Qualitative Template Matching Using Dynamic Process Models for State Transition Recognition of Robotic Assembly

1993 ◽  
Vol 115 (2A) ◽  
pp. 261-269 ◽  
Author(s):  
B. J. McCarragher ◽  
H. Asada
Keyword(s):  
State Transition ◽  
Process Models ◽  
New Method ◽  
Robotic Assembly ◽  
State Transitions ◽  
Assembly Process ◽  
Recognition Method ◽  
Discrete State ◽  
Model Based ◽  
Short Period

This paper presents a model-based approach to the recognition of discrete state transitions for robotic assembly. Sensor signals, in particular, force and moment, are interpreted with reference to the physical model of an assembly process in order to recognize the state of assembly in real time. Assembly is a dynamic as well as a geometric process. Here, the model-based approach is applied to the unique problems of the dynamics generated by geometric interactions in an assembly process. First, a new method for the modeling of the assembly process is presented. In contrast to the traditional quasi-static treatment of assembly, the new method incorporates the dynamic nature of the process to highlight the discrete changes of state, e.g., gain and loss of contact. Second, a qualitative recognition method is developed to understand a time series of force signals. The qualitative technique allows for quick identification of the change of state because dynamic modelling provides much richer and more copious information than the traditional quasi-static modeling. A network representation is used to compactly present the modelling state transition information. Lastly, experimental results are given to demonstrate the recognition method. Successful transition recognition was accomplished in a very short period of time: 7-10 ms.

scholarly journals Anterior cingulate cortex represents action-state predictions and causally mediates model-based reinforcement learning in a two-step decision task

2017 ◽  
Author(s):  
Thomas Akam ◽  
Ines Rodrigues-Vaz ◽  
Ivo Marcelo ◽  
Xiangyu Zhang ◽  
Michael Pereira ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Anterior Cingulate Cortex ◽  
State Transition ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
State Transitions ◽  
Decision Task ◽  
Transition Structure ◽  
Model Based ◽  
Subsequent Choice

SummaryThe anterior cingulate cortex (ACC) is implicated in learning the value of actions, but it remains poorly understood whether and how it contributes to model-based mechanisms that use action-state predictions and afford behavioural flexibility. To isolate these mechanisms, we developed a multi-step decision task for mice in which both action-state transition probabilities and reward probabilities changed over time. Calcium imaging revealed ramps of choice-selective neuronal activity, followed by an evolving representation of the state reached and trial outcome, with different neuronal populations representing reward in different states. ACC neurons represented the current action-state transition structure, whether state transitions were expected or surprising, and the predicted state given chosen action. Optogenetic inhibition of ACC blocked the influence of action-state transitions on subsequent choice, without affecting the influence of rewards. These data support a role for ACC in model-based reinforcement learning, specifically in using action-state transitions to guide subsequent choice.HighlightsA novel two-step task disambiguates model-based and model-free RL in mice.ACC represents all trial events, reward representation is contextualised by state.ACC represents action-state transition structure, predicted states, and surprise.Inhibiting ACC impedes action-state transitions from influencing subsequent choice.

scholarly journals Voice Control for an Industrial Robot as a Combination of Various Robotic Assembly Process Models

2017 ◽  
Vol 05 (11) ◽  
pp. 1-15 ◽  
Author(s):  
Svitlana Maksymova ◽  
Rami Matarneh ◽  
Vyacheslav V. Lyashenko ◽  
Nataliya V. Belova
Keyword(s):  
Industrial Robot ◽  
Process Models ◽  
Robotic Assembly ◽  
Assembly Process ◽  
Voice Control

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

Excavations at Can Hasan, 1967, Seventh Preliminary Report

1968 ◽  
Vol 18 ◽  
pp. 45-53 ◽  
Author(s):  
D. H. French
Keyword(s):  
Military Service ◽  
Preliminary Report ◽  
New Method ◽  
Turkish Government ◽  
Metal Framework ◽  
Short Period ◽  
Site Supervisors ◽  
Small Hand

