查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news originating from Cleveland, Ohio, by News Rx correspondents, research stated, "The expansive utility of polymeric 3D-print ing technologies and demand for high- performance lightweight structures has pro mpted the emergence of various carbon-reinforced polymer composite filaments." The news reporters obtained a quote from the research from NASA John H. Glenn Re search Center: "However, detailed characterization of the processing-microstruct ure-property relationships of these materials is still required to realize their full potential. In this study, acrylonitrile butadiene styrene (ABS) and two ca rbon-reinforced ABS variants, with either carbon nanotubes (CNT) or 5 wt.% chopped carbon fiber (CF), were designed in a bio-inspired honeycomb geometry. T hese structures were manufactured by fused filament fabrication (FFF) and invest igated across a range of layer thicknesses and hexagonal (hex) sizes. Microscopy of material cross-sections was conducted to evaluate the relationship between p rint parameters and porosity. Analyses determined a trend of reduced porosity wi th lower print-layer heights and hex sizes compared to larger print-layer height s and hex sizes. Mechanical properties were evaluated through compression testin g, with ABS specimens achieving higher compressive yield strength, while CNT-ABS achieved higher ultimate compressive strength due to the reduction in porosity and subsequent strengthening."
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 Intelligenc e is the subject of a report. According to news reporting from Wurzburg, Germany , by NewsRx journalists, research stated, "Cone-beam computed tomography (CBCT)- based online adaptation is increasingly being introduced into many clinics. Upon implementation of a new treatment technique, a prospective risk analysis is req uired and enhances workflow safety." The news correspondents obtained a quote from the research from University Hospi tal Wurzburg, "We conducted a risk analysis using Failure Mode and Effects Analy sis (FMEA) upon the introduction of an online adaptive treatment programme (Wege ner et al., Z Med Phys. 2022). A prospective risk analysis, lacking in-depth cli nical experience with a treatment modality or treatment machine, relies on imagi nation and estimates of the occurrence of different failure modes. Therefore, we systematically documented all irregularities during the first year of online ad aptation, namely all cases in which quality assurance detected undesired states potentially leading to negative consequences. Additionally, the quality of autom atic contouring was evaluated. Based on those quantitative data, the risk analys is was updated by an interprofessional team. Furthermore, a hypothetical radiati on therapist-only workflow during adaptive sessions was included in the prospect ive analysis, as opposed to the involvement of an interprofessional team perform ing each adaptive treatment. A total of 126 irregularities were recorded during the first year. During that time period, many of the previously anticipated fail ure modes (almost) occurred, indicating that the initial prospective risk analys is captured relevant failure modes. However, some scenarios were not anticipated , emphasizing the limits of a prospective risk analysis. This underscores the ne ed for regular updates to the risk analysis. The most critical failure modes are presented together with possible mitigation strategies."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Robotics is now available. Accordi ng to news reporting out of Harbin, People's Republic of China, by NewsRx editor s, research stated, "As a classic scan-to-map matching method, the correlative s can matching (CSM) algorithm may not be applicable if low-cost wheeled robots (l ike robot cleaners) are impacted. The first open issue is heavy dependence on tr ustworthy initial poses." Financial support for this research came from National Key Research and Developm ent Program of China. Our news journalists obtained a quote from the research from the Harbin Institut e of Technology, "Side slips caused by impacts are unobservable for rotary encod ers mounted on wheels, leading to huge localization errors. The second open issu e is the efficient processing of global localization using the CSM algorithm, wh ich is essential for impacted robots. The state-of-the-art hardware designs for large-scale multiresolution CSM have low energy efficiency. These two open issue s are properly addressed in this work, namely, RIA-CSM2. Based on lightweight de ep neural networks, we perform reliable impact detection in unforeseen environme nts using only low-cost proprioceptive sensors. To bind the rapid error growth o f conventional wheel-aided inertial navigation systems (INSs), delayed out-of-se quence measurements from CSM algorithms are integrated into an extended Kalman f ilter (EKF). The lightweight impact detection networks and INS are generally app licable for most embedded robotic systems with stringent energy, computing, and memory cost limitations. Once impacts are detected, large-scale multiresolution CSM algorithms will be performed on an energy-efficient hardware accelerator. Ex tensive experiments based on public datasets show that our work achieves high re al-time performance and energy efficiency. The frame rate of local-scale high-re solution CSM can reach up to 96.42 frames/s. Field experiments on wheeled robot platforms demonstrate the effectiveness of our impact detection network, which o utperforms our preliminary work in precision and false-alarm rate by a significa nt margin, with precision and recall rates reaching 100% and 97.8% , respectively."
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 Intelligenc e is the subject of a report. According to news reporting originating from Paris , France, by NewsRx correspondents, research stated, "Assessing the impact of SA RS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms o f viral replication. We combine label-free holotomographic microscopy with Artif icial Intelligence to visualize and quantify the subcellular changes triggered b y SARS-CoV-2 infection." Our news editors obtained a quote from the research from Universite Paris Cite, "We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipi d droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, pertur bs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifi cations of organelle cross-regulation that are triggered by infection and syncyt ia formation."
