Parallelized Seeded Region Growing Using CUDA

This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests

Simulation Method for the Physical Deformation of a Three-Dimensional Soft Body in Augmented Reality-Based External Ventricular Drainage

Objectives Intraoperative navigation reduces the risk of major complications and increases the likelihood of optimal surgical outcomes. This paper presents an augmented reality (AR)-based simulation technique for ventriculostomy that visualizes brain deformations caused by the movements of a surgical instrument in a three-dimensional brain model. This is achieved by utilizing a position-based dynamics (PBD) physical deformation method on a preoperative brain image. Methods An infrared camera-based AR surgical environment aligns the real-world space with a virtual space and tracks the surgical instruments. For a realistic representation and reduced simulation computation load, a hybrid geometric model is employed, which combines a high-resolution mesh model and a multiresolution tetrahedron model. Collision handling is executed when a collision between the brain and surgical instrument is detected. Constraints are used to preserve the properties of the soft body and ensure stable deformation. Results The experiment was conducted once in a phantom environment and once in an actual surgical environment. The tasks of inserting the surgical instrument into the ventricle using only the navigation information presented through the smart glasses and verifying the drainage of cerebrospinal fluid were evaluated. These tasks were successfully completed, as indicated by the drainage, and the deformation simulation speed averaged 18.78 fps. Conclusions This experiment confirmed that the AR-based method for external ventricular drain surgery was beneficial to clinicians

