gimi9 Pandas Profiling

Dataset statistics

Number of variables	16
Number of observations	312
Missing cells	12
Missing cells (%)	0.2%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	40.0 KiB
Average record size in memory	131.4 B

Variable types

Categorical	4
Text	9
Numeric	3

Dataset

Description	국방기술정보통합서비스(DTiMS)에서 제공 중인 국방기술품질원 보유 논문정보
Author	국방기술품질원
URL	https://www.data.go.kr/data/15038515/fileData.do

Alerts

`학술지시작페이지` is highly overall correlated with `학술지끝페이지`	High correlation
`학술지끝페이지` is highly overall correlated with `학술지시작페이지`	High correlation
`임팩트팩터` is highly overall correlated with `논문구분` and 1 other fields	High correlation
`논문구분` is highly overall correlated with `임팩트팩터` and 1 other fields	High correlation
`SCI 구분` is highly overall correlated with `임팩트팩터` and 1 other fields	High correlation
`논문구분` is highly imbalanced (63.8%)	Imbalance
`SCI 구분` is highly imbalanced (61.0%)	Imbalance
`학술지시작페이지` has 5 (1.6%) zeros	Zeros
`학술지끝페이지` has 5 (1.6%) zeros	Zeros
`임팩트팩터` has 258 (82.7%) zeros	Zeros

Reproduction

Analysis started	2023-12-12 14:29:02.967412
Analysis finished	2023-12-12 14:29:05.765208
Duration	2.8 seconds
Software version	ydata-profiling vv4.5.1
Download configuration	config.json

발행년도
Categorical

Distinct	4
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	2.6 KiB

2017	110
2015	103
2016	98
	1

Length

Max length	4
Median length	4
Mean length	3.9903846
Min length	1

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	2015
2nd row	2015
3rd row	2015
4th row	2015
5th row	2015

Common Values

Value	Count	Frequency (%)
2017	110	35.3%
2015	103	33.0%
2016	98	31.4%
	1	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2017	110	35.4%
2015	103	33.1%
2016	98	31.5%

논문관리번호
Text

Distinct	311
Distinct (%)	100.0%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	23
Median length	23
Mean length	21.675241
Min length	19

Characters and Unicode

Total characters	6741
Distinct characters	27
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	311 ?
Unique (%)	100.0%

Sample

1st row	dtaq-paper-2015-001
2nd row	dtaq-paper-2015-002
3rd row	dtaq-paper-2015-003
4th row	dtaq-paper-2015-004
5th row	dtaq-paper-2015-005

Value	Count	Frequency (%)
dtaq-paper-2015-001	1	0.3%
dtaq(pq)-paper-2017-107	1	0.3%
dtaq(pq)-paper-2017-100	1	0.3%
dtaq(pq)-paper-2017-101	1	0.3%
dtaq(pq)-paper-2017-102	1	0.3%
dtaq(pq)-paper-2017-103	1	0.3%
dtaq(pq)-paper-2017-104	1	0.3%
dtaq(pq)-paper-2017-105	1	0.3%
dtaq(pq)-paper-2017-094	1	0.3%
dtaq(pq)-paper-2017-108	1	0.3%
Other values (301)	301	96.8%

Most occurring characters

Value	Count	Frequency (%)
-	933	13.8%
0	678	10.1%
P	428	6.3%
a	402	6.0%
p	402	6.0%
1	389	5.8%
2	373	5.5%
Q	318	4.7%
A	220	3.3%
)	208	3.1%
Other values (17)	2390	35.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2177	32.3%
Lowercase Letter	1809	26.8%
Uppercase Letter	1406	20.9%
Dash Punctuation	933	13.8%
Close Punctuation	208	3.1%
Open Punctuation	208	3.1%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	678	31.1%
1	389	17.9%
2	373	17.1%
7	171	7.9%
5	164	7.5%
6	159	7.3%
3	62	2.8%
8	61	2.8%
4	61	2.8%
9	59	2.7%

Uppercase Letter

Value	Count	Frequency (%)
P	428	30.4%
Q	318	22.6%
A	220	15.6%
T	110	7.8%
D	110	7.8%
E	110	7.8%
R	110	7.8%

Lowercase Letter

Value	Count	Frequency (%)
a	402	22.2%
p	402	22.2%
r	201	11.1%
e	201	11.1%
t	201	11.1%
q	201	11.1%
d	201	11.1%

Dash Punctuation

Value	Count	Frequency (%)
-	933	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	208	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	208	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	3526	52.3%
Latin	3215	47.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
P	428	13.3%
a	402	12.5%
p	402	12.5%
Q	318	9.9%
A	220	6.8%
r	201	6.3%
e	201	6.3%
t	201	6.3%
q	201	6.3%
d	201	6.3%
Other values (4)	440	13.7%

Common

Value	Count	Frequency (%)
-	933	26.5%
0	678	19.2%
1	389	11.0%
2	373	10.6%
)	208	5.9%
(	208	5.9%
7	171	4.8%
5	164	4.7%
6	159	4.5%
3	62	1.8%
Other values (3)	181	5.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6741	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
-	933	13.8%
0	678	10.1%
P	428	6.3%
a	402	6.0%
p	402	6.0%
1	389	5.8%
2	373	5.5%
Q	318	4.7%
A	220	3.3%
)	208	3.1%
Other values (17)	2390	35.5%

논문구분
Categorical

HIGH CORRELATION IMBALANCE

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	2.6 KiB

국내학술지	272
국제학술지	39
<NA>	1

Length

Max length	5
Median length	5
Mean length	4.9967949
Min length	4

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	국내학술지
2nd row	국내학술지
3rd row	국내학술지
4th row	국내학술지
5th row	국내학술지

Common Values

Value	Count	Frequency (%)
국내학술지	272	87.2%
국제학술지	39	12.5%
<NA>	1	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
국내학술지	272	87.2%
국제학술지	39	12.5%
na	1	0.3%

학술지명
Text

Distinct	134
Distinct (%)	43.1%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	78
Median length	70
Mean length	16.019293
Min length	5

Characters and Unicode

Total characters	4982
Distinct characters	173
Distinct categories	9 ?
Distinct scripts	3 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	91 ?
Unique (%)	29.3%

Sample

1st row	한국방위산업학회지
2nd row	대한전자공학회지
3rd row	한국산업경영학회
4th row	한국항공운항학회지
5th row	한국추진공학회지

Value	Count	Frequency (%)
of	51	8.3%
journal	39	6.4%
and	37	6.1%
한국산학기술학회지	22	3.6%
한국군사과학기술학회지	21	3.4%
한국산학기술학회논문지	17	2.8%
한국기계기술학회지	15	2.5%
technology	14	2.3%
international	14	2.3%
한국품질경영학회지	14	2.3%
Other values (177)	367	60.1%

Most occurring characters

Value	Count	Frequency (%)
학	322	6.5%
	301	6.0%
n	269	5.4%
회	237	4.8%
o	219	4.4%
한	207	4.2%
지	207	4.2%
e	204	4.1%
a	196	3.9%
국	195	3.9%
Other values (163)	2625	52.7%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	2252	45.2%
Lowercase Letter	2001	40.2%
Uppercase Letter	392	7.9%
Space Separator	301	6.0%
Other Punctuation	11	0.2%
Close Punctuation	9	0.2%
Open Punctuation	9	0.2%
Decimal Number	4	0.1%
Dash Punctuation	3	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
학	322	14.3%
회	237	10.5%
한	207	9.2%
지	207	9.2%
국	195	8.7%
기	139	6.2%
술	104	4.6%
산	65	2.9%
공	52	2.3%
문	34	1.5%
Other values (108)	690	30.6%

Lowercase Letter

Value	Count	Frequency (%)
n	269	13.4%
o	219	10.9%
e	204	10.2%
a	196	9.8%
i	163	8.1%
t	144	7.2%
r	142	7.1%
l	118	5.9%
c	97	4.8%
s	92	4.6%
Other values (13)	357	17.8%

Uppercase Letter

Value	Count	Frequency (%)
I	46	11.7%
J	43	11.0%
S	39	9.9%
E	38	9.7%
C	29	7.4%
M	29	7.4%
T	28	7.1%
A	22	5.6%
R	20	5.1%
N	17	4.3%
Other values (13)	81	20.7%

