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Journal articles on the topic 'Sequential identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Larsson, Roger, and Martin Enqvist. "Sequential Aerodynamic Model Parameter Identification." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1413–18. http://dx.doi.org/10.3182/20120711-3-be-2027.00293.

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2

Perlini, Arthur H., and Andrew D. Silvaggio. "Eyewitness Misidentification: Single vs Double-Blind Comparison of Photospread Administration." Psychological Reports 100, no. 1 (February 2007): 247–56. http://dx.doi.org/10.2466/pr0.100.1.247-256.

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Interactive effects of photospread procedures (blind administration or has knowledge of suspect identity) and presentation techniques (sequential or simultaneous photospreads) on false eyewitness identification rates were examined. Of 117 subjects (57 men and 60 women), 87 served as a witness sample and 30 as a photospread administrator sample. Witnesses viewed a 20-sec. mock robbery video prior to viewing a photospread of six photographs either simultaneously or sequentially. Half of the administrators, prior to displaying the photospreads, were made aware of the photograph of the designated suspect (single-blind condition). Consistent with previous findings, sequential presentation was associated with both lower overall false identification rates as well as lower suspect designated-misidentification rates. In each case, there was evidence that administrators' knowledge increased false identification rates more in simultaneous than in sequential presentation conditions. Implications for administrations by police investigators are discussed.

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3

Rai, Alka, and Benjamin Nandy. "Employer brand to leverage employees’ intention to stay through sequential mediation model: evidence from Indian power sector." International Journal of Energy Sector Management 15, no. 3 (March 1, 2021): 551–65. http://dx.doi.org/10.1108/ijesm-10-2019-0024.

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Purpose This study aims to explain the linkage between employer branding and employee retention; a sequential mediation is hypothesized, where it is proposed that the relationship between employer branding and employee retention is sequentially mediated by person–organization fit (P-O fit) and organizational identification. Design/methodology/approach The sample belongs to 224 executive-level employees of the Indian power sector organization. The sequential mediation model is tested by using SPSS macro command of Preacher and Hayes. Findings The findings established that the relationship between employer brand and employees’ intention to stay is sequentially mediated by P-O fit and organizational identification. Practical implications The findings emphasize the role of employer brand on constructs such as P-O fit, organizational identification and intention to stay. In addition, the established mechanism emphasizes the role of P-O fit to realize the benefits such as organizational identification and employee retention. Originality/value Internal branding efforts may have a major impact on workforce attitude and behavior including engagement, job performance and retention. “Yet studies of the positive impact of employer brand on employee attitudes and behaviors, or of the factors that shape employer brand, are rare” (Charbonnier-Voirin et al., 2017, p. 2). Along the line of such gap, this study has taken up to test the unexplored sequential mediation mechanism between employer brand and employees’ intention to stay through P-O fit and organizational identification.

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4

Hwang, D. M., and C. T. Kelley. "Sequential Quadratic Programming for Parameter Identification Problems." IFAC Proceedings Volumes 22, no. 4 (June 1989): 259–63. http://dx.doi.org/10.1016/s1474-6670(17)53554-5.

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5

Yun, Chung-Bang, Hyeong-Jin Lee, and Chang-Gun Lee. "Sequential Prediction-Error Method for Structural Identification." Journal of Engineering Mechanics 123, no. 2 (February 1997): 115–22. http://dx.doi.org/10.1061/(asce)0733-9399(1997)123:2(115).

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6

Br�sio, Ana S. R., Andrey Romanenko, and Nat�rcia C. P. Fernandes. "Using Sequential Quadratic Programming for System Identification." Applied Mathematics & Information Sciences 9, no. 1 (January 1, 2015): 19–26. http://dx.doi.org/10.12785/amis/090103.

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7

Lin, F. B., Z. P. Bažant, J. C. Chern, and A. H. Marchertas. "Concrete model with normality and sequential identification." Computers & Structures 26, no. 6 (January 1987): 1011–25. http://dx.doi.org/10.1016/0045-7949(87)90118-0.

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8

Chen, Lin. "Graph Isomorphism and Identification Matrices: Sequential Algorithms." Journal of Computer and System Sciences 59, no. 3 (December 1999): 450–75. http://dx.doi.org/10.1006/jcss.1999.1642.