Evolutionary optimization of a technological knowledge network

Knowledge networks have two contradicting demands increasing flow and decreasing cost Maximizing the sum of these demands will result in an optimized network On the basis of the evolutionary optimization algorithm we suggest a quantitative way to produce an optimized technological knowledge network where optimality is defined as the maximization of overall benefit Applied to Korea s ICT network such a network results in a total knowledge flow increase of up to 75% in absolute measures and 150% with due regard to cost Our result supports the notion that self-organizing networks can be far from optimal and thus active intervention by policymakers or managers is necessary to take full advantage of the potential benefits Finally with clear-cut suggestions about where to add new links and remove old ones according to potential benefits this study sheds light on future policies with regard to evolutionary technology networks (C) 2010 Elsevier Ltd All rights reservedKwasnicki W, 1996, TECHNOL FORECAST SOC, V52, P31TEECE D, 1996, J ECON BEHAV ORGAN, V18, P1Powell WW, 1996, ADMIN SCI QUART, V41, P116FOGEL DB, 1995, EVOLUTIONARY COMPUTAFUKUYAMA F, 1995, TRUST SOCIAL VIRTUESJONES C, 1995, J POLITICAL EC, V110, P494SAVIOTTI PP, 1995, J EVOLUTIONARY EC, V5, P369SPENDER JC, 1994, INT BUSINESS REV, V3, P353WASSERMAN S, 1994, SOCIAL NETWORK ANALSorenson O, 2006, RES POLICY, V35, P994, DOI 10.1016/j.respol.2006.05.002Alcacer J, 2006, REV ECON STAT, V88, P774GOYAL S, 2007, CONNECTIONS INTRO ECCHEN X, 2007, ADV INTEL SYS RES, pNIL1Shin J, 2007, INF ECON POLICY, V19, P249, DOI 10.1016/j.infoecopol.2007.01.003Nieto MJ, 2007, TECHNOVATION, V27, P367, DOI 10.1016/j.technovation.2006.10.001Calia RC, 2007, TECHNOVATION, V27, P426, DOI 10.1016/j.technovation.2006.08.003Takeda Y, 2008, TECHNOVATION, V28, P531, DOI 10.1016/j.technovation.2007.12.006Lin JL, 2009, TECHNOVATION, V29, P763, DOI 10.1016/j.technovation.2009.05.001BOLTON R, 2003, EC DISAPPEARING DISTKeenan M, 2003, J FORECASTING, V22, P129, DOI 10.1002/for.849Hu AGZ, 2003, INT J IND ORGAN, V21, P849, DOI 10.1016/S0167-7187(03)00035-3*STEPI, 2004, AN TECHN IND LINK KOSAMPAT B, 2004, EXAMINING PATENT EXABalconi M, 2004, RES POLICY, V33, P127, DOI 10.1016/S0048-7333(03)00108-2Cowan R, 2004, J ECON DYN CONTROL, V28, P1557, DOI 10.1016/j.jedc.2003.04.002Morone P, 2004, J EVOL ECON, V14, P327, DOI 10.1007/s00191-004-0211-2Braha D, 2004, J INF TECHNOL, V19, P244, DOI 10.1057/palgrave.jit.2000030WILLIAMSON OE, 2000, J ECON LIT, V38, P595QUINN JB, 2000, SLOAN MANAGE REV, V41, P41Nonaka I, 2000, LONG RANGE PLANN, V33, P5*OECD, 2001, INN NETW COOP NAT IN*OECD, 2001, INN PEOPL MOB SKILL*NAT I SCI TECHN P, 2001, 71 NISTEP MIN ED CUL, P821HALL B, 2001, 8498 NAT BUR EC RES, P3BASRI E, 2001, INNOVATIVE NETWORKSNewman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.016131Grant RM, 1996, STRATEGIC MANAGE J, V17, P109TASCHLER DR, 1997, J TECHNOLOGY TRANSFE, V22, P29EDQUIST C, 1997, SYSTEMS INNOVATION TSCHWEITZER F, 1998, EVOL COMPUT, V5, P419Leonard D, 1998, CALIF MANAGE REV, V40, P112Nahapiet J, 1998, ACAD MANAGE REV, V23, P242Weitzman ML, 1998, Q J ECON, V113, P331Watts DJ, 1998, NATURE, V393, P440Peters L, 1998, RES POLICY, V27, P255Papaconstantinou G, 1998, RES POLICY, V27, P301Tijssen RJW, 1999, RES POLICY, V28, P519Simonin BL, 1999, STRATEGIC MANAGE J, V20, P595Almeida P, 1999, MANAGE SCI, V45, P905Kumaresan N, 1999, RES POLICY, V28, P563Amesse F, 2001, RES POLICY, V30, P1459Hagedoorn J, 2002, RES POLICY, V31, P477Zhuge H, 2002, EXPERT SYST APPL, V23, P23TUBKE A, 2003, 74 IPTS JRCSINGH J, 2003, SOCIAL NETWORK UNPUBSCHWEITZER F, 2003, BROWNIAN AGENTS ACTISCHAPPER MA, 2003, PROPOSAL CORE LIST I*OECD, 2003, DYN NAT INN SYSTPORTER AL, 2005, TECH MINING EXPLOITI*OECD, 2005, OECD INF TECHN OUTLDUGUET E, 2005, EC INNOVATION NEW TE, V14, P375Criscuolo P, 2005, RES POLICY, V34, P1350, DOI 10.1016/j.respol.2005.05.018Gay B, 2005, RES POLICY, V34, P1457, DOI 10.1016/j.respol.2005.07.001PITT L, 2006, SWEDISH BIOTECH SMES, P553Ejermo O, 2006, RES POLICY, V35, P412, DOI 10.1016/j.respol.2006.01.001Becheikh N, 2006, TECHNOVATION, V26, P644, DOI 10.1016/j.technovation.2005.06.016COASE RH, 1992, AM ECON REV, V82, P713LUNDVALL BA, 1992, NATL SYSTEMS INNOVATSCHRADER S, 1991, RES POLICY, V20, P153BARNEY J, 1991, J MANAGE, V17, P99DUNNING JH, 1991, INT NATL POLICY PERSMOGEE ME, 1991, TECHNOLOGY POLICY CRROMER PM, 1990, J POLIT ECON, V98, pS71WOMACK JP, 1990, MACHINE CHANGED WORLSTATA R, 1989, SLOAN MANAGE REV, V30, P63GOLDBERG DE, 1989, GENETIC ALGORITHMS SVONHIPPEL E, 1987, RES POLICY, V16, P291BOSENIUK T, 1987, PHYS LETT A, V125, P307WINTER SG, 1987, COMPETITIVE CHALLENG, P159PAVITT K, 1984, RES POLICY, V13, P343LIPPMAN SA, 1982, BELL J ECON, V13, P418ZIMMERMAN MB, 1982, BELL J ECON, V13, P297KILLING P, 1980, COLUMBIA J WORLD FAL, P38ALLEN TJ, 1977, MANAGING FLOW TECHNOTERLECKYJ N, 1974, EFFECTS R D PRODUCTIFORD LR, 1956, CAN J MATH, V8, P399