Other Punctuation

Value	Count	Frequency (%)
,	8	72.7%
·	2	18.2%
&	1	9.1%

Decimal Number

Value	Count	Frequency (%)
0	2	50.0%
1	2	50.0%

Space Separator

Value	Count	Frequency (%)
	301	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	9	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2393	48.0%
Hangul	2252	45.2%
Common	337	6.8%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
학	322	14.3%
회	237	10.5%
한	207	9.2%
지	207	9.2%
국	195	8.7%
기	139	6.2%
술	104	4.6%
산	65	2.9%
공	52	2.3%
문	34	1.5%
Other values (108)	690	30.6%

Latin

Value	Count	Frequency (%)
n	269	11.2%
o	219	9.2%
e	204	8.5%
a	196	8.2%
i	163	6.8%
t	144	6.0%
r	142	5.9%
l	118	4.9%
c	97	4.1%
s	92	3.8%
Other values (36)	749	31.3%

Common

Value	Count	Frequency (%)
	301	89.3%
)	9	2.7%
(	9	2.7%
,	8	2.4%
-	3	0.9%
·	2	0.6%
0	2	0.6%
1	2	0.6%
&	1	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2728	54.8%
Hangul	2252	45.2%
None	2	< 0.1%

Most frequent character per block

Hangul

Value	Count	Frequency (%)
학	322	14.3%
회	237	10.5%
한	207	9.2%
지	207	9.2%
국	195	8.7%
기	139	6.2%
술	104	4.6%
산	65	2.9%
공	52	2.3%
문	34	1.5%
Other values (108)	690	30.6%

ASCII

Value	Count	Frequency (%)
	301	11.0%
n	269	9.9%
o	219	8.0%
e	204	7.5%
a	196	7.2%
i	163	6.0%
t	144	5.3%
r	142	5.2%
l	118	4.3%
c	97	3.6%
Other values (44)	875	32.1%

None

Value	Count	Frequency (%)
·	2	100.0%

논문한글명
Text

Distinct	305
Distinct (%)	98.1%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	173
Median length	115
Mean length	45.996785
Min length	16

Characters and Unicode

Total characters	14305
Distinct characters	514
Distinct categories	11 ?
Distinct scripts	4 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	299 ?
Unique (%)	96.1%

Sample

1st row	유도탄 사격수량 도출 및 효율적인 시험평가 방안 연구
2nd row	낮은 변환 손실 및 높은 격리 특성의 W-band MMIC 믹서 모듈
3rd row	국방기술연구개발 사업의 효율성 개선 연구
4th row	소형 항공기 주익 복합재료 적용 사례 분석을 통한 개선 방향 연구
5th row	Hot Gas와 Cold Gas를 이용한 모사 이중펄스 로켓 추진기관의 내부 유동 특성

Value	Count	Frequency (%)
연구	131	4.6%
of	57	2.0%
관한	54	1.9%
및	46	1.6%
위한	35	1.2%
통한	34	1.2%
the	26	0.9%
이용한	23	0.8%
and	20	0.7%
분석	20	0.7%
Other values (1648)	2410	84.4%

Most occurring characters

Value	Count	Frequency (%)
	2557	17.9%
e	464	3.2%
i	427	3.0%
n	412	2.9%
o	383	2.7%
a	363	2.5%
t	338	2.4%
r	308	2.2%
s	275	1.9%
한	218	1.5%
Other values (504)	8560	59.8%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	6200	43.3%
Lowercase Letter	4485	31.4%
Space Separator	2557	17.9%
Uppercase Letter	817	5.7%
Decimal Number	120	0.8%
Dash Punctuation	70	0.5%
Other Punctuation	38	0.3%
Close Punctuation	8	0.1%
Open Punctuation	8	0.1%
Other Symbol	1	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
한	218	3.5%
구	181	2.9%
기	172	2.8%
의	157	2.5%
연	149	2.4%
에	125	2.0%
성	112	1.8%
용	99	1.6%
을	86	1.4%
정	85	1.4%
Other values (429)	4816	77.7%

Lowercase Letter

Value	Count	Frequency (%)
e	464	10.3%
i	427	9.5%
n	412	9.2%
o	383	8.5%
a	363	8.1%
t	338	7.5%
r	308	6.9%
s	275	6.1%
l	214	4.8%
c	197	4.4%
Other values (17)	1104	24.6%

Uppercase Letter

Value	Count	Frequency (%)
S	84	10.3%
A	82	10.0%
E	63	7.7%
T	60	7.3%
C	57	7.0%
I	50	6.1%
M	48	5.9%
R	44	5.4%
D	41	5.0%
P	36	4.4%
Other values (15)	252	30.8%

Decimal Number

Value	Count	Frequency (%)
0	35	29.2%
2	19	15.8%
1	15	12.5%
5	12	10.0%
3	11	9.2%
7	7	5.8%
4	7	5.8%
6	5	4.2%
8	5	4.2%
9	4	3.3%

Other Punctuation

Value	Count	Frequency (%)
,	9	23.7%
.	9	23.7%
/	9	23.7%
:	7	18.4%
&	2	5.3%
'	1	2.6%
·	1	2.6%

Space Separator

Value	Count	Frequency (%)
	2557	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	70	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	8	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	8	100.0%

Other Symbol

Value	Count	Frequency (%)
°	1	100.0%

Math Symbol

Value	Count	Frequency (%)
~	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Hangul	6200	43.3%
Latin	5301	37.1%
Common	2803	19.6%
Greek	1	< 0.1%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
한	218	3.5%
구	181	2.9%
기	172	2.8%
의	157	2.5%
연	149	2.4%
에	125	2.0%
성	112	1.8%
용	99	1.6%
을	86	1.4%
정	85	1.4%
Other values (429)	4816	77.7%

Latin

Value	Count	Frequency (%)
e	464	8.8%
i	427	8.1%
n	412	7.8%
o	383	7.2%
a	363	6.8%
t	338	6.4%
r	308	5.8%
s	275	5.2%
l	214	4.0%
c	197	3.7%
Other values (41)	1920	36.2%

Common

Value	Count	Frequency (%)
	2557	91.2%
-	70	2.5%
0	35	1.2%
2	19	0.7%
1	15	0.5%
5	12	0.4%
3	11	0.4%
,	9	0.3%
.	9	0.3%
/	9	0.3%
Other values (13)	57	2.0%

Greek

Value	Count	Frequency (%)
μ	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8102	56.6%
Hangul	6200	43.3%
None	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	2557	31.6%
e	464	5.7%
i	427	5.3%
n	412	5.1%
o	383	4.7%
a	363	4.5%
t	338	4.2%
r	308	3.8%
s	275	3.4%
l	214	2.6%
Other values (62)	2361	29.1%

Hangul

Value	Count	Frequency (%)
한	218	3.5%
구	181	2.9%
기	172	2.8%
의	157	2.5%
연	149	2.4%
에	125	2.0%
성	112	1.8%
용	99	1.6%
을	86	1.4%
정	85	1.4%
Other values (429)	4816	77.7%

None

Value	Count	Frequency (%)
°	1	33.3%
μ	1	33.3%
·	1	33.3%

ISSN_ISBN
Text

Distinct	112
Distinct (%)	36.0%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	10
Median length	9
Mean length	9.0032154
Min length	9

Characters and Unicode

Total characters	2800
Distinct characters	14
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	71 ?
Unique (%)	22.8%

Sample

1st row	1738-6144
2nd row	1229-635X
3rd row	1226-458X
4th row	1225-9705
5th row	0000-0000

Value	Count	Frequency (%)
1975-4701	50	16.1%
1229-1889	22	7.1%
1598-9127	21	6.8%
0000-0000	15	4.8%
1225-1348	12	3.9%
2092-0385	12	3.9%
1976-5622	8	2.6%
1738-494x	6	1.9%
1350-6307	5	1.6%
1598-2785	5	1.6%
Other values (99)	155	49.8%

Most occurring characters

Value	Count	Frequency (%)
2	396	14.1%
1	386	13.8%
0	323	11.5%
-	311	11.1%
9	274	9.8%
8	241	8.6%
7	237	8.5%
5	232	8.3%
4	153	5.5%
3	121	4.3%
Other values (4)	126	4.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2471	88.2%
Dash Punctuation	311	11.1%
Uppercase Letter	10	0.4%
Lowercase Letter	7	0.2%
Space Separator	1	< 0.1%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	396	16.0%
1	386	15.6%
0	323	13.1%
9	274	11.1%
8	241	9.8%
7	237	9.6%
5	232	9.4%
4	153	6.2%
3	121	4.9%
6	108	4.4%