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9

Lai, Tao, Ting-Hua Yi, and Hong-Nan Li. "Parametric study on sequential deconvolution for force identification." Journal of Sound and Vibration 377 (September 2016): 76–89. http://dx.doi.org/10.1016/j.jsv.2016.05.013.

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10

Choi, J. "Sequential loop closing identification of multivariable process models." Computers & Chemical Engineering 24, no. 2-7 (July 15, 2000): 809–14. http://dx.doi.org/10.1016/s0098-1354(00)00386-0.

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11

Jin Young, Choi, Lee Jietae, Jung Jae Hak, Lee Moonyong, and Han Chonghun. "Sequential loop closing identification of multivariable process models." Computers & Chemical Engineering 24, no. 2-7 (July 2000): 809–14. http://dx.doi.org/10.1016/s0098-1354(00)80003-4.

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12

G. Indrawan, G. Indrawan, B. Sitohang B. Sitohang, and S. Akbar S. Akbar. "Review of Sequential Access Method for Fingerprint Identification." TELKOMNIKA (Telecommunication Computing Electronics and Control) 10, no. 2 (June 1, 2012): 335. http://dx.doi.org/10.12928/telkomnika.v10i2.803.

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13

Küchler, Uwe, and Vyacheslav Vasiliev. "Sequential Identification of Linear Dynamic Systems with Memory." Statistical Inference for Stochastic Processes 8, no. 1 (2005): 1–24. http://dx.doi.org/10.1023/b:sisp.0000049119.79817.ee.

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14

Cho, Hokwon A. "Statistical Identification in Multinomial Models with Sequential Sampling." American Journal of Mathematical and Management Sciences 29, no. 1-2 (January 2009): 139–56. http://dx.doi.org/10.1080/01966324.2009.10737754.

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15

Kang, Young-Jin, O.-Kaung Lim, and Yoojeong Noh. "Sequential statistical modeling method for distribution type identification." Structural and Multidisciplinary Optimization 54, no. 6 (September 15, 2016): 1587–607. http://dx.doi.org/10.1007/s00158-016-1567-2.

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16

Baranov, V. V. "Sequential identification and adaptive control in stochastic systems." Cybernetics and Systems Analysis 28, no. 6 (November 1992): 890–904. http://dx.doi.org/10.1007/bf01291293.

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17

Kelley, C. T., and S. J. Wright. "Sequential quadratic programming for certain parameter identification problems." Mathematical Programming 51, no. 1-3 (July 1991): 281–305. http://dx.doi.org/10.1007/bf01586941.

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18

Brendstrup, Bjarne, and Harry J. Paarsch. "Identification and estimation in sequential, asymmetric, English auctions." Journal of Econometrics 134, no. 1 (September 2006): 69–94. http://dx.doi.org/10.1016/j.jeconom.2005.06.017.

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19

Carmi, Avishy Y. "Compressive system identification: Sequential methods and entropy bounds." Digital Signal Processing 23, no. 3 (May 2013): 751–70. http://dx.doi.org/10.1016/j.dsp.2012.12.006.

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20

Peri, Daniele. "Sequential quadrature methods for RDO." Communications in Applied and Industrial Mathematics 7, no. 1 (March 1, 2016): 23–47. http://dx.doi.org/10.1515/caim-2016-0017.

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AbstractThis paper presents a comparative study between a large number of different existing sequential quadrature schemes suitable for Robust Design Optimization (RDO), with the inclusion of two partly original approaches. Efficiency of the different integration strategies is evaluated in terms of accuracy and computational effort: main goal of this paper is the identification of an integration strategy able to provide the integral value with a prescribed accuracy using a limited number of function samples. Identification of the different qualities of the various integration schemes is obtained utilizing both algebraic and practical test cases. Differences in the computational effort needed by the different schemes is evidenced, and the implications on their application to practical RDO problems is highlighted.

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21

Wixted, John T., Laura Mickes, John C. Dunn, Steven E. Clark, and William Wells. "Estimating the reliability of eyewitness identifications from police lineups." Proceedings of the National Academy of Sciences 113, no. 2 (December 22, 2015): 304–9. http://dx.doi.org/10.1073/pnas.1516814112.

