system performance." Financial support for this research came from Youth Innovation Promotion Associa tion Chinese Academy of Sciences (CAS). Our news editors obtained a quote from the research from the University of Scien ce and Technology China, "In this article, we propose a machine-learning-based m ethod to address these mismatches across a broadband of input signal frequencies . Different from conventional approaches, this method avoids complex and specifi c matrix operations and reduces the compensation filter order required to achiev e a given reconstruction accuracy. To assess the efficacy of our proposed method,we designed a 5-Gs/s 12-bit TIADC system. Through extensive testing, the resul ts demonstrate notable improvements in the effective number of bits (ENOBs) foll owing real-time calibration."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Nanjing, People's Rep ublic of China, by NewsRx editors, research stated, "Groundwater Contaminant Sou rce Identification (GCSI) is important for addressing environmental concerns. Cu rrently, it is widely achieved using the Simulation/Optimization (S/O) method." Financial supporters for this research include National Key Research & Development Program of China, National Natural Science Foundation of China (NSFC ). Our news journalists obtained a quote from the research from Hohai University, " However, the utilization of optimization techniques may cause high computation c osts and parameter equifinality issues. We introduce two innovative GCSI methods,Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), ut ilizing the classical Artificial Neuro Network (ANN) model in the machine learni ng field. Both new methods eliminate the need for an optimization algorithm in G CSI, thus reducing the construction effort and improving efficiency. The first m ethod, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The seco nd method, OHML can estimate the spatial probability distribution of the contami nant location through one-hot encoding, addressing uncertainties in the contamin ant source location estimations realized by DFML. Evaluations demonstrate that b oth methods exhibit satisfying performances. DFML can estimate contaminant locat ion and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contam inant probabilities to regions containing true contaminant locations. The combin ation of DFML and OHML offers a comprehensive framework."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news originating from Wuhan, People's Republic of China, b y NewsRx correspondents, research stated, "Obstacle avoidance planning is the pr imary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C-space) provide the pose info rmation of any part of the manipulator and are preferentially considered in moti on planning." 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 Wuhan University of Technology, "However, it is difficult to express the environmental informat ion directly in the high dimensional C-space, limiting the application of C-spac e obstacle avoidance planning. This paper proposes a singular manifold splitting C-space method and designs a compatible obstacle avoidance strategy. The specif ic method is as follows: first, according to the specific structure of industria l robots, arm-wrist separation obstacle avoidance planning is proposed to fix th e robot as a 3R manipulator to reduce the dimension of C-space. Next, the C-spac e is segmented according to the singular manifolds, and the unique domain is del ineated to complete the streamlining of the volume of the C-space. Then, with th e help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo-obstacle map. Industrial robots' obstacle avoidan ce planning is completed based on the pseudo-obstacle map combined with an impro ved Rapidly-Exploring Random Trees (RRT) algorithm. This method dramatically imp roves the efficiency of obstacle avoidance planning in the C-space and avoids th e effect of singularities on industrial robots."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Wuhan, People's Republ ic of China, by NewsRx correspondents, research stated, "The southeastern Tibeta n Plateau (SETP) is a construction area of several key infrastructure projects i n China, such as the Sichuan-Tibet Railway and hydropower developments, which ha s historically faced the threat of glacier-related debris flows. However, a robu st assessment of such debris flow susceptibility is a challenge due to the compl ex and variable climate, terrain and glacial environment." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regr ession, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accur ately identify debris flow susceptible (DFS) areas. Topography, geology, and hyd rological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of t he study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accur acy of the DFS assessments. The CF-RF model showed the greatest improvement, wit h an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2 nd and 9th in the factor importance, respectively."
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 from Shanghai, People's Republic of China, by New sRx journalists, research stated, "Cephalopod skins evolve multiple functions in response to environmental adaptation, encompassing nonlinear mechanoreponse, da mage tolerance property, and resistance to seawater. Despite tremendous progress in skin-mimicking materials, the integration of these desirable properties into a single material system remains an ongoing challenge." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Fundamental Research Funds for the Central Universities, Sci ence & Technology Commission of Shanghai Municipality (STCSM).
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 Robotics have been published. According to news reporting originating in Chongqing, People's Republic of China, by NewsRx journalists, research stated, "Autoclaved lightweig ht concrete (ALC) wall panels are widely used in building envelope structures. D ue to the limitations of their sizes and architectural environment, installing A LC wall panels is very complex, requiring significant labor and time costs." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from Chongqing University, "Construction robots provide enhanced efficiency, yet current fa & ccedil;ade installation robots lack comprehensive onsite automation. In this st udy, an on-site wallboard installation robot with sensors, a mobile base, and a mechanical gripper was self-designed. Based on this robot, an automated installa tion scheme with integrated functions was proposed for the multiple steps from g rasping to installing the wall panel. The robot could recognize and judge the po sture of the wall panel through a robot vision algorithm and was able to navigat e according to the simultaneous localization and mapping (SLAM). The proposed sc heme achieved good results in both simulation and field testing."