Dash Punctuation

Value	Count	Frequency (%)
-	311	100.0%

Uppercase Letter

Value	Count	Frequency (%)
X	10	100.0%

Lowercase Letter

Value	Count	Frequency (%)
x	7	100.0%

Space Separator

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2783	99.4%
Latin	17	0.6%

Most frequent character per script

Common

Value	Count	Frequency (%)
2	396	14.2%
1	386	13.9%
0	323	11.6%
-	311	11.2%
9	274	9.8%
8	241	8.7%
7	237	8.5%
5	232	8.3%
4	153	5.5%
3	121	4.3%
Other values (2)	109	3.9%

Latin

Value	Count	Frequency (%)
X	10	58.8%
x	7	41.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2800	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
2	396	14.1%
1	386	13.8%
0	323	11.5%
-	311	11.1%
9	274	9.8%
8	241	8.6%
7	237	8.5%
5	232	8.3%
4	153	5.5%
3	121	4.3%
Other values (4)	126	4.5%

기여율
Categorical

Distinct	20
Distinct (%)	6.4%
Missing	0
Missing (%)	0.0%
Memory size	2.6 KiB

70	128
100	105
80	14
-	12
90	10
Other values (15)	43

Length

Max length	4
Median length	2
Mean length	2.2980769
Min length	1

Unique

Unique	6 ?
Unique (%)	1.9%

Sample

1st row	100
2nd row	70
3rd row	70
4th row	100
5th row	70

Common Values

Value	Count	Frequency (%)
70	128	41.0%
100	105	33.7%
80	14	4.5%
-	12	3.8%
90	10	3.2%
50	7	2.2%
30	5	1.6%
85	5	1.6%
97	4	1.3%
25	4	1.3%
Other values (10)	18	5.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
70	128	41.0%
100	105	33.7%
80	14	4.5%
	12	3.8%
90	10	3.2%
50	7	2.2%
30	5	1.6%
85	5	1.6%
25	4	1.3%
60	4	1.3%
Other values (10)	18	5.8%

주저자명
Text

Distinct	190
Distinct (%)	61.1%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	16
Median length	3
Mean length	4.4694534
Min length	2

Characters and Unicode

Total characters	1390
Distinct characters	161
Distinct categories	6 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	120 ?
Unique (%)	38.6%

Sample

1st row	안단
2nd row	안단
3rd row	박승
4th row	조일륜
5th row	조기홍

Value	Count	Frequency (%)
kim	11	3.0%
김장은	7	1.9%
한형석	7	1.9%
lee	7	1.9%
김준영	7	1.9%
han	7	1.9%
정윤식	6	1.6%
park	6	1.6%
이남례	5	1.3%
김병호	5	1.3%
Other values (198)	303	81.7%

Most occurring characters

Value	Count	Frequency (%)
n	73	5.3%
	63	4.5%
김	55	4.0%
o	46	3.3%
u	45	3.2%
e	39	2.8%
g	36	2.6%
이	34	2.4%
i	31	2.2%
a	31	2.2%
Other values (151)	937	67.4%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	789	56.8%
Lowercase Letter	388	27.9%
Uppercase Letter	126	9.1%
Space Separator	63	4.5%
Dash Punctuation	23	1.7%
Other Punctuation	1	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
김	55	7.0%
이	34	4.3%
재	30	3.8%
성	26	3.3%
영	23	2.9%
진	23	2.9%
정	23	2.9%
박	22	2.8%
형	18	2.3%
현	17	2.2%
Other values (112)	518	65.7%

Lowercase Letter

Value	Count	Frequency (%)
n	73	18.8%
o	46	11.9%
u	45	11.6%
e	39	10.1%
g	36	9.3%
i	31	8.0%
a	31	8.0%
y	17	4.4%
m	16	4.1%
k	14	3.6%
Other values (9)	40	10.3%

Uppercase Letter

Value	Count	Frequency (%)
H	25	19.8%
S	22	17.5%
J	15	11.9%
K	14	11.1%
Y	10	7.9%
P	8	6.3%
L	7	5.6%
C	4	3.2%
W	4	3.2%
N	4	3.2%
Other values (7)	13	10.3%

Space Separator

Value	Count	Frequency (%)
	63	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	23	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Hangul	789	56.8%
Latin	514	37.0%
Common	87	6.3%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
김	55	7.0%
이	34	4.3%
재	30	3.8%
성	26	3.3%
영	23	2.9%
진	23	2.9%
정	23	2.9%
박	22	2.8%
형	18	2.3%
현	17	2.2%
Other values (112)	518	65.7%

Latin

Value	Count	Frequency (%)
n	73	14.2%
o	46	8.9%
u	45	8.8%
e	39	7.6%
g	36	7.0%
i	31	6.0%
a	31	6.0%
H	25	4.9%
S	22	4.3%
y	17	3.3%
Other values (26)	149	29.0%

Common

Value	Count	Frequency (%)
	63	72.4%
-	23	26.4%
,	1	1.1%

Most occurring blocks

Value	Count	Frequency (%)
Hangul	789	56.8%
ASCII	601	43.2%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	73	12.1%
	63	10.5%
o	46	7.7%
u	45	7.5%
e	39	6.5%
g	36	6.0%
i	31	5.2%
a	31	5.2%
H	25	4.2%
-	23	3.8%
Other values (29)	189	31.4%

Hangul

Value	Count	Frequency (%)
김	55	7.0%
이	34	4.3%
재	30	3.8%
성	26	3.3%
영	23	2.9%
진	23	2.9%
정	23	2.9%
박	22	2.8%
형	18	2.3%
현	17	2.2%
Other values (112)	518	65.7%

공동저자명
Text

Distinct	252
Distinct (%)	81.0%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	91
Median length	66
Mean length	11
Min length	1

Characters and Unicode

Total characters	3421
Distinct characters	216
Distinct categories	6 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	230 ?
Unique (%)	74.0%

Sample

1st row
2nd row	이진구
3rd row	나중경
4th row
5th row	박정호;김의용

Value	Count	Frequency (%)
lee	15	3.2%
hyun	7	1.5%
park	6	1.3%
kim	6	1.3%
ho	5	1.1%
jin	5	1.1%
im	4	0.9%
chin	4	0.9%
choi	4	0.9%
sung	3	0.6%
Other values (357)	408	87.4%

Most occurring characters

Value	Count	Frequency (%)
;	372	10.9%
	234	6.8%
n	151	4.4%
김	124	3.6%
o	120	3.5%
e	110	3.2%
u	84	2.5%
g	76	2.2%
이	72	2.1%
a	70	2.0%
Other values (206)	2008	58.7%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	1612	47.1%
Lowercase Letter	853	24.9%
Other Punctuation	373	10.9%
Uppercase Letter	293	8.6%
Space Separator	234	6.8%
Dash Punctuation	56	1.6%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
김	124	7.7%
이	72	4.5%
현	62	3.8%
정	47	2.9%
재	43	2.7%
박	42	2.6%
성	40	2.5%
영	38	2.4%
수	36	2.2%
경	33	2.0%
Other values (162)	1075	66.7%

Lowercase Letter

Value	Count	Frequency (%)
n	151	17.7%
o	120	14.1%
e	110	12.9%
u	84	9.8%
g	76	8.9%
a	70	8.2%
i	54	6.3%
y	41	4.8%
h	30	3.5%
k	30	3.5%
Other values (10)	87	10.2%

Uppercase Letter

Value	Count	Frequency (%)
H	50	17.1%
J	43	14.7%
S	35	11.9%
K	27	9.2%
L	25	8.5%
Y	22	7.5%
C	18	6.1%
P	17	5.8%
B	14	4.8%
I	9	3.1%
Other values (10)	33	11.3%

Other Punctuation

Value	Count	Frequency (%)
;	372	99.7%
.	1	0.3%

Space Separator

Value	Count	Frequency (%)
	234	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	56	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Hangul	1612	47.1%
Latin	1146	33.5%
Common	663	19.4%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
김	124	7.7%
이	72	4.5%
현	62	3.8%
정	47	2.9%
재	43	2.7%
박	42	2.6%
성	40	2.5%
영	38	2.4%
수	36	2.2%
경	33	2.0%
Other values (162)	1075	66.7%

