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Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.
Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.
In the context of the Corona pandemic and its impact on teaching like digital lectures and exercises a new concept especially for freshmen in demanding courses of Smart Building Engineering became necessary. As there were hardly any face-to-face events at the university, the new teaching concept should enable a good start into engineering studies under pandemic conditions anyway and should also replace the written exam at the end. The students should become active themselves in small teams instead of listening passively to a lecture broadcast online with almost no personal contact. For this purpose, a role play was developed in which the freshmen had to work out a complete solution to the realistic problem of designing, construction planning and implementing a small guesthouse. Each student of the team had to take a certain role like architect, site manager, BIM-manager, electrician and the technitian for HVAC installations. Technical specifications must be complied with, as well as documentation, time planning and cost estimate. The final project folder had to contain technical documents like circuit diagrams for electrical components, circuit diagrams for water and heating, design calculations and components lists. On the other hand construction schedule, construction implementation plan, documentation of the construction progress and minutes of meetings between the various trades had to be submitted as well. In addition to the project folder, a model of the construction project must also be created either as a handmade model or as a digital 3D-model using Computer-aided design (CAD) software. The first steps in the field of Building information modelling (BIM) had also been taken by creating a digital model of the building showing the current planning status in real time as a digital twin. This project turned out to be an excellent training of important student competencies like teamwork, communication skills, and self -organisation and also increased motivation to work on complex technical questions. The aim of giving the student a first impression on the challenges and solutions in building projects with many different technical trades and their points of view was very well achieved and should be continued in the future.
Urban farming is an innovative and sustainable way of food production and is becoming more and more important in smart city and quarter concepts. It also enables the production of certain foods in places where they usually dare not produced, such as production of fish or shrimps in large cities far away from the coast. Unfortunately, it is not always possible to show students such concepts and systems in real life as part of courses: visits of such industry plants are sometimes not possible because of distance or are permitted by the operator for hygienic reasons. In order to give the students the opportunity of getting into contact with such an urban farming system and its complex operation, an industrial urban farming plant was set up on a significantly smaller scale. Therefore, all needed technical components like water aeriation, biological and mechanical filtration or water circulation have been replaced either by aquarium components or by self-designed parts also using a 3D-printer. Students from different courses like mechanical engineering, smart building engineering, biology, electrical engineering, automation technology and civil engineering were involved in this project. This “miniature industrial plant” was also able to start operation and has now been running for two years successfully. Due to Corona pandemic, home office and remote online lectures, the automation of this miniature plant should be brought to a higher level in future for providing a good control over the system and water quality remotely. The aim of giving the student a chance to get to know the operation of an urban farming plant was very well achieved and the students had lots of fun in “playing” and learning with it in a realistic way.
The worldwide Corona pandemic has severely restricted student projects in the higher semesters of engineering courses. In order not to delay the graduation, a new concept had to be developed for projects under lockdown conditions. Therefore, unused rooms at the university should be digitally recorded in order to develop a new usage concept as laboratory rooms. An inventory of the actual state of the rooms was done first by taking photos and listing up all flaws and peculiarities. After that, a digital site measuring was done with a 360° laser scanner and these recorded scans were linked to a coherent point cloud and transferred to a software for planning technical building services and supporting Building Information Modelling (BIM). In order to better illustrate the difference between the actual and target state, two virtual reality models were created for realistic demonstration. During the project, the students had to go through the entire digital planning phases. Technical specifications had to be complied with, as well as documentation, time planning and cost estimate. This project turned out to be an excellent alternative to on-site practical training under lockdown conditions and increased the students’ motivation to deal with complex technical questions.
Concerning current efforts to improve operational efficiency and to lower overall costs of concentrating solar power (CSP) plants with prediction-based algorithms, this study investigates the quality and uncertainty of nowcasting data regarding the implications for process predictions. DNI (direct normal irradiation) maps from an all-sky imager-based nowcasting system are applied to a dynamic prediction model coupled with ray tracing. The results underline the need for high-resolution DNI maps in order to predict net yield and receiver outlet temperature realistically. Furthermore, based on a statistical uncertainty analysis, a correlation is developed, which allows for predicting the uncertainty of the net power prediction based on the corresponding DNI forecast uncertainty. However, the study reveals significant prediction errors and the demand for further improvement in the accuracy at which local shadings are forecasted.
Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.
Improved efficiency prediction of a molten salt receiver based on dynamic cloud passage simulation
(2019)
ICSs (Industrial Control Systems) and its subset SCADA systems (Supervisory Control and Data Acquisition) are getting exposed to a constant stream of new threats. The increasing importance of IT security in ICS requires viable methods to assess the security of ICS, its individual components, and its protocols. This paper presents a security analysis with focus on the communication protocols of a single PLC (Programmable Logic Controller). The PLC, a Beckhoff CX2020, is examined and new vulnerabilities of the system are revealed. Based on these findings recommendations are made to improve security of the Beckhoff system and its protocols.
A novel solar sterilization and water destillation system : experiment and thermodynamic analysis
(1991)
The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.
Proceedings of the 2nd Humboldt Kolleg, Hammamet, Tunisia Organizer: Alexander von Humboldt Stiftung, Germany. pdf 184 p. Welcome Address Dear Participants, Welcome to the 2nd Humboldt Kolleg in “Nanoscale Science and Technology” (NS&T’12) in Tunisia, sponsored by the "Alexander von Humboldt" foundation. The NS&T’12 multidisciplinary scientific program includes seven "hot" topics dealing with "Nanoscale Science and Technology" covering basic and application-oriented research as well as industrial (market) aspects: - Molecular Biophyics, Spectroscopy Techniques, Imaging Microscopy - Nanomaterials Synthesis for Medicine and Bio-chemical Sensors - Nanostructures, Semiconductors, Photonics and Nanodevices - New Technologies in Market Industry - Environment, Electro-chemistry, Bio-polymers and Fuel Cells - Nanomaterials, Photovoltaic, Modelling, Quantum Physics - Microelectronics, Sensors Networks and Embedded Systems We are deeply indebted to all members of the Scientific Committee and General Chairs for joint Sessions and to all speakers and chairmen, who have dedicated invaluable time and efforts for the realization of this event. On behalf of the Organizing Committee, we are cordially inviting you to join the conference and hope that your stay will be fruitful, rewarding and enjoyable. Prof. Dr. Michael J. Schöning, Prof. Dr. Adnane Abdelghani
The ANM’09 multi-disciplinary scientific program includes topics in the fields of "Nanotechnology and Microelectronics" ranging from "Bio/Micro/Nano Materials and Interfacing" aspects, "Chemical and Bio-Sensors", "Magnetic and Superconducting Devices", "MEMS and Microfluidics" over "Theoretical Aspects, Methods and Modelling" up to the important bridging "Academics meet Industry".
Recently, in his vision for space exploration, US president Bush announced to extend human presence across the solar system, starting with a human return to the Moon as early as 2015 in preparation for human exploration of Mars and other destinations. In Europe, an exploration program, termed AURORA, was established by ESA in 2001 – funded on a voluntary basis by ESA member states – with a clear focus on Mars and the ultimate goal of landing humans on Mars around 2030 in international cooperation. In 2003, a Human Spaceflight Vision Group was appointed by ESA with the task to develop a vision for the role of human spaceflight during the next quarter of the century. The resulting vision focused on a European-led lunar exploration initiative as part of a multi-decade, international effort to strengthen European identity and economy. After a review of the situation in Europe concerning space exploration, the paper outlines an approach for a consistent positioning of exploration within the existing European space programs, identifies destinations, and develops corresponding scenarios for an integrated strategy, starting with robotic missions to the Moon, Mars, and near-Earth asteroids. The interests of the European planetary in-situ science community, which recently met at DLR Cologne, are considered. Potential robotic lunar missions comprise polar landings to search for frozen volatiles and a sample return. For Mars, the implementation of a modest robotic landing mission in 2009 to demonstrate the capability for landing and prepare more ambitious and complex missions is discussed. For near-Earth asteroid exploration, a low-cost in-situ technology demonstration mission could yield important results. All proposed scenarios offer excellent science and could therefore create synergies between ESA’s mandatory and optional programs in the area of planetary science and exploration. The paper intents to stimulate the European discussion on space exploration and reflects the personal view of the authors.
