1. Introduction 1.1 An Overview 1.2 Some Examples 1.3 A Brief History 1.4 A Chapter Summary 2. Probability 2.1 Introduction 2.2 Sample Spaces and the Algebra of Sets 2.3 The Probability Function 2.4 Conditional Probability 2.5 Independence 2.6 Combinatorics 2.7 Combinatorial Probability 2.8 Taking a Second Look at Statistics (Monte Carlo Techniques) 3. Random Variables 3.1 Introduction 3.2 Binomial and Hypergeometric Probabilities 3.3 Discrete Random Variables 3.4 Continuous Random Variables 3.5 Expected Values 3.6 The Variance 3.7 Joint Densities 3.8 Transforming and Combining Random Variables 3.9 Further Properties of the Mean and Variance 3.10 Order Statistics 3.11 Conditional Densities 3.12 Moment-Generating Functions 3.13 Taking a Second Look at Statistics (Interpreting Means) Appendix 3.A.1 MINITAB Applications 4. Special Distributions 4.1 Introduction 4.2 The Poisson Distribution 4.3 The Normal Distribution 4.4 The Geometric Distribution 4.5 The Negative Binomial Distribution 4.6 The Gamma Distribution 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) Appendix 4.A.1 MINITAB Applications Appendix 4.A.2 A Proof of the Central Limit Theorem 5. Estimation 5.1 Introduction 5.2 Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments 5.3 Interval Estimation 5.4 Properties of Estimators 5.5 Minimum-Variance Estimators: The Crami?½r-Rao Lower Bound 5.6 Sufficient Estimators 5.7 Consistency 5.8 Bayesian Estimation 5.9 Taking A Second Look at Statistics (Beyond Classical Estimation) Appendix 5.A.1 MINITAB Applications 6. Hypothesis Testing 6.1 Introduction 6.2 The Decision Rule 6.3 Testing Binomial Dataâ H0: p = po 6.4 Type I and Type II Errors 6.5 A Notion of Optimality: The Generalized Likelihood Ratio 6.6 Taking a Second Look at Statistics (Statistical Significance versus â Practicalâ Significance) 7. Inferences Based on the Normal Distribution 7.1 Introduction 7.2 Comparing Y-i?½ s/ vn and Y-i?½ S/ vn 7.3 Deriving the Distribution of Y-i?½ S/ vn 7.4 Drawing Inferences About i?½ 7.5 Drawing Inferences About s2 7.6 Taking a Second Look at Statistics (Type II Error) Appendix 7.A.1 MINITAB Applications Appendix 7.A.2 Some Distribution Results for Y and S2 Appendix 7.A.3 A Proof that the One-Sample t Test is a GLRT Appendix 7.A.4 A Proof of Theorem 7.5.2 8. Types of Data: A Brief Overview 8.1 Introduction 8.2 Classifying Data 8.3 Taking a Second Look at Statistics (Samples Are Not â Validâ !) 9. Two-Sample Inferences 9.1 Introduction 9.2 Testing H0: i?½X =i?½Y 9.3 Testing H0: s2X=s2Yâ The F Test 9.4 Binomial Data: Testing H0: pX = pY 9.5 Confidence Intervals for the Two-Sample Problem 9.6 Taking a Second Look at Statistics (Choosing Samples) Appendix 9.A.1 A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2) Appendix 9.A.2 MINITAB Applications 10. Goodness-of-Fit Tests 10.1 Introduction 10.2 The Multinomial Distribution 10.3 Goodness-of-Fit Tests: All Parameters Known 10.4 Goodness-of-Fit Tests: Parameters Unknown 10.5 Contingency Tables 10.6 Taking a Second Look at Statistics (Outliers) Appendix 10.A.1 MINITAB Applications 11. Regression 11.1 Introduction 11.2 The Method of Least Squares 11.3 The Linear Model 11.4 Covariance and Correlation 11.5 The Bivariate Normal Distribution 11.6 Taking a Second Look at Statistics (How Not to Interpret the Sample Correlation Coefficient) Appendix 11.A.1 MINITAB Applications Appendix 11.A.2 A Proof of Theorem 11.3.3 12. The Analysis of Variance 12.1 Introduction 12.2 The F Test 12.3 Multiple Comparisons: Tukeyâ s Method 12.4 Testing Subhypotheses with Contrasts 12.5 Data Transformations 12.6 Taking a Second Look at Statistics (Putting the Subject of Statistics togetherâ the Contributions of Ronald A. Fisher) Appendix 12.A.1 MINITAB Applications Appendix 12.A.2 A Proof of Theorem 12.2.2 Appendix 12.A.3 The Distribution of SSTR/(kâ 1) SSE/(nâ k)When H1 is True 13. Randomized Block Designs 13.1 Introduction 13.2 The F Test for a Randomized Block Design 13.3 The Paired t Test 13.4 Taking a Second Look at Statistics (Choosing between a Two-Sample t Test and a Paired t Test) Appendix 13.A.1 MINITAB Applications 14. Nonparametric Statistics 14.1 Introduction 14.2 The Sign Test 14.3 Wilcoxon Tests 14.4 The Kruskal-Wallis Test 14.5 The Friedman Test 14.6 Testing for Randomness 14.7 Taking a Second Look at Statistics (Comparing Parametric and Nonparametric Procedures) Appendix 14.A.1 MINITAB Applications Appendix: Statistical Tables Answers to Selected Odd-Numbered Questions Bibliography Index