Latin

Value	Count	Frequency (%)
n	151	13.2%
o	120	10.5%
e	110	9.6%
u	84	7.3%
g	76	6.6%
a	70	6.1%
i	54	4.7%
H	50	4.4%
J	43	3.8%
y	41	3.6%
Other values (30)	347	30.3%

Common

Value	Count	Frequency (%)
;	372	56.1%
	234	35.3%
-	56	8.4%
.	1	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1809	52.9%
Hangul	1612	47.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
;	372	20.6%
	234	12.9%
n	151	8.3%
o	120	6.6%
e	110	6.1%
u	84	4.6%
g	76	4.2%
a	70	3.9%
-	56	3.1%
i	54	3.0%
Other values (34)	482	26.6%

Hangul

Value	Count	Frequency (%)
김	124	7.7%
이	72	4.5%
현	62	3.8%
정	47	2.9%
재	43	2.7%
박	42	2.6%
성	40	2.5%
영	38	2.4%
수	36	2.2%
경	33	2.0%
Other values (162)	1075	66.7%

학술지 볼륨번호
Text

Distinct	168
Distinct (%)	54.0%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	6
Median length	5
Mean length	4.4115756
Min length	1

Characters and Unicode

Total characters	1372
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	105 ?
Unique (%)	33.8%

Sample

1st row	21(4)
2nd row	52(2)
3rd row	30(3)
4th row	23(1)
5th row	19(2)

Value	Count	Frequency (%)
n	12	3.9%
18(5	10	3.2%
18	9	2.9%
18(4	7	2.3%
18(2	7	2.3%
18(6	7	2.3%
45(3	7	2.3%
21(2	6	1.9%
18(3	6	1.9%
43(3	5	1.6%
Other values (158)	235	75.6%

Most occurring characters

Value	Count	Frequency (%)
(	249	18.1%
)	249	18.1%
1	201	14.7%
3	109	7.9%
2	108	7.9%
4	107	7.8%
8	77	5.6%
5	75	5.5%
6	63	4.6%
7	47	3.4%
Other values (3)	87	6.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	862	62.8%
Open Punctuation	249	18.1%
Close Punctuation	249	18.1%
Uppercase Letter	12	0.9%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	201	23.3%
3	109	12.6%
2	108	12.5%
4	107	12.4%
8	77	8.9%
5	75	8.7%
6	63	7.3%
7	47	5.5%
0	41	4.8%
9	34	3.9%

Open Punctuation

Value	Count	Frequency (%)
(	249	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	249	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	12	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1360	99.1%
Latin	12	0.9%

Most frequent character per script

Common

Value	Count	Frequency (%)
(	249	18.3%
)	249	18.3%
1	201	14.8%
3	109	8.0%
2	108	7.9%
4	107	7.9%
8	77	5.7%
5	75	5.5%
6	63	4.6%
7	47	3.5%
Other values (2)	75	5.5%

Latin

Value	Count	Frequency (%)
N	12	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1372	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
(	249	18.1%
)	249	18.1%
1	201	14.7%
3	109	7.9%
2	108	7.9%
4	107	7.8%
8	77	5.6%
5	75	5.5%
6	63	4.6%
7	47	3.4%
Other values (3)	87	6.3%

SCI 구분
Categorical

HIGH CORRELATION IMBALANCE

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	2.6 KiB

비SCI	267
SCI	44
<NA>	1

Length

Max length	4
Median length	4
Mean length	3.8589744
Min length	3

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	비SCI
2nd row	비SCI
3rd row	비SCI
4th row	비SCI
5th row	비SCI

Common Values

Value	Count	Frequency (%)
비SCI	267	85.6%
SCI	44	14.1%
<NA>	1	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
비sci	267	85.6%
sci	44	14.1%
na	1	0.3%

학술지시작페이지
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	246
Distinct (%)	79.1%
Missing	1
Missing (%)	0.3%
Infinite	0
Infinite (%)	0.0%
Mean	685.50482

Minimum	0
Maximum	27111
Zeros	5
Zeros (%)	1.6%
Negative	0
Negative (%)	0.0%
Memory size	2.9 KiB

Quantile statistics

Minimum	0
5-th percentile	2.5
Q1	111
median	309
Q3	628
95-th percentile	2241.5
Maximum	27111
Range	27111
Interquartile range (IQR)	517

Descriptive statistics

Standard deviation	1855.3479
Coefficient of variation (CV)	2.7065424
Kurtosis	135.84334
Mean	685.50482
Median Absolute Deviation (MAD)	235
Skewness	10.299251
Sum	213192
Variance	3442315.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	11	3.5%
0	5	1.6%
218	3	1.0%
125	3	1.0%
281	3	1.0%
220	3	1.0%
46	3	1.0%
75	2	0.6%
111	2	0.6%
609	2	0.6%
Other values (236)	274	87.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	5	1.6%
1	11	3.5%
4	1	0.3%
8	1	0.3%
9	1	0.3%
10	1	0.3%
12	1	0.3%
14	1	0.3%
15	2	0.6%
18	1	0.3%

Value	Count	Frequency (%)
27111	1	0.3%
9999	1	0.3%
7109	1	0.3%
6355	1	0.3%
5810	1	0.3%
5423	1	0.3%
5371	1	0.3%
4828	1	0.3%
4194	1	0.3%
3727	1	0.3%

학술지끝페이지
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	259
Distinct (%)	83.3%
Missing	1
Missing (%)	0.3%
Infinite	0
Infinite (%)	0.0%
Mean	693.63666

Minimum	0
Maximum	27120
Zeros	5
Zeros (%)	1.6%
Negative	0
Negative (%)	0.0%
Memory size	2.9 KiB

Quantile statistics

Minimum	0
5-th percentile	13.5
Q1	119
median	317
Q3	636.5
95-th percentile	2246.5
Maximum	27120
Range	27120
Interquartile range (IQR)	517.5

Descriptive statistics

Standard deviation	1855.3098
Coefficient of variation (CV)	2.6747574
Kurtosis	135.86573
Mean	693.63666
Median Absolute Deviation (MAD)	237
Skewness	10.299824
Sum	215721
Variance	3442174.5
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	5	1.6%
80	3	1.0%
287	3	1.0%
58	3	1.0%
52	3	1.0%
8	3	1.0%
205	2	0.6%
88	2	0.6%
167	2	0.6%
228	2	0.6%
Other values (249)	283	90.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	5	1.6%
6	2	0.6%
7	2	0.6%
8	3	1.0%
9	1	0.3%
10	1	0.3%
12	1	0.3%
13	1	0.3%
14	1	0.3%
16	1	0.3%

Value	Count	Frequency (%)
27120	1	0.3%
9999	1	0.3%
7117	1	0.3%
6367	1	0.3%
5818	1	0.3%
5431	1	0.3%
5379	1	0.3%
4834	1	0.3%
4198	1	0.3%
3735	1	0.3%

임팩트팩터
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	37
Distinct (%)	11.9%
Missing	1
Missing (%)	0.3%
Infinite	0
Infinite (%)	0.0%
Mean	0.16545338

Minimum	0
Maximum	3.625
Zeros	258
Zeros (%)	82.7%
Negative	0
Negative (%)	0.0%
Memory size	2.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1.078
Maximum	3.625
Range	3.625
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.49455075
Coefficient of variation (CV)	2.9890641
Kurtosis	19.685032
Mean	0.16545338
Median Absolute Deviation (MAD)	0
Skewness	4.0935754
Sum	51.456
Variance	0.24458045
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=37)

Value	Count	Frequency (%)
0.0	258	82.7%
1.0	6	1.9%
0.2	4	1.3%
0.32	4	1.3%
0.26	3	1.0%
1.028	3	1.0%
0.49	2	0.6%
0.951	2	0.6%
0.545	1	0.3%
0.73	1	0.3%
Other values (27)	27	8.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.0	258	82.7%
0.12	1	0.3%
0.16	1	0.3%
0.19	1	0.3%
0.2	4	1.3%
0.22	1	0.3%
0.26	3	1.0%
0.28	1	0.3%
0.32	4	1.3%
0.45	1	0.3%

Value	Count	Frequency (%)
3.625	1	0.3%
3.364	1	0.3%
3.171	1	0.3%
2.214	1	0.3%
2.18	1	0.3%
2.079	1	0.3%
2.0	1	0.3%
1.739	1	0.3%
1.497	1	0.3%
1.384	1	0.3%

