Journal articles: 'Innovation-tracking'

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Kessler, Jeff, and Daniel Sperling. "Tracking U.S. biofuel innovation through patents." Energy Policy 98 (November 2016): 97–107. http://dx.doi.org/10.1016/j.enpol.2016.08.021.

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O'Regan, Nicholas, Abby Ghobadian, and Martin Sims. "Fast tracking innovation in manufacturing SMEs." Technovation 26, no. 2 (February 2006): 251–61. http://dx.doi.org/10.1016/j.technovation.2005.01.003.

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Wu, Chun-Mu. "Adaptive parameters for tracking filters innovation system." Advances in Mechanical Engineering 8, no. 1 (January 28, 2016): 168781401662935. http://dx.doi.org/10.1177/1687814016629350.

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Qiu, Jing, and Dan Li. "Analyst tracking and innovation inconsistency in companies." Research in International Business and Finance 71 (August 2024): 102484. http://dx.doi.org/10.1016/j.ribaf.2024.102484.

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Moksyakov, Alexander, Yuandi Wu, Stephen Andrew Gadsden, John Yawney, and Mohammad AlShabi. "Object Detection and Tracking with YOLO and the Sliding Innovation Filter." Sensors 24, no. 7 (March 26, 2024): 2107. http://dx.doi.org/10.3390/s24072107.

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Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object’s trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications.

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Herrmann, Douglas K. "Tracking systems as a catalyst for incremental innovation." Management Decision 37, no. 10 (December 1999): 786–91. http://dx.doi.org/10.1108/00251749910302881.

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Nelson, Andrew, Andrew Earle, Jennifer Howard-Grenville, Julie Haack, and Doug Young. "Do innovation measures actually measure innovation? Obliteration, symbolic adoption, and other finicky challenges in tracking innovation diffusion." Research Policy 43, no. 6 (July 2014): 927–40. http://dx.doi.org/10.1016/j.respol.2014.01.010.

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., Sreeram Vishnumoorthy, Jancy Gupta ., and Shyam Suraj S. R. . "Tracking the evolution of dairy innovation system in Kerala." Indian Journal of Dairy Science 72, no. 1 (February 28, 2019): 108–14. http://dx.doi.org/10.33785/ijds.2019.v72i01.014.

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Zhu, Jun, Zhi-Zhuang Duan, and Chen Yao. "Technological Innovation: New Unmanned Aerial Vehicle Target Tracking Technology." Scholars Bulletin 10, no. 03 (March 4, 2024): 68–69. http://dx.doi.org/10.36348/sb.2024.v10i03.002.

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This article mainly discusses a new type of unmanned aerial vehicle target tracking technology, comprising a drone using a built-in GPS module and a device with a built-in GPS module worn by a human body. When the human body is in motion, the device worn by the human body and the drone generate pseudocodes; Perform pseudorange measurement at a certain moment in GPS time; Obtain a nonlinear equation system related to pseudorange, repeat iterative calculations to obtain accurate coordinates, use Dijkstra algorithm to calculate the shortest path in a weighted directed graph, and the drone flies according to the shortest movement trajectory from the starting point to the human target point; The drone and device of the present invention perform pseudorange measurement at a certain moment in GPS time, and repeat iterative calculations to reduce errors, which is conducive to obtaining accurate coordinates of the device and drone; Based on weighted graph search, the coordinates of the drone and the human body are treated as two endpoints, representing the starting and ending points. The algorithm calculates the shortest trajectory in the graph.

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Serdült, Uwe, and Yanina Welp. "How Sustainable is Democratic Innovation? Tracking Neighborhood Councils in Montevideo." Journal of Politics in Latin America 7, no. 2 (August 2015): 131–48. http://dx.doi.org/10.1177/1866802x1500700205.

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PYEON, Y. K. "VFF-PASTd Based Multiple Target Angle Tracking with Angular Innovation." IEICE Transactions on Communications E88-B, no. 3 (March 1, 2005): 1313–19. http://dx.doi.org/10.1093/ietcom/e88-b.3.1313.

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Schindel, William D. "System Life Cycle Trajectories: Tracking Innovation Paths Using System DNA." INCOSE International Symposium 25, no. 1 (October 2015): 648–63. http://dx.doi.org/10.1002/j.2334-5837.2015.00087.x.

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Yin, Shoulin, Jinfeng Wang, and Tianhua Liu. "Improved UFIR Tracking Algorithm for Maneuvering Target." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 2 (May 1, 2016): 344. http://dx.doi.org/10.11591/ijeecs.v2.i2.pp344-350.

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Maneuvering target tracking is a target motion estimation problem, which can describe the irregular target maneuvering motion. It has been widely used in the field of military and civilian applications. In the maneuvering target tracking, the performance of Kalman filter(KF) and its improved algorithms depend on the accuracy of process noise statistical properties. If there exists deviation between process noise model and the actual process, it will generate the phenomenon of estimation error increasing. Unbiased finite impulse response(UFIR) filter does not need priori knowledge of noise statistical properties in the filtering process. The existing UFIR filters have the problem that generalized noise power gain(GNPG) does not change with measurement of innovation. We propose an improved UFIR filter based on measurement of innovation with ratio dynamic adaptive adjustment at adjacent time. It perfects the maneuvering detect-ability. The simulation results show that the improved UFIR filter has the best filtering effect than KF when process noise is not accurate.

