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”I have never seen one who loves virtue as much as he loves beauty,” Confucius once said. If beauty is more important as goodness, it becomes clear why people invest so much effort in their first impression. The aesthetic of faces has many aspects and there is a strong correlation to all characteristics of humans, like age and gender. Often, research on aesthetics by social and ethic scientists lacks sufficient labelled data and the support of machine vision tools. In this position paper we propose the Aesthetic-Faces dataset, containing training data which is labelled by Chinese and German annotators. As a combination of three image subsets, the AF-dataset consists of European, Asian and African people. The research communities in machine learning, aesthetics and social ethics can benefit from our dataset and our toolbox. The toolbox provides many functions for machine learning with state-of-the-art CNNs and an Extreme-Gradient-Boosting regressor, but also 3D Morphable Model technolo gies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics.
This paper aims at presenting a solution that enables end customers of the energy system to participate in new local micro-energy-markets by providing them with a distributed, decentralized, transparent and secure Peer to Peer (P2P) payment system, which functions automatically applying new concepts of Machine to Machine (M2M) communication technologies. This work was performed within the German project VK_2G, funded by the DBU. The key results were: Providing means to perform microtransactions in a P2P fashion between end consumers and prosumers in local communities at low cost in a transparent and secure manner; Developing a platform with pre-defined smart contracts able to be tailored to different end customers ‘needs in an easy way and; Integrating both the market platform as well as the local control of generation and loads. This solution has been developed, integrated and tested in a laboratory prototype. This paper discusses this solution and presents the results of the first test.
3D assisted 2D face recognition involves the process of reconstructing 3D faces from 2D images and solving the problem of face recognition in 3D. To facilitate the use of deep neural networks, a 3D face, normally represented as a 3D mesh of vertices and its corresponding surface texture, is remapped to image-like square isomaps by a conformal mapping. Based on previous work, we assume that face recognition benefits more from texture. In this work, we focus on the surface texture and its discriminatory information content for recognition purposes. Our approach is to prepare a 3D mesh, the corresponding surface texture and the original 2D image as triple input for the recognition network, to show that 3D data is useful for face recognition. Texture enhancement methods to control the texture fusion process are introduced and we adapt data augmentation methods. Our results show that texture-map-based face recognition can not only compete with state-of-the-art systems under the same precon ditions but also outperforms standard 2D methods from recent years.
We discuss the fabrication technologies for IC chips in this chapter. We will focus on the main process steps and especially on those aspects that are of particular importance for understanding how they affect, and in some cases drive, the layout of ICs. All our analyses in this chapter will be for silicon as the base material; the principles and understanding gained can be applied to other substrates as well. Following a brief introduction to the fundamentals of IC fabrication (Sect. 2.1) and the base material used in it, namely silicon (Sect. 2.2), we discuss the photolithography process deployed for all structuring work in Sect. 2.3. We will then present in Sect. 2.4 some theoretical opening remarks on typical phenomena encountered in IC fabrication. Knowledge of these phenomena is very useful for understanding the process steps we cover in Sects. 2.5–2.8. We examine a simple exemplar process in Sect. 2.9 and observe how a field-effect transistor (FET) – the most important device in modern integrated circuits—is created. To drive the key points home, we provide a review of each topic at the end of every section from the point of view of layout design by discussing relevant physical design aspects.
In Germany, mobility is currently in a state of flux. Since June 2019, electric kick scooters (e-scooters) have been permitted on the roads, and this market is booming. This study employs a user survey to generate new data, supplemented by expert interviews to determine whether such e-scooters are a climate-friendly means of transport. The environmental impacts are quantified using a life cycle assessment. This results in a very accurate picture of e-scooters in Germany. The global warming potential of an e-scooter calculated in this study is 165 g CO2-eq./km, mostly due to material and production (that together account for 73% of the impact). By switching to e-scooters where the battery is swapped, the global warming potential can be reduced by 12%. The lowest value of 46 g CO2-eq./km is reached if all possibilities are exploited and the life span of e-scooters is increased to 15 months. Comparing these emissions with those of the replaced modal split, e-scooters are at best 8% above the modal split value of 39 g CO2-eq./km.