학술지 게재일자
Text

Distinct	130
Distinct (%)	41.8%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	3110
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	74 ?
Unique (%)	23.8%

Sample

1st row	2015-01-01
2nd row	2015-02-01
3rd row	2015-08-31
4th row	2015-03-31
5th row	2015-04-01

Value	Count	Frequency (%)
2016-06-01	12	3.9%
2017-06-30	12	3.9%
2017-09-01	10	3.2%
2017-06-01	9	2.9%
2017-09-30	9	2.9%
2015-10-01	8	2.6%
2015-09-30	8	2.6%
2015-06-01	8	2.6%
2017-05-31	7	2.3%
2016-12-01	7	2.3%
Other values (120)	221	71.1%

Most occurring characters

Value	Count	Frequency (%)
0	810	26.0%
-	622	20.0%
1	594	19.1%
2	390	12.5%
6	165	5.3%
5	136	4.4%
7	131	4.2%
3	128	4.1%
9	55	1.8%
8	45	1.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2488	80.0%
Dash Punctuation	622	20.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	810	32.6%
1	594	23.9%
2	390	15.7%
6	165	6.6%
5	136	5.5%
7	131	5.3%
3	128	5.1%
9	55	2.2%
8	45	1.8%
4	34	1.4%

Dash Punctuation

Value	Count	Frequency (%)
-	622	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	3110	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	810	26.0%
-	622	20.0%
1	594	19.1%
2	390	12.5%
6	165	5.3%
5	136	4.4%
7	131	4.2%
3	128	4.1%
9	55	1.8%
8	45	1.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3110	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	810	26.0%
-	622	20.0%
1	594	19.1%
2	390	12.5%
6	165	5.3%
5	136	4.4%
7	131	4.2%
3	128	4.1%
9	55	1.8%
8	45	1.4%

요약
Text

Distinct	307
Distinct (%)	98.7%
Missing	1
Missing (%)	0.3%
Memory size	2.6 KiB

Length

Max length	1024
Median length	832
Mean length	712.79421
Min length	35

Characters and Unicode

Total characters	221679
Distinct characters	683
Distinct categories	14 ?
Distinct scripts	4 ?
Distinct blocks	6 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	303 ?
Unique (%)	97.4%

Sample

1st row	본 논문에서는 소량의 유도탄 실사격 결과로 명중률 신뢰구간을 추정할 수 있는 방안을 제시하고 이를 바탕으로 유도탄의 사격 수량 도출 및 시험평가 방안을 제안하였다. 또한 사격수량에 따른 명중률 및 신뢰수준과의 상관관계 분석을 통해 유도무기에 따른 적절한 신뢰수준을 도출하고 유도탄의 시험평가 방안을 제시하였다.
2nd row	본 논문에서는 밀리미터파 센서 응용을 위한 낮은 변환손실 및 높은 LO-RF 격리도 특성의 W-band MMIC 믹서 모듈을 설계 및 제작하였다.제작된 MMIC 칩을 모듈화 하기 위해 CPW-도파관 변환기를 설계 및 제작하였으며, MMIC 믹서 모듈의 LO-RF 격리도는 94 GHz에서 30.4 dB의 양호한 측정 결과를 얻었다. 본 논문에서 개발된 W-band MMIC 믹서모듈은 기존의 발표된 W-band(75-110 GHz) MMIC 믹서와 비교하여 우수한 성능을 나타내었다.
3rd row	본 연구에서는 2006~2010년까지 연구개발된 55개 핵심기술을 대상으로 투입과 산출요소를 정의하고 효율성을 분석하였다. 효율성 분석은 비모수 접근법인 DEA를 활용하였다. 투입요소는 총사업비, 참여 연구원을 선정하였고, 산출요소는 특허와 실용화 건수를 사용하였다. 국방연구개발의 평균 순수기술효율성은 59%로 효율적인 DMU는 15%, 비효율적인 DMU는 85%로 나타났다. 한편 국방 연구개발에서 비효율성의 주된 원인은 순수기술적인 측면보다 규모에 의한 측면으로 나타났다. 또 한 투입요소 중 연구원 수는 효율성에 가장 큰 영향을 미치는 것으로 나타났다. 규모의 경제 측면에서는 규모수익유지(CRS)가 11%, 규모수익감소(DRS)가 56%, 규모수익확대(IRS)가 33% 분석되어 국방연구개발은 대부분 규모수익 감소가 필요한 것으로 분석되었다. 국방연구개발 분석에서 제시된 비효율성을 개선하기 위해 총사업비와 연구원은 각각 22%, 3% 줄일 필요가 있고, 특허와 실용화는 각각 현재 대비 약 6배 확대할 필요가 있는 것으로 제시했다.
4th row	composite materials are widely used as structural materials for manufacturing an aircraft, due to their : low weight, low thermal expansion coefficient, production efficiency, anisotropy, corrosion resistance and long fatigue life. The range of using composite materials has been extended from the fuselage and the wings to the entire aircraft structure. In this paper, by analyzing the problems which were generated while designing and fabricating aircraft structures using composite materials, the differences between metallic structures and composite structures are described. In addition, the methodological improvement directions on design and fabricating are described.
5th row	이중펄스 로켓 추진기관은 하나의 펄스분리장치에 의해 분리된 2개의 추진제 그레인을 가진 변형된 고체 추진기관이다. 이러한 추진기관의 주요 성능은 펄스분리장치 홀 면적대 노즐 목 면적비의 변화에 영향을 받는다. 본 연구에서는 펄스분리장치 홀 면적대 노즐 목 면적비 변화에 따른 내부유동특성을 고찰하기 위해 유동해석을 수행하였다. 유동해석에 사용된 기체로는 hot gas로 HTPB/AP계 복합추진제 연소가스와 cold gas로 질소가스를 사용하였다. 이중펄스 로켓 추진기관의 내부유동해석 결과는 공압실험 결과와 비교 분석을 통해 검정하였다. 본 논문에서는 상용 CFD 코드인 ANSUS FLUENT V14.5를 이용하여 유동을 모사하였다.

Value	Count	Frequency (%)
the	2166	5.9%
of	1260	3.5%
and	956	2.6%
to	635	1.7%
in	495	1.4%
a	455	1.2%
is	423	1.2%
for	304	0.8%
this	254	0.7%
on	247	0.7%
Other values (8906)	29217	80.2%

Most occurring characters

Value	Count	Frequency (%)
	36185	16.3%
e	18342	8.3%
t	13394	6.0%
i	11705	5.3%
a	11590	5.2%
o	10406	4.7%
n	10325	4.7%
r	9834	4.4%
s	9826	4.4%
d	5962	2.7%
Other values (673)	84110	37.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	145311	65.6%
Space Separator	36185	16.3%
Other Letter	28111	12.7%
Uppercase Letter	5222	2.4%
Other Punctuation	3821	1.7%
Decimal Number	1718	0.8%
Dash Punctuation	546	0.2%
Open Punctuation	337	0.2%
Close Punctuation	333	0.2%
Math Symbol	35	< 0.1%
Other values (4)	60	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
하	770	2.7%
을	654	2.3%
이	634	2.3%
에	611	2.2%
의	602	2.1%
다	583	2.1%
기	576	2.0%
는	510	1.8%
한	469	1.7%
수	432	1.5%
Other values (577)	22270	79.2%

Lowercase Letter

Value	Count	Frequency (%)
e	18342	12.6%
t	13394	9.2%
i	11705	8.1%
a	11590	8.0%
o	10406	7.2%
n	10325	7.1%
r	9834	6.8%
s	9826	6.8%
d	5962	4.1%
l	5635	3.9%
Other values (20)	38292	26.4%

Uppercase Letter

Value	Count	Frequency (%)
T	703	13.5%
S	464	8.9%
A	459	8.8%
I	425	8.1%
M	384	7.4%
C	334	6.4%
D	260	5.0%
E	259	5.0%
F	229	4.4%
R	205	3.9%
Other values (16)	1500	28.7%

Other Punctuation

Value	Count	Frequency (%)
.	1892	49.5%
,	1599	41.8%
%	88	2.3%
/	83	2.2%
:	81	2.1%
'	18	0.5%
?	18	0.5%
&	17	0.4%
;	12	0.3%
？	10	0.3%
Other values (2)	3	0.1%

