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Journal articles on the topic 'Human Performance Optimization'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Deuster, Patricia A., and Francis G. OʼConnor. "Human Performance Optimization." Journal of Strength and Conditioning Research 29 (November 2015): S52—S56. http://dx.doi.org/10.1519/jsc.0000000000001118.

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2

Nindl, Bradley C., Dianna P. Jaffin, Michael N. Dretsch, Samuel N. Cheuvront, Nancy J. Wesensten, Michael L. Kent, Neil E. Grunberg, et al. "Human Performance Optimization Metrics." Journal of Strength and Conditioning Research 29 (November 2015): S221—S245. http://dx.doi.org/10.1519/jsc.0000000000001114.

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3

Hatfield, Bradley D., Calvin M. Lu, and Jo B. Zimmerman. "Optimization of Human Performance." Kinesiology Review 9, no. 1 (February 1, 2020): 1–3. http://dx.doi.org/10.1123/kr.2019-0065.

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4

Austin, Krista G., and Patricia A. Deuster. "Monitoring Training for Human Performance Optimization." Journal of Special Operations Medicine 15, no. 2 (2015): 102. http://dx.doi.org/10.55460/evu4-w8lw.

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5

Russell, Adam, and Patricia A. Deuster. "Human Performance Optimization and Precision Performance: The Future of Special Operations Human Performance Efforts." Journal of Special Operations Medicine 17, no. 1 (2017): 80. http://dx.doi.org/10.55460/xfyj-equn.

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6

Chi, Chia-Fen, and Colin Drury. "Limits to human optimization in inspection performance." International Journal of Systems Science 32, no. 6 (January 2001): 689–701. http://dx.doi.org/10.1080/00207720117646.

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7

Sell, Timothy C., Robert H. Lutz, and Mallory S. Faherty. "The Warrior Model for Human Performance Optimization." Sports Medicine and Arthroscopy Review 27, no. 3 (September 2019): 99–106. http://dx.doi.org/10.1097/jsa.0000000000000248.

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8

Xu, Yangsheng, Jingyan Song, Michael C. Nechyba, and Yeung Yam. "Performance evaluation and optimization of human control strategy." Robotics and Autonomous Systems 39, no. 1 (April 2002): 19–36. http://dx.doi.org/10.1016/s0921-8890(02)00169-0.

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9

Zhang, Pinggai, Ling Wang, Jiaojie Du, Zixiang Fei, Song Ye, Minrui Fei, and Panos M. Pardalos. "Differential Human Learning Optimization Algorithm." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/5699472.

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Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

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Liu, Hao, Gang Xu, Gui-yan Ding, and Yu-bo Sun. "Human Behavior-Based Particle Swarm Optimization." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/194706.

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Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficientsc1andc2in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.

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Hallez, Axel, Guy De Tré, and Antoon Bronselaer. "Performance optimization of object comparison." International Journal of Intelligent Systems 24, no. 10 (October 2009): 1057–76. http://dx.doi.org/10.1002/int.20373.

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12

Worthington, Danielle, and Patricia A. Deuster. "Spiritual Fitness: An Essential Component of Human Performance Optimization." Journal of Special Operations Medicine 18, no. 1 (2018): 100. http://dx.doi.org/10.55460/2r9f-iefh.

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13

Ross, Jared, Lucas Miller, and Patricia A. Deuster. "Cognitive Agility as a Factor in Human Performance Optimization." Journal of Special Operations Medicine 18, no. 3 (2018): 86. http://dx.doi.org/10.55460/qu7u-8ice.

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14

Lunasco, Travis K., Rachel A. Chamberlin, and Patricia A. Deuster. "Human Performance Optimization: An Operational and Operator-Centric Approach." Journal of Special Operations Medicine 19, no. 3 (2019): 101. http://dx.doi.org/10.55460/sn5g-cmoa.

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15

McBratney, Colleen M., and Sarah J. de la Motte. "Collaboration Needed on Human Performance Optimization for Combat Athletes." Military Medicine 183, no. 7-8 (June 28, 2018): 143–45. http://dx.doi.org/10.1093/milmed/usy020.

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16

Mi, Zan, Jingzhou (James) Yang, and Karim Abdel-Malek. "Optimization-based posture prediction for human upper body." Robotica 27, no. 4 (July 2009): 607–20. http://dx.doi.org/10.1017/s0263574708004992.

