自動搬送制御を組み込んだ 医用分析装置プラットフォーム開発に関する
Contents
1.2. 99 2 1
3. 39
4. 1 1 3 3 1950
5. E E E E 1 2
6. 4 1 6 1 ee E lt
7. Gul aH HROBE EZ O O TRE 4 2 2 2 4 18 AIMS 1
8. AIMS ADM FIC Lo CRRA CS fe ADM ADM EV ADM ADM
9. 7 5 ADM 1 5 7 1 3 1 5 EECa T aTr Coo 7 2 ADM 7 2 1 ADM ADM ADM Step 2 Step 3
10. ADM ADM T CK
11. 1 AIMS Analyzer Integration Management Software AIMS AIMS Efrat
12. 2 1 1 1 3 2 2 3 3 1 2 1 1 1 3 1 14 2 gt G KA K 1 1 1 2 1 3 2 2 2 3 2 gt 3 1 3 2 LOO 2 1
13. AIMS 2 WED AIMS 82 6 ADM 6 3 ADM 2
14. 1 m Thm 0 i o LZ ClO 2 2 1 2
15. LT 1 3 1990 1
16. 1990 BA 2 2000 1 3 3 KA
17. 23 LBL gt FH 25 2 3 uh 1 2
18. 2 1 E
19. Bll LOC LOC Line Of Code 7 4 5 6 7 2 4 WBS 7 1
20. SET Mba CN kot 2
21. 2 3 2 1 2 2 1 3 EK 4
22. ADM ADM 4 EL ADM LAL ADM ADM ADM 1
23. STS DSTS KO 1 1 HED KE lt HILT 4S 2 1 fab OH E 4 200 STS PLS 380 SSTS 2 30 STS 111 HB Ae GEL Q
24. hop m 1 T bo 4 2 2 m 1 4 1 hop m 2 2 33 m 3 2 71 m 4 3 13 UT 1 2 A 1 3 4 1 5 1 PRAHA CIID Sz
25. m Riz 2 3 16 2
26. ADM Step 1 6 6 N N Step 2 6 5 EP Cay
27. A B C 8 3 3 3 8 3 87
28. 4 1a ITD 10 20
29. 15 T N m za Xa Ri Aaa SF les Le CEA e THE l my Bar code 2 2 1 D 2 1 2 2 3
30. 2 101 8 2002 C 41 LO 3 P D 4
31. ADM V EUV 9 STS vl 1 1 1 1 1 2
32. STS 1 b b 36 3 STS 3 3 STS STS
33. 1 2
34. 1997 P 4 4 40 8 1 1997 2 PP DP 3 pp ooo 4
35. 2015 3 Rt HY viii NA viii NA viii NA viii niy 2015 The Development of Clinical Laboratory Instrument Software Platform Embedding Automatic Transportation Control Ryuichiro Kodama Abstract A wide variety of clinical laboratory tests are conducted supporting medical diagnosis The testing is automated in the laboratory test field for specimen such as blood The laboratory testing has been automated by individual instruments that add reagent to specimen and calculate test data based on the chemical reaction While automation of testing by instruments was being realized laboratories are adopting the system in which multiple instruments connected to conveyors enhance the test processing performance The specimen is tra
36. 1 Estimated LOC for UI Assignment of leaders core total N x L LET N x L E B specific N x L I L otai UI Total N candidates for UI leaders 7 2 7 Work Breakdown Structure WBS 96 7 ADM WBS WBS
37. MAT 42 2 2006 431 2
38. 7 1 PLS PLS 1990 STS 4 STS STS BA STS
39. E 8 1 CH
40. ADM Architecture Domain Matrix AIMS Analyzer Integration Management Software ADM R ADM 8 k 8 1 1989
41. SPL Software Product Lines SPL Analyzer Integration Management System AIMS AIMS AIMS AIMS T AIMS T Architecture Domain Matrix ADM
42. m m 1 2 hop n n 42 4 STS makespan or An JE J 4 2 4 2 A A A out B B in B s2 Aout Bin B s1 s1 Bin
43. 1 Stoermer I27 MAP LAL 28 Koziolek 29
44. Mh 2
45. 104 9 MB E 3
46. TEE WES ID Tok
47. 4 5 2 1 5 3 SPL 2 2 5 2 AB C 0 8 2 0 2 5 ADM
48. Il19 AIMS Software Product Line SPL 201 21 22 AIMS
49. I11 18 2
50. Vol 50 No 11 pp 2654 2664 2009 K Schmid A comprehensive product line scoping approach and its 114 26 27 28 29 30 31 32 33 34 35 validation Proc the 24th International conference on software Engineering ICSE 02 pp 593 603 ACM 2002 L Northrop Software product lines essentials online Software Engineering Institute Carnegie Mellon University 2008 available from http www sei cmu edu library assets spl essentials pdf accessed 2014 02 04 C Stoermer L O Brien MAP mining architectures for product line evaluations Software Architecture 2001 Proc Working EEE IFIP Conference pp 35 44 2001 Knodel J John I Ganesan D et al Asset Recovery and Their Incorporation into Product Lines WCRE 05 Proc 12th Working Conference on Reverse Engineering Washington DC USA IEEE Computer Society pp 120 129 2005 H Koziolek T Coldschmidt T de Gooijer et al Experiences from Identifying Software Reuse Opportunities by Domain Analysis Proc 17th International Software Product Line Conference SPLC 2013 Jones L G and Bergey J K Exploring Acquisition Strategies for Adopting a Software Product Line No NPS AM 10 041 CARNEGIE MELLO