Decimal Number

Value	Count	Frequency (%)
0	364	21.2%
1	295	17.2%
2	267	15.5%
5	150	8.7%
4	147	8.6%
3	144	8.4%
8	91	5.3%
7	88	5.1%
6	86	5.0%
9	86	5.0%

Math Symbol

Value	Count	Frequency (%)
~	18	51.4%
+	8	22.9%
=	4	11.4%
<	2	5.7%
∼	1	2.9%
±	1	2.9%
×	1	2.9%

Final Punctuation

Value	Count	Frequency (%)
’	25	92.6%
”	2	7.4%

Other Symbol

Value	Count	Frequency (%)
℃	12	50.0%
°	12	50.0%

Initial Punctuation

Value	Count	Frequency (%)
‘	5	71.4%
“	2	28.6%

Space Separator

Value	Count	Frequency (%)
	36185	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	546	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	337	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	333	100.0%

Other Number

Value	Count	Frequency (%)
₂	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	150513	67.9%
Common	43035	19.4%
Hangul	28111	12.7%
Greek	20	< 0.1%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
하	770	2.7%
을	654	2.3%
이	634	2.3%
에	611	2.2%
의	602	2.1%
다	583	2.1%
기	576	2.0%
는	510	1.8%
한	469	1.7%
수	432	1.5%
Other values (577)	22270	79.2%

Latin

Value	Count	Frequency (%)
e	18342	12.2%
t	13394	8.9%
i	11705	7.8%
a	11590	7.7%
o	10406	6.9%
n	10325	6.9%
r	9834	6.5%
s	9826	6.5%
d	5962	4.0%
l	5635	3.7%
Other values (42)	43494	28.9%

Common

Value	Count	Frequency (%)
	36185	84.1%
.	1892	4.4%
,	1599	3.7%
-	546	1.3%
0	364	0.8%
(	337	0.8%
)	333	0.8%
1	295	0.7%
2	267	0.6%
5	150	0.3%
Other values (30)	1067	2.5%

Greek

Value	Count	Frequency (%)
α	8	40.0%
λ	7	35.0%
μ	4	20.0%
ε	1	5.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	193472	87.3%
Hangul	28111	12.7%
None	48	< 0.1%
Punctuation	35	< 0.1%
Letterlike Symbols	12	< 0.1%
Math Operators	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	36185	18.7%
e	18342	9.5%
t	13394	6.9%
i	11705	6.0%
a	11590	6.0%
o	10406	5.4%
n	10325	5.3%
r	9834	5.1%
s	9826	5.1%
d	5962	3.1%
Other values (69)	55903	28.9%

Hangul

Value	Count	Frequency (%)
하	770	2.7%
을	654	2.3%
이	634	2.3%
에	611	2.2%
의	602	2.1%
다	583	2.1%
기	576	2.0%
는	510	1.8%
한	469	1.7%
수	432	1.5%
Other values (577)	22270	79.2%

Punctuation

Value	Count	Frequency (%)
’	25	71.4%
‘	5	14.3%
”	2	5.7%
“	2	5.7%
…	1	2.9%

Letterlike Symbols

Value	Count	Frequency (%)
℃	12	100.0%

None

Value	Count	Frequency (%)
°	12	25.0%
？	10	20.8%
α	8	16.7%
λ	7	14.6%
μ	4	8.3%
₂	2	4.2%
·	2	4.2%
±	1	2.1%
×	1	2.1%
ε	1	2.1%

Math Operators

Value	Count	Frequency (%)
∼	1	100.0%

Heatmap
Table

	발행년도	논문구분	기여율	SCI 구분	학술지시작페이지	학술지끝페이지	임팩트팩터
발행년도	1.000	0.051	0.633	0.046	0.000	0.000	0.220
논문구분	0.051	1.000	0.345	0.940	0.217	0.217	0.684
기여율	0.633	0.345	1.000	0.353	0.000	0.000	0.669
SCI 구분	0.046	0.940	0.353	1.000	0.223	0.223	0.726
학술지시작페이지	0.000	0.217	0.000	0.223	1.000	1.000	0.290
학술지끝페이지	0.000	0.217	0.000	0.223	1.000	1.000	0.290
임팩트팩터	0.220	0.684	0.669	0.726	0.290	0.290	1.000

Heatmap
Table

	논문구분	발행년도	기여율	SCI 구분
논문구분	1.000	0.084	0.297	0.779
발행년도	0.084	1.000	0.417	0.076
기여율	0.297	0.417	1.000	0.304
SCI 구분	0.779	0.076	0.304	1.000