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SUMMARYA general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.

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17

Brownridge, Andrew, Peter Twigg, and Steve Sigurnjak. "Measurement of Human Motion in Performance." Measurement and Control 46, no. 1 (February 2013): 21–25. http://dx.doi.org/10.1177/002029401304600103.

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18

Anderson, Frank C., and Marcus G. Pandy. "Dynamic Optimization of Human Walking." Journal of Biomechanical Engineering 123, no. 5 (May 16, 2001): 381–90. http://dx.doi.org/10.1115/1.1392310.

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A three-dimensional, neuromusculoskeletal model of the body was combined with dynamic optimization theory to simulate normal walking on level ground. The body was modeled as a 23 degree-of-freedom mechanical linkage, actuated by 54 muscles. The dynamic optimization problem was to calculate the muscle excitation histories, muscle forces, and limb motions subject to minimum metabolic energy expenditure per unit distance traveled. Muscle metabolic energy was calculated by summing five terms: the basal or resting heat, activation heat, maintenance heat, shortening heat, and the mechanical work done by all the muscles in the model. The gait cycle was assumed to be symmetric; that is, the muscle excitations for the right and left legs and the initial and terminal states in the model were assumed to be equal. Importantly, a tracking problem was not solved. Rather, only a set of terminal constraints was placed on the states of the model to enforce repeatability of the gait cycle. Quantitative comparisons of the model predictions with patterns of body-segmental displacements, ground-reaction forces, and muscle activations obtained from experiment show that the simulation reproduces the salient features of normal gait. The simulation results suggest that minimum metabolic energy per unit distance traveled is a valid measure of walking performance.

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19

Dyreson, Mark S. "Optimizing Human Performance—A Brief History of Macro and Micro Perspectives." Kinesiology Review 9, no. 1 (February 1, 2020): 4–12. http://dx.doi.org/10.1123/kr.2019-0063.

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Since the origins of Homo sapiens 300,000 years ago, the quest to optimize human performance has shaped historical development. A macrohistorical perspective reveals that for 290,000 years the necessities of survival pushed hunter-forager cultures toward mass improvement of endurance capabilities and weapons skills. The agricultural revolution that began about 10,000 years ago changed those dynamics, focusing on enhancement for elite warriors while simultaneously diminishing the necessity of mass optimization. The multiple revolutions of modernity that began 500 years ago reanimated mass optimization while paradoxically removing physical enhancement from the realm of necessity through the increasing power of human-made motors rather than human locomotion. Microhistorical perspectives reveal that beyond the general patterns that shaped human cultures across time and place, the historical particularities vastly complicated optimization strategies. Employing macro- and microhistorical perspectives can enhance scientific understandings of optimal performance.

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20

Mallick, Zulquernain, Irfan Anjum Badruddin Magami, and Khaleed Hussain Tandur. "Operational characteristics optimization of human-computer system." IIUM Engineering Journal 8, no. 1 (September 29, 2010): 35–47. http://dx.doi.org/10.31436/iiumej.v8i1.86.

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Computer operational parameters are having vital influence on the operators efficiency from readability viewpoint. Four parameters namely font, text/background color, viewing angle and viewing distance are analyzed. The text reading task, in the form of English text, was presented on the computer screen to the participating subjects and their performance, measured in terms of number of words read per minute (NWRPM), was recorded. For the purpose of optimization, the Taguchi method is used to find the optimal parameters to maximize operators’ efficiency for performing readability task. Two levels of each parameter have been considered in this study. An orthogonal array, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) were employed to investigate the operators’ performance/efficiency. Results showed that Times Roman font, black text on white background, 40 degree viewing angle and 60 cm viewing distance, the subjects were quite comfortable, efficient and read maximum number of words per minute. Text/background color was dominant parameter with a percentage contribution of 76.18% towards the laid down objective followed by font type at 18.17%, viewing distance 7.04% and viewing angle 0.58%. Experimental results are provided to confirm the effectiveness of this approach.

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21

Neptune, R. R. "Optimization Algorithm Performance in Determining Optimal Controls in Human Movement Analyses." Journal of Biomechanical Engineering 121, no. 2 (April 1, 1999): 249–52. http://dx.doi.org/10.1115/1.2835111.