http://library.um.ac.id/free-contents/downloadpdf.php/buku/an-introduction-to-mathematical-statistics-and-its-applications-richard-j-larsen-morris-l-marx-12385.pdf

An introduction to mathematical statistical and its applications / Richard J. Larsen, Morris L. Marx

A method for predicting vehicle operator destinations including receiving vehicle position data for a vehicle. The vehicle position data for a current trip is compared to vehicle position data for a previous trip to predict a destination for the vehicle. A path to the destination is suggested.

Prediction of vehicle operator destinations

Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest RL algorithms have appeared that approximate dynamic programming on an incremental basis They can be trained on the basis of real or simulated experiences, focusing their computation on areas of state space that are actually visited during control, making them computationally tractable on very large problems If each member of a team of agents employs one of these algorithms, a new collective learning algorithm emerges for the team as a whole In this paper we demonstrate that such collective RL algorithms can be powerful heuristic methods for addressing large-scale control problems

Elevator group control serves as our testbed It is a difficult domain posing a combination of challenges not seen in most multi-agent learning research to date We use a team of RL agents, each of which is responsible for controlling one elevator car The team receives a global reward signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware These results demonstrate the power of multi-agent RL on a very large scale stochastic dynamic optimization problem of practical utility

/pdf/elevator-group-control-using-multiple-reinforcement-learning-9hqyoy6f2c.pdf

Elevator Group Control Using Multiple Reinforcement Learning Agents

Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

A review of literature related to fire evacuation in high-rise buildings was carried out with the following objectives, (1) to identify the key behavioural factors affecting the performance of people during a fire in a high-rise building, the singularities associated to this type of buildings and areas of future research; (2) to review the procedures and strategies currently adopted in high-rise buildings; (3) to review and analyse the capabilities of evacuation models by reviewing their current characteristics and applications in the context of high-rise building evacuations. The review included both findings on human behaviour in high-rise buildings and modelling techniques and tools. Different categories of building use were taken into account, namely office buildings, residential buildings and health care facilities. The individual or combined use of different egress components was analysed. Egress components include the use of stairs, elevators as well as alternative means of escape (e.g., sky-bridges, helicopters, etc.). The effectiveness of the egress components is strongly affected by the building use and the population involved. The review shows that evacuation models can be effectively employed to study relocation strategies and safety issues associated with high-rise buildings. The suitability of egress models for high-rise building evacuations is associated with their flexibility in representing different egress components and complex behavioural processes. The review highlights that there is not a definitive model to be used but that the predictive capabilities of evacuation modelling techniques would be enhanced if more than one model is employed to study different egress aspects. Future research and model developments should focus on the study of the impact of staff actions, group dynamics and people with disabilities. Given the increasing height of buildings and the gradual reduction in the physical abilities of the population, the effects of fatigue on evacuation need further studies.