Heatmap
Table

	학술지시작페이지	학술지끝페이지	임팩트팩터	발행년도	논문구분	기여율	SCI 구분
학술지시작페이지	1.000	1.000	0.059	0.000	0.264	0.000	0.272
학술지끝페이지	1.000	1.000	0.062	0.000	0.264	0.000	0.272
임팩트팩터	0.059	0.062	1.000	0.097	0.685	0.330	0.731
발행년도	0.000	0.000	0.097	1.000	0.084	0.417	0.076
논문구분	0.264	0.264	0.685	0.084	1.000	0.297	0.779
기여율	0.000	0.000	0.330	0.417	0.297	1.000	0.304
SCI 구분	0.272	0.272	0.731	0.076	0.779	0.304	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	발행년도	논문관리번호	논문구분	학술지명	논문한글명	ISSN_ISBN	기여율	주저자명	공동저자명	학술지 볼륨번호	SCI 구분	학술지시작페이지	학술지끝페이지	임팩트팩터	학술지 게재일자	요약
0	2015	dtaq-paper-2015-001	국내학술지	한국방위산업학회지	유도탄 사격수량 도출 및 효율적인 시험평가 방안 연구	1738-6144	100	안단		21(4)	비SCI	75	94	0.0	2015-01-01	본 논문에서는 소량의 유도탄 실사격 결과로 명중률 신뢰구간을 추정할 수 있는 방안을 제시하고 이를 바탕으로 유도탄의 사격 수량 도출 및 시험평가 방안을 제안하였다. 또한 사격수량에 따른 명중률 및 신뢰수준과의 상관관계 분석을 통해 유도무기에 따른 적절한 신뢰수준을 도출하고 유도탄의 시험평가 방안을 제시하였다.
1	2015	dtaq-paper-2015-002	국내학술지	대한전자공학회지	낮은 변환 손실 및 높은 격리 특성의 W-band MMIC 믹서 모듈	1229-635X	70	안단	이진구	52(2)	비SCI	232	236	0.0	2015-02-01	본 논문에서는 밀리미터파 센서 응용을 위한 낮은 변환손실 및 높은 LO-RF 격리도 특성의 W-band MMIC 믹서 모듈을 설계 및 제작하였다.제작된 MMIC 칩을 모듈화 하기 위해 CPW-도파관 변환기를 설계 및 제작하였으며, MMIC 믹서 모듈의 LO-RF 격리도는 94 GHz에서 30.4 dB의 양호한 측정 결과를 얻었다. 본 논문에서 개발된 W-band MMIC 믹서모듈은 기존의 발표된 W-band(75-110 GHz) MMIC 믹서와 비교하여 우수한 성능을 나타내었다.
2	2015	dtaq-paper-2015-003	국내학술지	한국산업경영학회	국방기술연구개발 사업의 효율성 개선 연구	1226-458X	70	박승	나중경	30(3)	비SCI	55	84	0.0	2015-08-31	본 연구에서는 2006~2010년까지 연구개발된 55개 핵심기술을 대상으로 투입과 산출요소를 정의하고 효율성을 분석하였다. 효율성 분석은 비모수 접근법인 DEA를 활용하였다. 투입요소는 총사업비, 참여 연구원을 선정하였고, 산출요소는 특허와 실용화 건수를 사용하였다. 국방연구개발의 평균 순수기술효율성은 59%로 효율적인 DMU는 15%, 비효율적인 DMU는 85%로 나타났다. 한편 국방 연구개발에서 비효율성의 주된 원인은 순수기술적인 측면보다 규모에 의한 측면으로 나타났다. 또 한 투입요소 중 연구원 수는 효율성에 가장 큰 영향을 미치는 것으로 나타났다. 규모의 경제 측면에서는 규모수익유지(CRS)가 11%, 규모수익감소(DRS)가 56%, 규모수익확대(IRS)가 33% 분석되어 국방연구개발은 대부분 규모수익 감소가 필요한 것으로 분석되었다. 국방연구개발 분석에서 제시된 비효율성을 개선하기 위해 총사업비와 연구원은 각각 22%, 3% 줄일 필요가 있고, 특허와 실용화는 각각 현재 대비 약 6배 확대할 필요가 있는 것으로 제시했다.
3	2015	dtaq-paper-2015-004	국내학술지	한국항공운항학회지	소형 항공기 주익 복합재료 적용 사례 분석을 통한 개선 방향 연구	1225-9705	100	조일륜		23(1)	비SCI	96	102	0.2	2015-03-31	composite materials are widely used as structural materials for manufacturing an aircraft, due to their : low weight, low thermal expansion coefficient, production efficiency, anisotropy, corrosion resistance and long fatigue life. The range of using composite materials has been extended from the fuselage and the wings to the entire aircraft structure. In this paper, by analyzing the problems which were generated while designing and fabricating aircraft structures using composite materials, the differences between metallic structures and composite structures are described. In addition, the methodological improvement directions on design and fabricating are described.
4	2015	dtaq-paper-2015-005	국내학술지	한국추진공학회지	Hot Gas와 Cold Gas를 이용한 모사 이중펄스 로켓 추진기관의 내부 유동 특성	0000-0000	70	조기홍	박정호;김의용	19(2)	비SCI	1	8	0.22	2015-04-01	이중펄스 로켓 추진기관은 하나의 펄스분리장치에 의해 분리된 2개의 추진제 그레인을 가진 변형된 고체 추진기관이다. 이러한 추진기관의 주요 성능은 펄스분리장치 홀 면적대 노즐 목 면적비의 변화에 영향을 받는다. 본 연구에서는 펄스분리장치 홀 면적대 노즐 목 면적비 변화에 따른 내부유동특성을 고찰하기 위해 유동해석을 수행하였다. 유동해석에 사용된 기체로는 hot gas로 HTPB/AP계 복합추진제 연소가스와 cold gas로 질소가스를 사용하였다. 이중펄스 로켓 추진기관의 내부유동해석 결과는 공압실험 결과와 비교 분석을 통해 검정하였다. 본 논문에서는 상용 CFD 코드인 ANSUS FLUENT V14.5를 이용하여 유동을 모사하였다.
5	2015	dtaq-paper-2015-006	국내학술지	한국항공우주학회지	Particle Swarm Optimization을 이용한 터보팬 엔진 다목표 성능 최적회 연구	1225-1348	70	최재원	정원철;성홍계	43(4)	비SCI	326	333	0.2	2015-04-01	최적화 프로그램과 연동시키기 위한 터보팬 엔진 성능해석 프로그램을 개발하고, 최적화 기법인 Particle Swarm Optimization을 이용하여 전투기 엔진의 주요 설계변수인 바이패스비, 팬 압축비, 고압압축기 압축비 및 버너출구온도에 대한 성능 최적화를 수행하였다. 최적화 목표는 순추력과 비연료소모율을 다목표 함수로 설정하였으며, 두 개의 목표에 대해 가중치를 주어 각 가중치별 최적 설계점을 도출하였다. 기본 모델은 F-18 전투기와 T-50 고등훈련기에 쓰이고 있는 F404 터보팬 엔진을 선정하여 분석을 수행하였다. 본 연구 결과로 네 개의 변수에 대한 최적 조건을 도출하고, 다양한 설계조건에 대한 최적 설계점 추이를 분석하였다.
6	2015	dtaq-paper-2015-007	국제학술지	Pervasive and Mobile Computing (International Journal)	Adaptive Landmark Recommendations for Travel Planning: Personalizing and Clustering Landmarks using Geo-Tagged Social Media	1574-1192	100	Jonghyun Han	Hyunju Lee	18(1)	SCI	4	17	2.079	2015-04-01	When travelers plan trips, landmark recommendation systems that consider the trip properties will conveniently aid travelers in determining the locations they will visit. Because interesting locations may vary based on the traveler and the situation, it is important to personalize the landmark recommendations by considering the traveler and the trip. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media. We first examine the impact of a trip’s spatial and temporal properties on the distribution of popular places through large-scale data analyses. In our approach, we compute the significance of landmarks for travelers based on their trip’s spatial and temporal properties. Next, we generate clusters of landmark recommendations, which have similar themes or are contiguous, using travel trajectory histories. Landmark recommendation performances based on our approach are evaluated against several baseline approaches. Our approach results in increased a
7	2015	dtaq-paper-2015-008	국내학술지	한국산학기술학회논문지	절충교역 업체의 전략 도출 프레임워크	1975-4701	70	김준영	최기용	16(5)	비SCI	3199	3209	0.0	2015-05-31	우리나라는 1983년에 최초로 절충교역 제도를 도입하여, 국방 전력 증가를 위한 수단 등으로 중요하게 활용하고 있다. 특히, 최근 절충교역을 수행하거나 참여를 원하는 업체들은 증가하고 있지만, 절충교역과 관련된 연구는 제도 개선 및 기술가치평가 등이 주를 이루고 있으며, 절충교역의 주요한 참여자인 절충교역 수행 업체 진단 및 차별화 전략 제시 관련 연구는 부족하다. 본 논문에서는 이에 착안하여 절충교역을 수행한 업체들에 대한 진단 및 차별화된 전략 도출을 위한 연구 프레임워크를 제시하고자 한다. 추가적으로, 제시한 연구 프레임워크를 실제 절충교역을 수행한 업체들에 적용하여 활용성 및 타당성에 대해 살펴본다. 한편, 연구 프레임워크는 크게 성과지표 도출, 만족도함수 및 AHP를 이용하여 각 성과지표에 대한 평가 그리고 IPA를 이용한 전략 도출 과정으로 구성된다.
8	2015	dtaq-paper-2015-009	국내학술지	한국액체미립화학회지	물/부동액-기반AI203나노유체를 이용한 차량용 냉각시스템 성능향상에 관한 실험적 연구	1226-2277	50	박용준	김현진;이승현;최태종;강예준;장석필	20(2)	비SCI	65	68	0.0	2015-06-01	In this study, the thermal performance of vehicle’s cooling system is experimentally investigated using the water/coolantbased Al2O3 nanofluids as working fluids. For the purpose, the water/coolant-based Al2O3 nanofluids are prepared by twostep method with gum arabic. In order to obtain the well-suspended nanofluids, the agglomerated Al2O3 nanoparticles are precipitated using centrifugal force and the experiments are performed with supernatant of them. The thermal conductivity is measured by transient hot wire method and the thermal conductivity of nanofluids is enhanced up to 4.8% as compared to that of base fluids. Moreover, the cooling performance of water/coolant-based Al2O3 nanofluids is evaluated using vehicle’s engine simulator under the constant RPM condition. The results show that the cooling performance of automobile engine increases up to 5.9% using prepared nanofluids. To investigate the effect of nanofluids on exhaust gas, the NOx emission is measured during the operation with respect to time and
9	2015	dtaq-paper-2015-010	국제학술지	IJAER	The framework for analyzing the efficiency of global arms-producing and military services companies based on DEA-AR and ANP	0973-4562	70	김준영	홍충의	10(10)	비SCI	27111	27120	0.0	2015-06-30	The importance of the defense industry has been increased for national security and synergy effects with other industries. This paper aims to propose a framework for analyzing the efficiency of global arms producing and military services companies. Generally, the DEA has been utilized for analysis of management efficiency but there have been some problems. This research applied the DEA-AR model to solve them and acquire more accurate analysis results. Based on the proposed framework, we did a case study to show the feasibility of it.