On the creation and evaluation of e-business model variants: The case of auction

E-business model variants aim at creating customer value through differentiation. At the core are two major components of the business model concept-business process and customer value. Focusing narrowly on these components, this paper provides a new and systematic way to identify both, create potentially competitive variants, and to evaluate them quantitatively. With a clear presentation of the whole business process, it is possible to highlight key sub-processes, and increase a shared understanding between multiple stakeholders. Moreover, understanding customer preference and the expected market share of potential variants can allow managers to benefit from clear value propositions for variants'''''''' potential profitability. Our method also promotes rapid and efficient consensus-building about the most competitive variant. Taken together, our proposed method will help both managers and researchers refine the business model more precisely. An e-auction case in Korea is presented to illustrate the empirical application of our method in detail. (C) 2008 Elsevier Inc. All rights reserved.Morris M, 2005, J BUS RES, V58, P726, DOI 10.1016/j.jbusres.2003.11.001Anderson BB, 2005, DECIS SUPPORT SYST, V39, P333, DOI 10.1016/j.dss.2003.12.001VANDERRAADT B, 2005, P 2005 13 IEEE INT C, P53Moore WL, 2004, INT J RES MARK, V21, P299, DOI 10.1016/j.ijresmar.2004.01.002Shen H, 2004, COMPUT IND, V54, P307, DOI 10.1016/j.compind.2003.07.009JOYCE P, 2004, VALUE CREATION E BUSKRIEGER AM, 2004, MONOGRAPH U PENNSYLVULRICH KT, 2003, PRODUCT DESIGN DEVGORDIJN J, 2003, REQUIREMENTS ENG J, V8, P114, DOI 10.1007/s00766-003-0169-xHAYNE SC, 2003, ELECT MARKETS, V13, P282, DOI 10.1080/1019678032000135536Chesbrough H, 2002, IND CORP CHANGE, V11, P529Magretta J, 2002, HARVARD BUS REV, V80, P86Huckvale M, 2002, COMPUT SPEECH LANG, V16, P165, DOI 10.1006/csla.2001.0187KIOLOV H, 2002, BUSINESS MODELS GUIDAmit R, 2001, STRATEGIC MANAGE J, V22, P493GREEN PE, 2001, INTERFACES, V31, P56PORTER ME, 2001, HARVARD BUS REV, V79, P63RAYPORT JF, 2001, E COMMERCEWEILL P, 2001, PLACE SPACEALT R, 2001, ELECTRON MARK, V11, P3HAN D, 2001, VALUE BASED STRATEGYAPPLEGATE L, 2001, HARVARD BUSINESS SCH, P61Haaijer R, 2000, J MARKETING RES, V37, P376AFUAH A, 2000, INTERNET BUSINESS MOFOWLER M, 2000, UML DISTILLED BRIEFMCFADDEN D, 2000, DISAGGREGATE BEHAV TERIKSSON H, 1999, BUSINESS MODELING UMORME B, 1999, ACA CBC BOTH EFFECTIVenkatraman N, 1998, SLOAN MANAGE REV, V40, P33Ben-Akiva M, 1998, J FORECASTING, V17, P175TIMMERS P, 1998, ELECT MARKETS, V8, P3VERNADAT FB, 1996, ENTERPRISE MODELINGSLYWOTZKY A, 1996, VALUE MIGRATIONRHYNE R, 1995, FUTURES, V27, P657OLUD MA, 1995, BUSINESS PROCESSES MWITTINK DR, 1994, INT J RES MARK, V11, P41KUKALIS S, 1994, INT J MANAGEMENT, V11, P676KOHJI R, 1991, J MARKETING RES, V28, P347GREEN PE, 1988, J ACAD MARKET SCI, V16, P114MARCA DA, 1988, STRUCTURED ANAL DESILOUVIERE JJ, 1983, J MARKETING RES, V20, P350BATSELL RR, 1981, J MARKETING RES, V18, P1RAO VR, 1978, 388 U ILL SCH BUS ADANDERSON NH, 1970, PSYCHOL REV, V77, P153ZWICKY F, 1969, DISCOVERY INVENTIONKRUSKAL JB, 1965, J ROY STAT SOC B, V27, P251LUCE RD, 1964, J MATH PSYCHOL, V1, P1SCHUMPETER JA, 1934, THEORY EC DEV INQUIR

Generation and Application of Patent Claim Map: Text Mining and

198-205Despite the fact that patents are under intensive scrutiny for years, patent claim, the most ample source of information has been relatively unexplored. Patent claims mean the right over a patent. Their overlaps by subsequently granted patents indicate the erosion of patent rights. In that regard, the issue of patent valuation and competitor strategy is very closely related with it. In addition, claims could be used to recognize technology relatedness. Therefore, in this research, an exploratory method to deal with patent claims using text-mining and network analysis has been proposed. First, a claim overlap profile is constructed to identify whether a specific claim overlaps another by applying text mining and domain expert knowledge. Secondly, network analysis is used to generate three kinds of patent claim map. This could help researchers, R&D managers and policy makers to evaluate patents and analyse competitors more accurately, and develop patent strategy more efficiently. In the long run, the patent claim profile and map could contribute to the overall technology management including new technology development, strategic positioning of technology and technology alliance

