Luleå University of Technology
  • Luleå, Norrbotten County, Sweden
Recent publications
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.
For rotordynamic analysis of hydropower units, the generator is treated as a rotating rigid body. However, previous studies have confirmed that certain designs of generators are elastic, so the complex geometry of generators cannot be considered rigid. This work produced a model of hydropower generators with floating rotor rims, consisting of a rigid hub and a flexible rotor rim coupled with flexible connections. The model takes into account the influence of centrifugal and Coriolis effects, and the electromagnetic interaction between rotor and stator. The model also reproduces the dynamics of the generator with static and dynamic eccentricities. A generator prototype was employed to test the model, showing its different applications. Once validated by empirical data, this model could be used when designing generators.
The main production route for steel in Europe is still via the blast furnace. Computational fluid dynamics (CFD) can be used to analyze the process virtually and thus improve its performance. Different reducing agents can be used to (partially) substitute the coke and consequently reduce overall emissions. To analyze different reducing agents effectively using CFD, their conversion process has to be modeled accurately. Under certain conditions, coal particles can cluster as the result of turbulence effects, which further reduces the mass transfer to the coal surface and consequently the conversion rate. We analyze the effect of turbulence under blast furnace raceway conditions on the conversion of coal particles and on the overall burnout. The model is applied in RANS to polydisperse particle systems and this is then compared to the simplified monodisperse assumption. Additionally, the model is extended by adding gasification reactions. Overall, we find that the turbulent effects on coal conversion are significant under blast furnace raceway conditions and should be considered in further simulations. Furthermore, we show that an a-priori assessment is difficult because the analysis via averaged quantities is impractical due to a strong variation of conditions in the furnace. Therefore, the effects of turbulence need to be correlated to the regions of conversion.
As the sawmill industry is moving towards thinner bandsaws for higher yields, it is important to study the cutting force in more detail. The cutting force can be split into two zones. Zone I concerns the force on the major cutting edge as well as the friction force on the major first flank. Zone II considers the forces on the minor cutting edges as well as the friction forces on the minor first flanks. Zone II cutting can significantly affect the cutting force and has not been studied in great detail. Frozen, non-frozen and dry heartwood of Norway spruce and Scots pine were cut using different tooth geometries and the cutting force was measured. The major cutting edge, clearance, band thickness, minor cutting edge angle and minor cutting edge clearance angle were investigated. The y-intercept of the cutting force–width graph was used as the Zone II force (at this point the Zone I forces are assumed to be zero). The Zone II force contribution to the cutting force was studied. The results show that frozen wood has less elastic spring-back and therefore less Zone II cutting. Dried wood showed a significantly higher degree of Zone II cutting (55−75% contribution to the cutting force). Changing the major cutting edge from 2.87 mm to 1.6 mm resulted in 10–15% higher Zone II force contributions.
Background Performance of high-intensity interval training (HIIT) by children and adolescents improves physical and health-related fitness, as well as cardiometabolic risk factors. Objectives To assess the impact of HIIT performed at school, i.e. both in connection with physical education (intra-PE) and extracurricular sports activities (extra-PE), on the physical fitness and health of children and adolescents. Methods PubMed and SPORTDiscus were searched systematically utilizing the following criteria for inclusion: (1) healthy children and adolescents (5–18 years old) of normal weight; (2) HIIT performed intra- and/or extra-PE for at least 5 days at an intensity ≥ 80% of maximal heart rate (HR max ) or peak oxygen uptake (VO 2peak ) or as Functional HIIT; (3) comparison with a control (HIIT versus alternative interventions); and (4) pre- and post-analysis of parameters related to physical fitness and health. The outcomes with HIIT and the control interventions were compared utilizing Hedges’ g effect size (ES) and associated 95% confidence intervals. Results Eleven studies involving 707 participants who performed intra-PE and 388 participants extra-PE HIIT were included. In comparison with the control interventions, intra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 5.89 ± 5.67 (range 1.88–9.90); ES number of push-ups: 6.22 (range n.a.); ES number of sit-ups: 2.66 ± 2.02 (range 1.24–4.09)), as well as ES fasting glucose levels (− 2.68 (range n.a.)) more effectively, with large effect sizes. Extra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 1.81 (range n.a.); ES number of sit-ups: 2.60 (range n.a.)) to an even greater extent, again with large effect sizes. Neither form of HIIT was more beneficial for parameters related to cardiorespiratory fitness than the control interventions. Conclusion Compared to other forms of exercise (e.g. low-to-moderate-intensity running or walking), both intra- and extra-PE HIIT result in greater improvements in neuromuscular and anaerobic performance, as well as in fasting levels of glucose in school children.
This paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. This is a rarely considered time scale in studies that could be important for the incorrect operation of end-user equipment. Moreover, measurements from multiple locations could be significant from the view of seeking pattern methods. To learn more about this time scale, we propose an unsupervised learning method that employs a Kernel Principal Component Analysis (KPCA) with a Cosine kernel to extract principal features from 10-min time series of voltage variations with a 1-s resolution followed by a k-means clustering to group the features. The scheme is applied to measurements from 57 low-voltage locations in 19 countries from 2009 to 2018. Fifteen initial clusters/patterns are then extracted and converted to ten new (general) patterns using a clusters' merging strategy with highly similar patterns employed in a new post-processing approach useful for multiple locations. Utilizing data from multiple locations in multiple countries ensures a level of generality of the patterns. It also allows comparing the locations. Next to the ten general patterns, some typical patterns are extracted separately for every location. A statistical indices analysis confirms that a complete picture of sub-10-min oscillations needs both statistical indices (quantifying level and variations) and the proposed framework (quantifying patterns). The extracted patterns could be used as a reference for testing/putting requirements on the grid-connected equipment and quantifying the grid's hosting capacity for different types of new distributed generations connected to the grid. The framework is scalable and computationally cheap, making it appropriate for seeking typical patterns in the big data domain. Applying the framework to the much less understood phenomenon will result in providing general knowledge in the field of power quality.
Triboelectric nanogenerators (TENGs) have potential to achieve energy harvesting and condition monitoring of oils, the “lifeblood” of industry. However, oil absorption on the solid surfaces is a great challenge for oil–solid TENG (O-TENG). Here, oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties. The designed O-TENG can generate an excellent electricity (with a charge density of 9.1 µC m ⁻² and a power density of 1.23 mW m ⁻² ), which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide. It also has a significant durability (30,000 cycles) and can power a digital thermometer for self-powered sensor applications. Further, a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils. The O-TENG can detect particle contaminants at least down to 0.01 wt% and water contaminants down to 100 ppm, which are much better than previous online monitoring methods (particle > 0.1 wt%; water > 1000 ppm). More interesting, the developed O-TENG can also distinguish water from other contaminants, which means the developed O-TENG has a highly water-selective performance. This work provides an ideal strategy for enhancing the output and durability of TENGs for oil–solid contact and opens new intelligent pathways for oil–solid energy harvesting and oil condition monitoring.
Strip rolling is a typical manufacturing process, in which conventional control approaches are widely applied. Development of the control algorithms requires a mathematical expression of the process by means of the first principles or empirical models. However, it is difficult to upgrade the conventional control approaches in response to the ever-changing requirements and environmental conditions because domain knowledge of control engineering, mechanical engineering, and material science is required. Reinforcement learning is a machine learning method that can make the agent learn from interacting with the environment, thus avoiding the need for the above mentioned mathematical expression. This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. Based on the proximal policy optimization (PPO), a multi-actor PPO is proposed. Each randomly initialized actor interacts with the environment in parallel, but only the experience from the actor that obtains the highest reward is used for updating the actors. Simulation results show that the proposed method outperforms the conventional control methods and the state-of-the-art reinforcement learning methods in terms of process capability and smoothness.
Online alkali measurements using surface ionization are employed to study alkali release during heating of used industrial fluidized bed materials and gasification of biomass-based char and bed material mixtures. The alkali release from the bed materials starts at 820 °C and increases with temperature, the time a bed material has experienced in an industrial process, and in the presence of CO2. Online alkali measurement during heating of char mixed with used bed material shows significant alkali uptake by the char. Complementary SEM-EDS studies confirm the alkali results and indicate that other important inorganic elements including Si, Mg, and Ca also migrate from the bed material to the char. The migration of elements initially enhances alkali release and char reactivity, but significantly reduces both during the final stage of the gasification. The observed effects on char gasification become more pronounced with increasing amount of bed material and increasing time the material experienced in an industrial process. The ash-layer on the used bed material is concluded to play an important role as a carrier of alkali and other active components. The char and bed material systems are closely connected under operational conditions, and their material exchange has important implications for the thermal conversion.
With the increasing deployment of solar power, high photovoltaic (PV) penetration is expected to adversely impact the distribution grid. One of the challenges relates to the power flow and transfer capacity of the distribution transformers. Transformers might not have sufficient capacity to accommodate for all the downstream PV to feed back to a higher voltage level during sunny periods with low consumption. In this paper, we estimate the transformer hosting capacity considering dynamic thermal rating for residential consumption with increasing amounts of PV penetration. This paper analyzes the impact of PV integration and increased consumption on the aging of a transformer. The potential of dynamic rating to enhance transformer hosting capacity is studied under varying environmental conditions.