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Laboratory-based mock crime studies have often been interpreted to mean that (i) eyewitness confidence in an identification made from a lineup is a weak indicator of accuracy and (ii) sequential lineups are diagnostically superior to traditional simultaneous lineups. Largely as a result, juries are increasingly encouraged to disregard eyewitness confidence, and up to 30% of law enforcement agencies in the United States have adopted the sequential procedure. We conducted a field study of actual eyewitnesses who were assigned to simultaneous or sequential photo lineups in the Houston Police Department over a 1-y period. Identifications were made using a three-point confidence scale, and a signal detection model was used to analyze and interpret the results. Our findings suggest that (i) confidence in an eyewitness identification from a fair lineup is a highly reliable indicator of accuracy and (ii) if there is any difference in diagnostic accuracy between the two lineup formats, it likely favors the simultaneous procedure.

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22

Dai, Bo, Chongshi Gu, Erfeng Zhao, Kai Zhu, Wenhan Cao, and Xiangnan Qin. "Improved online sequential extreme learning machine for identifying crack behavior in concrete dam." Advances in Structural Engineering 22, no. 2 (July 25, 2018): 402–12. http://dx.doi.org/10.1177/1369433218788635.

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Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BC a method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior.

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23

Kim, Byung-Jik. "Unstable Jobs Cannot Cultivate Good Organizational Citizens: The Sequential Mediating Role of Organizational Trust and Identification." International Journal of Environmental Research and Public Health 16, no. 7 (March 27, 2019): 1102. http://dx.doi.org/10.3390/ijerph16071102.

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Although existing works have investigated the influence of employee’s job insecurity on his or her perceptions or attitudes, those studies relatively have paid less attention to the influence of it on employee’s behaviors, as well as to its intermediating mechanisms of the relationship between job insecurity and the behaviors. Considering that employee’s behaviors substantially influence various organizational outcomes, I believe that studies which examine the impact of job insecurity on the behaviors as well as its underlying processes are required. Grounded on the context–attitude–behavior framework, I delved into the intermediating mechanism between job insecurity and organizational citizenship behavior with a sequential mediation model. In specific, I hypothesized that employee’s organizational trust and organizational identification would sequentially mediate the job insecurity–organizational citizenship behavior (OCB) link. Utilizing 3-wave time-lagged data from 303 employees in South Korea, I found that organizational trust and organizational identification function as sequential mediators in the link. The finding suggests that organizational trust and organizational identification are underlying processes to elaborately explain the job insecurity–OCB link.

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24

Fitrianah, Devi, and Hisyam Fahmi. "THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS." SINERGI 23, no. 3 (October 11, 2019): 184. http://dx.doi.org/10.22441/sinergi.2019.3.002.

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This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.

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25

KONISHI, Katsumi. "Sequential Matrix Rank Minimization Algorithm for Model Order Identification." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95.A, no. 10 (2012): 1788–91. http://dx.doi.org/10.1587/transfun.e95.a.1788.

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26

OHNO, Ryuzo, Miki NAKAYASU, and Masahi SOEDA. "KINESTHETIC SEQUENTIAL MEMORY AS A FACTOR OF PLACE IDENTIFICATION." Journal of Architecture and Planning (Transactions of AIJ) 67, no. 560 (2002): 173–78. http://dx.doi.org/10.3130/aija.67.173_3.

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27

Ubar, Raimund, Sergei Kostin, Maksim Jenihhin, Jaan Raik, and Lembit Jürimägi. "Fast identification of true critical paths in sequential circuits." Microelectronics Reliability 81 (February 2018): 252–61. http://dx.doi.org/10.1016/j.microrel.2017.11.027.

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28

File, Portia E., and John Todman. "Identification of sequential dependencies in conversations: A Pascal program." Behavior Research Methods, Instruments, & Computers 26, no. 1 (March 1994): 65–69. http://dx.doi.org/10.3758/bf03204568.

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29

Nakata, Paul A., and Michele M. McConn. "Sequential subtractive approach facilitates identification of differentially expressed genes." Plant Physiology and Biochemistry 40, no. 4 (April 2002): 307–12. http://dx.doi.org/10.1016/s0981-9428(02)01380-3.

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30

Syal, M., and M. S. Hsiao. "New techniques for untestable fault identification in sequential circuits." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 25, no. 6 (June 2006): 1117–31. http://dx.doi.org/10.1109/tcad.2005.855967.

