SlideShare a Scribd company logo
진법표현
charsyam@naver.com
학습 목표
• 2진수 10진수 16진수의 변환 방법을 알아보자.
진법
• 각 자리에서 사용할 수 있는 수의 개수를 정해두고 이를 이용하여 표현
하는 방식
• 10진수의 경우는 한자리가 0~9까지 10가지 문자(값)으로 수를 표현하는
방식
10진수
• 10진수의 경우는 한자리가 0~9까지 10가지 문자(값)으로 수를 표현하는
방식, 현재 우리가 가장 많이 쓰는 방식
• 10진수 2018 의 경우
2018
2 * 103 0 * 102 1 * 101
8 * 100
각 진법을 10진수로 바꿀때는
• n 진법의 경우 각 자리 k는 𝑛 𝑘
를 곱해주는 것으로 구할 수 있다.
2018
2 * 103 0 * 102 1 * 101
8 * 100
2진수 -> 10진수
• 2진수의 경우, 각 자리는 0, 1 두 개의 문자(값) 만을 사용할 수 있기
때문에 2진수로 불림
• 1011(2진수) = 1 * 23 + 0 * 22 + 1 * 21 + 1 * 20 = 11
1011 = 11(10진수)
1 * 23 0 * 22 1 * 21
1 * 20
16진수 -> 10진수
• 16진수의 경우, 각 자리는 0-9, A-F 총 16 개의 문자(값) 만을 사용
하여 표현
10진수 16진수 10진수 16진수
0 0 8 8
1 1 9 9
2 2 10 A(a)
3 3 11 B(b)
4 4 12 C(c)©
5 5 13 D(d)
6 6 14 E(e)
7 7 15 F(f)
16진수 -> 10진수
• 80FF(16진수) = 8 * 163
+ 0 * 162
+ 15 * 161
+ 15 * 160
• 33023(10진수)
80FF
8 * 163 0 * 162 15 * 161
15 * 160
2진수 <-> 16진수
• 2진수와 16진수는 2 𝑁
관계에 있기 때문에 쉽게 변경이 가능하다.
• 편의상 아래표에서는 4자리로 표현
16진수 2진수 16진수 2진수
0 0000 8 1000
1 0001 9 1001
2 0010 A 1010
3 0011 B 1011
4 0100 C 1100
5 0101 D 1101
6 0110 E 1110
7 0111 F 1111
2진수 -> 16진수
• 2진수 -> 16진수
• 4개씩 변환이 가능하다.
1111 1000
F 8
2진수 -> 16진수
• 2진수 -> 16진수
• 4개씩 변환이 가능하다.
1111 1000
F 8
16진수 -> 2진수
• 16진수 -> 2진수
F8
1111 1000
16진수 -> 2진수
• 16진수 -> 2진수
F8
1111 1000
2진수 -> 8진수
• 2진수 -> 8진수
• 3개씩 변환이 가능하다.
1111 1000
3 07
10진수를 2진수로 표현하기 #1
• 10진수 13을 2진수로 표현하기
132 1
10진수를 2진수로 표현하기 #2
• 10진수 13을 2진수로 표현하기
132
62
1
0
10진수를 2진수로 표현하기 #3
• 10진수 13을 2진수로 표현하기
132
62
32
1
0
1
10진수를 2진수로 표현하기 #4
• 10진수 13을 2진수로 표현하기
132
62
32
1
1
0
1
1101
10진수를 16진수로 표현하기 #1
• 10진수 100을 16진수로 표현하기
10016 4
10진수를 16진수로 표현하기 #2
• 10진수 100을 16진수로 표현하기
10016 4
6
64

More Related Content

More from DaeMyung Kang

The easiest consistent hashing
The easiest consistent hashingThe easiest consistent hashing
The easiest consistent hashing
DaeMyung Kang
 
How to name a cache key
How to name a cache keyHow to name a cache key
How to name a cache key
DaeMyung Kang
 
Integration between Filebeat and logstash
Integration between Filebeat and logstash Integration between Filebeat and logstash
Integration between Filebeat and logstash
DaeMyung Kang
 
How to build massive service for advance
How to build massive service for advanceHow to build massive service for advance
How to build massive service for advance
DaeMyung Kang
 
