THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

Survey on computational-intelligence-based UAV path planning

Adequate is better

As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.

RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm

As Unmanned Aerial Vehicle (UAV) having been applied in more complex and adverse environments, the requirements of automatic techniques for obstacle avoidance are becoming more and more important. Reinforcement learning (RL) is a well-known technique in the domain of Machine Learning (ML), which interacts with the environment and learning the knowledge without the requirement of massive priori training samples. Thus it is attractive to implement the idea of RL to support UAV tasks in unknown environments. This paper adopts an Adaptive and Random Exploration approach (ARE) to accomplish both the tasks of UAV navigation and obstacle avoidance. Search mechanisms will be conducted to guide the UAV escape to a proper path. Simulations on different scenarios show that our approach can effectively guide UAVs to reach their targets in quite rational paths.

Q learning algorithm based UAV path learning and obstacle avoidence approach

This paper presents a novel real-time path planning approach for unmanned aerial vehicles (UAVs) based on bi-level programming (BLP), in which the planning problem is described as a leader-follower decision making model. The proposed approach can fulfill an integrated path planning requirement, including several realistic abilities of convergence to target, obstacle avoidance, path length optimization, flight path smoothing and adaptability to the changes of the UAV's kinematic and sensory properties. In the BLP model, opposition obstacles and UAV's interrelated performances are described to construct path searching constraints, and variable planning time intervals are introduced to generate navigable flight paths only when necessary. A discretization solution algorithm embedded with five heuristic optimization strategies is particularly designed to speed up the planning. Moreover, convergence and computational cost, as well as potential extensions, are discussed to expose the efficiency and applicability of the approach. Numerous simulations in stochastic and representative scenarios demonstrate not only the effectiveness of the approach in generating optimized flight paths for UAVs, but also its clear advantages through comparisons with four typical methods, while synthetically considering the crucial abilities.

Zheng Zheng

Papers

Survey on computational-intelligence-based UAV path planning

Adequate is better

RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm

Q learning algorithm based UAV path learning and obstacle avoidence approach

Bi-level programming based real-time path planning for unmanned aerial vehicles