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Li, Can, Mona V. Makhija, and Danxue Gao. "Tracking Innovation Performance After Joint Ventures Termination: Have Local Firms learned?" Academy of Management Proceedings 2018, no. 1 (August 2018): 17322. http://dx.doi.org/10.5465/ambpp.2018.17322abstract.

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Rotroff, Susan I. "The Introduction of the Moldmade Bowl Revisited: Tracking a Hellenistic Innovation." Hesperia 75, no. 3 (October 2006): 357–78. http://dx.doi.org/10.2972/hesp.75.3.357.

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Zucker, Darby, and Fong. "Communitywide Database Designs for Tracking Innovation Impact: Comets, Stars and Nanobank." Annals of Economics and Statistics, no. 115/116 (2014): 277. http://dx.doi.org/10.15609/annaeconstat2009.115-116.277.

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Ryu, Chang-Soo, Jang-Sik Lee, and Kyu-Kyung Lee. "Multiple target angle-tracking algorithm with efficient equation for angular innovation." Electronics Letters 38, no. 10 (2002): 483. http://dx.doi.org/10.1049/el:20020328.

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Chambers, J., T. Thorat, MD Chenoweth, and PJ Neumann. "Fast-Tracking Innovation: A Scorecard for The Fda’s Expedited Review Process." Value in Health 18, no. 7 (November 2015): A517. http://dx.doi.org/10.1016/j.jval.2015.09.1572.

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Neuhouser, Kevin, Christian Smith, and Joshua Prokopy. "Latin American Religion in Motion: Tracking Innovation, Unexpected Change, and Complexity." Contemporary Sociology 30, no. 1 (January 2001): 59. http://dx.doi.org/10.2307/2654349.

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Nelson, Andrew, Andrew Earle, and Jennifer Howard-Grenville. "Obliteration, Symbolic Adoption, and Other Finicky Challenges in Tracking Innovation Diffusion." Academy of Management Proceedings 2012, no. 1 (July 2012): 13497. http://dx.doi.org/10.5465/ambpp.2012.296.

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Yang, Yingzhu, and Fengsheng Wu. "The Sustainability of the Project-Driven Innovation of Grassroots Governance: Influencing Factors and Combination Paths." Sustainability 14, no. 24 (December 15, 2022): 16862. http://dx.doi.org/10.3390/su142416862.

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Governance innovation is an important topic in public administration research. Based on the empirical evidence of governance innovation in China, this paper analyzes the pathways to the sustainability of project-driven innovation in urban grassroots governance and reveals its complex causal relationships. Using the selected cases, a multidimensional analysis framework is constructed, and the qualitative comparative analysis method of fuzzy sets is used. It is found that there are three combined paths for the sustainability of innovation, namely dependent development, social embeddedness and government–society cooperation. The first two paths are the most common for the sustainability of innovation, and they cover most of the selected cases. Case tracking also reveals that innovation driven by project operation can stimulate the behavior of grassroots governance innovation, but it does not guarantee the sustainability of the innovation. The sustainability of innovation requires more mobilization of the grassroots society to enhance social embeddedness. Moreover, this study results provide inspiration for the sustainability of innovation in the later stages of projects.

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Suprapto, Nadi, Abd Kholiq, Fang-Ying Yang, Septian Rahman Hakim, Raden Roro Hapsari Peni Agusti, Hasan Nuurul Hidaayatullaah, and Khoirun Nisa'. "The use of eye movement technology in education: a review and bibliometric study." Multidisciplinary Reviews 7, no. 9 (June 24, 2024): 2024214. http://dx.doi.org/10.31893/multirev.2024214.

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This study aims to analyze research patterns connected with eye-tracking innovation in education through bibliometric analysis and to investigate the level of commitment of scientists in the Scopus dataset. A total of 774 documents related to eye tracking or eye movement technology in education were found in the Scopus database, and this number has increased significantly every year. The most papers are held by the United States, while the institution that secured the first place was Open Universiteit. Most of the papers are in English and are of the article type. The most prolific writers on this topic are Klein, P., Kuhn, J., and Schindler, M. Research trends in eye-tracking technology in education are visualized in four clusters: (1) paper and eye-tracking framework; (2) tracking process and focus of study; (3) experimental treatment and duration of eye-tracking; (4) eye-tracking in terms of teaching and learning processes. Trends related to eye-tracking technology in education research worldwide can be identified through this research, providing an overview for further study.

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Yang, Jie, and Lee Byung Gook. "EVENT-TRIGGERED CONTROL OF MILLING PHOTOELECTRIC TRACKING SERVO SYSTEMS BASED ON MULTI-INNOVATION INTEREST PARAMETER IDENTIFICATION." DYNA 99, no. 5 (September 1, 2024): 538–46. http://dx.doi.org/10.52152/d11193.