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 from Guangzhou, People' s Republic of China, by NewsRx correspondents, research stated, "This article co ncentrates on the adaptive neural control approach of n-link flexible-joint elec trically driven robots. The presented control method only needs to know the posi tion and armature current information of the flexible-joint manipulator." Funders for this research include National Natural Science Foundation of China ( NSFC), China National Postdoctoral Program, Major Key Project of Peng Cheng Labo ratory, National Natural Science Foundation of Guangdong Province, Guangzhou Sci ence and Technology Planning Project. Our news editors obtained a quote from the research from the Guangdong Universit y of Technology, "An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to appr oximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all si gnals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero."
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 originating from Delft, Netherlands, by NewsRx cor respondents, research stated, "This paper provides an empirical and conceptual a ccount on seeing machine learning models as part of a sociotechnical system to i dentify relevant vulnerabilities emerging in the context of use. As ML is increa singly adopted in socially sensitive and safety-critical domains, many ML applic ations end up not delivering on their promises, and contributing to new forms of algorithmic harm." Financial support for this research came from Netherlands Organization for Scien tific Research (NWO). Our news journalists obtained a quote from the research from the Delft Universit y of Technology, "There is still a lack of empirical insights as well as concept ual tools and frameworks to properly understand and design for the impact of ML models in their sociotechnical context. In this paper, we follow a design scienc e research approach to work towards such insights and tools. We center our study in the financial industry, where we first empirically map recently emerging MLO ps practices to govern ML applications, and corroborate our insights with recent literature. We then perform an integrative literature research to identify a lo ng list of vulnerabilities that emerge in the sociotechnical context of ML appli cations, and we theorize these along eight dimensions. We then perform semi-stru ctured interviews in two real-world use cases and across a broad set of relevant actors and organizations, to validate the conceptual dimensions and identify ch allenges to address sociotechnical vulnerabilities in the design and governance of ML-based systems."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Support Vector Machines are present ed in a new report. According to news reporting from Inner Mongolia, People's Re public of China, by NewsRx journalists, research stated, "The state of energy (S OE) is a key indicator for lithium-ion battery management systems (BMS). Based o n the second-order resistance-capacitance equivalent circuit model and online pa rameter identification using the dynamic weights particle swarm optimization (DW PSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) a lgorithm is proposed to construct a particle filter to estimate the SOE of a lit hium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model , and use the LSSVM model to optimize the SOE estimates obtained by the PF algor ithm twice to improve the accuracy of SOE estimation for lithium-ion batteries."
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 artific ial intelligence. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Understanding how pricevolume i nformation determines future price movement is important for market makers who f requently place orders on both buy and sell sides, and for traders to split meta -orders to reduce price impact." Funders for this research include National Natural Science Foundation of China. The news editors obtained a quote from the research from Shanghai University: "G iven the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is current ly missing. On an intraday tick-level dataset of options on an exchange traded f und from the Chinese market, we apply a variety of machine learning methods, inc luding decision tree, random forest, logistic regression, and long short-term me mory neural network. As machine learning models become more complex, they can ex tract deeper hidden relationship from input features, which classic market micro structure models struggle to deal with. We discover that the price movement is p redictable, deep neural networks with time-lagged features perform better than a ll other simpler models, and this ability is universal and shared across assets. "
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 - Computational Intelligence have been published. According to news reportin g from Harbin, People's Republic of China, by NewsRx journalists, research state d, "Although Siamese trackers have become increasingly prevalent in the visual t racking domain, they are easily interfered by semantic distractors in complex en vironments, which results in the underutilization of feature information. Especi ally when multiple disturbances work together, the performance of many trackers often suffers severe degradation." Financial support for this research came from Natural Science Foundation of Heil ongjiang Province. The news correspondents obtained a quote from the research from Harbin Engineeri ng University, "To solve the above problem, this paper presents a robust Stereos copic Transformer network for improving tracking performance. Using a hybrid att ention mechanism, our method is composed of a channel feature awareness network (CFAN), a global channel attention network (GCAN), and a multi-level feature enh ancement unit (MFEU). Concretely, CFAN focuses on specific channel information, while highlighting the contained target features and weakening the semantic dist ractor features. As an intermediate hub, GCAN is mainly responsible for establis hing the global feature dependencies between the search region and the template, while selecting the concerned channel features to improve the distinguishing ab ility of the model. In particular, MFEU is used to enhance multi-level feature i nformation to facilitate feature representation learning for our method. Finally , a Transformer-based Siamese tracker (named VTST) is proposed to present an eff icient tracking representation, which can gain advantages over a variety of chal lenging attributes."
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 cy borg and bionic systems. According to news reporting originating from Tianjin, P eople's Republic of China, by NewsRx correspondents, research stated, "The param eter setting of functional electrical stimulation (FES) is important for active recovery training since it affects muscle health." Funders for this research include National Natural Fund Project; The Natural Sci ence Foundation of Hebei Province; Interdisciplinary Postgraduate Training Progr am of Hebei University of Technology;The Technology Nova of Hebei University of Technology; The National Natural Science 12 Cognitive Neurodynamics Foundation of China; The Key Research And Development Foundation of Hebei.
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