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匹配条件： “ Temporal Prediction” ，找到相关结果约10576条。
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Investigation of Relation between Solar Activity and Earthquakes with Deep Learning Method  [PDF]
Leilei Li, Hong Gu, Ryosuke Kikuyama, Ryuei Nishii, Pan Qin
International Journal of Geosciences (IJG) , 2021, DOI: 10.4236/ijg.2021.128040
Abstract: Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as tidal stress, rainfall, and the building of artificial water reservoirs. Here, we investigate the relation between SA and global earthquake numbers (GEN) by using a deep learning method to test the hypothesis. We use the daily data of GEN and SA (1996/01/01-2019/12/31) to construct a temporal convolution network (TCN). From the computational results, we confirm that the TCN captures the relation between SA and earthquakes with magnitudes from 4.0 to 4.9. We also find that the TCN achieves better fitting and prediction performance compared with previous work.
Temporal Prediction of Aircraft Loss-of-Control: A Dynamic Optimization Approach  [PDF]
Chaitanya Poolla, Abraham K. Ishihara
Intelligent Control and Automation (ICA) , 2015, DOI: 10.4236/ica.2015.64023
Abstract: Loss of Control (LOC) is the primary factor responsible for the majority of fatal air accidents during past decade. LOC is characterized by the pilot’s inability to control the aircraft and is typically associated with unpredictable behavior, potentially leading to loss of the aircraft and life. In this work, the minimum time dynamic optimization problem to LOC is treated using Pontryagin’s Maximum Principle (PMP). The resulting two point boundary value problem is solved using stochastic shooting point methods via a differential evolution scheme (DE). The minimum time until LOC metric is computed for corresponding spatial control limits. Simulations are performed using a linearized longitudinal aircraft model to illustrate the concept.
Representation of Reward Feedback in Primate Auditory Cortex
Michael Brosch,Henning Scheich
Frontiers in Systems Neuroscience , 2011, DOI: 10.3389/fnsys.2011.00005
Abstract: It is well established that auditory cortex is plastic on different time scales and that this plasticity is driven by the reinforcement that is used to motivate subjects to learn or to perform an auditory task. Motivated by these findings, we study in detail properties of neuronal firing in auditory cortex that is related to reward feedback. We recorded from the auditory cortex of two monkeys while they were performing an auditory categorization task. Monkeys listened to a sequence of tones and had to signal when the frequency of adjacent tones stepped in downward direction, irrespective of the tone frequency and step size. Correct identifications were rewarded with either a large or a small amount of water. The size of reward depended on the monkeys’ performance in the previous trial: it was large after a correct trial and small after an incorrect trial. The rewards served to maintain task performance. During task performance we found three successive periods of neuronal firing in auditory cortex that reflected (1) the reward expectancy for each trial, (2) the reward-size received, and (3) the mismatch between the expected and delivered reward. These results, together with control experiments suggest that auditory cortex receives reward feedback that could be used to adapt auditory cortex to task requirements. Additionally, the results presented here extend previous observations of non-auditory roles of auditory cortex and shows that auditory cortex is even more cognitively influenced than lately recognized.
The Use of a Nomogram to Visually Interpret a Logistic Regression Prediction Model for Giant Cell Arteritis
Edsel B. Ing,Royce Ing
- , 2018, DOI: 10.1080/01658107.2018.1425728
Abstract: Objective: To illustrate the utility of a nomogram for the prediction of giant cell arteritis (GCA)
TOWARDS TEMPORAL ABSENCE MODELLING: TEMPORAL ABSENCE CONNOTATION IN NETFLIX PRIZE DATA
MULANG’ ISAIAH ONANDO, WAWERU MWANGI
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Research on evaluating recommender systems shows that algorithms in this area are still deficient in prediction accuracy but recent works prove that modeling with temporal dynamics improves the degree of recommendation accuracy. Recommendations are invariably based on similarities of users and/or items in the user-item matrix of a system, user profiles, and rating information which presumes the presence of users or items in the matrix. The major difference being in the way the algorithms analyze data sources to develop notions of affinity between users for use in identifying well matched pairs. Not many have focused on the temporal absence as an indicator of preference or concept drift: and hence a factor for inclusion in the recommender algorithms and models to improve accuracy. In this paper we to define temporal absence in the context of recommender systems and find out, through examination of the Netflix Prize data, the extent of temporal absence and the significance of such information in future research and improvement of recommendation algorithms.
