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- 저자 : Skoglund, Erling (검색결과 6 건)
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Overview of Steel Bridges Containing High Strength Steel
Oskar Skoglund,John Leander,Raid Karoumi 한국강구조학회 2020 International Journal of Steel Structures Vol.20 No.4
The use of high strength steel has the potential to reduce the amount of steel used in bridges and thereby, facilitate a more sustainable construction. A survey of existing bridges built using high strength steel is presented in this paper with emphasis on the Swedish bridge stock. The survey aimed at identifying the steel grades that were used and where in the cross-section they have been used. A case study on the infl uence of fatigue shows that today’s regulations make it more diffi cult to use high strength steel in comparison to previous regulations.
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Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based Speech Enhancement
Lee, Jinkyu,Skoglund, Jan,Shabestary, Turaj,Kang, Hong-Goo IEEE 2018 IEEE signal processing letters Vol.25 No.8
<P>This letter presents a phase-sensitive joint learning algorithm for single-channel speech enhancement. Although a deep learning framework that estimates the time-frequency (T-F) domain ideal ratio masks demonstrates a strong performance, it is limited in the sense that the enhancement process is performed only in the magnitude domain, while the phase spectra remain unchanged. Thus, recent studies have been conducted to involve phase spectra in speech enhancement systems. A phase-sensitive mask (PSM) is a T-F mask that implicitly represents phase-related information. However, since the PSM has an unbounded value, the networks are trained to target its truncated values rather than directly estimating it. To effectively train the PSM, we first approximate it to have a bounded dynamic range under the assumption that speech and noise are uncorrelated. We then propose a joint learning algorithm that trains the approximated value through its parameterized variables in order to minimize the inevitable error caused by the truncation process. Specifically, we design a network that explicitly targets three parameterized variables: 1) speech magnitude spectra; 2) noise magnitude spectra; and 3) phase difference of clean to noisy spectra. To further improve the performance, we also investigate how the dynamic range of magnitude spectra controlled by a warping function affects the final performance in joint learning algorithms. Finally, we examined how the proposed additional constraint that preserves the sum of the estimated speech and noise power spectra affects the overall system performance. The experimental results show that the proposed learning algorithm outperforms the conventional learning algorithm with the truncated phase-sensitive approximation.</P>
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Green Body Behaviour of High Velocity Pressed Metal Powder
Jonsen P.,Haggblad H.A.,Troive L.,Furuberg J.,Allroth S.,Skoglund P. 한국분말야금학회 2006 한국분말야금학회 학술대회논문집 Vol.2006 No.1
High velocity compaction (HVC) is a production technique with capacity to significantly improve the mechanical properties of powder metallurgy (PM) parts. Investigated here are green body data such as density, tensile strength, radial springback, ejection force and surface flatness. Comparisons are performed with conventional compaction using the same pressing conditions. Cylindrical samples of a pre-alloyed water atomized iron powder are used in this experimental investigation. The HVC process in this study resulted in a better compressibility curve and lower ejection force compared to conventional quasi static pressing. Vertical scanning interferometry measurements show that the HVC process gives flatter sample surfaces.
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Uplink Waveform Channel With Imperfect Channel State Information and Finite Constellation Input
Do, Tan Tai,Oechtering, Tobias J.,Kim, Su Min,Skoglund, Mikael,Peters, Gunnar IEEE 2017 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.16 No.2
<P>This paper investigates the capacity limit of an uplink waveform channel assuming imperfect channel state information at the receiver (CSIR). Various realistic assumptions are incorporated into the problem, which make the study valuable for performance assessment of real cellular networks to identify potentials for performance improvements in practical receiver designs. We assume that the continuous-time received signal is first discretized by mismatched filtering based on the imperfect CSIR. The resulting discrete-time signals are then decoded considering two different decoding strategies, i.e., an optimal decoding strategy based on specific statistics of channel estimation errors and a sub-optimal decoding strategy treating the estimation error signal as additive Gaussian noise. Motivated by the proposed decoding strategies, we study the performance of the decision feedback equalizer for finite constellation inputs, in which inter-stream interferences are treated either using their true statistics or as Gaussian noise. Numerical results are provided to exemplify the benefit of exploiting the knowledge on the statistics of the channel estimation errors and inter-stream interferences. Simulations also assess the effect of the CSI imperfectness on the achievable rate, which reveal that finite constellation inputs are less sensitive to the estimation accuracy than Gaussian input, especially in the high SNR regime.</P>
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