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Designs, Volume 5, Issue 2 (June 2021) – 6 articles

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Open AccessArticle
Towards a Live Feedback Training System: Interchangeability of Orbbec Persee and Microsoft Kinect for Exercise Monitoring
Designs 2021, 5(2), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020030 - 15 Apr 2021
Viewed by 348
Abstract
Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the [...] Read more.
Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the interchangeability of the discontinued Kinect v2 and the all-in-one, image-based motion tracking system Orbbec Persee for the use in an exercise monitoring system prototype called ILSE. Nine functional training exercises were performed by six healthy subjects in front of both systems simultaneously. Comparing the systems’ internal tracking states from ’not tracked’ to ‘tracked’ showed that the Persee system is more confident during motion sequences, while the Kinect is more confident for hip and trunk joint positions. Assessing the skeleton tracking robustness, the Persee’s tracking of body segment lengths was more consistent. Furthermore, we used both skeleton datasets as input for the ILSE exercise monitoring including posture recognition and repetition-counting. Persee data from exercises with lateral movement and in uncovered full-body frontal view provided the same results as Kinect data. The Persee further preferred tracking of quasi-static lower limb motions and tight-fitting clothes. With these limitations in mind, we find that the Orbbec Persee is a suitable replacement for the Microsoft Kinect for motion sensing within the ILSE exercise monitoring system. Full article
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Open AccessArticle
Design Preconditions for Product–Service Integration
Designs 2021, 5(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020029 - 13 Apr 2021
Viewed by 441
Abstract
User expectations regarding new products and services are evolving rapidly, forcing innovative organizations to explore new avenues for innovation, combining products and services. This paper focuses on the integrative design of product–service systems (PSSs) and builds on the servitization and service-based innovation literature. [...] Read more.
User expectations regarding new products and services are evolving rapidly, forcing innovative organizations to explore new avenues for innovation, combining products and services. This paper focuses on the integrative design of product–service systems (PSSs) and builds on the servitization and service-based innovation literature. Many tools have been proposed for designing integrated PSS, with the intent to generate economic and/or sustainable impact. In this article, we focus on tools being used for bringing the user experience and intangibles in the design process. Although the literature is rich with tools and methods to optimize the PSS design process, it does not consider the full array of methods and their impact. This lack of research attention might hinder organizations developing PSS. Using in-depth interviews, this qualitative research systematically combines the extant conceptual literature on PSS design tools and processes with expert insights, thereby contextualizing how to lower thresholds in PSS design processes and how to increase the effectiveness of PSS design tools. The paper contributes to the literature on servitization and PSS by explicitly identifying twenty-one preconditions that support the PSS design process while integrating product and service innovation in close relation to the end-user. Full article
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Open AccessCase Report
Capacitor Commutation Method for MVDC Hybrid Circuit Breakers
Designs 2021, 5(2), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020028 - 13 Apr 2021
Viewed by 279
Abstract
The medium voltage DC (MVDC) type system can connect multiple terminals to a common MVDC bus, so it is possible to connect several renewable DC power sources to the common MVDC bus, but a DC circuit breaker is needed to isolate short circuit [...] Read more.
The medium voltage DC (MVDC) type system can connect multiple terminals to a common MVDC bus, so it is possible to connect several renewable DC power sources to the common MVDC bus, but a DC circuit breaker is needed to isolate short circuit accidents that may occur in the MVDC bus. For this purpose, the concept of a hybrid DC circuit breaker that takes advantage of a low conduction loss contact type switch and an arcless-breaking semiconductor switch has been proposed. During break the hybrid switch, a dedicated current commutation device is required to temporarily bypass the load current flowing through the main switch into a semiconductor switch branch. Existing current commutation methods include a proactive method and a reverse current injection method by a LC (Inductor-capacitor) resonant circuit. This paper proposes a power circuit of a new MVDC hybrid circuit breaker using a low withstanding voltage capacitor branch for commutation and a sequence controller according to it, and verifies its operation through an experiment. Full article
(This article belongs to the Section Electrical Engineering)
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Open AccessArticle
Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning
Designs 2021, 5(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020027 - 06 Apr 2021
Viewed by 493
Abstract
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. [...] Read more.
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies. Full article
(This article belongs to the Section Aerospace, Vehicles, and Civil Engineering Design Automation)
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Open AccessArticle
Proposal for a Methodology for Sustainable Rehabilitation Strategies of the Existing Building Stock—The Ponte Gêa Neighborhood
Designs 2021, 5(2), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020026 - 02 Apr 2021
Viewed by 470
Abstract
Having in mind the objectives of the United Nations Development Agenda 2030, which refers to the sustainable principles of a circular economy, it is urgent to improve the performance of the built environment. The existing buildings must be preserved and improved in order [...] Read more.
Having in mind the objectives of the United Nations Development Agenda 2030, which refers to the sustainable principles of a circular economy, it is urgent to improve the performance of the built environment. The existing buildings must be preserved and improved in order to reduce their environmental impact, in line with the need to revert climate change and reduce the occurrence of natural disasters. This work had as its main goal to identify and define a methodology for promoting the rehabilitation of buildings in the Ponte Gêa neighborhood, in the city of Beira, Mozambique, with an emphasis on energy efficiency, water efficiency, and construction and demolition waste management. The proposed methodology aims to create a decision support method for creating strategic measures to be implemented by considering the three specific domains—energy, water, and waste. This model allows for analyzing the expected improvement according to the action to be performed, exploring both individual and community solutions. It encompasses systems of standard supply that can reveal greater efficiency and profitability. Thus, the in-depth knowledge of the characteristics of urban space and buildings allows for establishing guidelines for the renovation process of the neighborhood. Full article
(This article belongs to the Special Issue Sustainable Architecture Design)
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Open AccessArticle
Spraying Cooling System for PV Modules: Experimental Measurements for Temperature Trends Assessment and System Design Feasibility
Designs 2021, 5(2), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020025 - 01 Apr 2021
Viewed by 314
Abstract
The hallmark of the PhotoVoltaic (PV) electricity generation is its sustainability, while its main weakness is the low conversion efficiency. A drawback to which is added the PV cell sensitivity to temperature variations: the higher the cell operating temperature, the lower the efficiency. [...] Read more.
The hallmark of the PhotoVoltaic (PV) electricity generation is its sustainability, while its main weakness is the low conversion efficiency. A drawback to which is added the PV cell sensitivity to temperature variations: the higher the cell operating temperature, the lower the efficiency. Considering that in-operation modules reach a conversion efficiency in the range of 10 to 15%, there is an urgent need to control their temperature to enhance the electricity generation. To this purpose, the authors developed a PV spraying cooling system able to drastically knockdown modules operating temperature. Using experimental measurements acquired through a dedicated test rig and after an in-depth literature review, the authors analyze the nozzles number, geometry, and position, as well as water and module’s temperature distribution, limestones formation, degradation of front glass properties, water consumption, and module power production with and without the cooling system. The experimental campaign shows that a cooling system equipped with three nozzles with a spraying angle of 90°, powered by water at 1.5 bar and managed in ON/OFF mode (30 s on to 180 s off), can improve the module’s efficiency from 11.18% to 13.27% thanks to a temperature reduction of up to 24 °C. Despite the improvement in electricity production (from 178.88 W to 212.31 W per single module), at the time of writing, the equipment and installation costs as well as the plant arrangement complexity make the investment not eligible for financing also in the case of a 1 MW floating PV facilities. Full article
(This article belongs to the Special Issue Planning for Home Renewable Energy Systems)
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