The seventh season of excavation at Can Hasan began on 9th September and stopped on 28th October; a further week was spent working on the finds and on the site. Site supervisors were Bay Altan Atılgan, and Messrs. S. W. Helms, R. Howell, and J. N. Postgate. In the House the work was undertaken by Bayan Behin Aksoy, Bayan Ülge Göker, Miss Carolyn Prater, Mrs. Cressida Ridley and Miss Monika van der Zwann. Bay Bedri Yalman represented the Turkish Government for a short period until called away to military service; his place was taken by Bay Cengiz Karadağ.A new method of sieving was introduced this year at the suggestion of Mr. Sebastian Payne. Instead of small hand sieves, “shakers”, built under Mr. Payne's supervision, were used. Basically this type of “shaker” is three removable trays with mesh of differing size (10 mm., 5 mm., 1 mm.) set on a sprung metal framework. It was also found more practicable with soils containing a lot of grain to “wash out” the grain from the soil remaining in the last tray after the soil had received preliminary hand-searching.

scholarly journals Traffic Flow Prediction with Relevance Vector Machine

2021 ◽  
Author(s):  
W.-Z. Xiong ◽  
X.-M. Shen ◽  
H.-J. Li ◽  
Z. Shen
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Sequence Data ◽  
Machine Method ◽  
Traffic Flow Prediction ◽  
Correlation Vector ◽  
Model Based ◽  
Flow Prediction ◽  
Short Period

Abstract Real-time prediction of traffic flow values in a short period of time is an importantelement in building a traffic management system. The uncertainty, complexity andnonlinearity of traffic flow data make it difficult to predict traffic flow in real time,and the accurate traffic flow prediction has been an urgent problem in the industry.Based on the research of scholars, a traffic flow prediction model based on thecorrelation vector machine method is constructed. The prediction accuracy of thecorrelation vector machine is better than that of the logistic regression and supportvector machine methods, and the correlation vector machine method has the functionof generating prediction error range for the actual traffic sequence data. Theprediction results are very satisfactory, and the prediction speed is significantlyfaster than the other two models, which meets the requirement of real-time trafficflow prediction and is suitable for real-time online prediction, and the predictionaccuracy of the used method is relatively high. The three-way comparison analysisshows that the traffic flow prediction by the correlation vector machine methodcan describe the nonlinear characteristics of traffic flow change more accurately,and the model performance and real-time performance are better. The case studyshows that the traffic flow prediction model based on the correlation vector machinecan improve the speed and accuracy of prediction, which is very suitablefor traffic flow prediction estimation with real-time requirements, and provides ascientific method for real-time traffic flow measurement.

scholarly journals Viscoelasticity and cell jamming state transition

2021 ◽  
Author(s):  
Ivana Pajic-Lijakovic ◽  
Milan Milivojevic
Keyword(s):  
Residual Stress ◽  
Wound Healing ◽  
Cell Migration ◽  
State Transition ◽  
State Transitions ◽  
Collective Cell Migration ◽  
Biological Processes ◽  
Cell Mobility ◽  
Controversial Topic ◽  
The Impact

Although collective cell migration (CCM) is a highly coordinated migratory mode, perturbations in the form of jamming state transitions and vice versa often occur even in 2D. These perturbations are involved in various biological processes, such as embryogenesis, wound healing and cancer invasion. CCM induces accumulation of cell residual stress which has a feedback impact to cell packing density. Density-mediated change of cell mobility influences the state of viscoelasticity of multicellular systems and on that base the jamming state transition. Although a good comprehension of how cells collectively migrate by following molecular rules has been generated, the impact of cellular rearrangements on cell viscoelasticity remains less understood. Thus, considering the density driven evolution of viscoelasticity caused by reduction of cell mobility could result in a powerful tool in order to address the contribution of cell jamming state transition in CCM and help to understand this important but still controversial topic. In addition, five viscoelastic states gained within three regimes: (1) convective regime, (2) conductive regime, and (3) damped-conductive regime was discussed based on the modeling consideration with special emphasis of jamming and unjamming states.

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