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The objective of this study was to evaluate the performance of different multivariate optimization algorithms by solving a “tracking” problem using a forward dynamic model of pedaling. The tracking problem was defined as solving for the muscle controls (muscle stimulation onset, offset, and magnitude) that minimized the error between experimentally collected kinetic and kinematic data and the simulation results of pedaling at 90 rpm and 250 W. Three different algorithms were evaluated: a downhill simplex method, a gradient-based sequential quadratic programming algorithm, and a simulated annealing global optimization routine. The results showed that the simulated annealing algorithm performed far superior to the conventional routines by converging more rapidly and avoiding local minima.

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22

Sutisna, Deden, Sri Wiludjeng SP, Suskim Riantani, and Yelli Eka Sumadhinata. "Optimization of Human Capital in Improving Competitive Advantage of Higher Education through Institution’s Performance: A Case Study of Widyatama University." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 12, 2020): 2880–90. http://dx.doi.org/10.37200/ijpr/v24i2/pr200587.

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23

Fattahi, Edris, Mahdi Bidar, and Hamidreza Rashidy Kanan. "Focus Group: An Optimization Algorithm Inspired by Human Behavior." International Journal of Computational Intelligence and Applications 17, no. 01 (March 2018): 1850002. http://dx.doi.org/10.1142/s1469026818500025.

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This paper presents a novel optimization algorithm, namely focus group (FG) algorithm, for solving optimization problems. The proposed algorithm is inspired by the behavior of group members to share their ideas (solutions) about a specific subject and trying to improve the solutions based on the cooperation and discussion. In the proposed algorithm, all the members present their solutions about the subject and all the suggested solutions proportional to their fitness, leave impact on the other solutions and incline them towards themselves. While trying to improve the quality of the candidate solutions, they converge to the optimal solution. To improve the performance of the proposed algorithm, two genetic operators are incorporated into the algorithm. The proposed algorithm is evaluated using several constrained and unconstrained benchmark functions commonly used in the area of optimization. Experimental results, in comparison with different well-known evolutionary techniques, confirm the high performance of the proposed algorithm in dealing with the optimization problems.

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24

Edy Jumady, Syamsul Alam, Yana Fajriah, Muhammad I. N. Hamiddin,. "Creating Culture Organizational Citizenship Behavior and Performance Through Human Research Optimization." Jurnal Manajemen 25, no. 3 (August 17, 2021): 479. http://dx.doi.org/10.24912/jm.v25i3.762.

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We conducted this study to examine and explore the effect of developing human resources, competence, and motivation on Organizational Citizenship Behavior (OCB) and the Performance of State Civil Apparatus. This study uses a quantitative approach involving 320 respondents. Our research data was collected by distributing questionnaires directly to respondents. The results of this study indicate that the development of human resources has a positive and significant effect on Organizational Citizenship Behavior. Competence has a positive and significant effect on Organizational Citizenship Behavior. Positive and significant motivation on Organizational Citizenship Behavior. HR development has a positive and significant impact on the performance of ASN. Competence has a positive and insignificant effect on ASN performance. Positive motivation and not significant to the performance of ASN. Positive and significant motivation on the performance of ASN.

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25

DUGAN, Cosmin, Robert Ionuț STANCIU, and Cristian VIZITIU. "Human Performance Optimization Concept Development and Applications In The Military Field." Romanian Military Thinking 2022, no. 2 (June 2022): 160–85. http://dx.doi.org/10.55535/rmt.2022.2.10.

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"In these times, dominated by accelerated cognitisation and sophisticated automation, rapid development of computers and multiplication of forms of communication, the human factor seems to have a secondary role. From our point of view, this is just an appearance, because the new cognitive instruments do not replace, but mirror and simulate aspects of people’s higher psychic functions, acting as a multiplier for talented, educated and original minds. The concept of Human Performance Optimization (HPO) represents a systematic effort to use the latest technologies and knowledge to achieve the maximum performance of individuals, teams and/or organisations. At the same time, HPO is an ethical way to optimize technological synapse between the forms of artificial and human intelligence."