Having in mind the topic of industrialised construction and the benefits of modular construction, sandwich panels are investigated to be utilised as load-bearing wall elements. To assess its full potential, the present paper tackles the linear elastic buckling response of axially loaded angle sandwich panels, by means of numerical and analytical calculations, as the upper bound of its load bearing capacity. The failures modes are obtained and framed for concentrically loaded angle panels with fixed and pin-ended supports. A parametric study of the angle panel comprising a series of finite element models is undertaken where responses are compared with analytical calculations based on the theory of sandwich panels. Boundaries for local and global buckling are identified and framed.
The Smart city is important for sustainability. Governments engaged in developing urban mobility in the smart city need to invest their limited financial resources wisely to realize sustainability goals. A key area for such sustainability investment is how to implement and invest in emerging technologies for urban mobility solutions. However, current frameworks on how to understand the impact of emerging technologies aligned with long-term sustainability strategies are understudied. This article develops a simulation-based comparison between different cities and autonomous vehicle (AV) adoption scenarios to understand which aspects of cities lead to positive AV implementation outcomes. As urban mobility and cities will become smart, the analysis represents a first attempt to explore the impact of AVs on a large scale across different cities around the world. Archetypes are formed and account for most, if not all, world cities. For three of our archetypes (car-centric giants, prosperous innovation centers, and high-density megacities), promoting AV-shuttle use would deliver the greatest advantage as measured by improvements in the model's KPIs. To develop urban powerhouses, however, micromobility would deliver greater benefits. For highly compact middleweights, a shift from private cars to other non-AV modes of transportation would be the smartest choice.
Laser beam welding is a promising technology to enable automated high-quality welding procedures at significantly higher processing speeds compared to conventional processes. However, its usability is often limited by gap bridgability. This disadvantage is related to the small laser beam spot sizes that require low gap sizes for joining, which are often practically not available, and the desired welding without additional filler materials to enable high processing speeds without direction restrictions. New possibilities of beam shaping for process control are also available now for high-power laser processing and they show promising results. The resulting complex effects require additional investigation to understand the mechanisms and the use of the technologies for process improvements. Therefore, in this work, advanced beam shaping optics with up to four separate laser beam spots was used to understand the impact of multiple-spot welding on the process dynamics and gap bridgability. Gap bridgability was measured by an opening gap setup, while spatter amounts as indicators of process dynamics were measured by high-speed imaging. It was shown that multiple-spot laser welding can increase the gap bridgability, probably due to the initiated melt flow toward the joining partners. Symmetric separation of the keyholes toward the sheets increased the gap bridgability, while additional low-intensity spots in the center were able to stabilize the melt pool and reduce spattering.
Studies that objectively investigate patterns of everyday physical activity in relation to well-being and that use measures specific to older adults are scarce. This study aimed to explore objectively measured everyday physical activity and sedentary behavior in relation to a morale measure specifically constructed for older adults. A total of 77 persons (42 women, 35 men) aged 80 years or older (84.3 ± 3.8) wore an accelerometer device for at least 5 days. Morale was measured with the Philadelphia Geriatric Center Morale Scale (PGCMS). PGCMS scores were significantly positively associated with number of steps, time spent stepping, and time spent stepping at >75 steps per minute. Sedentary behavior did not associate with PGCMS. Promoting PA in the form of walking at any intensity–or even spending time in an upright position—and in any quantity may be important for morale, or vice versa, or the influence may be bidirectional.
The aim of the paper is to describe the characteristics of voltage fluctuations induced by household devices in low voltage (LV) networks as well as the impact produced in the LED lamps’ flicker. A number of devices commonly found in households or offices have been studied and the results are described in terms of magnitude of resulting voltage fluctuation, rate of change and frequency of occurrence. The state of operation of the various devices have been linked to the resulting voltage fluctuations and a number of types or patterns of fluctuations with distinct characteristics have been defined. Measurements have been performed in a controlled environment using a source impedance representing a weak grid. Additional measurements were performed in a residential area where the source impedance is considered low. The flicker impact of the voltage fluctuations from the devices connected to the weak grid has been evaluated using the light flickermeter. The results show that there is a link between operation mode of the device and pattern of resulting voltage fluctuation, however it is not the case for all the operation modes in all devices. Several of the devices included in the study caused voltage fluctuations that produced flicker in LED lamps where the rate of change of the fluctuation is seen to have a significant impact. © 2017 Elsevier Inc. All rights reserved.
