Catastrophe Model-Based Assessment of Hurricane Risk and Estimates of Potential Insured Losses for the State of Florida

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Download Full-text

In operando monitoring of the state of charge and species distribution in zinc air batteries using X-ray tomography and model-based simulations

Physical Chemistry Chemical Physics ◽

10.1039/c4cp02878c ◽

2014 ◽

Vol 16 (40) ◽

pp. 22273-22280 ◽

Cited By ~ 37

Author(s):

Tobias Arlt ◽

Daniel Schröder ◽

Ulrike Krewer ◽

Ingo Manke

Keyword(s):

Species Distribution ◽

The State ◽

State Of Charge ◽

X Ray ◽

Model Based ◽

In Operando ◽

Model Based Analysis ◽

Zinc Air Batteries

A novel combination of in operando X-ray tomography and model-based analysis of zinc air batteries is introduced.

Download Full-text

Prediction of Road Congestion Level Based on Bayes Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1593 ◽

2014 ◽

Vol 599-601 ◽

pp. 1593-1596

Author(s):

Shou Bai Xiao

Keyword(s):

Traffic Congestion ◽

Road Traffic ◽

Evaluation Model ◽

The State ◽

Assessment Model ◽

Bayesian Algorithm ◽

Processing Efficiency ◽

Model Based ◽

Bayes Algorithm ◽

Road Traffic Congestion

Traffic jams increasingly threaten the normal city traffic, so our paper analyzes the state of the existing road traffic congestion, road traffic congestion found in the state is a relatively vague and random dynamic data model. Based on these two characteristics, we propose a road traffic congestion degree assessment model based on Bayesian algorithm. Based on the theoretical analysis of Bayesian algorithms to improve the processing efficiency of the algorithm to construct the road traffic congestion degree evaluation model based on Bayesian algorithm set, and the simulation experiments.

Download Full-text

State Estimation and Model-Based Control of a Pilot Anaerobic Digestion Reactor

Journal of Control Science and Engineering ◽

10.1155/2014/572621 ◽

2014 ◽

Vol 2014 ◽

pp. 1-19 ◽

Cited By ~ 12

Author(s):

Finn Haugen ◽

Rune Bakke ◽

Bernt Lie

Keyword(s):

Anaerobic Digestion ◽

Control Systems ◽

Predictive Control ◽

Gas Flow ◽

The State ◽

Pi Control ◽

State Estimator ◽

Model Based Control ◽

Methane Gas ◽

Model Based

A state estimator and various model-based control systems have been designed for a real anaerobic digestion (AD) pilot reactor fed with dairy manure. The model used is a modified Hill model which is a relatively simple dynamical AD process model. The state estimator is an Unscented Kalman Filter (UKF) which uses only methane gas flow measurement to update its states. The model and the state estimates are used in different control systems. One of the control systems aims at controlling the methane gas flow to a setpoint. Simulations indicate that the setpoint tracking performance of a predictive control system is considerably better comparing with PI control, while disturbance compensation is not much better. Consequently, assuming the setpoint is constant, the PI controller competes well with the predictive controller. A successful application of predictive control of the real reactor is presented. Also, three different control systems aiming at retaining the reactor at an operating point where the volatile fatty acids (VFA) concentration has a maximum, safe value are designed. A simulation study indicates that the best control solution among the three alternatives is PI control based on feedback from estimated VFA.

Download Full-text

A step-indexed Kripke model of hidden state

Mathematical Structures in Computer Science ◽

10.1017/s0960129512000035 ◽

2012 ◽

Vol 23 (1) ◽

pp. 1-54 ◽

Cited By ~ 2

Author(s):

JAN SCHWINGHAMMER ◽

LARS BIRKEDAL ◽

FRANÇOIS POTTIER ◽

BERNHARD REUS ◽

KRISTIAN STØVRING ◽

...

Keyword(s):

Metric Space ◽

Possible Worlds ◽

Operational Semantics ◽

Kripke Model ◽

The State ◽

Local State ◽

Modular Reasoning ◽

Proof Rules ◽

Model Based ◽

First Sound

Frame and anti-frame rules have been proposed as proof rules for modular reasoning about programs. Frame rules allow the hiding of irrelevant parts of the state during verification, whereas the anti-frame rule allows the hiding of local state from the context.We discuss the semantic foundations of frame and anti-frame rules, and present the first sound model for Charguéraud and Pottier's type and capability system including both of these rules. The model is a possible worlds model based on the operational semantics and step-indexed heap relations, and the worlds are given by a recursively defined metric space. We also extend the model to account for Pottier's generalised frame and anti-frame rules, where invariants are generalised to families of invariants indexed over preorders. This generalisation enables reasoning about some well-bracketed as well as (locally) monotone uses of local state.

Download Full-text

Digital Simulation of Ecological Industry Development Based on Grey Clustering Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3881 ◽

2014 ◽

Vol 556-562 ◽

pp. 3881-3885

Author(s):

Hui Shi

Keyword(s):

Industrial Development ◽

Clustering Algorithm ◽

Digital Simulation ◽

Subsequent Development ◽

The State ◽

Development Model ◽

Model Based ◽

Clustering Model ◽

Ecological Migration ◽

The Status

In order to protect the ecological environment of grassland and improve the living standards of the people in the prairie and promote the harmonious development of the economic environment, the state proposes ecological migration project. How to ensure the collaborative development of subsequent industry is the topic concerned the state and society. This article discusses the status and problems of subsequent industrial development after the grassland ecological migration and constructs subsequent development model based on gray clustering model. In order to verify the validity of the model, the paper establishes and analyzes computer simulation system. The result shows that the development model based on gray clustering algorithm can make an accurate assessment on the follow industry trends of grassland ecological migrant which provides reference for following industrial development of grassland ecological migrant.

Download Full-text

Planning with Expectation Models

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/506 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yi Wan ◽

Muhammad Zaheer ◽

Adam White ◽

Martha White ◽

Richard S. Sutton

Keyword(s):

Value Function ◽

The State ◽

Distribution Model ◽

Stochastic Environments ◽

Model Based ◽

Convergence Results ◽

Expectation Model ◽

Evaluation Algorithm ◽

Planning Algorithm ◽

Action Spaces

Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the state and action spaces are large. Expectation models, on the other hand, are relatively easier to learn due to their compactness and have also been widely used for deterministic environments. For stochastic environments, it is not obvious how expectation models can be used for planning as they only partially characterize a distribution. In this paper, we propose a sound way of using approximate expectation models for MBRL. In particular, we 1) show that planning with an expectation model is equivalent to planning with a distribution model if the state value function is linear in state features, 2) analyze two common parametrization choices for approximating the expectation: linear and non-linear expectation models, 3) propose a sound model-based policy evaluation algorithm and present its convergence results, and 4) empirically demonstrate the effectiveness of the proposed planning algorithm.

Download Full-text