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26

Deuster, Patricia A., Francis G. O'Connor, Kurt A. Henry, Valerie E. Martindale, Laura Talbot, Wayne Jonas, and Karl Friedl. "Human Performance Optimization: An Evolving Charge to the Department of Defense." Military Medicine 172, no. 11 (November 2007): 1133–37. http://dx.doi.org/10.7205/milmed.172.11.1133.

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27

Murdock, Richard C., Li Shen, Daniel K. Griffin, Nancy Kelley-Loughnane, Ian Papautsky, and Joshua A. Hagen. "Optimization of a Paper-Based ELISA for a Human Performance Biomarker." Analytical Chemistry 85, no. 23 (November 20, 2013): 11634–42. http://dx.doi.org/10.1021/ac403040a.

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28

Ong, Pio, and Jorge Cortés. "Opportunistic robot control for interactive multiobjective optimization under human performance limitations." Automatica 123 (January 2021): 109263. http://dx.doi.org/10.1016/j.automatica.2020.109263.

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29

Hadi, Ahmed Adnan, and Seyed Vahab AL-Din Makki. "Improved MANET Routing Protocols Performance by Using Hybrid Cat and Particle Swarm Optimization (CPSO)." Webology 19, no. 1 (January 20, 2022): 2182–95. http://dx.doi.org/10.14704/web/v19i1/web19148.

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The technology that used in communicating data and voice among certain mobile network nodes using wireless medium and radio spectrum transmission is called Mobile networking. Generally, Mobile refers to the intent, portable and lightweight devices that may be carried by their movie users. In this paper, we proposed a hybrid version of the swarm optimization model to improve the MANET routing protocols. The proposed optimization sets optimal parameters for the MANET networks. The proposed model combines between Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The methodology which will be developed in this research can be used for revealing the MANT networks or mobile sensor networks the study involve enhance mechanism(s) that can be used to avoid degraded routing issues to increase the performance. The result the obtain by proposed model satisfy best result compared with both PSO and CSO.

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30

ALRASHID, ENCE RAMLI. "Human Sigma Optimization: Improving Employee Engagement." Jurnal Muara Ilmu Ekonomi dan Bisnis 2, no. 1 (September 11, 2018): 128. http://dx.doi.org/10.24912/jmieb.v2i1.1678.

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Competition in banking industry from year to year increasingly fierce. Small capital banks must open compete with large banks in the same segment. Local banks must compete "apple to apple" with other local banks. In addition, local bank has to be able to compete with foreign banks in order to gain the largest market share. The level of customer engagement can be a leading indicator at the rate of return for investors. It means if the CE survey results produce a high level of engagement, then the company's financial performance expectation is also high. employee engagement level is the basic indicator of leading indicators. It means that if the EE survey results produce a high level of engagement then, engagement costumer is also high on products or services owned by a bank. Human Sigma is a model and approach by analyzing the value of Customer Engagement and Employee Engagement to determine Human Sigma Value that will describe the level of engagement from the point of view of employee and customer. The aim of this study is to look for top of pain factors the weakness of employee engagement in the retail banking company that caused the decrease of customer engagement and the level of company productivity by using the concept of human sigma.

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31

Geroy, Gary D., Amber Bray, and Donald L. Venneberg. "The CCM Model: A Management Approach to Performance Optimization." Performance Improvement Quarterly 18, no. 2 (October 22, 2008): 19–36. http://dx.doi.org/10.1111/j.1937-8327.2005.tb00331.x.

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32

Liu, Huashuo, Fei Wang, and Dan Zhang. "Inspiring Real-Time Evaluation and Optimization of Human–Robot Interaction with Psychological Findings from Human–Human Interaction." Applied Sciences 13, no. 2 (January 4, 2023): 676. http://dx.doi.org/10.3390/app13020676.

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The increasingly central role of robotic agents in daily life requires effective human–robot interaction (HRI). For roboticists to optimize interaction design, it is crucial to understand the potential effects of robotic agents on human performance. Yet a systematic specification of contributing factors is lacking, and objective measures of HRI performance are still limited. In these regards, the findings of research on human–human interaction can provide valuable insights. In this review, we break down the complex effects of robotic agents on interacting humans into some basic building blocks based on human–human interaction findings, i.e., the potential effects of physical presence, motor actions, and task co-representation in HRI. For each effect, we advise on future directions regarding its implication. Furthermore, we propose that the neural correlates of these effects could support real-time evaluation and optimization of HRI with electroencephalograph (EEG)-based brain–computer interface (BCI).

