Electroencephalogram (EEG) sensor data contain rich information about human emotionality. Emotion recognition based on EEG signals has attracted growing attention of researchers, especially with the ...fast progress of intelligent sensing technology. Numerous methods for the issue of EEG-based emotion classification have been presented in recent years. Although these methods have promoted the research development of this issue, the performance enhancement seems very slow, because the useful emotional information is quite weak compared with severe noise interferences and serious data variations. To capture the weak emotional information from the EEG signals that are distorted by various disturbances, this paper proposes a novel and effective approach, "Spatio-Temporal Field (STF)". This method extracts the Rational Asymmetry of Spectral Power features from the EEG signals at first, and then divides the feature space into the local field via the set-based discriminative measure, and finally employs the Bidirectional Long Short-Term Memory in the local field to exploit the local dynamic information for emotion classification. Experimental results have demonstrated the advantage of STF in EEG-based emotion classification by the public challenging databases, DEAP and DREAMER. STF can be regarded as an initial attempt to deal with the issue of EEG-based emotion classification from the local field perspective. We expect that the proposed method not only can provide inspirations for the further research on this issue, but may also enrich the field methodology for more general signal classification topics.
The issue of electroencephalogram (EEG)-based emotion recognition has great academic and practical significance. Currently, there are numerous research trying to address this issue in the literature. ...Particularly, transfer learning has gradually become a new methodological trend for the issue in company with the popularity of deep learning. Motivated by capturing the research panorama, summarizing the technological essence, and forecasting the advancement tendency of transfer learning for EEG-based emotion recognition, this article contributes a review work. This work mainly includes five aspects: 1) introducing the issue of EEG-based emotion recognition and expounding the importance of transfer learning for it; 2) analyzing the transfer learning framework and comparing it with the traditional ones; 3) elucidating the issue difficulties and explaining the suitability and capability of transfer learning for this issue; 4) summarizing, categorizing, and exemplifying the typical transfer learning methods for this issue; and 5) clarifying the methodological merits, discussing the challenging problems, and predicting the prospective development of transfer learning for the issue. We expect these contributions can inspire innovation and reformation of the transfer learning methodology for EEG-based emotion recognition as well as other relevant topics in the not-so-far future.
Abstract
To date, bipolar membrane electrodialysis (BMED) is being developed as a competitive technology for waste lithium‐ion battery recovery. However, the purity and concentration of lithium ...hydroxide generated from a BMED plant could not meet the product criteria for ternary lithium batteries, thus requiring additional condensation, purification, evaporation, and crystallization procedures. Herein, bipolar membrane crystallization (BMC) was proposed for the one‐step conversion of sulfate lithium into high‐purity lithium hydroxide monohydrate crystals. By mediating a continuous saturated feedstock in the salt compartment, it is possible to convert Li
2
SO
4
into 5+ mol/L LiOH at a current density higher than 500 A/m
2
. Therefore, this unique design allows the production of 99.9% LiOH∙H
2
O by taking the principle of water dissociation in the bipolar membrane and the simultaneous crystallization procedure. This proof‐of‐concept study proves the feasibility and competitiveness of the BMC for waste lithium recovery by abandoning the condensation and evaporation procedures.
High radix Booth encodings provide significant decrease on the number of partial products in the multiplication. However, due to the generation on hard multiples, additional delay and power are ...incurred, which in turn hampers the use of high radix Booth encodings. In this brief, an approximate radix-256 Booth encoding is proposed to circumvent the generation on hard multiples. A partial encoding approach is used to produce partial product pairs, which can be obtained easily by simple shifting and complementing operations. The exact encoding values are thus replaced by the sum of each corresponding partial product pair. A 16 <inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> 16-bit multiplier with proposed approximate radix-256 Booth encoding has been implemented for performance evaluation. Compared with the traditional radix-4 Booth encoding multiplier, the proposed design achieves 42.39%, 7.03%, 26.00% reduction in area, delay, and power, respectively. Additionally, 2-D discrete cosine transform (DCT) system with proposed multiplier is demonstrated as an application. 33.51% and 24.15% reduction on area and power consumption are obtained respectively with a penalty of 6.42dB peak signal to noise ratio loss in average when compared with the traditional design.
Objectives
To evaluate the diagnostic performance of zero echo time (ZTE) MRI in the depiction of structural lesions of sacroiliac joints (SIJs) in patients with the suspicion of sacroiliitis ...compared with T1-weighted fast spin echo (T1 FSE), using CT as the reference standard.
Methods
Forty patients with suspicion of sacroiliitis underwent both CT and MR scans of SIJs with 80 SIJs (160 bone articular surfaces) included for analysis. Two readers independently scored SIJs for structural lesions on CT and MR images. The diagnostic capability of ZTE MRI and T1 FSE were compared by the McNemar test, using CT as the reference standard. Agreements of diagnosis and sum scores of lesions between MR sequences and CT as well as between readers were also investigated using Cohen’s κappa tests and intraclass correlation coefficients.