51. STS PLS STS makespan makespan 4 1 47 4 2 1 1
52. 6 3600 6 600test h 33 3 100 T 1 T 100 N d N N d 3 1 3 1 100 N T
53. 12 2 2 13 DM TG EN i Ni 13 2 2 1 K 0Q IEEE 15 2 1 3 ee 17 2 20 ON SN 20 2 2 2 AIMS Analyzer Integration Management Software 22 2 23 DAD CC NO Sc On ee ci 24 2 2 5 25 DB FMF GEOOWOT7i ftEtpECPOOOO 29 1 2 1 2 1 1 2 1 2 PLS STS 2 1 3
54. 2 7 ADM a ADM ADM WBS 107 9 WBS
55. LK 5 3 105 SOR 1 4 STS PLS 30 5 4 4 8 1 3
56. Full 1501 STS FE 3 4 2 3 1 8 2 BC
57. PLS STS I341 60 5 Blocking Time 5 1 GANTT 5 2 STS 5 2 makespan 2 3 4 m 2 200 1 0 101 0 01 20 20 RO makespan 3 0 2 0 3 CP 61 5
58. Work Breakdown Structure WBS 8 1 5 2 5 4 5 2
59. th 4 4 Bh 43 40 4 4 1 makespan DNL 2 makespan STS PLS
60. TRE 5 1
61. 161 STS 1 17 2 2 2 2 1
62. ORE 1 1 1 2 A B 2 3 A QA B GB A B GA Bj 98 L A B 1 D A B 4
63. 0 WBS 6 4 ADM N N ADM
64. 2 30 WBS 9 SPL I51 XDDP Extreme Derivative Development Process SPL XDDP XDDP SPL ADM 29 2
65. 2007 150 1 95 1 EK MEK 80 1 102 8 ADM 8 TOR 4 5 2
66. 1 RO 2 1 1 PLS Pipe Line System PLS 1 PLS 1990
67. STS 70 6 ADM 6 ADM Cak SPE CO 180 eee OR RRO OO 2 ADM 6 1 SPL ADM 6 2 ffi AIMS 6 3 ADM 2 6 4 6 5 6 1 SPL SPE OBRYS ET UFA
68. 6 95 7 ADM ADM ADM L 7 2 1
69. ADM ADM ADM 6 5 WBS MAT ADM WBS
70. PLS STS 0 STS 1 63 1 000 0 900 0 800 0 700 0 600 it gu 0 500 0 400 0 300 0 200 0 100 0 000 PS 0780 o1a7 ooas oo7 1 000 0 900 0 800 0 700 0 600 0 500 0 400 0 300 0 200 0 100 0 000 PS 0630 0225 0088 0035 0 900 0 800 0 700 0 600 p 0 500 0 400 0 300 0 200 0 100 0 000 ps oses ozas 0205 0047 b m 3 N ON _ Wa w c m 4 lA 5 ORIGIN E PLS STS ORIGIN H PLS STS ORIGIN PLS gt STS 5 3 64 5 ORIGIN PLS STS 1 0 2 1 A ORIGIN 4 2 ORIGIN
71. 1 1V 2 RO 3 STS STS 4 STS ET STS STS 5 4 STS
72. 2 2 au 1 3 STS Side Track System STS PLS Pipe Line System 4 PLS STS STS STS
73. 17 C 2 7 2 3 7 4 o O O O O_ 7 4 87 7 ADM eR DB
74. ADM 1 SPL reactive proactive reactive proactive reactive proactive Cli reactive
75. GE COO 006 ADM 83 7 ADM 7 ADM FE 84 1 84 2 84 2 86 7 86 E WE o E E A EEE E E 87 7 3 ADM 88 7 4 ADM 91 TO i 96 ADM 7 1
76. WAZ 9 2 1 2 makespan 108 9
77. 3 3 WE FE 39 4 4 Ye Clk 4 2 ki OM fc RC CO GS 4 N RS Sat OSE ORA OCIS MAE TE ORE ee TE TENT CEE SIE E TENET AAA teem mane ee any TC ree TTI PLS STS
78. 88 7 ADM WBS WBS ADM WBS C C AA a al UI C w m a Ce aa 7 1 ADM WBS 7 3 ADM
79. WBS 6 5 ADM F 6 1 8SPL SPL Proactive Reactive Reactive A aA 6 2
80. kb ORAS s4 PLS STS 1 STS 1 PAT CPR 1 MAT s1 b s2 sl s2 b s3
81. 20 2 114 115
82. 3 4 3 1 131 3 1 l 32 3 T 1 3 1 tL 1 10
83. makespan 2 hop 2 1 5 1 CP CP 4 Q1 1 5 0 2 5 3 PLS STS 5 3 PLS STS 1 4 ORIGIN PLS STS PLS STS 400 40
84. PLS STS a KU makespan c 4 STS PLS 30 5 4 1 4
85. B Stand alone 2 8 Stand alone Product Product Product Product Product Product Product Product i a Core Assets Single Single Single Products Products Products integration ntegration N c M System Product 2 8 Stand alone SPL 2 SPL 35 F 27 2
86. 2 2 2 2 1 2 2 2 AIMS 1 2 2 8 AIMS 2 2 4 SPL Software Product Line 13 2 3 2 1 2 2 3 2 1 2 1 1
87. makespan 5 4 STS PLS STS makespan E STS STS 6 4 5 STS ADM Architecture Domain Matrix
88. 2009 20 145 108 9 FOR 9 1 1970 1971 1 100
89. LT A LSB 1 0 TOME 01 01 0 01 0 2 T 1 1 0 nr ntl 01 1 01 01 2 01 1 1 3 THE AE Lane _ 01 1 _ 1 b S1 T BEERE oO lt TRI EER sms EE a Bhs 4 5