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33

Liu, Li, and Seechen Yong. "Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model." Mobile Information Systems 2022 (July 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/2604761.

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Talent is an indispensable and important factor for the development and growth of an enterprise. Therefore, human resources have become an important resource of enterprises, and performance appraisal in human resource management has also become one of the important contents of modern enterprise management. Without a set of scientific and standardized performance appraisal systems, it will make it difficult for the daily human resource performance management of enterprises to achieve the expected goals. This study analyzes the actual needs, business processes, and existing problems of an enterprise in performance management and uses the multiobjective decision-making mathematical model to obtain an optimization plan for human resources performance assessment. Finally, a digital management system for enterprise performance appraisal is designed. The main work of this study is as follows: (1) based on the work characteristics of a certain enterprise, the key indicators of enterprise performance evaluation are analyzed; (2) a multiobjective decision-making mathematical model is constructed for the optimization of human resources performance evaluation; (3) the Hungarian algorithm is used to solve the construction The mathematical model of multiobjective decision-making can obtain the optimal work arrangement. This study combines performance appraisal with practical work and uses the proposed multiobjective decision-making mathematical model to obtain the optimization results of human resources, guide practical work, and improve the efficiency of enterprise operation.

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34

Ismail, A. R., M. H. M. Haniff, M. Y. M. Yusof, M. N. A. Rahman, and J. A. Ghani. "Optimization of Several Environmental Factors to Human Performance by Using Taguchi Method." Industrial Engineering and Management Systems 9, no. 2 (June 1, 2010): 157–64. http://dx.doi.org/10.7232/iems.2010.9.2.157.

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35

Kumar, K. L. Santhosh, and M. Lokanadha Reddy. "Strategic Human Resource Management:The Calibrated Catalysts for Indian IT-SMEs Performance Optimization." SDMIMD Journal of Management 10, no. 1 (March 12, 2019): 31–42. http://dx.doi.org/10.18311/sdmimd/2019/21493.

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36

Nindl, Bradley C., and Heikki Kyröläinen. "Editorial: Military human performance optimization: Contemporary issues for sustained and improved readiness." European Journal of Sport Science 22, no. 1 (January 2, 2022): 1–3. http://dx.doi.org/10.1080/17461391.2021.2019319.

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37

Damiati, E., G. Borsani, and Edoardo Giacopuzzi. "Amplicon-based semiconductor sequencing of human exomes: performance evaluation and optimization strategies." Human Genetics 135, no. 5 (March 22, 2016): 499–511. http://dx.doi.org/10.1007/s00439-016-1656-8.

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38

Yue, Xian, Yaliang Yang, Fei Xiao, Hao Dai, Chao Geng, and Yudong Zhang. "Optimization of Virtual Shack-Hartmann Wavefront Sensing." Sensors 21, no. 14 (July 9, 2021): 4698. http://dx.doi.org/10.3390/s21144698.

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Virtual Shack–Hartmann wavefront sensing (vSHWS) can flexibly adjust parameters to meet different requirements without changing the system, and it is a promising means for aberration measurement. However, how to optimize its parameters to achieve the best performance is rarely discussed. In this work, the data processing procedure and methods of vSHWS were demonstrated by using a set of normal human ocular aberrations as an example. The shapes (round and square) of a virtual lenslet, the zero-padding of the sub-aperture electric field, sub-aperture number, as well as the sequences (before and after diffraction calculation), algorithms, and interval of data interpolation, were analyzed to find the optimal configuration. The effect of the above optimizations on its anti-noise performance was also studied. The Zernike coefficient errors and the root mean square of the wavefront error between the reconstructed and preset wavefronts were used for performance evaluation. The performance of the optimized vSHWS could be significantly improved compared to that of a non-optimized one, which was also verified with 20 sets of clinical human ocular aberrations. This work makes the vSHWS’s implementation clearer, and the optimization methods and the obtained results are of great significance for its applications.

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39

Zaguri, Yanay, and Eran Gal. "An Optimization of Human Performance Technology Intervention Selection Model: A 360-Degree Approach to Support Performance." Performance Improvement 55, no. 6 (July 2016): 25–31. http://dx.doi.org/10.1002/pfi.21591.

