A Discrete Geese Swarm Algorithm for Spectrum Assignment of Cognitive Radio

In order to solve spectrum assignment problem, this paper proposes a discrete geese swarm algorithm (DGSA) based on particle swarm optimization and quantum particle swarm optimization, and we evaluate the performance of the DGSA through some classical benchmark functions. The proposed DGSA algorithm applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization. We also use it to solve cognitive radio spectrum assignment problem. The new spectrum allocation method has the ability to search global optimal solution under different network utility functions. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation algorithm is superior to some previous spectrum allocation algorithms

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods

2D-DOA Estimation for Cylindrical Array with Mutual Coupling

Most conventional direction-of-arrival (DOA) estimation algorithms are affected by the effect of mutual coupling, which make the performance of DOA estimation degrade. In this paper, a novel DOA estimation algorithm for conformal array in the presence of unknown mutual coupling is proposed. The special mutual coupling matrix (MCM) is applied to eliminate the effect of mutual coupling. With suitable array design, the decoupling between polarization parameter and angle information is accomplished. The two-demission DOA (2D-DOA) estimation is finally achieved based on estimation of signal parameters via rotational invariance techniques (ESPRIT). The proposed algorithm can be extended to conical conformal array as well. Two parameter pairing methods are illustrated for cylindrical and conical conformal array, respectively. The computer simulation verifies the effectiveness of the proposed algorithm

Experimental Investigation of the Ti-Nb-Sn Isothermal Section at 1173 K

Isothermal section of Ti-Nb-Sn at 1173 K was experimentally studied by back-scattered electron, electron probe microanalysis and X-ray diffraction analysis. Solid solution phase β(Ti, Nb), liquid Sn and eight intermetallic compounds Ti3Sn, Ti2Sn, Ti5Sn3, Ti6Sn5, Nb6Sn5, Nb3Sn, Ti3Nb2Sn2 and Ti3NbSn coexisted. Four ternary phase regions Ti3Sn + Ti3NbSn + β(Ti, Nb), Ti3NbSn + Ti3Nb3Sn2 + Ti3Sn, Ti2Sn + Ti3Sn + Ti3Nb3Sn2 and Ti6Sn5 + Ti3Nb3Sn2 + Nb3Sn were experimented. In addition, the proper composition range of the single phase was suggested. All the detected Ti-Sn and Nb-Sn compounds have a remarkable solubility along the isoconcentration of Sn. β(Ti, Nb) has a relatively large solution while liquid Sn has a little in the isothermal section

Faulty Feeder Identification Based on Data Analysis and Similarity Comparison for Flexible Grounding System in Electric Distribution Networks

Reliability and safety are the most important indicators in the electric system. When a ground fault occurs, the electrical equipment and personnel will be greatly threatened. Due to the zero-sequence voltage/current sensor networks applied in the system, the fault identification and diagnosis technology are developing rapidly, including the application of ground fault suppression. A flexible grounding system (FGS) is a new technology applied to arc extinguishing in medium and high voltage electric distribution networks. Its characteristic is that when the single-phase ground fault occurs, the power-electronic-based device is put into the electric system to compensate and suppress the ground point current to be close to zero in a very short time. In order to implement the above process, the corresponding faulty feeder identification method needs to meet the requirements of rapidity and accuracy. In this article, based on the real-time sampled data from the zero-sequence current/voltage sensors, an improved faulty feeder identification method combining wavelet packet transform (WPT) and grey T-type correlation degree is proposed, which features both accuracy and rapidity. The former is used to reconstruct the transient characteristic signal, and the latter is responsible for calculating and comparing the similarity of relative variation trend. Simulation results verify the rationality and effectiveness of the proposed method and analysis

Survival Outcomes and Failure Patterns in Patients with Inoperable Non-Metastatic Pancreatic Cancer Treated with Definitive Radiotherapy

This study investigated the long-term results, failure patterns, and prognostic factors of patients with initially inoperable non-metastatic pancreatic cancer (PC) receiving definitive radiotherapy (RT). Between January 2016 and December 2020, a total of 168 non-metastatic PC patients, who were surgically unresectable or medically inoperable, were enrolled to receive definitive RT, with or without chemotherapy. Overall survival (OS) and progression-free survival (PFS) were evaluated using the Kaplan–Meier method with a log-rank test. The cumulative incidence of locoregional and distant progression was estimated using the competing risks model. The Cox proportional-hazards model was used to determine the influence of prognostic variables on OS. With a median follow-up of 20.2 months, the median OS (mOS) and median PFS (mPFS) from diagnosis were 18.0 months [95% confidence interval (CI), 16.5–21.7 months] and 12.3 months (95% CI, 10.2–14.3 months), respectively. The mOS and mPFS from RT were 14.3 months (95% CI, 12.7–18.3 months) and 7.7 months (95% CI, 5.5–12.0 months), respectively. The corresponding 1-year, 2-year, and 3-year OS from diagnosis and RT were 72.1%, 36.6%, and 21.5% as well as 59.0%, 28.8%, and 19.0%, respectively. In a multivariate analysis, stage I–II (p = 0.032), pre-RT CA19–9 ≤ 130 U/mL (p = 0.011), receiving chemotherapy (p = 0.003), and a biologically effective dose (BED10) > 80 Gy (p = 0.014) showed a significant favorable influence on OS. Among the 59 available patients with definite progression sites, the recurrences of local, regional, and distant progression were 33.9% (20/59), 18.6% (11/59), and 59.3% (35/59), respectively. The 1-year and 2-year cumulative incidences of locoregional progression after RT were 19.5% (95% CI, 11.5–27.5%) and 32.8% (95% CI, 20.8–44.8%), respectively. Definitive RT was associated with long-term primary tumor control, resulting in superior survival in patients with inoperable non-metastatic PC. Further prospective randomized trials are warranted to validate our results in these patients