Massive service basic
Massive service basicMassive service basic
Massive service basic
DaeMyung Kang
 
Data Engineering 101
Data Engineering 101Data Engineering 101
Data Engineering 101
DaeMyung Kang
 
How To Become Better Engineer
How To Become Better EngineerHow To Become Better Engineer
How To Become Better Engineer
DaeMyung Kang
 
Kafka timestamp offset_final
Kafka timestamp offset_finalKafka timestamp offset_final
Kafka timestamp offset_final
DaeMyung Kang
 
Kafka timestamp offset
Kafka timestamp offsetKafka timestamp offset
Kafka timestamp offset
DaeMyung Kang
 
Data pipeline and data lake
Data pipeline and data lakeData pipeline and data lake
Data pipeline and data lake
DaeMyung Kang
 
Redis acl
Redis aclRedis acl
Redis acl
DaeMyung Kang
 
Coffee store
Coffee storeCoffee store
Coffee store
DaeMyung Kang
 
Scalable webservice
Scalable webserviceScalable webservice
Scalable webservice
DaeMyung Kang
 
webservice scaling for newbie
webservice scaling for newbiewebservice scaling for newbie
webservice scaling for newbie
DaeMyung Kang
 
Internet Scale Service Arichitecture
Internet Scale Service ArichitectureInternet Scale Service Arichitecture
Internet Scale Service Arichitecture
DaeMyung Kang
 
Bloomfilter
BloomfilterBloomfilter
Bloomfilter
DaeMyung Kang
 
Redis From 2.8 to 4.x(unstable)
Redis From 2.8 to 4.x(unstable)Redis From 2.8 to 4.x(unstable)
Redis From 2.8 to 4.x(unstable)
DaeMyung Kang
 
Redis From 2.8 to 4.x
Redis From 2.8 to 4.xRedis From 2.8 to 4.x
Redis From 2.8 to 4.x
DaeMyung Kang
 
Soma search
Soma searchSoma search
Soma search
DaeMyung Kang
 
Redis 2017
Redis 2017Redis 2017
Redis 2017
DaeMyung Kang
 

More from DaeMyung Kang (20)

The easiest consistent hashing
The easiest consistent hashingThe easiest consistent hashing
The easiest consistent hashing
 
How to name a cache key
How to name a cache keyHow to name a cache key
How to name a cache key
 
Integration between Filebeat and logstash
Integration between Filebeat and logstash Integration between Filebeat and logstash
Integration between Filebeat and logstash
 
How to build massive service for advance
How to build massive service for advanceHow to build massive service for advance
How to build massive service for advance
 
Massive service basic
Massive service basicMassive service basic
Massive service basic
 
Data Engineering 101
Data Engineering 101Data Engineering 101
Data Engineering 101
 
How To Become Better Engineer
How To Become Better EngineerHow To Become Better Engineer
How To Become Better Engineer
 
Kafka timestamp offset_final
Kafka timestamp offset_finalKafka timestamp offset_final
Kafka timestamp offset_final
 
Kafka timestamp offset
Kafka timestamp offsetKafka timestamp offset
Kafka timestamp offset
 
Data pipeline and data lake
Data pipeline and data lakeData pipeline and data lake
Data pipeline and data lake
 
Redis acl
Redis aclRedis acl
Redis acl
 
Coffee store
Coffee storeCoffee store
Coffee store
 
Scalable webservice
Scalable webserviceScalable webservice
Scalable webservice
 
webservice scaling for newbie
webservice scaling for newbiewebservice scaling for newbie
webservice scaling for newbie
 
Internet Scale Service Arichitecture
Internet Scale Service ArichitectureInternet Scale Service Arichitecture
Internet Scale Service Arichitecture
 
Bloomfilter
BloomfilterBloomfilter
Bloomfilter
 
Redis From 2.8 to 4.x(unstable)
Redis From 2.8 to 4.x(unstable)Redis From 2.8 to 4.x(unstable)
Redis From 2.8 to 4.x(unstable)
 
Redis From 2.8 to 4.x
Redis From 2.8 to 4.xRedis From 2.8 to 4.x
Redis From 2.8 to 4.x
 
Soma search
Soma searchSoma search
Soma search
 
Redis 2017
Redis 2017Redis 2017
Redis 2017
 

Number system