Temporal Construal Level Theory:A Survey
LI Dan,YIN Hua-zhan, LI Zuo-shan,LI Zuo-shan
Journal of Chongqing Normal University , 2010,
Abstract: Temporal construal level theory explored the mechanism that the subjective evaluation of events changed with the temporal distance from now, which on one hand connected time psychology and decision psychology, and on the other hand provided basis for human rational decision and the most profit. Construal level theory proposes that temporal distance thanges people's responses to future events by changing the way people mentally represent those events. The greater the temporal distance, the more likely are events to be represented in terms of a few abstract features that convey the percerved essence of the events (high-level construals ) rather than in terms of more concrete and incidental details of the events (low-level construals ).The informational and evaluative imlications of high-level construals, compared with those of low-level construals, should therefore have more impact on responses to distant-future events than neaar-future events. In the present article, at first , the basis, intention, and proof were introduced, and the applied research about prediction and evaluation was described, and finally three problems about mechanism, number of segmentation, and post time temporal construal level were pointed out to be breach of the future research. In addition, the suggestion to the research in China was also be pointed out.
Statistical Modeling of the Residents Activity Interval in Smart Homes
M.R. Alam,M.B.I. Reaz,M.A.M. Ali
Journal of Applied Sciences , 2011,
Abstract: The activities of residents in smart homes possess temporal information which can be used to classify and model psychological behavior of the resident. In this study, a learning algorithm is proposed to predict the activity interval of smart home inhabitants. The algorithm is based on the hypothesis that residents activity intervals follow a normal distribution. To predict the starting time of the following activity, it incrementally utilizes mean and standard deviation of previous history which are applied according to the central limit theory of statistical probability. The prediction algorithm exhibits 88.3 to 95.3% prediction accuracies for different ranges of mean and standard deviations when verified by practical smart home data. Further stochastic analyses prove that the time difference between the residents activities follows normal distribution which was merely an assumption previously.
Study on similarity indices for link prediction in opportunistic networks
Jian Shu,Linlan Liu,Xulin Cai
- , 2018, DOI: 10.1177/1687814018803190
Abstract: Link prediction aims to estimate the existence of links between nodes, using information of network structures and node properties. According to the characteristics of node mobility, node intermittent contact, and high delay of opportunistic network, novel similarity indices are constructed based on CN, AA, and RA. The indices CN, AA, and RA do not consider the historic information of networks. Similarity indices, T_CN, T_AA, and T_RA, based on temporal characteristics are proposed. These take the historic information of network evolution into consideration. Using historic information of the evolution of opportunistic networks and 2-hop neighbor information of the nodes, similarity indices based on the temporal-spatial characteristics, O_CN, O_AA, and O_RA, are proposed. Based on the imote traces cambridge (ITC) and detected social network (DSN) datasets, the experimental results indicate that similarity indices O_CN, O_AA, and O_RA outperform CN, AA, and RA. Furthermore, index O_AA has superior performance
How Dynamic Brain Networks Tune Social Behavior in Real Time
Brian Silston,Danielle S. Bassett,Dean Mobbs
- , 2018, DOI: 10.1177/0963721418773362
Abstract: During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context-appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions
Spatiotemporal correlation–based adaptive sampling algorithm for clustered wireless sensor networks
Meiyan Zhang,Wenyu Cai
- , 2018, DOI: 10.1177/1550147718794614
Abstract: Energy efficiency is one of the most crucial concerns for WSNs, and almost all researches assume that the process for data transmission consumes more energy than that of data collection. However, a few sophisticated collection processes of sensory data will consume much more energy than traditional transmission processes such as image and video acquisitions. Given this hypothesis, this article proposed an adaptive sampling algorithm based on temporal and spatial correlation of sensory data for clustered WSNs. First, according to spatial correlations between sensor nodes, a distributed clustering mechanism based on data gradient and residual energy level is proposed, and the whole network is divided into several independent clusters. Afterwards, each cluster head maintains an autoregressive prediction model for sensory data, which is derived from historical data in the temporal domain. With that, each cluster head has the ability of self-adjusting temporal sampling intervals within each cluster. Consequently, redundant data transmission is reduced by adjusting temporal sampling frequency while ensuring desired prediction accuracy. Finally, several distinct sampler collection sets are selected within each cluster following intra-cluster correlation matrix, and only one sampler collection needs to be activated at each round time. Sensory data of non-sampler can be substituted by those of sampler due to strong spatial correlation between them. Simulation results demonstrate the performance benefits of proposed algorithm
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