Results
Diagnostic accuracy of ZTE MRI was higher than that of T1 FSE for erosions, sclerosis, and joint space changes (e.g., joint space changes: 91.3% vs 75.0%). ZTE MRI also improved sensitivity for detection of erosions and sclerosis (e.g., erosions at the joint level: 98.2% vs 80.0%) as well as specificity for detection of joint space changes (93.0% vs 67.4%). ZTE MRI had more consistency with CT than T1 FSE for both diagnosis and sum scores. Inter-reader agreements were higher for CT and ZTE MRI than those for T1 FSE.
Conclusions
ZTE MRI showed superior diagnostic performance in the depiction of SIJ structural lesions compared with routine T1-weighted MRI and had reliability comparable to CT.
Key Points
• ZTE MRI can provide CT-like bone contrast for the depiction of osseous structural lesions of the sacroiliac joints.
• ZTE MRI showed superior diagnostic performance than conventional T1 FSE in the detection of osseous structural lesions of sacroiliitis, using CT as the reference standard.
• In terms of inter-reader reliability, ZTE MRI performed comparably to CT and better than conventional T1 FSE.
The generalized orthopair fuzzy set is more favored by decision-makers and extensively utilized in areas like supply chain management, risk investment, and pattern recognition because it offers a ...broader decision information boundary than the intuitionistic fuzzy set and Pythagorean fuzzy set. This enables it to express fuzzy information more comprehensively and accurately in multi-attribute decision-making problems. To this end, this paper combines the ability of the power average (PA) operator to eliminate the impact of extreme values and the advantage of the Bonferroni mean (BMs,t) operator in reflecting the relationships between variables, then incorporates weight indicators for different attributes to define the generalized orthopair fuzzy weighted power Bonferroni mean operator. The effectiveness of this operator is demonstrated through aggregation laws for generalized orthopair fuzzy information. Subsequently, the desirable properties of this operator are discussed. Based on these findings, a novel generalized orthopair fuzzy multi-attribute decision-making method, with a correlation between attributes, is proposed. Lastly, an investment decision-making example illustrates the feasibility and superiority of this method.
Abstract Slender objects have a large aspect ratio and are generally oriented, resulting in poor performance of current general detectors on slender object detection tasks. Therefore, an adaptive ...label assignment scheme for slender object detection is proposed in this paper. Specifically, the central axis prior to positive training samples is proposed to make the final position distribution of positive training samples more reasonable. Secondly, it is proposed that the number of positive training samples of slender objects could be further increased to solve the problem of positive training sample imbalance between slender objects and regular objects. Experimental results on the MS COCO dataset demonstrate the effectiveness of the proposed method.
Spacecraft autonomous relative navigation is widely required in the field of on-orbit services. In previous works, it has been shown that the monocular sequential images (MSIs) is a cost-effective ...autonomous relative navigation method. However, given the lack of size and initial orbit of noncooperative targets, the relative navigation system (RNS) with MSIs cannot ensure to estimate the entire relative motion states during the whole observation process. It is critical to perform an online observability analysis for RNS with MSIs to ensure autonomous relative motion estimation validity and accuracy. An analytical nonlinear observability analysis method is proposed in this article to improve the computational efficiency. We study the attributes of the relative orbit dynamics model and Lie algebra operations, and find that the observability matrix is only related to the limited states. The relationship among those states, rank, and the Frobenius norm of observability matrix is demonstrated, which simplify the observability criteria conditions and the degree measurements in analytical forms. In addition, based on the proposed method, we analyze the influence of the orbital manifolds on the observability degree of RNS with MSIs. Several suggestions are proposed for orbital manifolds detecting. The conclusion is a theoretical supplement to the past numerical research results. Finally, we implement the numerical simulations, and the current study's findings verified the effectiveness of the proposed observability analysis method.
Hou, B.W.; Zeng, Q.E., and Li, J.J., 2020. Underwater acoustic characteristics of high-speed railway subsea tunnel. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, ...and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 43–46. Coconut Creek (Florida), ISSN 0749-0208. Focusing on the distribution characteristics of marine acoustics caused by high-speed railway channel tunnels, a high-speed railway channel tunnel-ocean bed-ocean fluid-solid coupling dynamic model is established based on the finite element method and fluid-solid coupling theory. By applying the wheel-rail interaction forces which is calculated with the wheel-rail coupling dynamics model as the excitation, the distribution characteristics of the ocean sound pressure has been studied. The spatial propagation law of marine acoustics has been illustrated. Results show that when the train is running at 250km/h, the maximum vibration of the surface of the ocean bed does not appear directly above the tunnel, but on the path that is transmitted upward by 45° on both sides of the tunnel. The maximum underwater sound level is about 136.2∼143.9dB, and the dominant frequency is mainly concentrated in the range below 200Hz. In the vertical direction, the sound level decreases by 3.6∼7.6dB within a depth of 20m. In the horizontal direction, the variation of the sound level at the same sea level is within 2dB ranging from 0∼40m.
Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music ...characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.
