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Product placement on YouTube: An explorative study on YouTube creators’ experiences with advertisers
(2017)
During its process of diffusion, YouTube has become increasingly attractive for advertisers, not only in terms of classical commercials (like pre-roll ads) but also in terms of product placement opportunities. This article aims to explore the processes between YouTube creators and advertisers concerning product placement enquiries. For this purpose, YouTube creators from Germany ( n = 57) answered an online survey about their experiences with advertisers. The creators covered a wide range of YouTube channel genres (beauty, fashion, comedy, gaming, cooking) and differed in number of subscribers. Some creators who took part in the survey had less than 20,000 subscribers, others had between 20,000 and 500,000 subscribers and still others had more than 500,000 subscribers. How often do YouTube creators use product placement? Is paid-for placement an important source of finance for them? How professional are the relations and processes between YouTube creators and advertisers? Before presenting and discussing the results of the empirical survey of the current use of product placement, a brief history of how YouTube has become a marketing space for advertisers is provided.
A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control applica-tion (MPC) with a flexible objective function and on-line tunable weights, which runs on a In-dustrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dy-namics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the re-ceiver was developed. By parallelising the model solver, the estimation of service-life con-sumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and archi-tecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies.
Magnetic Resonance (MR) elastography is a method for measuring tissue elasticity via phase images acquired with an MR scanner. The propagation of periodic mechanical waves through the tissue can be captured by means of a modified phase contrast sequence. These waves are generated with a mechanical oscillator (actuator) and coupled into the tissue through the skin. The actuator must be capable of generating a sinusoidal excitation with excellent phase and amplitude stability, while not disturbing the MR imaging process. In this work, an actuator based on a piezoelectric principle was developed. Based on the imaging evaluation of several material samples, the housing for the piezoelectric ceramic was constructed of aluminum. Smaller parts of the housing were manufactured from brass and titanium to fulfill the mechanical constraints. A lever was used to transfer the oscillation generated by the piezoelectric ceramic to the point of excitation. The lever amplifies the piezoelectric motion, allowing for a more compact design. Three different lever designs were characterized by an acceleration sensor both outside and inside the magnet. It was shown that the rigidity of the lever, as determined by its material and form, was decisive in determining the resonant frequency of the system and therefore the maximum practical frequency of operation. It was also shown that the motion of the oscillator is unaffected by the electromagnetic fields of the MR imager. The final design can be placed directly in the magnet bore within a few centimeters of the tissue volume to be imaged without generating significant artifacts. An amplitude range of 0–1 mm in the frequency range from 0 to over 300 Hz was achieved, sufficient for performing most MR elastography applications. © 2002 Wiley Periodicals, Inc. Concepts in Magnetic Resonance (Magn Reson Engineering) 15: 239–254, 2002
Men afflicted with hypogonadism have reduced fat-free mass and in some cases a loss in muscle strength. We hypothesize that muscle elasticity of the lower extremities differs between patients with hypogonadism and healthy controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in applied force.
Men afflicted with chronic obstructive pulmonary disease (COPD) show signs of muscle dysfunction, such as increased muscle fatigue and acidosis during exercise. We hypothesize that muscle elasticity of the lower extremities differs between patients with COPD and age-related controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in the maximum applied force.
The Chylla–Haase polymerization reactor is widely accepted as a benchmark process for the evaluation of control strategies for batch reactors. In this contribution a control concept based on Exact I/O-Linearization is proposed and compared to a conventional cascade control structure. In order to adapt the exact linearization control strategy to various polymerization products and batch conditions, an advanced probabilistic inference algorithm (Sigma-Point Kalman Filter) is applied and investigated. Sigma-Point Kalman Filters have the major improvement of simplified implementation compared to local linearization methods (i.e. Extended Kalman Filter) because no analytical Jacobians are required. Stochastic simulation studies are introduced and show the effectiveness, accuracy and benefit of the control concept. Within several scenarios a satisfying robustness against structural errors in the underlying model equations for the nonlinear control law and the inference algorithm is demonstrated. Furthermore it is pointed out, that with little effort in reassembling the plant design, control performance can be improved significantly.
This article deals with the cost optimal commitment of power systems. System stability is often only considered after solving the commitment problem. We show by example that a cost optimization may result in a mode of operation that, while economically optimal, is unstable or has otherwise unacceptable dynamics. As a remedy, we propose to use the so-called normal vector method, which has been developed for the optimization of nonlinear dynamical systems with stability boundaries and uncertain parameters. We apply the method to a small sample power grid, where the optimization goal is to minimize the energy production costs for a given power consumption.
