Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.
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The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an (Formula presented.) -greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.
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Professional development of teacher educators is an important topic, because teacher educators need to maintain and enhance their expertise in order to educate our future teachers (Kools & Koster, n.d. ; Dengerink, Lunenberg & Kools, 2015). How do teacher educators fulfil this task, especially within the hectic timeframe of everyday work? I asked four colleges to participate in a group to share their experiences, actions or behaviour in the organisation about their development in their profession of being a teacher educator. My purpose is to bring awareness and movement into that group. My research focusses on teacher educators in a large teacher education department in the Netherlands and the opportunities for action available to them. During this study we are currently creating a learning environment in which mutual cooperation increases the learning potential of all participants. In this group participants take or make time to learn, giving words to their scopes . Researcher and participants discuss and explore on the basis of equality, reciprocity and mutual understanding. By deploying methods borrowed from ‘Appreciative Inquiry’(Massenlink et al., 2008) the enthusiasm of a study group is raised and the intrinsic motivation of the participants stimulated. Our study group will convene three times. Its goal is to stimulate cooperation among teacher educators through optimisation of existing qualities, a method that could be described as empowerment, or a process of collective reinforcement ‘To learn’ involves experiencing that what one does really matters, as well as developing one’s own persona in the local community. Intervention, action, reflection and study group meetings alternate in the course of our research. In addition to audio and video recordings, data consists of reports drawn up on the basis of member checks. Data is analysed qualitatively by coding the interview texts and reports. After applying the codes, the researcher discusses the coding in a research group and with the participants of the study group (membercheck). Working collaboratively can offer learning challenges that catalyse growth as a professional, teacher educators become acquainted and approach each other from the perspective of their respective professional and functional responsibilities. This study offers perspectives for other teacher educators to recognize these possibilities in their own situation. Moreover the study offers a description of a way to organise collegial exchange. The research is related to the RDC professional development of teacher educators.
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This report deals with the possibilities for touristic and recreational development on the Wadden Sea coast of the Dutch province of Friesland. The topic is approached against the background of possible dyke reinforcements which might become necessary in future years. The data on this topic were collected by conducting three interviews with stakeholders responsible for or influenced by these changes. In addition 70 questionnaires were handed out to respondents who are using the dykes for touristic or recreational purposes. Results of the research show that a complete reinforcement is not planned so far. Still, the people using the dykes have a certain demand for new developments in this field and are not completely satisfied with the current state. It also becomes clear that the possibilities for touristic improvements in the dyke area are limited in many ways. The protection is the main purpose of the dykes and interventions risking the safety are stopped by existing laws. Concluding it can be said that there is a potential for further touristic and recreational improvements in this area. Stakeholders and people using the dykes, all have a certain interest in new developments. Nevertheless, these changes can only be limited to small developments, building up on already existing tourist activities. Furthermore, stakeholders have to improve their cooperation in order to work towards a common goal.
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University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting.
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University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting. https://www.scienceguide.nl/2021/06/leren-van-docentteams-vraagt-om-gezamenlijk-leiderschap/
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Krewerd is een klein dorp in het Noordoosten van het Groningen-gasveld. Een kerk, een dorpshuis en ruim veertig huizen. Stuk voor stuk zijn ze in meer of mindere mate beschadigd. Alle bewoners wachten al jaren op een versterkingsadvies.
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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