/pdf/fire-evacuation-in-high-rise-buildings-a-review-of-human-3ms24kb5gw.pdf

Fire evacuation in high-rise buildings: a review of human behaviour and modelling research

Passenger waiting time in a lobby and ride time in a car give a good indication of the service capability of an elevator group. If several cars are in the same group, these parameters are too complicated to model in a closed form. In this paper, procedures for modelling elevator performance and passenger traffic in a generic building are described. Elevator dynamics and control, call allocation and passenger traffic modelling are described. Simulated interval and load values are compared with theoretical results, and variances in simulation results are investigated. According to typical simulation results, there is no direct connection between interval and passenger waiting times, as is often assumed. The interval depends on the elevator bank configuration and elevator performance capabilities. Performance indicators, such as the number of starts and cycle times, become saturated even at low traffic intensities. The waiting times, in addition to elevator performance, depend on the passenger traffic patter...

Elevator Traffic Simulation

In this report a novel control that optimizes passenger service in an elevator group is described. Fuzzy logic and artificial intelligence are applied in the control when allocating landing calls to the elevators. Fuzzy logic is used to recognize the traffic pattern and the traffic peaks from statistical forecasts. In order to form the statistical forecasts, the passenger traffic flow in the building is measured. In the statistics the passenger traffic flow is learned day by day, and the control adapts to the prevailing traffic situation. The validity of the forecast data is confirmed before applying it in the control. The measured passenger arrival rate at each floor is used in optimizing the passenger waiting times and the ride times inside a car. The methods developed in this work are utilized in real group controls. With these controls average waiting times and waiting time distribution floor by floor are decreased compared with conventional controls that optimize landing call times. An example of an office building where an old electronic control was modernized with the TMS9000 control shows a considerable improvement in the resulting landing call times.

Elevator Group Control with Artificial Intelligence

Optimal control of double-deck elevator group using genetic algorithm

The passenger service level in an elevator system depends on the planning of the elevator group and on the control of the elevators. In this thesis the contribution of passenger and elevator traffic for an elevator group with multiple shafts is studied by simulations and theoretical analysis. During this work an elevator control system, the Traffic Master System 9000 (TMS9000), was developed. The TMS9000 group control system optimizes passenger waiting times instead of hall call times, and the control actions can be started in advance, which is not the case with conventional controls. With the TMS9000 control system, average passenger waiting times became shorter and more balanced floor by floor compared to conventional controls. The functional properties of the TMS9000 group control system were tested with the Advanced Lift Traffic Simulator (ALTS), which is described in the thesis. ALTS has also been applied in elevator planning to predict hall call times in new buildings, or to estimate improvement potential in hall call times for existing elevator groups. Simulation has revealed new correlation between the passenger and elevator traffic. In this thesis, new equations and planning criteria for the mean passenger journey time and ride time inside the car during an up-peak are introduced, and the results are verified with simulations. 3 PREFACE This thesis is based on work that was carried out in the KONE Research an Development Department and, later, in the KONE Elevators Research Center. My starting point was to develop an elevator traffic simulator, and to get acquainted with the elevator planning problems. I am very grateful to Mr. Nils-Robert Roschier, and also to the late Mr. Matti Kaakinen, for their encouragement and enthusiasm shown towards my work. Later on the work continue with the development of group control principles. I owe my thanks to Mr. Risto Kontturi, who also gave me the opportunity to finish this thesis. I also want to express my gratitude to my supervisor, Prof. Raimo Hämäläinen, for his interest in my work. The group control study included in this thesis started in 1985 as a research project funded by the Technology Development Centre in Finland (TEKES). The objective was to apply an expert system framework for embedded applications. One of the applications in this project was the elevator group control, where cooperation was made with Helsinki University of Technology (HUT). The original idea of the work was subsequently rejected, but the study …

Planning and Control Models for Elevators in High-Rise Buildings

The invention relates to a procedure for controlling an elevator group consisting of double-deck elevators. According to the invention, landing calls are allocated to the elevators and after that to the elevator decks in such a way that the passenger journey time is optimised. The procedure of the invention takes into account the time the call has been on and the estimated time of arrival to the destination floor.

Marja-Liisa Siikonen

Papers

Elevator Traffic Simulation

Elevator Group Control with Artificial Intelligence

Optimal control of double-deck elevator group using genetic algorithm

Planning and Control Models for Elevators in High-Rise Buildings

Procedure for control of an elevator group consisting of double-deck elevators, which optimises passenger journey time