90. EC 881 ADM 2 ADM 2 TT 6 ADM Architecture Workflow Domain Validation Scenario Team1 Team2 Team3 Team4 Core Product Development Organization Core Asset Codes Product Dependent Codes 6 5 Architecture Domain Matrix 6 4
91. c d a c d a c N c c d a c cd a Step 2 Step 4 ADM AX VER LADEAM EATS 81 6 ADM Step 5 WBS ADM
92. FE 3 4 a OUT Empty IN 5 a Bt A OUT Qa gO b IN Full IN OUT j 3 4 Full Empty 2 38 3 Full Empty 3 4 a 32 Bt Empty Full 3 40 It FE
93. 109 9 Vernet aoe 3 ADM ADM 0 6 0
94. ADM mB E 9 E V ADM 1 5 3
95. OED INL Ch ADM 2 ADM ADM 8 3 3 ADM
96. D WBS 4 5 2 ADM ADM 91 7 ADM CN 7 4 ADM
97. 1 1 2 2 3 2 2 101
98. 1 5 SPL 2 5 2 7 3 2 90 ADM 4 5 2 ADM 1 ADM
99. Feature Oriented Domain Analysis FODA 87 L WS beste ALY JAN r PE E Br a O METAF 2 9 2 9 I24 1
100. 4 1 O 4 2 P OH 52 4 4 1 c no ea gata 4 1 28 2 1000 14 5 1 at 48 b 3 1 ooo 4 1 1260 3 210 20 6 W1010 122 rs Wiit0 1112 9 WO01 41 4 2 A Hk eh 2 3 2 1 1 kP k nwt 27e AE DE TE Pays 2 HBR 8 7 lt 15 8 7 7 aC eee ee P e lt es 2 roe Eeoa 53 4 STS
101. 34 3 a STS weet lt Q b 3 2 1 STS 3 2 a 18
102. 4 1 makesparn KU PLS STS 4 2 STS 4 3 43 1 PLS STS makespan 4 8 2 PLS STS makespan 4 3 3 PLS STS 4 3 4
103. 58 5 5 DM Ch 58 SS OMRON 60 SS cd rig Sy TOE ME een en en ee 62 TO 65 67 5 1 PLS STS 5 2 STS 5 3 makespan makespan 2 4 5 4 STS 1 8 4 8 5 5
104. ADM 2 2 76 6 ADM ADM ADM 6 5
105. ADM 7 ADM ADM ADM ADM 8 ADM 11 1 1990 STS ADM BOR CS STS
106. B 2 b1 b2 A 4 A 1 0 STS 54 4 b1 b2 b3 b4 1 1 PLS cC 3 1 FEC b4 a8 2 1 2 PaeDr Drd n 1 a Prett Ert t I n22 Ps n 4 3 3 55 4 be ER EC DS n d a Poro n l a Pa n 2 ee 4 3 4 Pss n 4 3 3
107. 1 1 5 3 iS joj oisi 0196 mes ME f ozs 0226 0315 SS ZEA Ke HA Tre 0 2a tele 0 0 3 2 2 1 67 5 5 4 5 4 8 1 8 bn 1 bn 3 bn 2 bn 3 8 bn 1 33 bn 2 38 5 4
108. 2 4 3 2 23231312231121222311 20 v 01 20 01 01011110011101000111 12 0 8 2211131124111 01 1 21 1 13 nip 8311 1 13 R 6 4 gt 1 aa E z PIE 2 gt Ta 4 4 4 4 2 BO 2 LSB Least Significant Bit 48 4 A MSB Most Significant Bit B
109. Wiese J EEF MICDORAEROKEMT
110. Reactive 36 Reactive Proactive 72 6 ADM AIMS Reactive 6 2 1 SPL N N N
111. STS STS STS 6 STS ADM Architecture Domain Matrix ADM zW Ca ia eas 7 ADM ADM 8 STS 1990 STS
112. Pa 6 2 WHA BC A B C 6 3 A B C 74 6 ADM A BB C 2 AIMS AIMS A B C a b c AIMS AIMS a b c AIMS AIMS
113. 1 STS 2 4 1 STS STS 17 2 1 STS 2 STS
114. OUT ad b5 makespan PLS 7 7 STS 4 7 STS PLS 61 Eo T STS PLS 89 STS PLS PLS gs 1 A 2 B 2 STS 1 STS ABE PLS 2 1 AHAHBHBID REGENCE ARA B STS 2 AA A B7 A
115. 2 I12 n 2 13 1 vr 2 5 19 2 Job Scheduling Problem JSP 2 5
116. 1977 4 1981 8 1981 4 1988 4 1989 11 2001 10 2013 4 2015 3 Rochester Institute of Technology College of Applied Science and Technology Master of Science Rochester Institute of Technology College of Applied Science and Technology Master of Science
117. A B C 25 No 2 5 3 3 86 7 ADM 7 2 2 7 3 100 7 3 17 3 ADM
118. 3 a Gil uh a 2 Sun 2E LT Wor
119. 1 2 1 8 1 1 1 1 1 X 2 1 1980 10
120. 18 A LAL
121. IOC OF VAI View COST UES E OF TE ARETE 57 4 4 3 PLS STS 2 A
122. T ADM 1 ADM 92 7 ADM
123. 01 makespan 4 3 3 PLS STS 2 1 T 0 STS makespan PLS makespan STS 0 2 0 PLS makespan STS 0
124. Roche 44 86 55 000 144 2006
125. P Step 3 cLd a 85 7 ADM R 7 2 BA B C ADM DB _ TARTUP SU a lelelelglgldl alalal GM ldglglglalalalalglalglaglalalalal lglglalalalalglalalalalalalal gt t N 7 et 1 Dull H CD a Fie Bs U D fe fe lela lela lela SESSE SE lalalslalglgldldlalalalal ldldldldldlalalalalaldldldlalalalal LdldlglslalalsaldlglalgWaslalalal ldlglgldlalalalalglgldjdlalalalal 1d lalslalal alalalal lslslaslal lad C 6 QC he AIBIC IPIAIBICIPIAIBIC IPIAIBIClIPIAIBIClPlAIBICI Lot Le L Le 25 25 SHUTDOWN SD Step 2 Step 3
