The Experts below are selected from a list of 17160 Experts worldwide ranked by ideXlab platform
Zhiwei Zhu - One of the best experts on this subject based on the ideXlab platform.
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extended range forecasting of chinese summer surface air temperature and heat waves
2018Co-Authors: Zhiwei ZhuAbstract:Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5–30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial–Temporal Projection models (STPMs). Based on the training data during 1960–1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10–80 day SAT over China. STPMs are therefore constructed using the Projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000–2013), the STPMs can reproduce EOF-filtered 30–80 day SAT at all lead times of 5–30 days over most part of China, and observed 30–80 and 10–80 day SAT at 25–30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5–30-day lead times against EOF-filtered and observed 30–80 day SAT, and at a 20-day lead time against observed 10–80 day SAT. The STPMs perform poorly in reproducing 10–30 day SAT. Forecasting for the first two modes of 10–30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10–30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10–80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.
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statistical extended range forecast of winter surface air temperature and extremely cold days over china
2017Co-Authors: Zhiwei ZhuAbstract:An extremely cold day (ECD) in boreal winter over China is often accompanied by freezing rainfall or snow, leading to power outages, paralysed traffic and damaged ecosystems. Extended-range (5–30 days lead) forecast of Chinese winter surface air temperature (SAT) and ECD has become a critical demand nationwide. In the present study, based on training data during 1960/1961–1999/2000, a statistical spatial–Temporal Projection model (STPM) is conducted to carry out an independent extended-range forecast of winter SAT and ECD over China. For the independent forecast period (2000/2001–2012/2013), STPM is able to capture the empirical orthogonal function (EOF)-filtered 10–80 days SAT anomaly at all 5–30 days lead times. Verification against the observed 10–80 days SAT anomaly shows that significant Temporal correlation coefficient skill persists to 25–30 days lead times over most parts of China, except for northeastern China and the Tibetan Plateau where useful skill is up to a 15 days lead time. Significant pattern correlation coefficients between forecasted and the EOF-filtered/observed 10–80 days SAT anomaly account for over 63%/59% of total forecasts for all 5–30 days lead times. The forecast local ECD is determined based on reconstructed SAT by adding the lower frequency (longer than 80 days) climatological SAT to the forecast 10–80 days SAT anomaly. Except for southeastern China and the Tibetan Plateau, STPM can hit above 30% of local ECDs over most parts of China at least 15 days in advance.
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a spatial Temporal Projection model for extended range forecast in the tropics
2015Co-Authors: Zhiwei Zhu, Pangchi HsuAbstract:An extended singularity value decomposition based statistical model, namely the spatial–Temporal Projection model (STPM), was constructed for the extended-range (10–30-day) forecast of tropical outgoing longwave radiation anomalies (OLRA). The special feature of this empirical model is using the spatial and Temporal information of predictor–predictand coupled patterns to predict the Temporally varying predictand field at all-time leads (i.e., 10–35 days) at once. A 10-year hindcast result shows that, different from previous statistical models, the skill scores of the STPM dropped slowly with forecast lead times. Useful skills can be detected at 30–35 day leads over most tropical regions. The highest Temporal correlation coefficient of 0.4–0.5 appears over the Maritime Continent (Indian and western North Pacific monsoon regions) in boreal winter (summer), exceeding a 99 % confidence level. The STPM is also capable in predicting the spatial evolutions of convective anomalies, including the zonal and meridional propagation of OLRA. The forecast of the Real-time Multivariate MJO indices shows that the STPM attains a higher skill than previous statistical models. The STPM also shows comparable skills with the state-of-the-art dynamic models during the Dynamics of the Madden–Julian Oscillation campaign period, especially at 15-day and longer leads.
Michael Beetz - One of the best experts on this subject based on the ideXlab platform.
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fast Temporal Projection using accurate physics based geometric reasoning
2013Co-Authors: Lorenz Mosenlechner, Michael BeetzAbstract:Temporal Projection is the computational problem of predicting what will happen when a robot executes its plan. Temporal Projection for everyday manipulation tasks such as table setting and cleaning is a challenging task. Symbolic Projection methods developed in Artificial Intelligence are too abstract to reason about how to place objects such that they do not hinder future actions. Simulation-based Projection is fine-grained enough but computationally too expensive as it is not able to abstract away from the execution of uninteresting actions (such as navigation). In this paper we propose a novel Temporal Projection mechanism that combines the strengths of both approaches: it is able to abstract away from the execution of continuous but uninteresting actions and provides the realism and fine grainedness needed to reason about critical situations.