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40

Akbar, Estelee Elora, Heni Noviarita, and Erike Anggraeni. "OPTIMIZATION OF HUMAN RESOURCES QUALITY IN IMPROVING THE PERFORMANCE OF ISLAMIC BANKING EMPLOYEES." JURNAL PERSPEKTIF EKONOMI DARUSSALAM 6, no. 2 (September 17, 2020): 136–44. http://dx.doi.org/10.24815/jped.v6i2.14491.

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The purpose of this study was to find and review the optimization of human resourcesquality in improving the performance of Islamic banking employees. The approach used in this study was a quantitative approach by using primary data obtained through questionnaires to the Islamic banking customers located in Lampung. Sampling technique used in this studywas purposive sampling with a sample size of 100 people. Analysis of the data used in this study was simple linear regression analysis. Based on the result, the study found that there are significant quality of human resources in improving the performance of Islamic banking at 10.3%. Therefore,it needs to do the optimization of human resources quality through various efforts such as the recruitment system, the planning and training given by the company toward the employees, improving the intelligence, creativity, perseverance, as well as the professionalism of the employees.

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41

Agarwal, Sakshi, Mohd Nor Akmal Khalid, and Hiroyuki Iida. "Game refinement theory: Paradigm shift from performance optimization to comfort in mind." Entertainment Computing 32 (December 2019): 100314. http://dx.doi.org/10.1016/j.entcom.2019.100314.

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42

Yin, Jiawei, Agung Julius, John T. Wen, Zhen Wang, Chuanlin He, and Lei Kou. "Human Alertness Optimization with a Three-Process Dynamic Model." Mathematics 10, no. 11 (June 2, 2022): 1916. http://dx.doi.org/10.3390/math10111916.

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Circadian rhythm is an important biological process for humans as it modulates a wide range of physiological processes, including body temperature, sleep-wake cycle, and cognitive performance. As the most powerful external stimulus of circadian rhythm, light has been studied as a zeitgeber to regulate the circadian phase and sleep. This paper addresses the human alertness optimization problem, by optimizing light exposure and sleep schedules to relieve fatigue and cognitive impairment, in cases of night-shift workers and subjects with certain mission periods based on dynamics of the circadian rhythm system. A three-process hybrid dynamic model is used for simulating the circadian rhythm and predicting subjective alertness and sleepiness. Based on interindividual difference in sleep type and living habits, we propose a tunable sleep schedule in the alertness optimization problem, which allows the appropriate tuning of sleep and wake times based on sleep propensity. Variational calculus is applied to evaluate the impacts of light and sleep schedules on the alertness and a gradient descent algorithm is proposed to determine the optimal solutions to maximize the alertness level in various cases. Numerical simulation results demonstrate that the cognitive performance during certain periods can be significantly improved by optimizing the light input and tuning sleep/wake times compared to empirical data.

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Zong, Z., K. Y. Lam, and Tessa Gan. "Limiting Performance Analysis of Underwater Shock Isolation of a System with Biodynamic Response Using Genetic Algorithm." Shock and Vibration 7, no. 5 (2000): 321–32. http://dx.doi.org/10.1155/2000/679157.

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Biodynamic response of shipboard crew to underwater shock is of a major concern to navies. An underwater shock can produce very high accelerations, resulting in severe human injuries aboard a battleship. Protection of human bodies from underwater shock is implemented by installing onboard isolators. In this paper, the optimal underwater shock isolation to protect human bodies is studied. A simple shock-structure-isolator-human interaction model is first constructed. The model incorporates the effect of fluid-structure interaction, biodynamic response of human body, isolator influence. Based on this model, the optimum shock isolation is then formulated. The performance index and restriction are defined. Thirdly, GA (genetic algorithm) is employed to solve the formulated optimization problem. GA is a powerful evolutionary optimization scheme suitable for large-scale and multi-variable optimization problems that are otherwise hard to be solved by conventional methods. A brief introduction to GA is given in the paper. Finally, the method is applied to an example problem and the limiting performance characteristic is obtained.

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Wang, Hailin. "Research on Human Resource Management Performance Evaluation Method Based on Chaos Optimization Algorithm." Security and Communication Networks 2022 (August 1, 2022): 1–8. http://dx.doi.org/10.1155/2022/9201618.