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Given the coupling of speed and current and the nonlinearity of electronic devices, the photoelectric tracking servo system for milling machines make it difficult to determine the model parameters, and traditional proportional–integral (PI) control hardly meets the high-performance requirements of milling machine optoelectronic tracking servo systems, especially in the development of system networking. In this study, an event-triggered intelligent PI position control strategy based on a multi-innovation identification model was proposed to improve the low control accuracy and dynamic performance caused by the strong coupling and nonlinearity in the optoelectronic tracking servo system of computerized numerical control (CNC) milling machines. A discredite model of the system was established through a multi-innovation identification model, and the PI control parameter was quickly determined using an improved multiverse optimization (IMVO) algorithm. At the same time, an event-triggering mechanism was introduced, thus reducing the number of controller triggers and saving system resources while ensuring the dynamic performance of the system. Finally, experiment results were compared with typical second-order system engineering design PI (SSED-PI) control, pole placement PI (PP-PI) control, and multiverse optimization (MVO)-PI control. Results demonstrate that the proposed multi-innovation stochastic gradient identification model fully utilizes the historical turning angle information of the optoelectronic tracking servo system and has higher accuracy than traditional stochastic gradient identification (parameter accuracy improved by 6.9 times, quantization error reduced by 6.7 times). The proposed event-triggered IMVO-PI (ET-IMVO-PI) has a triggering frequency of 3.5% compared with time-triggered IMVO-PI, with an overshoot of less than 0.5%, which can meet the needs of most engineering practices (less than 5%). Compared with event-triggered SSED-PI, PP-PI, and IMVO-PI, ET-IMVO-PI has higher dynamic performance and fewer triggering times, which can effectively meet the requirements of high-performance network control. The proposed method serves a crucial theoretical guide and important reference for the upgrading and transformation of the photoelectric tracking servo system of CNC milling machines. Keywords: Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system

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Gao, Jian, Zhi Quan Feng, and Xian Hui Song. "One Hand Tracking Algorithm Based on Behavioral Model of Grasping Object and Particle Filter." Applied Mechanics and Materials 462-463 (November 2013): 230–36. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.230.

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A novel human hand tracking algorithm based on a single-view camera is put forward. First, we remove the deformity gesture before tracking employing hand physical constraint and motion constraint. Second, we get data from digital glove in the process of hand grasping object, then we obtain the polynomial law of joint motion by analyzing the data to reduce the dimension. Finally, we fuse the behavioral model and optimized particle filter to improve the result of tracking. The innovation of this paper is to establish the behavioral model of grasping object. The experiments show that the proposed algorithm can track movement of hand accurately and quickly.

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Wang, Yuan-Kai, Tung-Ming Pan, and Chi-En Hu. "Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework." Applied Sciences 14, no. 22 (November 15, 2024): 10540. http://dx.doi.org/10.3390/app142210540.

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Multi-object tracking faces critical challenges, including occlusions, ID switches, and erroneous detection boxes, which significantly hinder tracking accuracy in complex environments. To address these issues, this study proposes a single-task joint learning (STJL) model integrated into an online multi-object tracking framework to enhance feature extraction and model robustness across diverse scenarios. Employing cross-dataset training, the model has improved generalization capabilities and can effectively handle various tracking conditions. A key innovation is the refined tracker initialization strategy that combines detection and tracklet confidence, which significantly reduces the number of false positives and ID switches. Additionally, the framework employs a combination of Mahalanobis and cosine distances to optimize data association, further improving tracking accuracy. The experimental results demonstrate that the proposed model outperformed state-of-the-art methods on standard benchmark datasets, achieving superior MOTA and reduced ID switches, confirming its effectiveness in dynamic and occlusion-heavy environments.

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Nakaya, Makoto, Tatenobu Seki, Kyojiro Kawaguchi, Yasushi Onoe, and Tetsuya Ootani. "MODEL PARAMETER ESTIMATION BY TRACKING SIMULATOR FOR THE INNOVATION OF PLANT OPERATION." IFAC Proceedings Volumes 41, no. 2 (2008): 2168–73. http://dx.doi.org/10.3182/20080706-5-kr-1001.00366.

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Yu, Shiguan, Hongshuo Zhang, Shilong Mu, Shizhong Liu, and Haojie Ding. "Analysis and innovation prospect of intelligent ship visual tracking and recognition system." Journal of Physics: Conference Series 1982, no. 1 (July 1, 2021): 012055. http://dx.doi.org/10.1088/1742-6596/1982/1/012055.

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Holmes, Amy Foshee, Michael Wilkins, and Shage Zhang. "Engagement, Innovation, and Impact: Tracking Faculty Activities Under the 2013 AACSB Standards." Organization Management Journal 14, no. 1 (January 2, 2017): 22–33. http://dx.doi.org/10.1080/15416518.2017.1293433.