126. a3 b2 a3 A b2 B 4 3 aJ KAAN A B OU E b2 a1 b2 a1 a4 a3 a3 makespan 7 7 STS D 45 4 OUT B A IN 0 2 4 6 8 MAKESPAN 7 7 ALL Ale g H 2 S Transfer Time Blocking Time MAKESPAN 4 7 1 A A in 1 A out 1 a1
127. 6 2 1 j 79 6 ADM C min CC 7 CD gt CA 6 4 1 Li 00 CD ar Seer Uo Save 4 3 ADM CO
128. 2 2 4 1 1 OEY 5 1 2 4 4 1 5 4 8
129. IN A 46 4 A in 1 a1 IN A in 1 al IN A in 1 RI A m 1 A a1 a1 A out 1 A out 1 ORRE a4 A in 1 a4 al a1 A out 1 OUD al B
130. WBS DB WBS VAT C
131. ADM 89 7 ADM 7 5 2 2 A 3 B 2 CC 2 LT 1 5 3 SPL 2 2 5 2 AB C 0 8 2 0 2 5 7 5
132. 111 S x 3 4 RUF D BRI Architecture Domain Matrix Vol 55 No 8 pp 1796 1806 2014 Ryuichiro Kodama Jun Shimabukuro Yoshimitsu Takagi Shinobu Koizumi Shun ichi Tano Experiences with Commonality Control Procedures to Develop Clinical Instrument System Proceedings 18th International Software Product Line Conference SPLC pp 254 263 2014 6 7 2014 9 TOM 112 1 2 3 A 5 6 7 8 9 10 11 12 13 Vol 89 No 12 pp 958 959 2007 25 9 6
133. FE 3 3 OUT XH IN UN OUT OUT IN OUT FE FE 1 1 Full 0 Empty FE 3 1 j ETS IN i Full QIN 1 OOUT Empty ii Step 1 IN 1 37 3 Step 2 IN 1 OUT Step 3 OUT ij
134. STS STS 1 1 1 4 9 48 1000kLOC Line Of Codes 92 48
135. 83 HEBHSIELT Byer 4 3 4 3 1 PLS STS PLS STS A B 2 tAHBHAHAHBHBHAHB OIR T 8 1 8 al b2 a3 a4 b5 b6 a7 b8 a b A B 44 4 PLS PLS A B al null b2 a1 a3 b2 a4 a3 b5 a4 b6 b5 a7 b6 b8 a7 ullb8 null 2 a1 b5 a4 b8 a7 A B
136. 6 4 LAL k X rann hk Le EK S COIRE ORL AIMS 75 6 ADM Bl 6 4 AIMS 6 3 ADM Architecture Domain Matrix ADM ADM 2 6 5 ADM L
137. N N d xT a 3 2 T Ox
138. OF 80 6 ADM 3 c core d dual a application N Step 3 N 6 5 N N c dja oda O 3 De Gd Qa N
139. ADM 93 7 ADM ADM 5 2 3 ADM ADM 2 SPL
140. 7 2 ADM 7 2 1 8 ADM 7 2 2 ADM 7 2 3 7 2 4 WBS 7 3 ADM 7 4 ADM 7 5 84 7 ADM 7 1 ADM 3 1 5 7 1 3
141. 1 2 2 1 cD 4 6 6 a2b1 b2 b3 b4 a3 Fa b A B STS 0 6 A 4 K JO7 ACS 1 a2 LEE X T al a2 b1 b4 1 0
142. 4 5 3 4 5 4 2 2 2 1 6 ADM Archi
143. Ei 8 3 4 6 2 10 31 3 3 3 2 HABIBI Fy NA a kk 3 8 K KRK KONOQONMAATWE 2 3 1 3 2 3 3
144. ADM 94 7 ADM SOP Standard Operating Procedures SOP SOP SOP SOP SOP 5
145. STS 2 30 4 8 2 5 1 2 2
146. 3 AIMS MG 6 2 3 1 73 BOR ADM 1
147. 49 4 4 5 a b b a 1 ROC a b 28 01 1 01 0 a b 1 1 a b 81 82 01 S2 01 S1 01 S1 gkespg7 S2 gkespg7 Sem SIM makespan _ S1 01 S2 01 SIMO _ SI SI S2
148. 5 1 n m m n Q PLS Step 1 m Step 2 Q m Step 3 i 1 59 5 Step 4 1 Q 1 Step 3 Q STS STS 3 ER WARK
149. PLS 1990 FRET T 1990 Side Track System STS PLS STS 2 1
150. PLS 3 8 3 STS 2 4 2 STS 2 4 1 SLI SEER a b a c 2 lt 8 b 1 3 STS 2 4 STS 2 1 3 1
151. PLS STS slide 5 3 1 5 3 2 Porc Q Porc 2 n Gh a Popo 2 a Por 3 B Pore 3 n 2 d a Pors 3 a Pors 4 L Pore 9 L Pore 3 n 3 A a Pn a Po n 1 B Ppe n 1 B Pro n 4 5 3 1 Porc l Porc 2 amp Porc O n l j d a Pore 3 a Pope 4 1 8 Ga S rs o P 4 a Porc 5 1 B 8 1 a Pre in 3 d a Popg Nt I Py n 2 1 B B Pyeg n in 4 5 3 2 slide B 8 0 slide R 5 2 a 5 3 1 5 3 2 ORIGIN PLS STS co 8 4 3 1 5 b c
152. 3 1 1 3 EE i SEDA 5 DAD DL ci T be Oi ii A E mea E tie 8 EOE OA 9 2 E g 12 2 OO 13 PWV AeA TE Dj edad 13 2 1 2 15 2 1 3 17 20 2 20 2 2 2 AIMS Analyzer Integration Management Software 22 20S AIMS oy fy Ss as gc at CC Oe ee ee 23 2 ON Cc 24 2 2 5 25 2 3 II 29 3 cccccccsccccnsscccncscccscsccsecccncscccscsccnssscccscenscsccessseees 31 3 1 es 31 3 2 A Fee SF Po Ls kk 33 Sa Pya ey ds aA wae 0 115 ss Cee ee ee ee eee 36 38 4 ae 39 r 40 O Ol Ol A OT A Ww N e O H Q A QA QA Q Ol A wW N e 41 43 43 2 47 50 MU 51 BO eect A tahoe A EAE AE AEE A AA T A te 56 Pe E EA R ia EA TA 58 es 58 TO 60 PESCA T A STS OENE E ora ee 62 SS 65 67 MEF ADM 70 SPE AE i iii 70 Pg O A PEES ES A A E EE AT 72 DI 75 MO NIS ne Pe E 77