Brownian agent-based technology forecasting

Today''''''''s innovation process is best characterized by nonlinearity and interaction. Agent-based models build on these concepts, but have not been useful in practice because they are either too complex or too simple to make a good match with reality. As a remedy, we employ a Brownian agent model with intermediate complexity to produce value-added technology forecasting. As an illustration with Korea''''''''s software industry data, computer simulation is carried out. Attracted by higher technology value, agents concentrate on specific technology regions, and form co-existing major technology regions of high density. A rough comparison with actual software production data exhibits a fair reflection of reality, and supports the underlying idea that economic motivation of agents should be considered. (C) 2009 Elsevier Inc. All rights reserved.*KOSA, 2006, KOR SOFTW IND STAT*OECD, 2006, OECD SCI TECHN IND SGordon TJ, 2005, TECHNOL FORECAST SOC, V72, P1064, DOI 10.1016/j.techfore.2004.11.008*OECD, 2005, OECD INF TECHN OUTLPORTER AL, 2005, TECH MINING*KOSA, 2004, ANN REP KOR SOFTW IN*KOSA, 2004, KOR SOFTW FIRMS REPGordon TJ, 2003, TECHNOL FORECAST SOC, V70, P397, DOI 10.1016/S0040-1625(02)00323-2SCHWEITZER F, 2003, BROWNIAN AGENTS ACTISCHAAPER M, 2003, UNCTAD M MEAS EL COMGoldenberg J, 2001, TECHNOL FORECAST SOC, V68, P293LUNA F, 2000, EC SIMULATIONS SWARMCHOPARD B, 1998, CELLULAR AUTOMATA MODURRETT R, 1998, THEOR POPUL BIOL, V53, P33SCHWEITZER F, 1998, ADV COMPLEX SYST, V1, P11Lemons DS, 1997, AM J PHYS, V65, P1079Watts RJ, 1997, TECHNOL FORECAST SOC, V56, P25Moore C, 1997, J STAT PHYS, V88, P795Galam S, 1997, PHYSICA A, V238, P66MULLER JP, 1997, INTELLIGENT AGENTS, V3EPSTEIN JM, 1996, GROWING ARTIFICIAL SBenJacob E, 1995, FRACTALS, V3, P849STEELS L, 1995, BIOL TECHNOLOGY INTEHARADA Y, 1994, RES POPUL ECOL, V36, P237SILVERBERG G, 1994, J EVOLUTIONARY EC, V4, P207BHARGAVA SC, 1993, TECHNOL FORECAST SOC, V44, P87ARTHUR WB, 1993, J EVOLUTIONARY EC, V3, P1LAM L, 1993, COMPUT PHYS, V7, P534WALDORP MM, 1992, COMPLEXITYWOUDENBERG F, 1991, TECHNOL FORECAST SOC, V40, P131WEIDLICH W, 1991, PHYS REP, V204, P1SCHNARRS S, 1989, MEGAMISTAKES FORECASTOFFOLI T, 1987, CELLULAR AUTOMATA MAMAKRIDAKIS S, 1986, INT J FORECASTING, V2, P15DENDRINOS DS, 1984, GEOGR ANAL, V16, P287HAKEN H, 1978, SYNERGETICS INTRO NOVONNEUMANN J, 1966, THEORY SELF REPROD A

Novelty-focused patent mapping for technology opportunity analysis

Patent maps are an effective means of discovering potential technology opportunities. However, this method has been of limited use in practice since defining and interpreting patent vacancies, as surrogates for potential technology opportunities, tend to be intuitive and ambiguous. As a remedy, we propose an approach to detecting novel patents based on systematic processes and quantitative outcomes. At the heart of the proposed approach is the text mining to extract the patterns of word usage and the local outlier factor to measure the degree of novelty in a numerical scale. The meanings of potential technology opportunities become more explicit by identifying novel patents rather than patent vacancies that are usually represented as a simple set of keywords. Finally, a novelty-focused patent identification map is developed to explore the implications on novel patents. A case study of the patents about thermal management technology of light emitting diode (LED) is exemplified. We believe the proposed approach could be employed in various research areas, serving as a starting point for developing more general models.close0

Measurement of depreciation rate of technological knowledge: Technology cycle time approach

121-127In this paper, a new method is proposed for estimating the depreciation rate of technological knowledge based on the analysis of technology cycle time (TCT). Patent citation data are used in an empirical analysis. The following features characterize the proposed TCT-based method: i) Estimation of the depreciation rate is measured by using the entire set of patents; and ii) The current approach generates the sector-specific depreciation rates for individual industrial sectors. Overall, the results of empirical analysis are in accordance with expectation. The average depreciation rate (13.3 %) is rather higher than other estimates of previous research. At the same time, consistent upward trends are found over time. Regarding the sectoral variation among industries, emerging and high-tech sectors show faster pace of technical progress but at the same time higher rate of technical obsolescence, vis-à-vis traditional manufacturing sectors or light industries