After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. This book presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), a novel interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic approach, similar to the roundup of a sheep herd, is to let autonomous layout modules interact with each other inside a successively tightened layout zone. Considering various principles of self-organization, remarkable overall solutions can result from the individual, local, selfish actions of the modules. Displaying this fascinating phenomenon of emergence, examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. From an academic point of view, SWARM combines the strengths of procedural generators with the assets of optimization algorithms, thus paving the way for a new automation paradigm called bottom-up meets top-down.
Energy efficient electric control of drives is more and more important for electric mobility and manufacturing industries. Online dynamic optimization of induction machines is challenging due to the computational complexity involved and the variable power losses during dynamic operation of induction machines. This paper proposes a simple technique for sub-optimal online loss optimization using rotor flux linkage templates for energy efficient dynamic operation of induction machines. Such a rotor flux linkage template is given by a rotor flux linkage trajectory which is optimal for a specific scenario. This template is calculated in an offline optimization process. For a specific scenario during real time operation the rotor flux linkage is calculated by appropriately scaling the given template.
With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.
Deep learning-based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning-based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.
Investigation of tympanic membrane influences on middle-ear impedance measurements and simulations
(2020)
This study simulates acoustic impedance measurements in the human ear canal and investigates error influences due to improperly accounted evanescence in the probe’s near field, cross-section area changes, curvature of the ear canal, and pressure inhomogeneities across the tympanic membrane, which arise mainly at frequencies above 10 kHz. Evanescence results from strongly damped modes of higher order, which can only be found in the near field of the sound source and are excited due to sharp cross-sectional changes as they occur at the transition from the probe loudspeaker to the ear canal. This means that different impedances are measured depending on the probe design. The influence of evanescence cannot be eliminated completely from measurements, however, it can be reduced by a probe design with larger distance between speaker and microphone. A completely different approach to account for the influence of evanescence is to evaluate impedance measurements with the help of a finite element model, which takes the precise arrangement of microphone and speaker in the measurement into account. The latter is shown in this study exemplary on impedance measurements at a tube terminated with a steel plate. Furthermore, the influences of shape changes of the tympanic membrane and ear canal curvature on impedance are investigated.
Urgent action is needed to keep the chance of limiting global warming to 1.5°C or even 2.0°C. Current outlooks by IPCC, and many other organisations forecast that this will be impossible at current pace of emission 'reductions' – Germany has already hit 1.5° warming this year. Across 2019, particularly during the UN New York Climate summit, numerous organisations declared their ambition to become net carbon neutral. Amongst these were investors and companies, including quite a number of German ones.
We apply a mixed methods approach, utilising data gathered from approx. 900 companies after Climate Week in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans announced and initiatives pledging climate action.
With this, we analyse how German companies in the manufacturing sectors react to rising societal pressure and emerging policies, particularly what measures they have taken or plan to implement to reduce the footprint of their company, their products and their supply chain. In this, we particularly analyse whether and in what way energy- and resource consumption, as well as carbon emissions are considered in the development and lifecycle of goods manufactured. This is of huge relevance as these goods determine the future footprint of buildings, vehicles and industry.
Regarding the supply chain, current articles indicate that small and medium-sized enterprises (SME) are particularly challenged by increasing demands from their large corporate clients and an alleged lack of preparedness to be able to take and afford prompt decarbonisation action themselves (Buchenau et. al. 2019). Notably the automotive industry recently announced new models that will be 100% carbon neutral all the way through (ibid). We thus analyse if and how factors such as company size, energy intensity and sector affiliation influence a company’s plan to fully decarbonize. Ownership structure and corporate culture, it appears, significantly impact on the degree of decarbonisation action underway.
In this work, a brushless, harmonic-excited wound-rotor synchronous machine is investigated which utilizes special stator and rotor windings. The windings magnetically decouple the fundamental torque-producing field from the harmonic field required for the inductive power transfer to the field coil. In contrast to conventional harmonic-excited synchronous machines, the whole winding is utilized for both torque production and harmonic excitation such that no additional copper for auxiliary windings is needed. Different rotor topologies using rotating power electronic components are investigated and their efficiencies have been compared based on Finite-Element calculation and circuit analysis.