	발행년도	논문관리번호	논문구분	학술지명	논문한글명	ISSN_ISBN	기여율	주저자명	공동저자명	학술지 볼륨번호	SCI 구분	학술지시작페이지	학술지끝페이지	임팩트팩터	학술지 게재일자	요약
302	2017	DTAQ(PQ)-PAPER-2017-009	국내학술지	신뢰성응용연구	Wiener Process 및 D-Optimality 조건 하에서 계단형 가속열화시험 설계	1738-9895	100	김헌길		17(2)	비SCI	129	135	0.0	2017-06-01	Wiener Process 및 D-Optimality 조건 하에서 계단형 가속열화시험 설계
303	2017	DTAQ(PQ)-PAPER-2017-008	국내학술지	한국산학기술학회	차세대 국방기술정보통합서비스 구축에 관한 연구	1975-4701	100	김미정		18(6)	비SCI	636	645	0.0	2017-06-30	국방기술정보 관리를 위한 정보체계는 국방관련기관에서 개별적으로 보유하고 있는 데이터를 관리하고 적시에 제공하여 사용자의 업무 및 관리자의 의사결정을 지원해야한다. 본 연구의 목적은 국방기술정보관리체계인 DTiMS의 차세대 서비스 구축 방안을 제시함에 있다. 그동안 DTiMS는 법/제도적으로 부여된 임무인 국방과학기술 수집/관리/유통 역할을 수행함에 있어 수집관리 분야에 집중되어 왔다. 따라서 서비스 운영에 있어서 기술정보 유통분야 강화 및 수집관리방안 재정립이 필요한 시점이다. 이를 위해 수요자 관점에서 정보 활용가치를 높일 수 있는 서비스 방향과 무기체계 총수명주기/기술획득 전 순기를 토대로 수집관리대상 기술정보 재정립 방안을 제시하였다. 본 연구에서는 국내외 유관기관의 관리대상정보와 운영방안을 조사하여 비교 분석하였다. 또한 기존 서비스의 현황분석을 통해 관리대상정보 재정립, 정보 간 연관성과 추정성, 사용자의 활용성 강화를 위한 인터페이스 적용의 개선과제를 제시했다. 제안된 구축방향은 차세대 국방기술정보 통합서비스(Defense Technology inforMation Service, DTiMS) 구축을 통하여 구현하였다. 본 연구의 결과는 향후 무기체계 총수명주기 서비스로 발전하는 데 기여할 것으로 기대되며, 국방 기술기획, 연구개발, 정책 의사결정을 효과적으로 지원할 수 있는 도구로써 활용 될 것이다.
304	2017	DTAQ(PQ)-PAPER-2017-007	국내학술지	한국산학기술학회논문지	절충교역 획득기술 활용성과 정량화를 통한 성과관리 제고 방안	1975-4701	70	박태완	정태윤	18(2)	비SCI	609	618	0.0	2017-02-28	본 논문에서는 절충교역을 통해 획득된 국방과학기술에 대한 정량적 성과평가 모형을 제시하였다.
305	2017	DTAQ(PQ)-PAPER-2017-006	국내학술지	한국산학기술학회논문지	국방 R&D기술 등급평가 방법론 개발 연구	1975-4701	70	정유진	김준영; 정태윤	18(2)	비SCI	158	167	0.0	2017-02-28	본 논문에서는 국방 R&D기술 평가에 적용 가능한 기술등급평가 방법론을 제시하였다.
306	2017	DTAQ(PQ)-PAPER-2017-005	국내학술지	한국산학기술학회논문지	유정용 강관의 미세조직 및 기계적 성질에 미치는 열처리의 영향	1975-4701	70	최종민	노상우; 이원재	18	비SCI	252	261	0.0	2017-05-31	This study examined the effect of heat treatment on the microstructure and mechanical properties of J55 line pipe steel. The experiments were carried out at under the following various conditions: austenization temperature(880℃, 910℃, 940℃), cooling methods(water quenching, oil quenching) and tempering temperature(none, 550℃, 650℃). The phase diagram and CCT curve were simulated based on the chemical composition of J55 steel to predict the microstructures. In the results, A1, A3 temperature decreased. As the austenization temperature increased, existing austenite grains grew exponentially which seriously degraded their mechanical properties. Various microstructures, including martensite, bainite, ferrite, and pearlite, developed in accordance with the heat treatments and were closely correlated with hardness, tensile strength and toughness. Martensite was formed after water quenching, but bainite and ferrite appeared after oil quenching. FeC precipitation formed and coarsened during tempering, which improved
307	2017	DTAQ(PQ)-PAPER-2017-004	국내학술지	한국산학기술학회논문지	채널별 색상정보 외삽법 기반 시간적 초해상도 기법을 활용한 전자광학 센서의 프레임률 향상 연구	1975-4701	100	노상우		18	비SCI	125	132	0.0	2017-05-31	The temporal super resolution is a method for increasing the frame rate. Electro-optical sensors are used in various surveillance and reconnaissance weapons systems, and the spatial resolution and temporal resolution of the required electro-optical sensors vary according to the performance requirement of each weapon system. Because most image sensors capture images at 30~60 frames/second, it is necessary to increase the frame rate when the target moves and changes rapidly. This paper proposes a method to increase the frame rate using color channel extrapolation. Using a DMD, one frame of a general camera was adjusted to have different consecutive exposure times for each channel, and the captured image was converted to a single channel image with an increased frame rate. Using the optical flow method, a virtual channel image was generated for each channel, and a single channel image with an increased frame rate was converted to a color channel image. The performance of the proposed temporal super resolution me
308	2017	DTAQ(PQ)-PAPER-2017-003	국내학술지	한국산학기술학회논문지	차량 안전 통신을 위한 새로운 혼잡 제어 알고리즘 제안	1975-4701	100	이원재		18	비SCI	120	124	0.0	2017-05-31	Vehicular safety service reduces traffic accidents and traffic congestion by informing drivers in advance of threats that may occur while driving using vehicle-to-vehicle (V2V) communications in a wireless environment. For vehicle safety services, every vehicle must broadcasts a Basic Safety Message(BSM) periodically. In congested traffic areas, however, network congestion can easily happen, reduce the message delivery ratio, increase end-to-end delay and destabilize vehicular safety service system. In this paper, to solve the network congestion problem in vehicle safety communications, we approximate the relationship between channel busy ratio and the number of vehicles and use it to estimate the total network congestion. We propose a new context-aware transmit power control algorithm which controls the transmission power based on total network congestion. The performance of the proposed algorithm is evaluated using Qualnet, a network simulator. As a result, the estimation of total network congestion is accu
309	2017	DTAQ(PQ)-PAPER-2017-002	국내학술지	Journal of the Korean Institute of Information Security and Cryptology	사이버 무기체계 핵심기술 실현시기의 영향 요인 분석	1598-3986	70	Ho-gyun Lee	Jong-in Lim; Kyung-ho Lee	27(2)	비SCI	281	292	0.0	2017-04-01	It is demanded to promote research and development of cyber weapons system and core technology in response to the ongoing cyber attack of North Korea. In this paper, core technologies of the future cyber weapon system are developed and the factors affecting the realization timing of core technologies were analyzed. 9 core technology groups and 36 core technologies are derived. Afterwards, these core technology groups are compared to the operation phase of the joint cyber warfare guideline and the cyber kill chain of Lockheed Martin. As a result of the comparison, it is confirmed that the core technology groups cover all phases of the aforementioned tactics. The results of regression analyses performed on the degree of influence by each factor regarding the moment of core technology realization show that the moment of core technology realization approaches more quickly as factors such as technology level of the most advanced country, technology level of South Korea, technology transfer possibility from the mil
310	2017	DTAQ(PQ)-PAPER-2017-001	국내학술지	한국정보기술학회지	Assessment of Indoor Localization Algorithm Performance under Realistic Conditions	1598-8619	100	노승회		15	비SCI	95	102	0.0	2017-06-30	지역위치시스시템(LPS) 같은 non-cooperative 위치추정이라 불리는 기존 기술은 anchor node, 즉 위치가 알려 진 센서노드들만을 활용하여 찾고자 하는 목표물, 즉 찾고자 하는 센서노드의 위치추정을 수행하였다. 하지만 위치가 알려진 센서들만을 활용하여 위치추정을 수행하는 것은 정확도에 제한을 줄 수밖에 없다. 이러한 문제 를 해결하기 위해 cooperative 위치추정기법은 고안 및 연구되어져왔다. cooperative 위치추정은 위치가 알려진 센서 뿐만 아니라 위치가 알려지지 않은 센서들도 활용함으로써 정확도와 안정성을 향상시키는 기술이다. 이 논문에서는 실험결과가 현실적이라는 증거를 보여주기 위하여 실제 실내 환경에서 실제로 측정된 데이터를 활 용하여 cooperative 위치추정이 non-cooperative 위치추정보다 더 성능이 우수하다는 결과를 보여줄 것이다. 또한, 실내실험환경에서 실제로 측정된 데이터와 시뮬레이션 데이터를 사용한 위치추정결과를 비교하여 시뮬레이션 을 이용한 위치추정이 얼마나 현실적인 데이터인지 보여준다.
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