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Mohammadi, Arash, and Amir Asif. "Distributed Consensus $+$ Innovation Particle Filtering for Bearing/Range Tracking With Communication Constraints." IEEE Transactions on Signal Processing 63, no. 3 (February 2015): 620–35. http://dx.doi.org/10.1109/tsp.2014.2367468.

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de Oliveira, Matheus Noschang, Letícia Rezende Mosquéra, Patricia Helena dos Santos Martins, André Luiz Marques Serrano, Guilherme Dantas Bispo, Guilherme Fay Vergara, Gabriela Mayumi Saiki, Clovis Neumann, and Vinícius Pereira Gonçalves. "Tracking Biofuel Innovation: A Graph-Based Analysis of Sustainable Aviation Fuel Patents." Energies 17, no. 15 (July 26, 2024): 3683. http://dx.doi.org/10.3390/en17153683.

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The use of biofuels represents a promising means of achieving a sustainable future and offers considerable economic and environmental benefits. Since they are derived from organic sources, such as vegetable oils and animal fats, biofuels can mitigate the effects of greenhouse gas emissions, improve air quality, support local agriculture, create employment opportunities, and enhance energy security by reducing dependence on fossil fuels. However, introducing these alternative fuels to the aviation sector remains a significant challenge. Thus, it is vital to investigate the potential of sustainable aviation fuel (SAF) and discover how to overcome the technological obstacles to its integration into mainstream aviation to attain broader decarbonization objectives. This article seeks to contribute to a discussion about SAF by examining how it has evolved and its connections to related patents. This article is a comprehensive study of biofuel innovation, highlighting the complex relationships between academia, industry, and other stakeholders. It is hoped that the findings from this study will provide a clearer understanding of the catalysts involved in SAF innovation and provide valuable insights for policymakers, academics, and professionals in the field who are committed to shaping the trajectory of sustainable energy technologies in the future.

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Liu, Yunfeng, Jidong Suo, Hamid Reza Karimi, and Xiaoming Liu. "A Filtering Algorithm for Maneuvering Target Tracking Based on Smoothing Spline Fitting." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/127643.

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Maneuvering target tracking is a challenge. Target’s sudden speed or direction changing would make the common filtering tracker divergence. To improve the accuracy of maneuvering target tracking, we propose a tracking algorithm based on spline fitting. Curve fitting, based on historical point trace, reflects the mobility information. The innovation of this paper is assuming that there is no dynamic motion model, and prediction is only based on the curve fitting over the measured data. Monte Carlo simulation results show that, when sea targets are maneuvering, the proposed algorithm has better accuracy than the conventional Kalman filter algorithm and the interactive multiple model filtering algorithm, maintaining simple structure and small amount of storage.

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Khattak, Ahmad, Gulistan Raja, and Nadeem Anjum. "Adaptive Framework for Multi-Feature Hybrid Object Tracking." Applied Sciences 8, no. 11 (November 19, 2018): 2294. http://dx.doi.org/10.3390/app8112294.

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Object tracking is a computer vision task deemed necessary for high-level intelligent decision-making algorithms. Researchers have merged different object tracking techniques and discovered a new class of hybrid algorithms that is based on embedding a meanshift (MS) optimization procedure into the particle filter (PF) (MSPF) to replace its inaccurate and expensive particle validation processes. The algorithm employs a combination of predetermined features, implicitly assuming that the background will not change. However, the assumption of fully specifying the background of the object may not often hold, especially in an uncontrolled environment. The first innovation of this research paper is the development of a dynamically adaptive multi-feature framework for MSPF (AMF-MSPF) in which features are ranked by a ranking module and the top features are selected on-the-fly. As a consequence, it improves local discrimination of the object from its immediate surroundings. It is also highly desirable to reduce the already complex framework of the MSPF to save resources to implement a feature ranking module. Thus, the second innovation of this research paper introduces a novel technique for the MS optimization method, which reduces its traditional complexity by an order of magnitude. The proposed AMF-MSPF framework is tested on different video datasets that exhibit challenging constraints. Experimental results have shown robustness, tracking accuracy and computational efficiency against these constraints. Comparison with existing methods has shown significant improvements in term of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE.

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Ge, Baoshuang, Hai Zhang, Liuyang Jiang, Zheng Li, and Maaz Butt. "Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance." Sensors 19, no. 6 (March 19, 2019): 1371. http://dx.doi.org/10.3390/s19061371.

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The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

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K.Sanjeevaiah, B.Monika Lakshmi, B.Rohini, D.Swetha, and T.Praveen. "EYE CONTROLLED VIRTUAL MOUSE." international journal of engineering technology and management sciences 8, no. 3 (2024): 241–45. http://dx.doi.org/10.46647/ijetms.2024.v08i03.030.