This book covers the fundamental knowledge of layout design from the ground up, addressing both physical design, as generally applied to digital circuits, and analog layout. Such knowledge provides the critical awareness and insights a layout designer must possess to convert a structural description produced during circuit design into the physical layout used for IC/PCB fabrication.
The objective of the project presented here is to develop an intelligent control algorithm for an energy system consisting of a biogas CHP (combined heat and power), various storage technologies, such as thermal energy storages (TES) and gas storages, and other renewable energy sources, such as photovoltaics. A corresponding algorithm based on the Monte-Carlo method has already been developed at Reutlingen University for CHP units running on natural gas and for heat pumps. The project presented here concentrates on the further development of this algorithm for application to biogas CP units. In this context, an adequate implementation of the gas storage is of primary importance, as it mainly determines the flexibility of the plant. In the course of the validation of the new optimization algorithm, simulations were carried out based on data from the Lower Lindenhof, an agricultural experimental station of the University of Hohenheim. Both an optimization with regard to onsite electricity utilization and an optimization driven by residual load were investigated. Preliminary results show that the optimization algorithm can improve the operation of the biogas CHP unit depending on the selected target function.
Despite strong political efforts across Europe, small and medium- sized enterprises (SMEs) seem to neglect adopting effective measures for energy efficiency. Adopting a cultural perspective and based on a study among industrial SMEs in Southern Germany, we investigate what drives decisions for energy efficiency in SMEs and how energy management contributes to closing the energy efficiency gap. The study follows a mixed-methods approach and combines eleven ethnographic case studies and a quantitative survey among 500 manufacturing SMEs in Southern Germany.
The main contribution of the paper is to offer a perspective on energy efficiency in SMEs beyond the diffusion of energyefficient technology. By contrast, our results strongly suggest that the diffusion of energy efficiency in industrial companies should not be solely reduced to decisions for technical measures. We shed light on how energy efficiency is established and the importance of energy management in SMEs.
Our study shows that energy efficiency is well established in the investigated SMEs. At the same time, establishment cannot be explained by company size or energy demand. By contrast, the contextual environment of the company and the individual leadership of the company appear to have a more substantial influence. The embedding of energy efficiency in corporate strategy, a broad spectrum of different practices, the involvement of the employees, actions for raising awareness in everyday work life, and distributing attention by organizational measures constitute the driving forces in establishing energy efficiency, and these drivers can be subsumed under the label of energy management.
Despite strong political efforts in Europe, industrial small- and medium sized enterprises (SMEs) seem to neglect adopting practices for energy effciency. By taking a cultural perspective, this study investigated what drives the establishment of energy effciency and corresponding practices in SMEs. Based on 10 ethnographic case studies and a quantitative survey among 500 manufacturing SMEs, the results indicate the importance of everyday employee behavior in achieving energy savings. The studied enterprises value behavior related measures as similarly important as technical measures. Raising awareness for energy issues within the organization, therefore, constitutes an essential leadership task that is oftentimes perceived as challenging and frustrating. It was concluded that the embedding of energy efficiency in corporate strategy, the use of a broad spectrum of different practices, and the empowerment and involvement of employees serve as major drivers in establishing energy effciency within SMEs. Moreover, the findings reveal institutional influences on shaping the meanings of energy effciency for the SMEs by raising attention for energy effciency in the enterprises and making energy effciency decisions more likely. The main contribution of the paper is to offer an alternative perspective on energy effciency in SMEs beyond the mere adoption of energy-effcient technology.
Purpose. To improve the efficiency of the closed-cycle operation of the field-orientation induction machine in dynamic behavior when load conditions are changing, considering the nonlinearities of the main inductance.
Methodology. The optimal control problem is defined as the minimization of the time integral of the energy losses. The algorithm observed in this paper uses the Matlab/Simulink, dSPACE real-time interface, and C language. Handling real-time applications is made in ControlDesk experiment software for seamless ECU development.