153. 1 Cio AUUT LURE Ch PS 2 2 2 AIMS Analyzer Integration Management Software
154. 2 2 4 1 makespan PLS STS GANTT BM 5 1 20 PLS STS GANTT PLS STS 4 3 3 makespan 1 makespan
155. 52 I521 3 10 ATV a YF
156. 13 1975 2011 PAB FRR II Vol 44 No 10 pp 601 608 2000 113 14 15 16 17 18 19 20 21 22 23 24 25 Vol 44 No 2 pp 102 109 1993 ZAHNG Heng Vol 54 No 1 pp 11 18 2003 AAR ii CER C Vol 49 No 437 pp 133 141 1983 Bo s
157. 90 7 ADM A B A B C LAL DIEE A B 60 7 6 UI DB 4 5B lt c ADM WBS
158. Core Asset Development Product Development 3 35l SPL 3 6 1 71 6 ADM Business Plan Estimation Domain Engineering Q Core Assets a 3 5 Application Engineering PED T SPL Proactive
159. 21 MB E E TUNA EIR OR 1 1 1 2 1 1 1 1
160. Vol 60 No 4 pp 218 225 2009 ZHI Vol 79 No 10 pp 757 762 1997 ROA ESE http www meti go jp committee materials downloadfiles g70124b0Gj pdf 2018 09 08 Software Engineering Institute Carnegie Mellon University Software Product Lines Overview online available from http www sei cmu edu productlines accessed 2013 09 08 SFE 5 lt gt Vol 50 No 4 pp 295 302 2009 Vol 46 No 8 pp 2482 2491 2005 M Fowler Refactoring Improving The Design of Existing Code Addition Wesley 1999
161. RO 7 2 2 100 117 3 300 117 CH 7 2 8 ADM 7 2 4 ADM WBS WBS 97 7 ADM 7 3 1 2
162. 150 1 CORR
163. STS PIER IN COUC HET GI 4 1 makespan LE FR 4 2 STS makespan 2 1 4 3 1 PLS
164. s2 s1 s2 B s2 s2 s1 A out Brin makespan STS k 5 conv hop 4 2 1 43 4 Tom Te
165. 51 4 4 3 4 3 4 01 4 1 a 8 4 01 a 4 110 1 1 11 1 10 2 1 1 2 ga bd CHS HAMIL 4 1 58 2 25 P k 1 q q 4 3 2 Pk 01 k q 0 P k 1 Pk xq KU Pk 1
166. AIMS 23 2 2 2 3 AIMS AIMS UI User Interface R DB 6 2 7 AIMS System Controller Architecture Layer QDU Instrument Controller SH EEE omw ico A B C ll ECOM 2 7 AIMS AIMS
167. STS 4 3 2 1 2 makespan 4 3 3 PLS STS 0 PLS STS STS 4 3 4
168. Z 91 2 2 6 2 6
169. a amsa Pe B IN C DEE Eee R 3 7 3 1 171 SPL 1 2 5 2 X 1 25 1 17 1 46 3 1 5 2 7 UI DB 7 6
170. 2 ADM ADM ADM LICE 3 ADM 2 4 ADM 5 Reactive 6 ADM 98 7 ADM 7 WBS
171. STS 2 35 3 3 2 PLS STS STS STS y N
172. http www jasis jp 2013 pdf result 130905 04 kakudo pdf 2014 09 15 Vol 70 No 2 pp 62 67 2000 Vol 73 No 11 pp 1003 1008 1991 ERMA Vol 61 No 1 pp 59 64 1996 3175729 2001 R Kodama H Mitsumaki T Mimura T Noda Method of conveying sample rack and automated analyzer in which sample rack is conveyed U S Patent 6 599 749 2003 07 29 R Kodama H Mitsumaki T Mimura T Noda Method of conveying sample rack and automated analyzer in which sample rack is conveyed European Patent 0 801 308 2006 01 18 JEITA 2009 Vol 78 No 12 pp 1105 1111 2012 OR
173. 2014 10 01 AMAA FIV AVA E 7700 http www hitachi hitec com science medical 7700series html 2014 10 01 EERI Vol 93 No 3 pp 40 45 2011 http www hitachi hitec com news_events ir 2014 nr20140409 html 2014 10 01 http wwwr hitachi hitec com ir products_info 2008_2 html 116 46 47 48 49 50 51 52 2013 10 01 E i DR A Architecture Domain Matrix Vol 55 No 8 pp 1796 1806 2014 Ryuichiro Kodama Jun Shimabukuro Yoshimitsu Takagi Shinobu Koizumi Shun ichi Tano Experiences with Commonality Control Procedures to Develop Clini
174. 6 6 ADM ATIMS N N N N ADM 78 6 ADM N TITY 5a N Yes 6 6 ADM N NOS
175. 7 1 ADM slslslzlslsfwlmlwiilslslsTslslzss ULEULLLLLL 1 1 IIIllIlIlIlIllIlIll ll II Il see O a amec AA B 2 QA C QA QA B 7 2 ADM 7 2 1 3 7 2 A B C8 ADM AIMS
176. 24 1 7 Schmidl25 LAL 28 2 3 SPL 1I26 SPL
177. 3 8 PLS STS 2 3 69 5 1 2 4 5 2 DED 1 1 STS AHO STS PLS 4 0 PLS STS
178. DDPP 1996 6 2003 7 I81 8 1 a 8 B A AWA BAD BHAA DIREA B 8 1 8 2 8 2 5 8 1 1 2
179. 1 makespan makespan makespan 1 1 4 1 5 5 3 2 3 4 PLS STS 4 PLS STS 4
