giới thiệu về kính an toàn

Ngày tải lên: 03/10/2015, 15:07

... Source: Economic Trends Annual Supplement, Labour Market Trends 20.5 Inflation in selected European countries Germany France Belgium EU Finland UK Spain Italy Portugal Greece % change 1998 compared ... in selected European countries Germany France Belgium EU Finland UK Spain Italy Portugal Greece 10 15 20 % unemployment (ILO measure) 1998 20.9 Unemployment in UK, USA and Germany 10 % p.a 1960-73 ... USA 1981-90 1990-98 Germany 20.10 Economic growth in UK, USA and Germany % p.a 1960-73 1973-81 UK USA 1981-90 1990-98 Germany 20.11 The circular flow of income, expenditure and output I C S C+I...

Ngày tải lên: 06/08/2014, 02:22

... quan hệ công việc khác (hình ) Ngày Cơng việc Tháo van điều tiết Sửa van Đường dẫn nước Thay & sửa đường dây Lắp van Hình : Sơ đồ GANTT Hình : Biểu đồ mũi tên Sửa van GV: Cao Thị Hoàng Trâm Trang ... luyện, cố vấn cho đai xanh - Giới thiệu để chứng nhận cho đai xanh • Đai xanh (Green belt): thành viên đội dự án, đảm nhận công việc bán thời gian dự án cải tiến Đai xanh thường đảm nhận công ... yếu tố mối quan hệ chúng Ghi mức độ quan hệ yếu tố vào vị trí giao cột hàng Ký hiệu thường sử dụng: ʘ : có quan hệ mạnh Ο: quan hệ trung bình Δ: quan hệ yếu Để trống: khơng có quan hệ d Ví dụ:...

Ngày tải lên: 25/09/2012, 16:52

... lý (Management & Trainning Project)f Dự án bảo dưỡng lớn (Major Maintenance Project)g Dự án viện trợ phát triển / phúc lợi công cộng (Public / Welfare / Development Project) GIỚI THIỆU VỀ QUẢN ... cổ điển  Phương pháp thời gian bù vốn – Tbv : thời gian cần thiết để lượng tiền thu bù lại tiền đầu tư ban đầu Thời gian bù vốn khơng xét đến suất chiết tính Thời gian bù vốn có xét đến suất chiết ... theo kế hoạch hướng đến mục tiêu Kiểm soát = Giám sát + So sánh + Sửa sai GIỚI THIỆU VỀ NHÀ QUẢN LÝ DỰ ÁN (PROJECT MANAGER - PM) PM : Là người chịu trách nhiệm việc QLDA 3.1 Vai trò trách nhiệm...

Ngày tải lên: 04/10/2012, 11:59

... programs and try to flesh out more (2) Lecture Introduction and Document Distance 6.006 Spring 2008 Document Distance in Practice Computing Document Distance: docdist1.py The python code and results ... as: � θ(D1 , D2 ) = arccos D1 · D2 � D1 � ∗ � D2 � � ≤ θ ≤ π/2 An angle metric of means the two documents are identical whereas an angle metric of π/2 implies that there are no common words Number ... D1 · D2 = � D1 (w) · D2 (w) (1) w Angle Metric: The angle between the vectors D1 and D2 gives an indication of overlap between the documents Mathematically this angle is expressed as: � θ(D1 ,...

Ngày tải lên: 15/11/2012, 10:24

... complexity and efficiency of the various algorithms for approaching a given problem (here Document Distance) • Document Distance Summary - place everything we did last time in perspective • Translate ... Python) 2n lg(n) better than Insertion Sort (in C) 0.01n2 ? Aside: Note the 20X constant factor difference between Insertion Sort written in Python and that written in C Answer: Merge Sort wins ... Insertion Sort routine – Divide and Conquer – Analysis of Recurrences • Get rid of sorting altogether? Readings CLRS Chapter Asymptotic Notation General Idea For any problem (or input), parametrize...

Ngày tải lên: 15/11/2012, 10:24

... “Reservations” for future landings • When plane lands, it is removed from set of pending events • Reserve req specify “requested landing time” t • Add t to the set of no other landings are scheduled ... sorted(R) land: t = R[0] if (t != now) return error R = R[1: ] (drop R[0] from R) Can we better? • Sorted list: A minute check can be done in O(1) It is possible to insert new time/plane rather than ... precision time or verifying width slots for landing Key Lesson: Need fast insertion into sorted list New Requirement Rank(t): How many planes are scheduled to land at times ≤ t? The new requirement...