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logic programming with simulation based Temporal Projection for everyday robot object manipulation
2011Co-Authors: Lars Kunze, Mihai Emanuel Dolha, Michael BeetzAbstract:In everyday object manipulation tasks, like making a pancake, autonomous robots are required to decide on the appropriate action parametrizations in order to achieve desired (and to avoid undesired) outcomes. For determining the right parameters for actions like pouring a pancake mix onto a pancake maker, robots need capabilities to predict the physical consequences of their own manipulation actions. In this work, we integrate a simulation-based approach for making Temporal Projections for robot manipulation actions into the logic programming language PROLOG. The realized system enables robots to determine action parameters that bring about certain effects by utilizing simulation-based Temporal Projections within PROLOG's chronological backtracking mechanism. For a set of formal parameters and their respective ranges of values, the developed system translates the manipulation problems into physical simulations, monitors and logs the relevant data structures of the simulations, translates the logged data back into first-order time-interval-based representations, called timelines, and eventually evaluates the individual timelines with respect to specified performance criteria. Integrating the proposed approach into robot control programs allow robots to mentally simulate the consequences of different action parametrizations before committing to them and thereby to reduce the number of undesired outcomes.
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simulation based Temporal Projection of everyday robot object manipulation
2011Co-Authors: Lars Kunze, Mihai Emanuel Dolha, Emitza Guzman, Michael BeetzAbstract:Performing everyday manipulation tasks successfully depends on the ability of autonomous robots to appropriately account for the physical behavior of task-related objects. Meaning that robots have to predict and consider the physical effects of their possible actions to take.In this work we investigate a simulation-based approach to naive physics Temporal Projection in the context of autonomous robot everyday manipulation. We identify the abstractions underlying typical first-order axiomatizations as the key obstacles for making valid naive physics predictions. We propose that Temporal Projection for naive physics problems should not be performed based on abstractions but rather based on detailed physical simulations. This idea is realized as a Temporal Projection system for autonomous manipulation robots that translates naive physics problems into parametrized physical simulation tasks, that logs the data structures and states traversed in simulation, and translates the logged data back into symbolic time-interval-based first-order representations. Within this paper, we describe the concept and implementation of the Temporal Projection system and present the example of an egg-cracking robot for demonstrating its feasibility.
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using physics and sensor based simulation for high fidelity Temporal Projection of realistic robot behavior
2009Co-Authors: Lorenz Mosenlechner, Michael BeetzAbstract:Planning means deciding on the future course of action based on predictions of what will happen when an activity is carried out in one way or the other. As we apply action planning to autonomous, sensor-guided mobile robots with manipulators or even to humanoid robots we need very realistic and detailed predictions of the behavior generated by a plan in order to improve the robot's performance substantially. In this paper we investigate the high-fidelity Temporal Projection of realistic robot behavior based on physics-and sensor-based simulation systems. We equip a simulator and interpreter with means to log simulated plan executions into a database. A logic-based query and inference mechanism then retrieves and reconstructs the necessary information from the database and translates the information into a first-order representation of robot plans and the behavior they generate. The query language enables the robot planning system to infer the intentions, the beliefs, and the world state at any projected time. It also allows the planning system to recognize, diagnose, and analyze various plan failures typical for performing everyday manipulation tasks.
Pangchi Hsu - One of the best experts on this subject based on the ideXlab platform.
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a spatial Temporal Projection model for extended range forecast in the tropics
2015Co-Authors: Zhiwei Zhu, Pangchi HsuAbstract:An extended singularity value decomposition based statistical model, namely the spatial–Temporal Projection model (STPM), was constructed for the extended-range (10–30-day) forecast of tropical outgoing longwave radiation anomalies (OLRA). The special feature of this empirical model is using the spatial and Temporal information of predictor–predictand coupled patterns to predict the Temporally varying predictand field at all-time leads (i.e., 10–35 days) at once. A 10-year hindcast result shows that, different from previous statistical models, the skill scores of the STPM dropped slowly with forecast lead times. Useful skills can be detected at 30–35 day leads over most tropical regions. The highest Temporal correlation coefficient of 0.4–0.5 appears over the Maritime Continent (Indian and western North Pacific monsoon regions) in boreal winter (summer), exceeding a 99 % confidence level. The STPM is also capable in predicting the spatial evolutions of convective anomalies, including the zonal and meridional propagation of OLRA. The forecast of the Real-time Multivariate MJO indices shows that the STPM attains a higher skill than previous statistical models. The STPM also shows comparable skills with the state-of-the-art dynamic models during the Dynamics of the Madden–Julian Oscillation campaign period, especially at 15-day and longer leads.