180. 1 4 8 4 1 2 AICS CWS a slide1 Porg 1 Poste 2 Porg 3 Porg 4 Pore 5 Porg 6 Ld od Ld rd pd Ps 1 Ps 2 Ps 3 Ps 4 Ps 5 Ps 6 b slide2 Porg 1 Porg 2 Porg 3 Porg 4 Pete 2 Pore G Pss 1 Pss 2 Pss 3 Pss 4 Pss 5 Pss 6 4 7 4 7 CHS slide1 shide2 Porg n slide1 vc 1 L EYI side2 cg 0 siide1 oc 0 slide2 oc 1 2 56 4 4 4 PLS
181. 3 2 2 1 4 5 1 5 5 68 5 5 1 PLS STS STS 3 jE makespan GANTT 5 2 2 3 4
182. instrument STS has its potential capacity in processing performance so that it is of great significance to pursue its possibility This study clarifies the following 1 The performance significantly diminishes when the conveyance time per one specimen for a conveyor exceeds the processing time of a specimen number of the devices 1 in STS 2 the processing time of STS is 30 shorter than the one of PLS in average where both systems process the same 200 samples which drop in four instruments randomly and those instruments have one buffer in each of entrance and exit of the side track and 3 the increase of buffers does not have impact more than 30 performed by one buffer Our performance analysis focuses the sequence of random specimen samples which skip a specific instrument and placed STS as the architecture to compress the space caused by skipping This study confirms possibility of STS quantitatively and in this way builds the design guideline for the conveyor and the buffer of STS 2 The development method of integration software platform This study proposes the software development method based on Software Product Line SPL approach employed for Analyzer Integration Management Software AIMS to systemize heterogeneous clinical analyzers It is difficult to make a development plan to connect a new analyzer to AIMS because an analyzer requires its own particular management and various portion of AIMS software should be changed to
183. implement the new management To solve this problem the study devised the method called Architecture Domain Matrix ADM method in which each architecture component is further decomposed into clinical operation flow elements and core asset of software is extracted from those elements This method controls development cost of core asset in a cost estimate phase and enhances productivity of software development because Work Breakdown Structure WBS can be generated by collecting all change specifications for each operational flow element and a development team suitable for change can be designed by adding up all changes for each architecture component After applying this method to a real project the project integrated embedded software of three different analyzers in one year and a half and achieved 2 5 times embedded software productivity compared with the past non SPL methods The technologies described in the above 1 and 2 are applied to the real clinical instruments Both 1 and 2 contribute to the development of clinical instruments which occupy a high share of the world market The configuration of each chapter is as follows Chapter 1 describes the background and purpose of this study Chapter 2 builds the problem scope for this study which is composed of the transportation performance design and the platform development method Chapter 3 shows how to control the specimen transportation for STS and clarifies that STS can overcome
184. 1 2 3 AYBIA BEI 2 2 00 2 4 3 4 c n m m n 41 4 a 2 b 3 c 4 4 1 4 2 STS 1 1
185. 400 40 makespan 65 5 a b c c SD 5 3 1 5 3 2 PLS STS STS PLS 4 30 a b 0 306 mean 0 8099 5 2 a oe are aie ee aoe ee b PLS STS T eeren EEF M AAA mean m2 5 4 STS 2 3 5 3 66 5 bn bn bn 2 bn 3 PLS STS bn 1 5 2