Significant use of forest biomass in the iron and steel industry (ISI) to mitigate fossil CO2 emissions will affect the biomass availability for other users of the same resource. This paper explores the market effects of increased forest biomass competition when promoting the use of forest-based bio-products in the ISI, as well as the interactions between the ISI and the forest industries. We employ a soft-linking approach that combines a geographically explicit techno-economic energy system model and an economic partial equilibrium model of the forest industries and forestry sectors. This allows for iterative endogenous modelling of new equilibrium price developments for different biomass assortments, determining locational choice of bio-products and assessing optimal bio-products technology choices. The results indicate an upward pressure on biomass prices when bio-products are introduced in the ISI (up to 62%), which affects both forest industries and the ISI itself. Prudence is thus warranted not to render bio-production investments uneconomical ex-post by neglecting to include potential price effects in investment decisions. The estimated price effects can be mitigated by increased domestic biomass supply, adjustments of international trade or by revising relevant policies. Even though the results suggest that the price effects will affect the geographical preferences for individual bio-production plants, proximity to the ISI production facility and integration benefits are more important than the proximity to cheaper biomass feedstocks. Product gas production integrated at ISI sites emerges as particularly attractive, while charcoal production exhibits sensitivity to fluctuating markets, both regarding resulting cost for the ISI, and preferred production locations.
Gasoline subsidies distort the gasoline market resulting in inefficiencies and a costly burden in government budget. In Indonesia, they have taken up to 15 % of the government expenditures that arguably could be better spent elsewhere. Governments are aware of these costs, yet face difficulties in removing the policy. Governments would like to release the subsidy fund for other programs while still maintaining political power. Simultaneously, a reform will reduce the purchasing of the population and thus, it is commonly met with strong public resistance. The general population can influence the government's decision to carry out a reform by exerting pressure that may affect the country's political stability. There is a vast economics literature analysing the economic impact from a subsidy reform. Meanwhile, the government's hesitancy is analysed in the political science literature. We combined these two fields by developing a quantitative game theory model to show the interaction between the government and the general population. The model is based on Indonesian data but provides a framework that can be applied elsewhere. Different policy removal schemes are simulated including completely or partially phasing-out the subsidy with and without compensation. An important take-away from our analysis is that it provides a framework showing governments what they have to quantify in order to make an informed policy decision. Another important implication is that the success of the policy reform is highly dependent on the selectorates trust to the government. It strongly supports the political science recommendations of building trust through transparency and inclusion.
Objectives The objective of this study is to assess the determinants of willingness to pay to enhance pandemic preparedness in Mauritius. Study design A contingent valuation method is used to estimate willingness to pay to pay for enhancing pandemic preparedness using a sample of working people in Mauritius. Methods A two-phase decision process analysis is carried out to model the willingness to pay to enhance pandemic preparedness. The first phase is to analyse the respondents' decision of whether or not to pay for enhancing pandemic preparedness using a Probit model. The second phase is to estimate the determinants of the amount of money respondents are willing to pay using a Tobit model. Results Income earners are willing to pay an average of Rs. 1,900 (approximately USD 50) per annum to enhance pandemic preparedness. ‘Perceived Response Efficacy’, ‘Awareness of the Need and Responsibility for Paying’, ‘Subjective Obligation to Pay’ and the ‘Theory of Planned Behaviour’ are found to affect both stages of of the decision-making process. Knowledge on COVID-19 is found to have a positive impact on the decision to pay and health responsibility attitude is found to have a negative impact on the amount people are willing to pay. Conclusions On average, the government can potentially expect to mobilise an additional Rs. 1,047,470,000 (USD 27,565,000) from taxpayers to spend on enhancing pandemic preparedness in Mauritius. To increase willingness to pay for enhanced pandemic preparedness, the government can focus on improving knowledge on a pandemic, perceived response efficacy and awareness on need and responsibility of paying.
The development of immersive virtual reality (IVR) applications for design reviews is a major trend in the design field. While many different applications have been developed, there is little consensus on the functionalities necessary for these applications. This paper proposes a classification scheme for IVR functionalities related to design reviews (DRs), combining conceptual-to-empirical and empirical-to-conceptual strategies. The classification scheme consists of eight class categories (Input, Representation, Navigation, Manipulation, Collaboration, Edit, Creation, and Output), 22 class subcategories, and 55 classes. The classification scheme has been validated by analysing several commercial IVR applications for DRs. As part of the classification scheme development, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilised to review 70 articles that develop IVR applications for DRs. The results from systematic literature reviews suggest the development of solutions that integrate several class categories, are better connected to current design workflows, include various design information, support a DR planning cycle, and support distributed work. The proposed classification scheme helps to orient the future development of IVR applications for DRs and provides a framework to systematically accumulate evidence on the effect of such applications on DRs.
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3,388 members
Tao Song
  • Department of Engineering Sciences and Mathematics
Rene Laufer
  • Department of Computer Science, Electrical and Space Engineering (SRT)
Agneta Larsson
  • Division of Health Medicine and Rehabilitation
Nadhir Al-Ansari
  • Department of Civil, Environmental and Natural resources engineering
Lulea university of technology, 97187, Luleå, Norrbotten County, Sweden
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