Stability and cost optimality of power generation and supply systems must not be considered independently from one another. Simple examples show that optimizing cost without considering stability may result in modes of operation that, while economically optimal, are unstable. We demonstrate that stability, robustness, and optimality can be considered systematically and simultaneously by combining bifurcation theory and nonlinear optimization. Essentially, the proposed method enforces a backoff distance between the optimal point of operation and operational or stability boundaries in the space of the optimization variables, where bifurcation theory is used to describe nonlinear stability boundaries.
We present a control scheme for a parabolic trough power plant that is equipped with a molten salt thermal energy storage system. We show that the multivariable control problem for the solar field can be decoupled by controlling the three way valve that splits the heat transfer fluid between the storage system and the steam generator. The steam generation cycle is regulated with inlet pressure control. The subsystems of the plant are modeled based on first principles, where we focus on the steam generation cycle. A multi-group extraction turbine with pre- and reheater is considered, which is suitable for solar power plants. We demonstrate the benefits of the proposed control scheme with a day-to-day simulation.
We derive a nonlinear model of the steam cycle of a solar power plant, estimate its unknown parameters with measured data, and design a linear model predictive controller based on the resulting model. Simulations show that the linear model predictive controller is able to regulate the electrical power of the nonlinear steam process of the plant to a given reference trajectory very well. Moreover, it results in very good disturbance rejection.
Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual’s eyes. It allows for an evaluation of the individual’s gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness.
Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.
Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery.
Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur
A method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device. The object is recognized with an object-class by means of an image processing system. Respective position data are determined from the images using a pinhole camera model based on the object-class. Position data indicating in world coordinates the position of a reference point of the object with respect to the plane of the road is used with a scaling factor of the pinhole camera model estimated by means of a Bayes estimator using the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability. The distance of the object from the automated vehicle is calculated from the estimated scaling factor using the pinhole camera model.
Despite the higher uptake of digital media also in older age groups, there are still challenges for people of high ages and with cognitive or motoric problems, which prevent them from learning and using devices like smartphones or tablets. Tangible user interfaces provide an opportunity to connect these people to social media, thereby increasing their digital participation in society and reducing loneliness. In this paper, we discuss the challenges of intergenerational communication and research the opportunities affiliated with tangible technologies to overcome technology barriers for adults of high ages. We present a hybrid system for social media interactions between young smartphone users and their old relatives, who do not use mobile technologies. The system consists of a tangible user interface designed specifically for old adults that connects to a standard mobile messenger service. TAMI was designed in a user-centered process to fulfill the needs of older adults and tested in a longer-term field study with pairs of young and old relatives.
User-centered and Participatory Design approaches have become widespread within HCI, also when designing digital media for older adults. These methodologies are seen as crucial to adapt technology design to older people's needs and increase their acceptance of digital technology. Nevertheless, it is often difficult to reach a diverse group of older people, for instance, people with limited technology skills, limited mobility, or cognitive issues. In addition, gerontologists criticized lately how older people's participation unfolds in technology design projects, e.g. that people are pushed into the role of users. Little is known about the motivations and needs of older adults regarding their participation in HCI research. To close this gap, we conducted an online survey (N=46) and semi-structured interviews (N=8) focusing on motivations, willingness and experiences regarding participation. Results show that people are generally willing to participate due to interest in the topic, learning new things and social bonding. However, despite the conceptions within HCI that long-term engagement is crucial within participatory design, older people in the study preferred less commitment and more open formats.
The healthcare sector's digital transformation necessitates the involvement of stakeholders through participation and co-creation processes. This study aims to investigate the understanding and implementation of participation and co-creation in the development of hybrid interaction technologies in healthcare. Eight interdisciplinary research projects were examined through semi-structured group interviews with 49 participants from academia, industry, and the application field. The interviews focused on participants' perspectives on project goals, user groups, access to the field, methods of participation and co-creation, and user motivation. The results highlight the diverse understandings of participation and co-creation, with some perceiving them as research-driven and others as already embedded in user- centered design. The projects emphasized user needs but often excluded stakeholders from collaborative decision-making. Challenges in achieving continuous involvement and co-decision were acknowledged. The findings underscore the need for reflexive practice and evaluation of participation and co-creation throughout the development process, as well as further research on participation across projects. By addressing these challenges and embracing participatory approaches, digital health applications can better address ethical concerns, build trust, and increase acceptance and adoption in the healthcare sector.
Wear it or fear it: exploration of drivers & barriers in smartwatch acceptance by senior citizens
(2018)
Probing the Potential of Multimedia Artefacts to Support Communication of People with Dementia
(2015)
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.
Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.
Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test.
Computational investigation of oxy-combustion of pulverized coal and biomass in a swirl burner
(2022)
Experimental investigation of heat transfer performance of corrugated tube with spring tape inserts
(2019)
The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.
Asset management for distribution networks with high penetration of distributed energy resources
(2018)
Large-Scale Storage Technologies in the Context of future German Transmission Grid Congestions
(2012)