186. 81 ADM es 83 eo 84 ADM Fs E Sacchi ene erated aetna E A 84 ci MAA SORT SO Tet i penaa 84 86 NN 86 87 ADM 88 ADM Se she torch rede i a 91 vill 8 Et 9 9 1 Jez gt ja 99 103 SN iter eee ree eee ee ea eee Tee ee ee eT Te ee eee ee ee ee eee ee ee ee eee ee EE EE EE ee tp 2 S ed es 110 111 112 11 1 1 3 SE CO 5 A SN i 7 OU CO i i 8 OO i 9 1 1 1 1 1 1 1 2 1 1 3 1990 1 1 4
187. Critical Point makespan CP 1 1 CP 0 0 15 0 65 1 CP 5 1 Ul O O NS O O c O Y U9 O O 02 04 06 08 1 5 2 makespan 62 5 5 1 1 ax b 2 ax b CP CP me a aes es 1 1 152 8 7 40 7 13 4 370 13 1 2 a b CP CP 1
188. LEASE 1 2 1 2 1990 2 a Pipe Line System PLS Side Track System STS L FES PLS TR 5 STS I18 1996 6 2003 7 8 1990 PLS STS 1
189. N UNIV COLORADO SPRINGS CO 2010 AL lt gt Vol 73 No 11 pp 995 1002 1991 lt gt Vol 50 No 4 pp 225 228 2005 C Vol 63 No 611 pp 2296 2301 1997 R Core Team 2013 R A language and environment for statistical computing R Foundation for Statistical Computing Vienna Austria URL http www R project org accessed 2014 08 19 Software Engineering Institute Carnegie Mellon University A Framework for Software Product Line Practice Version 5 0 online available from http www sei cmu edu productlines frame_report index html accessed 2013 09 08 115 36 37 38 39 40 41 42 43 44 45 Frakes W B and Kang K C Software reuse research Status and future IEEE Transactions on Software Engine
190. S2 SIRS S S2 4 3 1 Romp 4 3 1 ADD 2 makespan 01 01 4 4 makespan IEC D 01 makespan 01 01 makespan 01 2 81 AME S2 2 81 4 8 1 K 2 4 5 50 4 m m 01
191. STS Side Track System STS LOT STS PLS STS 1990 4 STS STS STS
192. cal Instrument System Proceedings 18th International Software Product Line Conference SPLC pp 254 263 2014 100 2014 9 25 B TOM 2014 9 NXT 260 260 6 2010 AE XDDP4SPL EMB 2013 EMB 28 9 pp 1 6 2013 EZR AL 2003 3 pp 17 24 2003 117
193. ering Vol 31 No 7 pp 529 536 2005 Kang K Cohen S Hess J et al Feature Oriented Domain Analysis FODA Feasibility Study CMU SEI 90 TR 021 ADA235785 online Software Engineering Institute Carnegie Mellon University available from http www sei cmu edu library abstracts reports 90tr021 cfm accessed 2013 09 08 Hofman P Pohley T Bermann A et al Domain Specific Feature Modeling for Software Product Lines Proc 16th International Software Product Line Conference SPLC pp 229 238 2012 Takebe Y Fukaya N Chikahisa M Hanawa T and Shirai O Experiences with software product line engineering in product development oriented organization in Proceedings of the 13th International Software Product Line Conference Carnegie Mellon University pp 275 283 2009 2 lamay a eko A a http www hitachi co jp New cnews 9704 0415 html 2014 10 01 http wwwr hitachi hitec com news_events product72002 nr020906 html
194. k Breakdown Structure WBS SPL 2 2 5 1 SPL Software Product Line SPL SPL 26 2 ae AIMS 6 E E
195. nsported to designated instruments by conveyors The system requires software platform to connect instruments to conveyors It is important to design system performance corresponding to connected instruments and to apply a software development method to maintain platform containing unchanged core assets This study is a system development thesis to report how to realize the integrated software platform embedding automatic transportation control for specimens This thesis is composed of 1 the design of automatic transportation control to enhance system processing performance and 2 the development method of integration software platform to allow flexible connection of instruments to the system 1 The design of automatic transportation control This theme is to propose the basic design of automatic transportation control and conduct quantitative performance analysis for the