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 originating in Turin, Italy, by NewsRx j ournalists, research stated, "The present study investigates the best seismic pa rameters for modeling the dynamic response of various nonlinear structural syste ms by comparing different Machine Learning (ML) algorithms. A total of 400 synth etic excitations were generated and analyzed against 23 seismic parameters." Financial support for this research came from European Union-Next Generation E U. The news reporters obtained a quote from the research from Polytechnic University Torino, "These signals were used in a step-by-step numerical analysis to calcu late the dynamic responses of 1000 singledegree- of-freedom (SDOF) systems with varying mechanical properties. The data obtained from these responses were proce ssed using 20 ML algorithms, including linear regression, tree, support vector m achine (SVM), boosted and bagged trees, and artificial neural network (ANN). Eac h ML algorithm used a single seismic parameter as input to determine the most pr edictive parameters for modeling structural responses, defining the high predict ive seismic parameters (HPSP) set. To validate the obtained results, the most ef fective model predictions have been compared with the results of the parametric step-by-step analyses performed for a new group of natural ground motions. The f indings demonstrate that with a properly calibrated training phase, considering the specific site hazard and selecting seismic parameters from the HPSP set, the ML model can accurately estimate seismic responses whit a significantly reduced computational effort."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Budapest, Hung ary, by NewsRx journalists, research stated, "We aimed to externally validate th e SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset-a large multi-centre c ohort of patients undergoing CRT implantation. The SEMMELWEIS-CRT score is a mac hine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation." Funders for this research include European Union, Ministry of Innovation and Tec hnology of Hungary from the National Research, Development and Innovation Fund, National Research, Development, and Innovation Office of Hungary, New National E xcellence Program, Ministry of Culture and Innovation in Hungary from the Nation al Research.
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 out of Shanghai, People's Republic o f China, by NewsRx editors, research stated, "This study provides a reliability improvement control method for robotic arms with clearance joints. Firstly, the dynamical model of a six-DOF robotic arm with joint clearance is established, an d the Archard model is utilized to describe joint wear, considering its effect o n clearance evolution." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Tongji University, "The kinematic and dynamic characteristics of the robotic arm with clearances ar e analyzed concerning the contact and operation state variations. Then, the infl uence of clearance wear on the operational reliability of the robotic arm is stu died as joint wear in the robotic arm contains interval uncertainty. To provide the uncertainty factors caused by the interval, we introduce Chebyshev functions to describe the dynamic response uncertainty and reliability. The non-probabili stic reliability index is given to evaluate the reliability of the robotic arm b ased on the stress intensity interference theory. Lastly, to improve operational accuracy and reliability, a novel compound control strategy containing collisio n force feedforward and PD feedback is carried out. It is compared with the trad itional PD control strategy. Also, the sensitivity and robustness of the propose d compound control strategies are discussed. The results show that the proposed control strategy can effectively enhance the dynamics precision and reliability of the robotic arm, with satisfactory robustness."
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 from Helwan, Egypt, by NewsRx journ alists, research stated, "This study explores a novel approach to emotion recogn ition through machine learning, addressing the limitations of previous methods. While deep learning has shown promise in this field, it often requires significa nt computational resources and time." The news correspondents obtained a quote from the research from Helwan University, "In response, we propose a multimodal approach utilizing Feature-level Fusion (FLF) and Decision-level Fusion (DLF) to enhance performance while reducing com plexity. The study focuses on integrating electroencephalogram (EEG), electromyo graphy (EMG), and electrooculogram (EOG) signals. Signal preprocessing involves extracting statistical features, power spectral density (PSD), and incremental e ntropy analysis. Recursive Feature Elimination (RFE) is employed as a feature se lector, facilitating the fusion of different signal features. Three fusion strat egies are explored: EEG with EOG, EEG with EMG, and a combination of EEG with EO G and EMG. For classification, the Bagging Classifier and K-Nearest Neighbors Al gorithm are chosen. Results demonstrate promising accuracy rates, with 95.7% for arousal, 96.41% for valence in subject-dependent classificatio n, 93.68% for arousal, and 93.23% for valence in sub ject-independent classification."
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