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In recent years, the development of assistive technologies has significantly enhanced the quality of life for individuals with physical disabilities. One such innovation is the eyecontrolled virtual mouse, a system designed to enable hands-free computer interaction using eye-tracking technology. This paper presents a comprehensive overview of the design, implementation, and performance of an eye-controlled virtual mouse. The system leverages advanced eye-tracking hardware to capture and interpret eye movements, translating them into cursor movements and click commands on a computer screen. Key features include calibration routines to accommodate individual differences in eye movement, algorithms for precise gaze estimation, and user-friendly software interfaces that ensure accessibility and ease of use. Our evaluation, conducted with a diverse group of participants, demonstrates the system’s high accuracy and responsiveness, making it a viable alternative for users with limited motor abilities. This innovation not only broadens the scope of assistive technology but also paves the way for further advancements in human-computer interaction.

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Yang, Lei, Chuansheng Tang, Jie Yang, Yongxin Li, and Tao Li. "Output Delay Sliding Mode Tracking Control of SRM based on Multi-innovation Model Identification." Journal of Engineering Science and Technology Review 13, no. 5 (2020): 114–21. http://dx.doi.org/10.25103/jestr.135.15.

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The drive system of switched reluctance motor (SRM) is a complex nonlinear system that is composed of many links. The delay in the measurement of the speed and position signal of SRM is caused by the factors that affect the measuring sensor. To effectively improve the influence of the SRM rotor position and speed signal delay on the system performance, a sliding mode position tracking method based on output delay observation was proposed in this study. First, the model was discretized according to the structure and characteristics of SRM and the mathematical parameters of the system were identified using a multi-innovation stochastic gradient (MISG) algorithm. Second, a delay state observer was constructed on the basis of an SRM system model with output delay. Then, the sliding mode tracking control method based on the delay state observation compensation was proposed and combined with sliding mode control theory. Lastly, the effectiveness of the designed model parameter identification, delay state observation, and output delay control methods were compared through numerical simulation. Results show that when uncertain factors, such as noise, are present in the system, the MISG identification method can rapidly and accurately identify the parameters of the SRM model compared with the stochastic gradient identification method; the identification accuracy of the former is four times higher than that of the latter. Similarly, the sliding mode position tracking control method based on output delay observer can rapidly and accurately track the position and speed within 0.5 s. However, its position (0.2 rad) and velocity (0.233 rad/s) tracking exhibit large steady-state errors when no delay observation compensation is present. The proposed method not only demonstrates high position tracking accuracy, but also possesses strong robustness to output delay.

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Silver, Rita Elaine, Galyna Kogut, and Thi Canh Dien Huynh. "Learning “New” Instructional Strategies: Pedagogical Innovation, Teacher Professional Development, Understanding and Concerns." Journal of Teacher Education 70, no. 5 (May 4, 2019): 552–66. http://dx.doi.org/10.1177/0022487119844712.

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Teacher professional development (TPD) through supported pedagogical innovations relies on teacher understanding (TU) of what is proposed, how the innovation can be enacted, and ways in which an innovation might be effectively adapted to local conditions. This article describes evolving TU during a 1-year reading comprehension innovation. Analysis, based on a two-dimensional neo-Bloomian framework, revealed that types of TU aligned with specific strategies used at specific times in the innovation program to some extent. Initial concerns about cultural appropriateness tended to fade as participating teachers came to their own understandings of how to employ the new instructional strategies. Other teacher concerns shifted from hypothetical to more concrete concerns about how to further enhance classroom interactions, evidencing evolving TU. Importantly, while examining in-class practices shows only the outcome of an innovation, tracking teachers’ developing understanding was important for gaining insights into the ongoing TPD process.

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Romaniuk, Svitlana Zakharivna, and Tetiana Fedirchyk. "Management of innovation activity in establishments of general secondary education of Ukraine." Studia Gdańskie. Wizje i rzeczywistość XV (June 15, 2019): 335–54. http://dx.doi.org/10.5604/01.3001.0014.0493.

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The essence of the concepts “innovation”, “innovation activity of the pedagogue”, “innovation potential of the institution of education”, “modern approaches to the management of innovation activity” have been clarified in the article. It has been grounded that the basis of inno-vation activity is the ability to construct the conceptual foundations of pedagogical innovations, which include diagnostics, forecasting, devel-opment of the program of the experiment, analysis of its implementa-tion, as well as realization of the innovation program, tracking the course and the result of its implementation, correction and reflection of innova-tion actions. The main functions of innovation management, which in-clude analytical-forecasting, planning, organization, control and regula-tion, have been characterized. The eight main stages of management of innovation activity have been singled out and analyzed. For its imple-mentation it is necessary to decide on the ability of pedagogues to crea-tive work in general, to prove the necessity of changes, to offer the per-fect monitoring system of the innovation process of the institution of education. The article distinguishes five levels of teacher’s innovation activity: zero, adaptive, reproductive, heuristic, and creative.

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Caetano, Mauro, Cláudio Jorge Pinto Alves, and Lucas Bispo de Oliveira Alves. "Model for measuring airport innovation." Revista Brasileira de Inovação 20 (October 5, 2021): e21012. http://dx.doi.org/10.20396/rbi.v20i00.8661362.

