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Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

This paper aims to explore the evolutionary dynamics of innovation ecosystems in the food industry by adopting both open innovation and convergence approaches to derive practical policy implications to develop impactful innovation ecosystems to promote food production sustainably.

Starting from the cutting-edge case of Foodvalley in the Netherlands, the study adopts a backcasting approach to reach a future vision of food ecosystems from a sustainable food production perspective. The authors set the backcasting analysis in four steps: (1) description of the present and trends analysis, (2) selection of trustworthy criteria and goals, (3) development of future images and (4) analysis of how to reach the images.

The trends analysis has identified three existing innovation fields – protein shift, circular agrifood and food and health – and two strategic directions – convergence and localness decrease. The study reports how a long-term commitment may lead the valley toward a best future scenario.

The study suggests that policymakers and stakeholders can promote innovation strategies in sustainable food production ecosystems by encouraging collaboration between different sectors, reducing regulatory barriers, attracting innovative actors, and investing in education and training programs. To achieve measurable environmental and social impact outcomes, policies should promote entrepreneurship and create an enabling environment that encourages innovation and risk-taking.

The originality of this paper concerns the backcasting perspective applied to the analysis of the food ecosystem. This approach facilitates the identification of a path direction for successfully managing open innovation and industry convergence toward a desirable future of sustainable food production.

Before designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile.

The review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space.

The performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified.

The information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

This paper aims to present assessment of models and simulation results used in the development process of flight stabilisation system that uses trim tabs for PZL-130 Orlik turboprop military trainer aircraft. Flight test of the system allowed to compare software and hardware simulation results with real flight recordings.

Proposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation, software and hardware in the loop testing. The project reached flight testing stage which allowed to gather data to verify models and simulation results and asses their quality.

Results of the comparison showed that the trim tab actuator model used in simulation can be improved by adding play. This reduced the difference between simulation and real flight system output – actuator angle. The influence of airloads on the flying actuator angle compared to hardware in the loop simulation in lab is less than ± 0.6°.

Proposed flight stabilisation system that uses trim tabs has several benefits over classic automatic flight system in terms of weight, energy consumption and structure simplicity and does not need aircraft primary control modification. It was developed using modern techniques of model-based design, automatic code generation and hardware in the loop simulations.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

In response to environmental uncertainty, organizations rely on independent, empowered teams to increase their responsiveness to change. The purpose of this paper is to analyze how objectives and key results (OKRs) can contribute to creating a shared vision which keeps the different, empowered teams aligned.

Through a qualitative study, this paper develops a conceptual model of how OKRs lead to a shared vision. The authors interview professionals in nine organizations differing in size and industry who recently introduced agile, focusing on the main hurdles encountered and benefits obtained from the methodology.

The findings confirm that OKRs are an effective goal-setting methodology and suggest that their successful implementation builds on the new ways of organizing introduced by the agile methods. Moreover, the findings suggest that OKRs contribute to developing a shared vision and increase the alignment: first, the organizational vision becomes clear and its definition is participatory; second at the team level, individuals align around a shared team vision through participative definition of objectives.

The contribution of the paper is two-fold: this study provide an overview on the methodology, which is diffused from a practice perspective, but has not yet been addressed from a theoretical point of view. And this study connects OKRs to extant theory on goal-setting and vision development. This study shows they act as a coupling mechanism which helps empowered and independent teams together.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

The purpose of this study is to explore the concept of long-term thinking in a non-business context to gain deeper insights into bridging the gap between the theory of long-term thinking and its application as a management strategy.

To explore the concept of long-thinking further in a non-business setting, a grounded theory study was conducted with preschool leaders in a municipality in Sweden to examine how the leaders describe, define and apply the concept of long-term thinking in their schools. Interviews with school leaders, both written and oral, were used for data collection.

This study illustrates that the concept of long-term thinking can be twofold. First, the description can be as an anchor that reflects a mission. Second, the description can be a steering mechanism that guides decision-making. The findings also reinforce the importance of organisations developing an organisational culture that connect their vision and goals with the values and needs of their customers.

This study was carried out in a single organisation and shows a snapshot of the organisation's status at the time the data were collected. Therefore, the findings are not generalisable to all organisational settings; rather the findings may be transferable to other settings.

The results can be used to help identify areas where preschools in a municipal context can engage with sustainable quality development in order to build systems that support work with quality in a more structured way.

Long-term thinking is seen, within both theory and organisations, as necessary to achieve success in terms of sustainable development and quality, and this study contributes with knowledge about the current gap between theories of long-term thinking and practice in organisations.

This article details findings on how leaders of deeper learning schools establish, maintain, and propel unique teaching and learning environments. In this case study, the authors present findings from data collected through interviews with 30 leaders of self-proclaimed deeper learning initiatives and site visits to those elementary and secondary schools.

Using a case study approach, the authors collected data from interviews and observations of 30 school leaders.

The study's findings indicate how leaders of schools that engage in deeper learning tend to adhere to three core practices. First, the leaders of deeper learning schools in this study intently listened to the community to ascertain needs and desires; this drove the vision. Second, leaders of deeper learning schools created learning spaces that empowered students and gave them voice, agency, and choice. Third, leaders of deeper learning schools sought to humanize the schooling experience.

This study provides actionable examples of what leaders currently do to engage kids and teachers in deeper learning. These leaders offer insights into specific actions and practices that they espoused to make the schooling experience markedly different.

Previous studies focused on the deeper learning of schools and students. This is one of the first studies to focus on the inteplay between deeper learning and school leaders.