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This paper presents the first part of a research-work conducted at the University of Applied Sciences (HFT- Stuttgart). The aim of the research was to investigate the potential of low-cost renewable energy systems to reduce the energy demand of the building sector in hot and dry areas. Radiative cooling to the night sky represents a low-cost renewable energy source. The dry desert climate conditions promote radiative cooling applications. The system technology adopted in this work is based on uncovered solar thermal collectors integrated into the building’s hydronic system. By implementing different control strategies, the same system could be used for cooling as well as for heating applications. This paper focuses on identifying the collector parameters which are required as the coefficients to configure such an unglazed collector for calibrating its mathematical model within the simulation environment. The parameter identification process implies testing the collector for its thermal performance. This paper attempts to provide an insight into the dynamic testing of uncovered solar thermal collectors (absorbers), taking into account their prospective operation at nighttime for radiative cooling applications. In this study, the main parameters characterizing the performance of the absorbers for radiative cooling applications are identified and obtained from standardized testing protocol. For this aim, a number of plastic solar absorbers of different designs were tested on the outdoor test-stand facility at HFT-Stuttgart for the characterization of their thermal performance. The testing process was based on the quasi-dynamic test method of the international standard for solar thermal collectors EN ISO 9806. The test database was then used within a mathematical optimization tool (GenOpt) to determine the optimal parameter settings of each absorber under testing. Those performance parameters were significant to compare the thermal performance of the tested absorbers. The coefficients (identified parameters) were used then to plot the thermal efficiency curves of all absorbers, for both the heating and cooling modes of operation. Based on the intended main scope of the system utilization (heating or cooling), the tested absorbers could be benchmarked. Hence, one of those absorbers was selected to be used in the following simulation phase as was planned in the research-project.
During the first years of the last decade, Egypt used to face recurrent electricity cut-offs in summer. In the past few years, the electricity tariff dramatically increased. Radiative cooling to the clear night sky is a renewable energy source that represents a relative solution. The dry desert climate promotes nocturnal radiative cooling applications. This study investigates the potential of nocturnal radiative cooling systems (RCSs) to reduce the energy consumption of the residential building sector in Egypt. The system technology proposed in this work is based on uncovered solar thermal collectors integrated into the building hydronic system. By implementing different control strategies, the same system could be used for both cooling and heating applications. The goal of this paper is to analyze the performance of RCSs in residential buildings in Egypt. The dynamic simulation program TRNSYS was used to simulate the thermal behavior of the system. The relevant issues of Egypt as a case-study are firstly overviewed. Then the paper introduces the work done to develop a building model that represents a typical residential apartment in Egypt. Typical occupancy profiles were developed to define the internal thermal gains. The adopted control strategy to optimize the system operation is presented as well. To fully understand and hence evaluate the operation of the proposed RCS, four simulation cases were considered: 1. a reference case (fully passive), 2. the stand-alone operation of the RCS, 3. ideal heating & cooling operation (fully-active), and 4. the hybrid-operation (when the active cooling system is supported by the proposed RCS). The analysis considered the main three distinct climates in Egypt, represented by the cities of Alexandria, Cairo and Asyut. The hotter and drier weather conditions resulted in a higher cooling potential and larger temperature differences. The simulated cooling power in Asyut was 28.4 W/m² for a 70 m² absorber field. For a smaller field area of 10 m², the cooling power reached 109 W/m² but with humble temperature differences. To meet the rigorous thermal comfort conditions, the proposed sensible RCS cannot fully replace conventional air-conditioning units, especially in humid areas like Alexandria. When working in a hybrid system, a 10% reduction in the active cooling energy demand could be achieved in Asyut to keep the cooling set-point at 24 °C. This percentage reduction was nearly doubled when the thermal comfort set-point was increased by two degrees (26 °C). In a sensitivity analysis, external shading devices as a passive measure as well as the implementation of the Egyptian code for buildings (ECP306/1–2005) were also investigated. The analysis of this study raised other relevant aspects to discuss, e.g. system-sizing, environmental effects, limitations and recommendations.
Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
Development work within an experimental environment, in which certain properties are investigated and optimized, requires many test runs and is therefore often associated with long execution times, costs and risks. This can affect product, material and technology development in industry and research. New digital driver technologies offer the possibility to automate complex manual work steps in a cost-effective way, to increase the relevance of the results and to accelerate the processes many times over. In this context, this article presents a low-cost, modular and open-source machine vision system for test execution and evaluates it on the basis of a real industrial application. For this purpose a methodology for the automated execution of the load intervals, the process documentation and for the evaluation of the generated data by means of machine learning to classify wear levels. The software and the mechanical structure are designed to be adaptable to different conditions, components and for a variety of tasks in industry and research. The mechanical structure is required for tracking the test object and represents a motion platform with independent positioning by machine vision operators or machine learning. An evaluation of the state of the test object is performed by the transfer learning after the initial documentation run. The manual procedure for classifying the visually recorded data on the state of the test object is described for the training material. This leads to an increased resource efficiency on the material as well as on the personnel side since on the one hand the significance of the tests performed is increased by the continuous documentation and on the other hand the responsible experts can be assigned time efficiently. The presence and know-how of the experts are therefore only required for defined and decisive events during the execution of the experiments. Furthermore, the generated data are suitable for later use as an additional source of data for predictive maintenance of the developed object.