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31

Tekumalla, R. C., and P. R. Menon. "Identification of primitive faults in combinational and sequential circuits." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 20, no. 12 (2001): 1426–42. http://dx.doi.org/10.1109/43.969436.

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32

Krolikowski, A. "Sequential identification and control for bounded-noise ARX systems." IEEE Transactions on Automatic Control 46, no. 2 (2001): 325–31. http://dx.doi.org/10.1109/9.905703.

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33

Hirsch, Markus, and Luigi del Re. "Sequential Identification of Engine Subsystems by Optimal Input Design." SAE International Journal of Engines 2, no. 2 (September 13, 2009): 562–69. http://dx.doi.org/10.4271/2009-24-0132.

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34

Beshah, Kebede. "Sequential identification of polymer microstructures by heteronuclear NMR correlations." Macromolecular Symposia 86, no. 1 (September 1994): 35–46. http://dx.doi.org/10.1002/masy.19940860105.

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35

Baillie, Clive F., and Paul D. Coddington. "Cluster Identification Algorithms for Spin Models — Sequential and Parallel." Concurrency: Practice and Experience 3, no. 2 (April 1991): 129–44. http://dx.doi.org/10.1002/cpe.4330030205.

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36

Baron, Claude, Jean-Claude Geffroy, and César Zamilpa. "Identification of evolutionary sequential systems-part 1: unified approach." Communications in Numerical Methods in Engineering 17, no. 9 (July 23, 2001): 623–30. http://dx.doi.org/10.1002/cnm.437.

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37

Mercère, Guillaume, Stéphane Lecœuche, and Christian Vasseur. "SEQUENTIAL CORRELATION BASED PROPAGATOR ALGORITHM FOR RECURSIVE SUBSPACE IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 922–27. http://dx.doi.org/10.3182/20050703-6-cz-1902.00155.

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38

Dette, Holger, and Robert Kwiecien. "Finite sample performance of sequential designs for model identification." Journal of Statistical Computation and Simulation 75, no. 6 (June 2005): 477–95. http://dx.doi.org/10.1080/00949650412331270879.

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39

Dayanik, Savas, Warren B. Powell, and Kazutoshi Yamazaki. "Asymptotically optimal Bayesian sequential change detection and identification rules." Annals of Operations Research 208, no. 1 (April 12, 2012): 337–70. http://dx.doi.org/10.1007/s10479-012-1121-6.

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40

Chen, Wei, Amna Saeed Khalifa, and Ken T. Trotman. "Facilitating Brainstorming: Impact of Task Representation on Auditors' Identification of Potential Frauds." AUDITING: A Journal of Practice & Theory 34, no. 3 (November 1, 2014): 1–22. http://dx.doi.org/10.2308/ajpt-50986.

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SUMMARY Individual auditor brainstorming is an important input to group brainstorming. In a setting where multiple potential frauds exist, we examine the effect of brainstorming task representation (simultaneous versus sequential unpacking of potential frauds) on individual auditors' identification of potential frauds and fraud risk assessments. Results from an experiment with experienced auditors show that the sequential unpacking of the brainstorming task into risk categories (potential frauds identified for each category individually) leads to a greater quantity and quality of potential frauds identified by auditors compared to the simultaneous unpacking (potential frauds identified for the categories together). Additional analysis suggests that the positive effect of sequential unpacking on the quality of frauds identified persists when auditors subsequently receive frauds identified by other team members. We also find a potential negative effect of sequential unpacking as it reduces auditors' fraud risk assessments thus potentially reducing the level of professional skepticism. Data Availability: Contact the authors.

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41

Rudenko, A., Th Ergler, K. Zrost, B. Feuerstein, V. L. B. de Jesus, C. D. Schröter, R. Moshammer, and J. Ullrich. "From non-sequential to sequential strong-field multiple ionization: identification of pure and mixed reaction channels." Journal of Physics B: Atomic, Molecular and Optical Physics 41, no. 8 (April 3, 2008): 081006. http://dx.doi.org/10.1088/0953-4075/41/8/081006.

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42

Dobolyi, David G., and Chad S. Dodson. "Eyewitness confidence in simultaneous and sequential lineups: A criterion shift account for sequential mistaken identification overconfidence." Journal of Experimental Psychology: Applied 19, no. 4 (2013): 345–57. http://dx.doi.org/10.1037/a0034596.