processing time of clinical instrument system connected by a conveyor PLS Pipe Line System is one of the architectures to implement the system where clinical instruments are aligned along the conveyor and each instrument pipettes samples on the conveyor PLS was used to deal with almost uniformly requested tests for samples in the 1990s After that as a variety of tests 1s made wide and requested tests are deviated in sample by sample STS Side Track System was devised and is now working STS has a buffer function and passing function for samples to skip unrequested
196. tecture Domain Matrix ADM 30 2 7 6 2 7 2 1 0 4 3 5 4 Hr tty ee Spree ADM Architecture Domain Matrix 7 7 6
197. the PLS deficiency caused by deviation of instrument paths which each specimen follows Chapter 4 compares STS and PLS in processing performance in an analytical manner First the limit performance of a conveyor to keep STS system performance is analyzed Second probability distribution of idle time sequence length is defined so that a transportation system is modeled as a filter transforming that distribution This clarifies the mechanism for STS to increase the processing performance by reducing idle time Chapter 5 verifies the analytical results described in Chapter 4 It verifies that the limit performance of a conveyor does exist the filter model of transportation system can explain the idle time reduction in digit and STS is superior to the old system by calculating compression rate of makespan In addition it reports and discusses how the number of instruments and buffers affects the system performance quantitatively Chapter 6 proposes how to develop STS software platform The method is called ADM Architecture Domain Matrix method The problem to be solved by ADM is that every time a new instrument is connected to the platform the source code changes are scattered everywhere in the architecture ADM allows achieving higher resolution to analyze source codes by two axes architecture elements and domain elements This enhances estimate precision and ADM can appropriately control the development of core assets Chapter 7 verifies
198. the high productivity achieved by ADM method and reviews the result after ADM method is applied to a real project Chapter 8 describes the real products which the transportation performance design and the platform development method are applied to STS which was devised in 1990s not only enables customers to provide a variety of instrument configurations but also characterizes the STS products as the system which can avoid unnecessary congestion of samples on a conveyor This leads to the high share of clinical instruments in the world market Also the software platform was developed by adopting the ADM method so that many varieties of clinical instruments were built with high productivity This also contributes to the high world wide share of clinical instruments Chapter 9 summarizes the problems set in the transportation performance design and the platform development method and their solutions described in the above chapters so that those solutions contribute to the world wide share of the system products As the result of this study the system processing performance and the development productivity has been enhanced establishing how to build the software platform of clinical instrument system from a design phase to an implementation phase The system products which adopt the methods in this study are utilized in the world to contribute to the cost reduction and the testing speed in clinical laboratories
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