Ngày tải lên: 15/11/2012, 10:24

... Adel’son-Velsii and Landis 1962 Bayer and McCreight 1972 (see CLRS 18) Nievergelt and Reingold 1973 CLRS Chapter 13 Sleator and Tarjan 1985 Pugh 1989 Galperin and Rivest 1993 Seidel and Aragon 1996 ... Balanced Binary Search Trees 6.006 Spring 2008 Lecture 4: Balanced Binary Search Trees Lecture Overview • The importance of being balanced • AVL trees – Definition – Balance – Insert • Other balanced ... Original Russian article: Adelson-Velskii, G.; E M Landis (1962) An algorithm for the organization of information” Proceedings of the USSR Academy of Sciences 146: 263266 (English translation by...

Ngày tải lên: 15/11/2012, 10:24

... m = 2r and w-bit machine words and a = odd integer between 2( w − 1) and 2w Good Practise: a not too close to 2(w−1) or 2w Key Lesson: Multiplication and bit extraction are faster than division ... keys per slot Expected performance of chaining: assuming simple uniform hashing The performance is likely to be O(1 + α) - the comes from applying the hash function and access slot whereas the ... i e when m divides | k1 − k2 | • fine if keys you store are uniform random • but if keys are x, 2x, 3x, (regularity) and x and m have common divisor d then use only 1/d of table This is likely...

Ngày tải lên: 15/11/2012, 10:24

... CLRS 17.4) String Matching Given two strings s and t, does s occur as a substring of t? (and if so, where and how many times?) E.g s = ‘6.006’ and t = your entire INBOX (‘grep’ on UNIX) Lecture ... time • space can get big with respect to n e.g nì insert, nì delete solution: when n decreases to m/4, shrink to half the size =⇒ O(1) amortized cost for both insert and delete - analysis is harder; ... Large should Table be? • want m = θ(n) at all times • don’t know how large n will get at creation • m too small =⇒ slow; m too big =⇒ wasteful Idea: Start small (constant) and grow (or shrink) as...

Ngày tải lên: 15/11/2012, 10:24

... function, define a whole family and select one at random • e.g multiplication method with random a • can prove P r (over random h) {h(x) = h(y)} = m for every (i.e not random) x �= y • =⇒ O(1) expected ... Addressing, Probing Strategies • Uniform Hashing, Analysis • Advanced Hashing Readings CLRS Chapter 11.4 (and 11.3.3 and 11.5 if interested) Open Addressing Another approach to collisions • no linked ... Addressing: better cache performance and rarely allocates memory Chaining: less sensitive to hash functions and α Lecture Hashing III: Open Addressing 6.006 Spring 2008 Advanced Hashing Universal Hashing...

Ngày tải lên: 15/11/2012, 10:24

... want sorted A[1: n] w/o auxiliary space?? Figure 2: Merge Sort Example In-Place Sorting Numbers re-arranged in the array A with at most a constant number of them sorted outside the array at any ... right(i) if l ≤ heap-size(A) and A[l] > A[i] then largest ← l else largest ← i if r ≤ heap-size(A) and A[r] > largest then largest ← r if largest = i � then exchange A[i] and A[largest] MAX HEAPIFY(A, ... time Can we improve to O(lg n)? Lecture Sorting I: Heaps 6.006 Spring 2008 5 4 i=1 θ(n ) time in-place Figure 3: Selection Sort Example Heaps (Not garbage collected storage) A heap is an array...

Ngày tải lên: 15/11/2012, 10:24

... (A,3) Swap A[3] and A[7] 4 14 3 16 7 10 10 MAX-HEAPIFY (A,2) Swap A[2] and A[5] Swap A[5] and A[10] 4 14 10 16 16 10 14 MAX-HEAPIFY (A,5) no change MAX-HEAPIFY (A,4) Swap A[4] and A[8] 10 9 10 ... the tree and can’t have children BUILD MAX HEAP(A): heap size(A) = length(A) O(n) times for i ← � length[A]/2� downto O(lg n) time MAX HEAPIFY(A, i) O(n lg n) overall See Figure for an example ... HEAP(A): n times for i =length[A] downto exchange A[1] ←→ A[i] heap size[A] = heap size[A] − O(lg n) MAX HEAPIFY(A, 1) O(n lg n) overall See Figure for an illustration Lecture Sorting II: Heaps...