The use of additive manufacturing technologies for industrial production is constantly growing. This technology differs from the known production proecdures. The areas for scheduling, detailed and sequence planning are particularly important for additive production due to the long print times and flexible use of the production area. Therefore, production-relevant variables are considered and used for the production planning and control (PPC) of additive manufacturing machines. For this purpose, an optimization model is presented which shows a time-oriented build space utilization. In the implementation, a nesting algorithm is used to check the combinability of different models for each individual print job.
The promise of immutable documents to make it easier and less expensive for consumers and producers to collaborate in a verifiable way would represent an enormous progress, especially as companies strive for establish service contracts which are based on the flow of many small transactions using machine-to-machine communication. The blockchain technology logs these data, verifies the authenticity and make them available for service offers. This work deals with an architecture enabling to setup order processing between consumers and produceers using blockchain. In this way, the technical feasibility is shown and the special characteristics of blockchain production networks will be discussed.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
Sleep is an essential part of human existence, as we are in this state for approximately a third of our lives. Sleep disorders are common conditions that can affect many aspects of life. Sleep disorders are diagnosed in special laboratories with a polysomnography system, a costly procedure requiring much effort for the patient. Several systems have been proposed to address this situation, including performing the examination and analysis at the patient's home, using sensors to detect physiological signals automatically analysed by algorithms. This work aims to evaluate the use of a contactless respiratory recording system based on an accelerometer sensor in sleep apnea detection. For this purpose, an installation mounted under the bed mattress records the oscillations caused by the chest movements during the breathing process. The presented processing algorithm performs filtering of the obtained signals and determines the apnea events presence. The performance of the developed system and algorithm of apnea event detection (average values of accuracy, specificity and sensitivity are 94.6%, 95.3%, and 93.7% respectively) confirms the suitability of the proposed method and system for further ambulatory and in-home use.
Zero or plus energy office buildings must have very high building standards and require highly efficient energy supply systems due to space limitations for renewable installations. Conventional solar cooling systems use photovoltaic electricity or thermal energy to run either a compression cooling machine or an absorption-cooling machine in order to produce cooling energy during daytime, while they use electricity from the grid for the nightly cooling energy demand. With a hybrid photovoltaic-thermal collector, electricity as well as thermal energy can be produced at the same time. These collectors can produce also cooling energy at nighttime by longwave radiation exchange with the night sky and convection losses to the ambient air. Such a renewable trigeneration system offers new fields of applications. However, the technical, ecological and economical aspects of such systems are still largely unexplored.
In this work, the potential of a PVT system to heat and cool office buildings in three different climate zones is investigated. In the investigated system, PVT collectors act as a heat source and heat sink for a reversible heat pump. Due to the reduced electricity consumption (from the grid) for heat rejection, the overall efficiency and economics improve compared to a conventional solar cooling system using a reversible air-to-water heat pump as heat and cold source.
A parametric simulation study was carried out to evaluate the system design with different PVT surface areas and storage tank volumes to optimize the system for three different climate zones and for two different building standards. It is shown such systems are technically feasible today. With a maximum utilization of PV electricity for heating, ventilation, air conditioning and other electricity demand such as lighting and plug loads, high solar fractions and primary energy savings can be achieved.
Annual costs for such a system are comparable to conventional solar thermal and solar electrical cooling systems. Nevertheless, the economic feasibility strongly depends on country specific energy prices and energy policy. However, even in countries without compensation schemes for energy produced by renewables, this system can still be economically viable today. It could be shown, that a specific system dimensioning can be found at each of the investigated locations worldwide for a valuable economic and ecological operation of an office building with PVT technologies in different system designs.