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a spatial Temporal Projection model for 10 30 day rainfall forecast in south china
2015Co-Authors: Pangchi Hsu, Lijun You, Jianyun Gao, Hongli RenAbstract:Extended-range (10–30 days) forecast, lying between well-developed short-range weather and long-range (monthly and seasonal) climate predictions, is one of the most challenging forecast currently faced by operational meteorological centers around the world. In this study, a set of spatial–Temporal Projection (STP) models was developed to predict low-frequency rainfall events at lead times of 5–30 days. We focused on early monsoon rainy season (mid April–mid July) in South China. To ensure that the model developed can be used for real-time forecast, a non-filtering method was developed to extract the low-frequency atmospheric signals of 10–60 days without using a band-pass filter. The empirical models were built based on 12-year (1996–2007) data, and independent forecast was then conducted for a 5 year (2008–2012) period. The assessment of the 5-year forecast of rainfall over South China indicates that the ensemble prediction of the STP models achieved a useful skill (with a Temporal correlation coefficient exceeding 95 % confidence level) at a lead time of 20 days. The amplitude error was generally less than one standard deviation at all lead times of 5–30 days. Furthermore, the STP models provided useful probabilistic forecasts with the ranked probability skill score between 0.3–0.5 up to 30-day forecast in advance. The evaluation demonstrated that the STP models exhibited useful 10–30 days forecast skills for real-time extended-range rainfall prediction in South China.
Lorenz Mosenlechner - One of the best experts on this subject based on the ideXlab platform.
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fast Temporal Projection using accurate physics based geometric reasoning
2013Co-Authors: Lorenz Mosenlechner, Michael BeetzAbstract:Temporal Projection is the computational problem of predicting what will happen when a robot executes its plan. Temporal Projection for everyday manipulation tasks such as table setting and cleaning is a challenging task. Symbolic Projection methods developed in Artificial Intelligence are too abstract to reason about how to place objects such that they do not hinder future actions. Simulation-based Projection is fine-grained enough but computationally too expensive as it is not able to abstract away from the execution of uninteresting actions (such as navigation). In this paper we propose a novel Temporal Projection mechanism that combines the strengths of both approaches: it is able to abstract away from the execution of continuous but uninteresting actions and provides the realism and fine grainedness needed to reason about critical situations.
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using physics and sensor based simulation for high fidelity Temporal Projection of realistic robot behavior
2009Co-Authors: Lorenz Mosenlechner, Michael BeetzAbstract:Planning means deciding on the future course of action based on predictions of what will happen when an activity is carried out in one way or the other. As we apply action planning to autonomous, sensor-guided mobile robots with manipulators or even to humanoid robots we need very realistic and detailed predictions of the behavior generated by a plan in order to improve the robot's performance substantially. In this paper we investigate the high-fidelity Temporal Projection of realistic robot behavior based on physics-and sensor-based simulation systems. We equip a simulator and interpreter with means to log simulated plan executions into a database. A logic-based query and inference mechanism then retrieves and reconstructs the necessary information from the database and translates the information into a first-order representation of robot plans and the behavior they generate. The query language enables the robot planning system to infer the intentions, the beliefs, and the world state at any projected time. It also allows the planning system to recognize, diagnose, and analyze various plan failures typical for performing everyday manipulation tasks.
Samu Taulu - One of the best experts on this subject based on the ideXlab platform.
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effectively combining Temporal Projection noise suppression methods in magnetoencephalography
2020Co-Authors: Maggie Clarke, Eric Larson, Kambiz Tavabi, Samu TauluAbstract:Abstract Background Magnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG’s limited spatial resolution and superior Temporal resolution, noise suppression methods that operate in the Temporal domain can be favorable. New Method We examined the independent and joint effects of two Temporal Projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; Temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled Temporal Projection (OTP). Results We found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO). Comparison with existing Methods Although the mathematical description of OTP has been described before ( Larson and Taulu, 2017 ), OTP’s effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used Temporal noise suppression algorithms (i.e., tSSS) has not been shown. Conclusions This finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.
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reducing sensor noise in meg and eeg recordings using oversampled Temporal Projection
2018Co-Authors: Eric Larson, Samu TauluAbstract:Objective: Here, we review the theory of suppression of spatially uncorrelated, sensor-specific noise in electro- and magentoencephalography (EEG and MEG) arrays, and introduce a novel method for suppression. Our method requires only that the signals of interest are spatially oversampled, which is a reasonable assumption for many EEG and MEG systems. Methods: Our method is based on a leave-one-out procedure using overlapping Temporal windows in a mathematical framework to project spatially uncorrelated noise in the Temporal domain. Results: This method, termed “oversampled Temporal Projection” (OTP), has four advantages over existing methods. First, sparse channel-specific artifacts are suppressed while limiting mixing with other channels, whereas existing linear, time-invariant spatial operators can spread such artifacts to other channels with a spatial distribution which can be mistaken for one produced by an electrophysiological source. Second, OTP minimizes distortion of the spatial configuration of the data. During source localization (e.g., dipole fitting), many spatial methods require corresponding modification of the forward model to avoid bias, while OTP does not. Third, noise suppression factors at the sensor level are maintained during source localization, whereas bias compensation removes the denoising benefit for spatial methods that require such compensation. Fourth, OTP uses a time-window duration parameter to control the tradeoff between noise suppression and adaptation to time-varying sensor characteristics. Conclusion: OTP efficiently optimizes noise suppression performance while controlling for spatial bias of the signal of interest. Significance: This is important in applications where sensor noise significantly limits the signal-to-noise ratio, such as high-frequency brain oscillations.