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All enterprise components and business activities are centered on the customer. Whether human resource management is reasonable and effective not only is a matter of human resource management, but also directly influences whether other resources can be used reasonably and effectively, and determines the efficiency of business operations. In human resource management, perfect performance evaluation criteria are developed based on the actual situation in order to evaluate employee productivity and work ethic. Nonetheless, in the process of actual enterprise HR management performance evaluation work, there are not only imperfect performance evaluation standards, but also relatively objective evaluation standards, and HR cannot conduct a comprehensive analysis of each position, which has a negative impact on the quality of enterprise HR management work. This paper improves the performance evaluation model for the original data mining and proposes a human resource management performance evaluation method based on chaotic optimization algorithm, which generates the initial values of evaluation data through chaotic logistic mapping, bringing the initial data closer to the optimal value, reducing the impact of random initialization on the algorithm’s performance, and when the model is trained, it can make the algorithm’s performance more stable. Capability thereby addresses the flaws in the original evaluation method. Experiments demonstrate that our method significantly improves the precision of model training and prediction.

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Afsar, Bekir, Kaisa Miettinen, and Francisco Ruiz. "Assessing the Performance of Interactive Multiobjective Optimization Methods." ACM Computing Surveys 54, no. 4 (May 2021): 1–27. http://dx.doi.org/10.1145/3448301.

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Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

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Li, Wanyi, Feifei Zhang, Qiang Chen, and Qian Zhang. "Projection Analysis Optimization for Human Transition Motion Estimation." International Journal of Digital Multimedia Broadcasting 2019 (June 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/6816453.

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It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human motion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is necessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency will be improved. Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate the human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds the object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained to generate the human transition motion. The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the needed transition motion. The excellent performance of LSOPA will be tested by the experiments.

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Hu, Si Qi. "Cloudy-Based Optimization of the Human Resource Management System." Advanced Materials Research 998-999 (July 2014): 1557–62. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1557.

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With the development of cloud-based data centers, cloud-based system has been widely used in many kinds of enterprises. Human resource management is an important issue in enterprise management. In this paper, we proposed a cloud based system for human resource management. Under the help of our design, the performance of the human resource management system can be improved apparently. In order to illustrate its superiority,we use some experiment to evaluate the proposed system. The experimental results indicated that the cloudy-based system would consume less memory and cpu resources while it has faster connection speed.

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Breevaart, Kimberley, and Hannes Zacher. "Daily Selection, Optimization, and Compensation Strategy Use and Innovative Performance." Journal of Personnel Psychology 18, no. 2 (April 2019): 71–83. http://dx.doi.org/10.1027/1866-5888/a000224.

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Abstract. Knowledge on how to improve employees’ daily innovative performance is imperative, because innovation contributes importantly to organizational competitiveness. We tested a model in which daily use of selection, optimization, and compensation (SOC) strategies mediates the relationship between daily job autonomy and daily innovative performance. Moreover, we predicted that the association between daily SOC strategy use and daily innovative performance is stronger on days when time pressure is high (vs. low). Hypotheses were tested using a daily diary study in which employees filled out a short questionnaire at the end of their workday for a period of five workdays ( N = 91; 381 daily entries). Results of structural equation modeling analyses supported our mediation, but not our moderation hypothesis.

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Genaidy, Ashraf, Waldemar Karwowski, and Doran Christensen. "Principles of work system performance optimization: A business ergonomics approach." Human Factors and Ergonomics in Manufacturing 9, no. 1 (1999): 105–28. http://dx.doi.org/10.1002/(sici)1520-6564(199924)9:1<105::aid-hfm6>3.0.co;2-s.

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Huang, Yan-yan. "Research on the Performance Appraisal of Human Resources in Public Institutions." Proceedings of Business and Economic Studies 4, no. 6 (December 31, 2021): 61–66. http://dx.doi.org/10.26689/pbes.v4i6.2856.

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Under the background of economic globalization, the rapid development of the society and the intricate changes also require a leap in the economy and management level. For state-owned institutions, it is of great significance to maximize the enthusiasm of employees and managers as well as establish a scientific and efficient performance appraisal mechanism. This paper analyzes the existing problems and deficiencies of performance appraisal in public institutions and puts forward corresponding optimization countermeasures and plans.

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