The global demand for resources such as energy, land, or water is constantly increasing. It is therefore not sur- prising that research on the Food-Energy-Water (FEW) nexus has become a scientific as well as a general focus in recent years. A significant increase in publications since 2015 can be observed, and it can be expected that this trend will continue. A multilevel (macro, meso, and micro) perspective is essential, as the FEW nexus has cross- sectoral interdependencies. Several review studies on the FEW nexus can be found in the literature, in general, it can be concluded that the FEW nexus is a multi-disciplinary and complex topic. The studies examined identify essential fields of action for research, policy, and society. However, questions such as what are the main research fields at each level? Is it possible to divide the research into specific clusters? and do the clusters correlate with the levels, and what are the methods of modeling used in the clusters and levels? are still not fully discussed in the literature. An extensive literature review was conducted to get insight into the existing research areas. Especially in such fields as the FEW nexus, the amount of literature can get huge, and a human could get lost analyzing the literature manually. For that, we created word clouds and performed a cluster- and network-analysis to support the selection of most relevant papers for a detailed reading. In 2021, the most publications were published, with 173 publications, which corresponds to a share of 26.6 %. There has been a significant increase since 2015, and it can be expected that this trend will continue in the coming years. Most of the first authors come from the USA (25.4 %), followed by China with 22.4 %. From the word cloud and the top 20 words, which appear in the title and abstract, it can be deduced that the topic water is the most represented. However, the terms system, resource, model, study, change, development, and management also appear to be very important, which indi- cates the importance of a holistic approach to the topic. In total 9 clusters could be identified at the different levels. It can be seen that three clusters form well. For the others, a rather diffuse picture can be observed. In order to find out which topics are hidden behind the individual clusters, 6 publications from each cluster were subjected to a more detailed examination. With these steps, a number of 54 publications were identified for de- tailed consideration. The modeling approaches that are currently being applied in research can be classified into domain-specific tools (e. g. global water models, crop models or global climate models) and into more general tools to perform for example a life cycle analysis, spatial analysis using geographic information system, or system dynamics for a general understanding of the links between the domains. With the domain-specific tools, detailed research questions can be addressed to answer questions for a specific domain. However, these tools have the disadvantage that especially the links between the sectors food, energy, and water are not fully considered. Many implementations that are made today are at lowest level (micro) relate to bounded spatial areas and are derived from macro and meso level goals.
Theory and practice of implementing a successful enterprise IoT strategy in the industry 4.0 era
(2021)
Since the arrival of the internet and affordable access to technologies, digital technologies have occupied a growing place in industries, propelling us towards a 4th industrial revolution: Industry 4.0. In today’s era of digital upheaval, enterprises are increasingly undergoing transformations that are leading to their digitalization. The traditional manufacturing industry is in the throes of a digital transformation that is accelerated by exponentially growing technologies (e.g., intelligent robots, Internet of Things, sensors, 3D printing). Around the world, enterprises are in a frantic race to implement solutions based on IoT to improve their productivity, innovation, and reduce costs and improve their markets on the international scene. Considering the immense transformative potential that IoTs and big data have to bring to the industrial sector, the adoption of IoT in all industrial systems is a challenge to remain competitive and thus transform the industry into a smart factory. This paper presents the description of the innovation and digitalization process, following the Industry 4.0 paradigm to implement a successful enterprise IoT strategy.
Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study
(2020)
Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time.
On the design of an urban data and modeling platform and its application to urban district analyses
(2020)
An integrated urban platform is the essential software infrastructure for smart, sustainable and resilitent city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimized smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.
Context: Companies increasingly strive to adapt to market and ecosystem changes in real time. Gauging and understanding team performance in such changing environments present a major challenge.
Objective: This paper aims to understand how software developers experience the continuous adaptation of performance in a modern, highly volatile environment using Lean and Agile software development methodology. This understanding can be used as a basis for guiding formation and maintenance of high-performing teams, to inform performance improvement initiatives, and to improve working conditions for software developers.
Method: A qualitative multiple-case study using thematic interviews was conducted with 16 experienced practitioners in five organisations.
Results: We generated a grounded theory, Performance Alignment Work, showing how software developers experience performance. We found 33 major categories of performance factors and relationships between the factors. A cross-case comparison revealed similarities and differences between different kinds and different sizes of organisations.
Conclusions: Based on our study, software teams are engaged in a constant cycle of interpreting their own performance and negotiating its alignment with other stakeholders. While differences across organisational sizes exist, a common set of performance experiences is present despite differences in context variables. Enhancing performance experiences requires integration of soft factors, such as communication, team spirit, team identity, and values, into the overall development process. Our findings suggest a view of software development and software team performance that centres around behavioural and social sciences.
Context: Development of software intensive products and services increasingly occurs by continuously deploying product or service increments, such as new features and enhancements, to customers. Product and service developers must continuously find out what customers want by direct customer feedback and usage behaviour observation. Objective: This paper examines the preconditions for setting up an experimentation system for continuous customer experiments. It describes the RIGHT model for Continuous Experimentation (Rapid Iterative value creation Gained through High-frequency Testing), illustrating the building blocks required for such a system. Method: An initial model for continuous experimentation is analytically derived from prior work. The model is matched against empirical case study findings from two startup companies and further developed. Results: Building blocks for a continuous experimentation system and infrastructure are presented. Conclusions: A suitable experimentation system requires at least the ability to release minimum viable products or features with suitable instrumentation, design and manage experiment plans, link experiment results with a product roadmap, and manage a flexible business strategy. The main challenges are proper, rapid design of experiments, advanced instrumentation of software to collect, analyse, and store relevant data, and the integration of experiment results in both the product development cycle and the software development process.
Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.
Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.
Comparison of sleep characteristics measurements: a case study with a population aged 65 and above
(2020)
Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.