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Identifying and measuring innovation at airports becomes necessary not only to point out theelements of its management or infrastructure to be improved, but also to raise opportunities for innovation, with the aim to collaborate for the best efficiency of air transport. In this sense, this study concerns innovation management applied to airports given the limitations in the state of the art related to measuring innovation in this type of organization. The integrated perception of different airport stakeholders has been collected from a survey with 70 Brazilian professionals specialized in the subject, namely researchers, airports, airlines, and the aeronautics industry managers, in a proposal to measure the level of airport innovation from different indicators. The results proposed an algorithm for an Airport Integrated Innovation Index (AI3), composed by 38 measurable variables related to innovation practices, procedures, and infrastructure elements such as new biometric passenger identification systems, real-time passenger flight tracking systems, new types of runway pavement, and actions to reduce airport operational restrictions. This model can be used in airports of different categories (national or international) and sizes (small, medium, or large). It may be adapted for different countries and contexts according to their markets and organizational cultures.

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Wang, Bingshu, Qiang Li, Qianchen Mao, Jinbao Wang, C. L. Philip Chen, Aihong Shangguan, and Haosu Zhang. "A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods." Drones 8, no. 9 (September 23, 2024): 518. http://dx.doi.org/10.3390/drones8090518.

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The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.

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Hua, Bing, Guang Yang, Yunhua Wu, and Zhiming Chen. "Angle-Only Target Tracking Method for Optical Imaging Micro-/Nanosatellite Based on APSO-SSUKF." Space: Science & Technology 2022 (October 27, 2022): 1–13. http://dx.doi.org/10.34133/2022/9898147.

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To ensure the safety of the space station and improve the accuracy of the estimated trajectory tracking of noncooperative target, an optical imaging micro-/nanosatellite based on APSO-SSUKF (adaptive particle swarm optimization-spherical simplex unscented Kalman filter) is proposed to track low-orbit target using angle-only measurement. First, the algorithm considers the effect of J2 perturbation, uses the angle-only data as the observation vector, and uses spherical simplex unscented Kalman filter (SSUKF) to reduce the cost of calculation of the UKF in space noncooperative target tracking. Secondly, it is proposed to use the actual and theoretical covariance of the innovation sequence for real-time estimation of measurement noise, designing the adaptive particle swarm optimization (APSO) algorithm for real-time tracking of the process noise in the SSUKF that improves the accuracy of the filter in angle-only tracking. Finally, the tracking simulation of low-orbit satellite is carried out by using optical imaging micro-/nanosatellite, and the result shows that, compared with UKF, SSUKF, and PSO-SSUKF, APSO-SSUKF reduces the root mean square of the error in predicting the position in space target tracking by 45.44%, 35.26%, and 20.94%, and APSO-SSUKF reduces the root mean square of the error in velocity by 45.58%, 33.53%, and 16.33%, respectively; in the angle-tracking target, APSO-SSUKF improves the convergence and estimated accuracy of the algorithm in tracking.

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Sun, Minghan, Yiwei Jia, Jian Wei, and Jewel X. Zhu. "Exploring the Green-Oriented Transition Process of Ship Power Systems: A Patent-Based Overview on Innovation Trends and Patterns." Energies 16, no. 6 (March 8, 2023): 2566. http://dx.doi.org/10.3390/en16062566.

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The shipping industry has accelerated the transformation of its carbon emission reduction and decarbonization, and relevant patents are rapidly increasing, but the industry still lacks consensus on the low-carbon development route of ship propulsion technology. We used the Derwent Innovation Index to collect the global patent information on ship power systems between 1965 and 2022 and proposed a new patent information mining framework. It is used for the dynamic tracking and analysis of global technology correlation characteristics, hot technology topics, and competitive situations. The findings indicate that: (1) the innovation of ship power systems is more radical and concentrated in the fuel field represented by LNG technology, whereas technical innovation in the field of pure electric propulsion is more scattered. Small tonnage ships, underwater operations, and recreation technology are among its innovation hotspots. (2) Pure electric propulsion technology is dominated by combined innovation with other propulsion methods (hybrid propulsion technology) and Chinese universities have recently begun to lead this technology. (3) Fuel cells and remote control have become innovation hotspots. Fuel cell technology, which combines electric, fuel, and hybrid power technology, is now on the cutting edge of innovation and has the potential for disruptive innovation.

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Yang, Zihan, and Zhiquan Xiao. "A Review of the Sustainable Development of Solar Photovoltaic Tracking System Technology." Energies 16, no. 23 (November 25, 2023): 7768. http://dx.doi.org/10.3390/en16237768.

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In the face of the traditional fossil fuel energy crisis, solar energy stands out as a green, clean, and renewable energy source. Solar photovoltaic tracking technology is an effective solution to this problem. This article delves into the sustainable development of solar photovoltaic tracking technology, analyzing its current state, limiting factors, and future trends. The adjustment of solar panel orientation using solar tracking technology to maximize energy generation efficiency has been widely implemented in various fields, including solar power plants. Currently, limiting factors for this technology include energy generation efficiency, costs, and the complexity of various environmental conditions. In terms of sustainable development, this article emphasizes the importance of photovoltaic materials and manufacturing innovation, energy efficiency improvements, as well as the integration of smart and digital technologies. Future trends include higher precision, broader applications, and lower costs. Solar photovoltaic tracking technology will play a pivotal role in global energy production, fostering the realization of a clean and sustainable energy future.

