Jones Research Notebook

A* algorithm is a feasibility and quickly search method for path finding problems.

Here is the 8-Pizzle example for describe the algorithm.

In computer science, A* (pronounced "A star") is a best-first, graph search algorithm that finds the least-cost path from a given initial node to one goal node (out of one or more possible goals).

It uses a distance-plus-cost heuristic function (usually denoted f(x)) to determine the order in which the search visits nodes in the tree. The distance-plus-cost heuristic is a sum of two functions: the path-cost function (usually denoted g(x), which may or may not be a heuristic) and an admissible "heuristic estimate" of the distance to the goal (usually denoted h(x)). The path-cost function g(x) is the cost from the starting node to the current node.

Since the h(x) part of the f(x) function must be an admissible heuristic, it must not overestimate the distance to the goal. Thus for an application like routing, h(x) might represent the straight-line distance to the goal, since that is physically the smallest possible distance between any two points (or nodes for that matter).

The algorithm was first described in 1968 by Peter Hart, Nils Nilsson, and Bertram Raphael. In their paper, it was called algorithm A. Since using this algorithm yields optimal behavior for a given heuristic, it has been called A*.

To get more detail information,you may checkout the Wikipedia.

資料來源:
金週刊. 2008.05.12

Topic: A Group Tour Guide System with RFIDs and Wireless Sensor Networks
Arthurs:Po Yu Chen、Wen Tseun Chen、Cheng Han Wu、Yu-Chee Tseng、Chi-Fu Huang
ABSTRACT:
This paper proposes a new application framework for group tour guiding services based on RFIDs and wireless sensor network. We consider a sensing field mixed with multiple independent tourist groups, each with a leader and several members. Members of a
group will follow the moving path of their leader, but may occasionally roam around randomly based on their interest. Sensor nodes have to track leaders’ locations and maintain following paths from members to leaders. A member may ask where his/her leader is, and a leader may “recall” his/her members. We propose a feasible solution to such an application by using existing technologies.
A group guiding protocol is presented. The design enables reliable group guiding at low cost and low traffic load.

這系統不需要使用大量的演算法，只是透過Sensor Network與RFID的群組的方式，來達成團隊呼叫的模式，有如線上的母雞帶小雞的服務模式，應該也可以應用在KOISK的環境上。

Source Link

There are four major functionality that can be used to support engineering project management.
1. Improved product information structuring and packaging
2. Improved information access
3. Improved information security and tailoring by organization modelling
4. Workflow management
5. Lifecycle management

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由於模型社的緣故
認識了萬博士
他是玩趣村中玩藝復興村的村長
今天就受邀參加村民大會的討論
認識了不少人
感覺是個比較輕鬆的創意團隊
也希望能在這次的活動中
學習發想創意的流程 ^^

真的粉棒呢 ^^

說明一下所謂的玩趣村概念吧
這是由創意中心所領導的跨領域創意發想團隊
共分為五個村莊
分別是
@大宅門（Otaku Power）
@玩藝復興
@DOLLnDESIRE
@Rotine Breaker
@創作玩趣創作
五大村莊

這五個村莊各有目標
我當然就加入 玩藝復興村 摟

藉由不同領域的撮合與腦力激盪
而且可能可以買粉多的玩具
來做實驗器材
這種不用繃緊神經的計畫
怎有不加入的道理呢 ^^

為了建立 u-learning 環境的測驗與評量活動，需先定義所需考慮的參數，對真實世界中的
學習活動，有五項情境參數如下所列：

 1. 對學習者個人情境的感知：包含學習者所在地點、到達時間、溫度、濕度、心跳速度、
血壓…等。

 2. 對學習環境參數的感知：包含感應器的 ID 及位置、溫度、濕度、空氣梯度與感應器周圍
的其他環境參數，以及其他可能靠近感應器的物體。

 3. 行動學習載具的感應器所回傳的信息：包含目標物件的感應值(如溫度、水的酸鹼值、空
氣污染值、樹的形狀及顏色)與顏色。

 4. 從資料庫取得的個人資料與學習歷程：包含學習者的資料與學習歷程，例如學習者的預
定行事曆、線上討論的發言內容、預定學習活動的開始時間、可接受學習活動的最短與
最長時間、學習地點、學習活動課程歷程及次序、學習活動課程的約束與限制等。

 5. 從資料庫取得的環境資料：包含學習地點的詳細資料，例如學習活動地點的行程、場地
的限制與管理規則、場地的使用記錄、場地所具備的配備、場地管理者與使用者等。

Source:
U-Learning 環境的構成要件與情境參數

這讓我覺得日本人的實驗精神真的粉厲害
畢竟這不是三兩個就可以完成的實驗
你說它有意義嗎
我並不知道
但是我覺得它們真的很有實驗精神呢!!