Findings. Adiscrete-time model with an integrated predictive control scheme where the optimization is performed online at every sampling step has been developed. The optimal field-producing current trajectory is determined, so that the copper losses are minimized over a wide operational range. Additionally, the comparison of measurement results with conventional methods is provided, which validates the advantages and performance of the control scheme.
Originality. To solve the given problem, the information vector on the current state of the coordinates of the electromechanical system is used to form a controlling influence in the dynamic mode of operation. For the first time, the formation process of controls has considered the current state and the desired future state of the system in the real-time domain.
Practical value. Apredictive iterative approach for optimal flux level of an induction machine is important to generate the required electromagnetic torque and to reduce power losses simultaneously.
In an effort to make the cultural and institutional aspects of energy efficiency in industrial organizations more visible, this article introduces a theoretical framework of decision-making processes. Taking a sociological perspective and viewing organizations as cultural systems embedded in wider social contexts, I have developed a multilevel framework addressing institutional, organizational, and individual dimensions shaping decisions on energy efficiency. The framework's development is based on qualitative empirical fieldwork and integrates insights into organizational theory; neo-institutional theory, the attention-based view of the firm, and organizational culture theories. I conclude that decisions on energy efficiency are results of problematization and theorization processes. These processes emerge between the institutional issue-field, the organization, and its members. The model explains decisions shaped by environment (external and material), organizational processes (energy-efficiency practices, climate and culture) and individuals’ characteristics. The framework serves several purposes: introducing a meta-theory of decision making, providing a concept for empirical analysis, and enabling connectivity to the research on barriers.
Heat pumps in combination with a photovoltaic system are a very promising option for the transformation of the energy system. By using such a system for coupling the electricity and heat sectors, buildings can be heated sustainably and with low greenhouse gas emissions. This paper reveals a method for dimensioning a suitable system of heat pump and photovoltaics (PV) for residential buildings in order to achieve a high level of (photovoltaic) PV self-consumption. This is accomplished by utilizing a thermal energy storage (TES) for shifting the operation of the heat pump to times of high PV power production by an intelligent control algorithm, which yields a high portion of PV power directly utilized by the heat pump. In order to cover the existing set of building infrastructure, 4 reference buildings with different years of construction are introduced for both single- and multi-family residential buildings. By this means, older buildings with radiator heating as well as new buildings with floor heating systems are included. The simulations for evaluating the performance of a heat pump/PV system controlled by the novel algorithm for each type of building were carried out in MATLAB-Simulink® 2017a. The results show that 25.3% up to 41.0% of the buildings’ electricity consumption including the heat pump can be covered directly from the PV installation per year. Evidently, the characteristics of the heating system significantly influence the results: new buildings with floor heating and low supply temperatures yield a higher level of PV self-consumption due to a higher efficiency of the heat pump compared to buildings with radiator heating and higher supply temperatures. In addition, the effect of adding a battery to the system was studied for two building types. It will be shown that the degree of PV self-consumption increases in case a battery is present. However, due to the high investment costs of batteries, they do not pay off within a reasonable period.
The design process for a single phase, smart, universal charger for light electric vehicles, is presented. With a step up, power factor correction circuit, followed by a phase shifted, full bridge converter, with synchronous rectification on the secondary side. Due to the resistor-capacitor-diode snubber on the secondary side, the current peak at the start of power transfer, leads to false triggering during light load control with peak current mode control. The solution developed for light loads, is to change from peak current control to voltage control. This is achieved by limiting the maximum phase shift, instead of changing the reference value. For the power factor correction stage, measured and calculated efficiencies are compared as a function of the output power. The voltage and current waveforms are shown for the power factor correction circuit, and for the phase shifted bridge, the measured current waveform is compared with simulation.
The generous feed-in tariffs (FiTs) introduced in Germany—which resulted in major growth in decentralized solar photovoltaic (PV) systems—will phase out in the coming years, making many of the existing distributed generation assets stranded. This challenge creates an opportunity for community-focused energy utilities, such as Elektrizitätswerke Schönau eG (EWS) based in Schönau, Germany, to try a new approach to assist its customers, makes the transition to a more sustainable future. This chapter describes how EWS is developing products and offering community-based solutions including peer-to-peer trading using automated platforms. Such innovative offering may lead to successful differentiation in a competitive and highly decentralized future.