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Gao, Ming, and Shixin Yang. "CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes." IECE Transactions on Emerging Topics in Artificial Intelligence 1, no. 1 (May 29, 2024): 44–57. http://dx.doi.org/10.62762/tetai.2024.240529.

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In the era of rapid technological advancement, the demand for sophisticated Multi-Object Tracking (MOT) systems in applications such as intelligent surveillance and autonomous navigation has become increasingly critical.However, existing models often struggle with accuracy and efficiency in densely populated or dynamically complex environments. Addressing these challenges, we introduce a novel deep learning-based MOT model that incorporates the latest CT-DETR detection technology and an advanced ReID module for improved pedestrian tracking. Experimental results demonstrate the model's superior performance in accurately identifying and tracking multiple targets across varied scenarios, significantly outperforming existing benchmarks.This research not only marks a significant leap forward in the field of video surveillance technology but also lays a foundational framework for future advancements in intelligent system applications, underscoring the importance of innovation in deep learning methodologies for real-world challenges.

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Szajerman, Dominik, Piotr Napieralski, and Jean-Philippe Lecointe. "Joint analysis of simultaneous EEG and eye tracking data for video images." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 5 (September 3, 2018): 1870–84. http://dx.doi.org/10.1108/compel-07-2018-0281.

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Purpose Technological innovation has made it possible to review how a film cues particular reactions on the part of the viewers. The purpose of this paper is to capture and interpret visual perception and attention by the simultaneous use of eye tracking and electroencephalography (EEG) technologies. Design/methodology/approach The authors have developed a method for joint analysis of EEG and eye tracking. To achieve this goal, an algorithm was implemented to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. All parameters have been measured as a function of the relationship between the tested signals, which, in turn, allowed for a more accurate validation of hypotheses by appropriately selected calculations. Findings The results of this study revealed a coherence between EEG and eye tracking that are of particular relevance for human perception. Practical implications This paper endeavors both to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. Eye tracking provides a powerful real-time measure of viewer region of interest. EEG technologies provides data regarding the viewer’s emotional states while watching the movie. Originality/value The approach in this paper is distinct from similar studies because it takes into account the integration of the eye tracking and EEG technologies. This paper provides a method for determining a fully functional video introspection system.

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Wang, Lufei, Dong Joon Lee, Han Han, Lixing Zhao, Hiroshi Tsukamoto, Yong-IL Kim, Adele M. Musicant, et al. "Application of bioluminescence resonance energy transfer-based cell tracking approach in bone tissue engineering." Journal of Tissue Engineering 12 (January 2021): 204173142199546. http://dx.doi.org/10.1177/2041731421995465.

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Bioluminescent imaging (BLI) has emerged as a popular in vivo tracking modality in bone regeneration studies stemming from its clear advantages: non-invasive, real-time, and inexpensive. We recently adopted bioluminescence resonance energy transfer (BRET) principle to improve BLI cell tracking and generated the brightest bioluminescent signal known to date, which thus enables more sensitive real-time cell tracking at deep tissue level. In the present study, we brought BRET-based cell tracking strategy into the field of bone tissue engineering for the first time. We labeled rat mesenchymal stem cells (rMSCs) with our in-house BRET-based GpNLuc reporter and evaluated the cell tracking efficacy both in vitro and in vivo. In scaffold-free spheroid 3D culture system, using BRET-based GpNLuc labeling resulted in significantly better correlation to cell numbers than a fluorescence based approach. In scaffold-based 3D culture system, GpNLuc-rMSCs displayed robust bioluminescence signals with minimal background noise. Furthermore, a tight correlation between BLI signal and cell number highlighted the robust reliability of using BRET-based BLI. In calvarial critical sized defect model, robust signal and the consistency in cell survival evaluation collectively supported BRET-based GpNLuc labeling as a reliable approach for non-invasively tracking MSC. In summary, BRET-based GpNLuc labeling is a robust, reliable, and inexpensive real-time cell tracking method, which offers a promising direction for the technological innovation of BLI and even non-invasive tracking systems, in the field of bone tissue engineering.

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Dai, Jingyi. "Advancements in deep learning for visual object tracking." Applied and Computational Engineering 82, no. 1 (November 8, 2024): 130–36. http://dx.doi.org/10.54254/2755-2721/82/20240997.

