Sequential Bayesian Filtering via Minimum Distortion Quantization

2010 ◽  
pp. 203-213
Author(s):  
Graham C. Goodwin ◽  
Arie Feuer ◽  
Claus Müller
Keyword(s):  
Bayesian Filtering ◽  
Minimum Distortion

GUTERL D-2

Alloy Digest ◽  
1981 ◽  
Vol 30 (4) ◽  
Keyword(s):  
Fracture Toughness ◽  
Compressive Strength ◽  
Corrosion Resistance ◽  
Physical Properties ◽  
Heat Treating ◽  
High Carbon ◽  
High Hardenability ◽  
Die Steel ◽  
High Chromium ◽  
Minimum Distortion

Abstract GUTERL D-2 is a high-hardenability tool and die steel of the high-carbon, high-chromium type. It has high compressive strength, his resistance to abrasion, minimum distortion in hardening but only fair toughness. Among its many uses are rolls, punches, shear blades, lathe centers and many types of cutting and forming dies. This datasheet provides information on composition, physical properties, hardness, and elasticity as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, machining, and surface treatment. Filing Code: TS-380. Producer or source: Guterl Special Steel Corporation.

CYCLOPS SCK

Alloy Digest ◽  
1979 ◽  
Vol 28 (3) ◽  
Keyword(s):  
Heat Treatment ◽  
Wear Resistance ◽  
Physical Properties ◽  
Tool Steel ◽  
Cold Work ◽  
Heat Treating ◽  
Optimum Combination ◽  
Cold Work Tool Steel ◽  
Minimum Distortion

Abstract CYCLOPS SCK is a cold-work tool steel with a balanced composition to provide air hardening and an optimum combination of toughness, wear resistance and minimum distortion during heat treatment. Typical applications are shear blades, trimming dies and forming rolls, including grade rolls for cutlery and flatware. This datasheet provides information on composition, physical properties, hardness, and elasticity. It also includes information on forming, heat treating, and machining. Filing Code: TS-346. Producer or source: Cyclops Corporation.

DEWARD

Alloy Digest ◽  
1976 ◽  
Vol 25 (12) ◽  
Keyword(s):  
Heat Treatment ◽  
Fracture Toughness ◽  
Wear Resistance ◽  
Compressive Strength ◽  
Physical Properties ◽  
Heat Treating ◽  
Die Steel ◽  
Satisfactory Performance ◽  
Specialty Steel ◽  
Minimum Distortion

Abstract DEWARD is an oil-hardening, non-deforming, manganese die steel that is characterized by uniformity, good machinability and satisfactory performance in service. Its composition permits a relatively low hardening temperature to give minimum distortion after heat treatment and little danger of cracking. It has good wear resistance and gives excellent results when used for all kinds of intricate tools. This datasheet provides information on composition, physical properties, hardness, elasticity, and compressive strength as well as fracture toughness. It also includes information on forming, heat treating, and machining. Filing Code: TS-310. Producer or source: AL Tech Specialty Steel Corporation.

scholarly journals Lattice-Based Minimum-Distortion Data Hiding

2021 ◽  
pp. 1-1
Author(s):  
Jieni Lin ◽  
Junren Qin ◽  
Shanxiang Lyu ◽  
Shanxiang Lyu ◽  
Bingwen Feng ◽  
...  
Keyword(s):  
Data Hiding ◽  
Minimum Distortion

scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

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