Simple MOSFET models intended for hand analysis are inaccurate in deep sub-micrometer process technologies and in the moderate inversion region of device operation. Accurate models, such as the Berkeley BSIM6 model, are too complex for use in hand analysis and are intended for circuit simulators. Artificial neural networks (ANNs) are efficient at capturing both linear and non-linear multivariate relationships. In this work, a straightforward modeling technique is presented using ANNs to replace the BSIM model equations. Existing open-source libraries are used to quickly build models with error rates generally below 3%. When combined with a novel approach, such as the gm/Id systematic design method, the presented models are sufficiently accurate for use in the initial sizing of analog circuit components without simulation.
Based on a survey among customers of seven German municipal utilities, we estimate two regression models to identify the most prospective customer segments and their preferences and motivations for participating in peer-to-peer (P2P) electricity trading and develop implications for decision-makers in the energy sector and policy-makers for this currently relatively unknown product. Our results show a large general openness of private households towards P2P electricity trading, which is also the main predictor of respondents' intention to participate. It is mainly influenced by individuals’ environmental attitude, technical interest, and independence aspiration. Respondents with the highest willingness to participate in P2P electricity trading are mainly motivated by the ability to share electricity, and to a lesser extent by economic reasons. They also have stronger preferences for innovative pricing schemes (service bundles, time-of-use tariffs). Differences between individuals can be observed depending on their current ownership (prosumers) or installation probability of a microgeneration unit (consumers, planners). Rather than current prosumers, especially planners willing to install microgeneration in the foreseeable future are considered to be the most promising target group for P2P electricity trading. Finally, our results indicate that P2P electricity trading could be a promising niche option in the German energy transition.
Steady state efficiency optimization techniques for induction motors are state of the art and various methods have already been developed. This paper provides new insights in the efficiency optimized operation in dynamic regime. The paper proposes an anticipative flux modification in order to decrease losses during torque and speed transients. These trajectories are analyzed based on a numerical study for different motors. Measurement results for one motor are given as well.
This paper presents a novel emulation concept for the test of smart contracts and Distributed Ledger Technologies (DLT) in distribute control or energy economy tasks and use cases. The concept uses state of the art behavioral modeling tools such as Matlab Simulink but presents a possible way to solve the shortfall of Simulink in communicating to DLT-Nodes directly. This is solved through a middleware solution. After this, an example used in verifying the test bed is presented and the target demonstration object is described. Finally, the possible expansion of the system is discussed and presented.
Heat pumps are a vital element for reaching the greenhouse gas (GHG) reduction targets in the heating sector, but their system integration requires smart control approaches. In this paper, we first offer a comprehensive literature review and definition of the term control for the described context. Additionally, we present a control approach, which consists of an optimal scheduling module coupled with a detailed energy system simulation module. The aim of this integrated two part control approach is to improve the performance of an energy system equipped with a heat pump, while recognizing the technical boundaries of the energy system in full detail. By applying this control to a typical family household situation, we illustrate that this integrated approach results in a more realistic heat pump operation and thus a more realistic assessment of the control performance, while still achieving lower operational costs.
A methodology for designing planar spiral antennas with a feeding network embedded within a dielectric is presented. To avoid a purely academic work which may not be manufactured with available standard technologies, the approach takes into account manufacturing process requirements by choice of used materials in the simulation. General design rules are provided. They encompass amongst others, selection criteria for dielectric material, aspects to consider when sketching the radiating element design, as well as those for the implementation of the feeding network. A rule of thumb, which maybe helpful in the determination of the antenna supporting substrate’s height, has been found. The appeal of the method resides in the fact that it eases up the design process and helps to minimize errors, saving time and money. The approach also enables the design of a compact and small-size spiral antenna as antenna-in-package (AiP), and provides the opportunity to assemble the antenna with other RF components/systems on the same layer stack or on the same integration platform.