University technology transfer stakeholders lack a simple, yet meaningful way to measure how effectively and quickly a university is able to license patents into commercially…
Abstract
University technology transfer stakeholders lack a simple, yet meaningful way to measure how effectively and quickly a university is able to license patents into commercially successful products and to spin off startups that in turn, create jobs. Current leading count-based measures fail to account for the fact that many significant technology transfer outcomes follow a skewed distribution that when summed, provide inadequate insight into a university's ability to quickly place its patent portfolio into productive external use. This article introduces a set of three core index-based measures that overcome the limitations of conventional metrics and econometric models: a commercialization health index, job creation health index, and a licensing-speed health index. The concept underlying the technology transfer health indexes is borrowed from the h index utilized by university tenure committees to measure scholarly impact and productivity over time. The index-based measures described in this article are simple for technology transfer practitioners to apply, can be calculated using existing data, and are immune to skewing by atypical outcomes such a single, high-earning patent, and be difficult to intentionally manipulate. With little cost and no additional infrastructure, index-based measures of university technology transfer activity yield meaningful metrics that could be input into larger, economic impact studies. The index-based measures described here reward universities that have sustained and impactful technology transfer activity over time; widespread application of index-based measures would incent universities to become better stewards of federally funded scientific research.
This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither…
Abstract
Purpose
This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.
Design/methodology/approach
Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.
Findings
One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.
Originality/value
One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.
Details
Keywords
Hualei Zhang and Mohammad Asif Ikbal
In response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method…
Abstract
Purpose
In response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.
Design/methodology/approach
The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.
Findings
First, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.
Originality/value
The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.