Ngày tải lên: 15/11/2012, 10:24

... Insertion sort, merge sort and heap sort are all comparison sorts The best worst case running time we know is O(n lg n) Can we better? Decision-Tree Example Sort < a1 , a2 , · · · an > 1:2 1:3 2:3 2:3 ... a3) 2:3 (a2 ≤ a3) ≤ 231 4≤ 6≤ Figure 2: Decision Tree Execution Decision Tree Model Can model execution of any comparison sort In order to sort, we need to generate a total ordering of elements ... of algo: length of path taken • Worst-case running time: height of the tree Theorem Any decision tree that can sort n elements must have height Ω(n lg n) Proof: Tree must contain ≥ n! leaves since...

Ngày tải lên: 15/11/2012, 10:24

... Sort and Heap: Find maximum element and put it at end of array (swap with element at end of array) NOT STABLE! 2a 2b ← 2b 2a define 2a

Ngày tải lên: 15/11/2012, 10:24

... edges if directed (See Fig for an example) colorBlue(u, v) • in Python: Adj = dictionary of list/set values vertex = any hashable object (e.g., int, tuple) • advantage: multiple graphs on same ... i.e., Adj[u] Incidence Lists: • can also make edges objects (see Figure 6) • u.edges = list of (outgoing) edges from u • advantage: storing data with vertices and edges without hashing a Lecture ... row and j = column, and � aij = if (i, j) � E φ otherwise See Figure • good for dense graphs where | E |≈ (| V |)2 • space requirement = Θ(V ) • cool properties like A2 gives length-2 paths and...

Ngày tải lên: 15/11/2012, 10:24

... edges back edge: to ancestor forward edge: to descendant cross edge (to another subtree) Figure 7: Edge Classification To compute this classification, keep global time counter and store time interval ... {z, d, c} frontier3 = {f, v} (not x, c, d) level level Figure 3: Breadth-First Search Frontier Analysis: • vertex V enters next (& then frontier) only once (because level[v] then set) base case: ... Lecture 13 Searching II 6.006 Spring 2008 Lecture 13: Searching II: Breadth-First Search and Depth-First Search Lecture Overview: Search of • Breadth-First Search • Shortest Paths • Depth-First...

Ngày tải lên: 15/11/2012, 10:24

... shortest solution: UNSOLVED n × n Chess: Given n × n board & some configuration of pieces, can WHITE force a win? • can be formulated as (αβ) graph search • every algorithm needs time exponential in n: ... (efficient) NP = all decision problems whose YES answers have short (polynomial-length) “proofs” checkable by a polynomial-time algorithm e.g., Rubik’s cube and n2 − puzzle: is there a solution of length ... generating proofs/solutions is harder than checking them NP-complete = in NP & NP-hard NP-hard = as hard as every problem in NP = every problem in NP can be efficiently converted into this problem...

Ngày tải lên: 15/11/2012, 10:24

... Theorem If p� is shorter than pij , cut out pij and replace with p� ; result is shorter than p ij ij Contradiction v7 Lecture 15 Shortest Paths I: Intro 6.006 Spring 2008 Triangle Inequality: Theorem: ... −1 < 0! Shortest path S −→ C (or B, D, E) is undefined Can go around B → D → C as many times as you like Shortest path S −→ A is defined and has weight Lecture 15 Shortest Paths I: Intro 6.006 ... ⎪ p ⎨ w(p) : if ∃ any such path δ(u, v) = u −→ v ⎪ ⎩ ∞ otherwise (v unreachable from u) Single Source Shortest Paths: Given G = (V, E), w and a source vertex S, find δ(S, V ) [and the best path]...

Ngày tải lên: 15/11/2012, 10:24

... Shortest Paths II: Bellman-Ford 6.006 Spring 2008 Lecture 16: Shortest Paths II: Bellman-Ford Lecture Overview • Review: Notation • Generic S.P Algorithm • Bellman Ford Algorithm – Analysis – Correctness ... w(u, v) : ⎢ ⎣ d[v] ← d[u] + w(u, v) π[v] ← u until you can’t relax any more edges or you’re tired or Lecture 16 Shortest Paths II: Bellman-Ford 6.006 Spring 2008 Complexity: Termination: Algorithm ... A D ∞ ∞ 1 -3 -2 End of pass (and and 4) Figure 5: The numbers in circles indicate the order in which the δ values are computed Lecture 16 Shortest Paths II: Bellman-Ford 6.006 Spring 2008 Theorem:...

Ngày tải lên: 15/11/2012, 10:24