Solar sails are propelled in space by reflecting solar photons off large mirroring surfaces, thereby transforming the momentum of the photons into a propulsive force. This innovative concept for low-thrust space propulsion works without any propellant and thus provides a wide range of opportunities for highenergy low-cost missions. Offering an efficient way of propulsion, solar sailcraft could close a gap in transportation options for highly demanding exploration missions within our solar system and even beyond. On December 17th, 1999, a significant step was made towards the realization of this technology: a lightweight solar sail structure with an area of 20 m × 20 m was successfully deployed on ground in a large facility at the German Aerospace Center (DLR) at Cologne. The deployment from a package of 60 cm × 60 cm × 65 cm with a total mass of less than 35 kg was achieved using four extremely light-weight carbon fiber reinforced plastics (CFRP) booms with a specific mass of 100 g/m. The paper briefly reviews the basic principles of solar sails as well as the technical concept and its realization in the ground demonstration experiment, performed in close cooperation between DLR and ESA. Next possible steps are outlined. They could comprise the in-orbit demonstration of the sail deployment on the upper stage of a low-cost rocket and the verification of the propulsion concept by an autonomous and free flying solar sail in the frame of a scientific mission. It is expected that the present design could be extended to sail sizes of about (40 m)2 up to even (70 m)2 without significant mass penalty. With these areas, the maximum achievable thrust at 1 AU would range between 10 and 40 mN – comparable to some electric thrusters. Such prototype sails with a mass between 50 and 150 kg plus a micro-spacecraft of 50 to 250 kg would have a maximum acceleration in the order of 0.1 mm/s2 at 1 AU, corresponding to a maximum ∆V-capability of about 3 km/s per year. Two near/medium-term mission examples to a near-Earth asteroid (NEA) will be discussed: a rendezvous mission
and a sample return mission.
Humic substances possess distinctive chemical features enabling their use in many advanced applications, including biomedical fields. No chemicals in nature have the same combination of specific chemical and biological properties as humic substances. Traditional medicine and modern research have demonstrated that humic substances from different sources possess immunomodulatory and anti-inflammatory properties, which makes them suitable for the prevention and treatment of chronic dermatoses, allergic rhinitis, atopic dermatitis, and other conditions characterized by inflammatory and allergic responses [1-4]. The use of humic compounds as agentswith antifungal and antiviral properties shows great potential [5-7].
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation.
Micromachined thermal heater platforms offer low electrical power consumption and high modulation speed, i.e. properties which are advantageous for realizing nondispersive infrared (NDIR) gas- and liquid monitoring systems. In this paper, we report on investigations on silicon-on-insulator (SOI) based infrared (IR) emitter devices heated by employing different kinds of metallic and semiconductor heater materials. Our results clearly reveal the superior high-temperature performance of semiconductor over metallic heater materials. Long-term stable emitter operation in the vicinity of 1300 K could be attained using heavily antimony-doped tin dioxide (SnO2:Sb) heater elements.
Wing weight estimation methodology for highly non-planar lifting systems during conceptual design
(2013)
The recently proposed NASA and ESA missions to Saturn and Jupiter pose difficult tasks to mission designers because chemical propulsion scenarios are not capable of transferring heavy spacecraft into the outer solar system without the use of gravity assists. Thus our developed mission scenario based on the joint NASA/ESA Titan Saturn System Mission baselines solar electric propulsion to improve mission flexibility and transfer time. For the calculation of near-globally optimal low-thrust trajectories, we have used a method called Evolutionary Neurocontrol, which is implemented in the low-thrust trajectory optimization software InTrance. The studied solar electric propulsion scenario covers trajectory optimization of the interplanetary transfer including variations of the spacecraft's thrust level, the thrust unit's specific impulse and the solar power generator power level. Additionally developed software extensions enabled trajectory optimization with launcher-provided hyperbolic excess energy, a complex solar power generator model and a variable specific impulse ion engine model. For the investigated mission scenario, Evolutionary Neurocontrol yields good optimization results, which also hold valid for the more elaborate spacecraft models. Compared to Cassini/Huygens, the best found solutions have faster transfer times and a higher mission flexibility in general.