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43

Jung, Dawoon, Joono Cheong, Dong Il Park, and Chanhun Park. "Backward sequential approach for dynamic parameter identification of robot manipulators." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141875857. http://dx.doi.org/10.1177/1729881418758578.

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This article proposes a sequential optimization approach to efficiently identify non-minimal dynamic parameters of robot manipulators, possibly having large degrees of freedom. A back-substitution-based parameter identification from the last link to inward links is enabled due to the block upper triangular form of inherent regressor matrix. Starting with the dynamic model using the non-minimal parameters, we derive a generic compact formulation for the linear regression equation. We then establish a sequential optimization procedure taking into account physical feasibility of parameters. Numerical case examples demonstrate the validity of the proposed approach.

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44

Aguirregabiria, Victor, and Pedro Mira. "Identification of games of incomplete information with multiple equilibria and unobserved heterogeneity." Quantitative Economics 10, no. 4 (2019): 1659–701. http://dx.doi.org/10.3982/qe666.

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This paper deals with identification of discrete games of incomplete information when we allow for three types of unobservables: payoff‐relevant variables, both players' private information and common knowledge, and nonpayoff‐relevant variables that determine the selection between multiple equilibria. The specification of the payoff function and the distributions of the common knowledge unobservables is nonparametric with finite support (i.e., finite mixture model). We provide necessary and sufficient conditions for the identification of all the primitives of the model. Two types of conditions play a key role in our identification results: independence between players' private information, and an exclusion restriction in the payoff function. When using a sequential identification approach, we find that the up‐to‐label‐swapping identification of the finite mixture model in the first step creates a problem in the identification of the payoff function in the second step: unobserved types have to be correctly matched across different values of observable explanatory variables. We show that this matching‐type problem appears in the sequential estimation of other structural models with nonparametric finite mixtures. We derive necessary and sufficient conditions for identification, and show that additive separability of unobserved heterogeneity in the payoff function is a sufficient condition to deal with this problem. We also compare sequential and joint identification approaches.

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45

Guo, Xiaotao, Xing Wang, Dongqing Zhou, and Yubing Wang. "Sequential emitter identification method based on D-S evidence theory." Engineering review 38, no. 2 (2018): 155–65. http://dx.doi.org/10.30765/er.38.2.3.

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This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information.

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46

Tubino, M., A. C. Biondo, M. M. D. C. Vila, L. Pezza, and H. R. Pezza. "Spot-test identification and rapid quantitative sequential analysis of dipyrone." Eclética Química 35, no. 1 (2010): 41–46. http://dx.doi.org/10.1590/s0100-46702010000100005.

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A qualitative spot-test and tandem quantitative analysis of dipyrone in the bulk drug and in pharmaceutical preparations is proposed. The formation of a reddish-violet color indicates a positive result. In sequence a quantitative procedure can be performed in the same flask. The quantitative results obtained were statistically compared with those obtained with the method indicated by the Brazilian Pharmacopoeia, using the Student's t and the F tests. Considering the concentration in a 100 µL aliquot, the qualitative visual limit of detection is about 5×10-6 g; instrumental LOD ≅ 1.4×10-4 mol L-1 ; LOQ ≅ 4.5×10-4 mol L-1.

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47

WAIZUMI, Yuji, Tsuyoshi SATO, and Kazuyuki TANAKA. "Network Application Identification Using Sequential Transition Patterns of Payload Length." Interdisciplinary Information Sciences 18, no. 2 (2012): 189–96. http://dx.doi.org/10.4036/iis.2012.189.

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48

Canudas de Wit, C., and A. Aubin. "Parameters Identification of Robots Manipulators via Sequential Hybrid Estimation Algorithms." IFAC Proceedings Volumes 23, no. 8 (August 1990): 163–68. http://dx.doi.org/10.1016/s1474-6670(17)51730-9.

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49

Elhami, M. R., and D. J. Brookfield. "Sequential identification of coulomb and viscous friction in robot drives." Automatica 33, no. 3 (March 1997): 393–401. http://dx.doi.org/10.1016/s0005-1098(96)00183-5.

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50

Huang, T. W., B. Houshmand, and T. Itoh. "Fast sequential FDTD diakoptics method using the system identification technique." IEEE Microwave and Guided Wave Letters 3, no. 10 (October 1993): 378–80. http://dx.doi.org/10.1109/75.242267.

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