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Abstract. Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. This comprehensive review delves into the transformative impact of deep learning on the domain of visual object tracking. Since the inception of AlexNet in 2012, deep learning has revolutionized feature extraction, leading to significant advancements in tracking accuracy and robustness. The article explores the integration of deep learning with various tracking algorithms, including deep correlation filters, classification-based approaches, Siamese networks, gradient-based methods, and the innovative application of Transformer architectures. Moreover, the role of tracking datasets in fostering algorithm development and innovation is highlighted, with an emphasis on the expansion in scale, diversity, and annotation quality. Furthermore, the article also examines the multifaceted evaluation metrics for tracking algorithms, encompassing precision, robustness, efficiency, generalization, and real-time capabilities. Looking ahead, the review outlines future research directions, such as algorithm optimization for lightweight and accelerated performance, enhancing generalizability, leveraging multimodal data fusion, and refining Transformer models for improved temporal information processing. The challenges of long-term tracking and the growing importance of algorithm interpretability and transparency are also discussed. In summary, the article underscores the promising trajectory of deep learning in visual object tracking, with ongoing research poised to make tracking technologies smarter, more efficient, and robust, catering to a wide array of practical applications and environments.

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Homma, Noriyasu, Yoshihiro Takai, Haruna Endo, Kei Ichiji, Yuichiro Narita, Xiaoyong Zhang, Masao Sakai, et al. "Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity." Journal of Medical Engineering 2013 (December 8, 2013): 1–8. http://dx.doi.org/10.1155/2013/340821.

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We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray. The tumor can then be extracted by decomposing the fluoroscopic intensity into the tumor intensity component and the others. The decomposition problem for more than two structures is ill posed, but it can be transformed into a well-posed one by temporally accumulating constraints that must be satisfied by the decomposed moving tumor component and the rest of the intensity components. The extracted tumor image can then be used to achieve accurate tumor motion tracking without implanted markers that are widely used in the current tracking techniques. The performance evaluation showed that the extraction error was sufficiently small and the extracted tumor tracking achieved a high and sufficient accuracy less than 1 mm for clinical datasets. These results clearly demonstrate the usefulness of the proposed method for markerless tumor motion tracking.

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Zhao, Huarong, Li Peng, and Hongnian Yu. "Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology." Sensors 20, no. 15 (July 27, 2020): 4164. http://dx.doi.org/10.3390/s20154164.

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This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.

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Jin, Qingchen, Xin Chen, Pengfei Zhang, Jing Yuan, and Shang Li. "State Estimation of Wireless Sensor Networks Under False Data Injection." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2216/1/012016.

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Abstract This paper studies the problem of false external data injection information transmitted through wireless sensor networks. Firstly, this paper models the nonlinear system and improves the unscented Kalman filter to solve the problem of false data injection in the transmission process replaces the abnormal innovation data by setting the innovation confidence interval in advance and deduces the error covariance of this method. Then the stability of the designed algorithm is verified. Finally, the effectiveness of the filtering algorithm designed in this paper is illustrated by tracking the state of the system and the mean square error of the simulation system.

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Jones, Graham, Bernardita Chirino Chace, and Justin Wright. "Cultural diversity drives innovation: modeling in the global pharmaceutical industry." International Journal of Innovation Science 13, no. 2 (February 8, 2021): 133–44. http://dx.doi.org/10.1108/ijis-06-2020-0087.

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Purpose The innovative capacity of an organization is typically realized through unit-level teams. Previous studies correlate innovation performance with cultural diversity of teams, but note that team dynamics need to be optimized to derive maximum benefit. Herein, this study offers an assessment of available team building instruments through the lens of diverse innovation teams. In a demonstration project in the pharmaceutical industry, this study then outlines specific tools and approaches which can be successfully deployed through team coaching and mentoring. Design/methodology/approach A cluster of nine innovation teams with varying degrees of cultural diversity was provided with assessment and management instruments which had been identified and field tested by a mentoring team. Content included cultural awareness tools, innovation team profiling methods and Team Science (SciTS) ideology. Teams were funded, coached and mentored through a six-month performance period and assessed at regular intervals. Findings Team assessments provided correlations between performance (measured by project completion and new intellectual property generated) and diversity together with wealth of information on intra-team culture and dynamics. Concrete recommendations from the study include adoption of appropriate communication standards to promote inclusivity, use of SciTS operational tracking metrics to enhance engagement, use of the FourSight group profiling methodology and cultural quotient scale cultural awareness instruments at team-forming stage to promote effective dynamics and enhance inclusivity. Practical implications Cultural diversity has a positive impact on innovation teams. This said, for maximum benefit cultural awareness of team members should be optimized to avoid unintended conflicts developing. Such issues can be exacerbated when teams are deployed remotely and preventative measures should be established. These issues became of heightened significance as a result of telecommuting imposed by the COVID-19 pandemic and have longer-term implications, as corporations consider global air travel reduction through environmental concerns. A tracking tool is described to monitor team engagement and promote inclusivity. It is expected that the learnings can influence how teams can best form, normalize and operate within corporate innovation programs and form the basis of long-term impact studies. Originality/value This represents the first systematic study on the impact of cultural diversity and team dynamics within innovation programs in the pharmaceutical industry. The tools and methodologies deployed are widely available and can be adopted by innovation teams in many adjacent industries with established innovation ecosystems.

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Journal articles on the topic 'Innovation-tracking'

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