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Social robots are robots interacting with humans not only in collaborative settings, but also in personal settings like domestic services and healthcare. Some social robots simulate feelings (companions) while others just help lifting (assistants). However, they often incite both fascination and fear: what abilities should social robots have and what should remain exclusive to humans? We provide a historical background on the development of robots and related machines (1), discuss examples of social robots (2) and present an expert study on their desired future abilities and applications (3) conducted within the Forum of the European Active and Assisted Living Programme (AAL). The findings indicate that most technologies required for the social robots' emotion sensing are considered ready. For care robots, the experts approve health-related tasks like drawing blood while they prefer humans to do nursing tasks like washing. On a larger societal scale, the acceptance of social robots increases highly significantly with familiarity, making health robots and even military drones more acceptable than sex robots or child companion robots for childless couples. Accordingly, the acceptance of social robots seems to decrease with the level of face-to-face emotions involved.
Deafblindness is a condition that limits communication capabilities primarily to the haptic channel. In the EU-funded project SUITCEYES we design a system which allows haptic and thermal communication via soft interfaces and textiles. Based on user needs and informed by disability studies, we combine elements from smart textiles, sensors, semantic technologies, image processing, face and object recognition, machine learning, affective computing, and gamification. In this work, we present the underlying concepts and the overall design vision of the resulting assistive smart wearable.
This paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for the performance criteria, the single-linkage method has achieved better results compared to other agglomeration methods.
In public transportation, the motor pool often consists of various different vehicles bought over a duration of many years. Sometimes, they even differ within one batch bought at the same time. This poses a considerable challenge in the storage and allocation of spare parts, especially in the event of damage to a vehicle. Correctly assigning these parts before the vehicle reaches the workshop could significantly reduce both the downtime and, therefore, the actual costs for companies. In order to achieve this, the current software uses a simple probability calculation. To improve the performance, the data of specific companies was analysed, preprocessed and used with several modelling techniques to classify and, therefore, predict the spare parts to be used in the event of a faulty vehicle. We summarize our experience running through the steps of the Cross Industry Standard Process for Data Mining and compare the performance to the previously used probability. Gradient Boosting Trees turned out to be the best modeling technique for this special case.
Colored glass products with various printing technologies are becoming more important in industry. The aim is to achieve individual solution in a very short delivery time. Conventional thermal treatment of burning glasses in oven for tempered color printing has predominant issues with high time consumption, energy consumption and manufacturing cost. It requires alternative process development.
This paper proposes laser process to overcome issues in conventional treatment with the latest results of tempering colored glass. Samples have been analyzed with the scanning electron microscope (SEM). Two different laser systems have been applied and the glass has been printed with black paste.
CONTEXT
The paper addresses the needs of medium and small businesses regarding qualification of R&D specialists in the interdisciplinary cross-industry innovation, which promises a considerable reduction of investments and R&D expenditures. The cross-industry innovation is commonly understood as identification of analogies and transfer of technologies, processes, technical solutions, working principles or business models between industrial sectors. However, engineering graduates and specialists frequently lack the advanced skills and knowledge required to run interdisciplinary innovation across the industry boundaries.
PURPOSE
The study compares the efficiency of the cross-industry innovation methods in one semester project-oriented course. It identifies the individual challenges and preferred working techniques of the students with different prior knowledge, sets of experiences, and cultural contexts, which require attention by engineering educators.
APPROACH
Two parallel one-semester courses were offered to the mechanical and process engineering students enrolled in bachelor’s and master’s degree programs at the faculty of mechanical and process engineering. The students from different years of study were working in 12 teams of 3…6 persons each on different innovation projects, spending two hours a week in the classroom and additionally on average two hours weekly on their project research. Students' feedback and self-assessments concerning gained skills, efficiency of learned tools and intermediate findings were documented, analysed, and discussed regularly along the course.
RESULTS
Analysis of numerous student projects allows to compare and to select the tools most appropriate for finding cross-industry solutions, such as thinking in analogies, web monitoring, function-oriented search, databases of technological effects and processes, special creativity techniques and others. The utilization of learned skills in practical innovation work strengthens the motivation of students and enhances their entrepreneurial competences. Suggested learning course and given recommendations help facilitate sustainable education of ambitious specialists.
CONCLUSIONS
The structured cross-industry innovation can be successfully run as a systematic process and learned in one semester course. The choice of the preferred working teqniques made by the students is affected by their prior knowledge in science, practical experience, and cultural contexts. Major outcomes of the students’ innovation projects such as feasibility, novelty and customer value of the concepts are primarily influenced by students’ engineering design skills, prior knowledge of the technologies, and industrial or business experience.