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Deep convolutional networks are of great significance for the automatic semantic annotation of remotely sensed images. Object position and semantic labeling are equally important in semantic segmentation tasks. However, the convolution and pooling operations of the convolutional network will affect the image resolution when extracting semantic information, which makes acquiring semantics and capturing positions contradictory. We design a duplex restricted network with guided upsampling. The detachable enhancement structure to separate opposing features on the same level. In this way, the network can adaptively choose how to trade-off classification and localization tasks. To optimize the detailed information obtained by encoding, a concentration-aware guided upsampling module is further introduced to replace the traditional upsampling operation for resolution restoration. We also add a content capture normalization module to enhance the features extracted in the encoding stage. Our approach uses fewer parameters and significantly outperforms previous results on two very high resolution (VHR) datasets: 84.81% (vs 82.42%) on the Potsdam dataset and 86.76% (vs 82.74%) on the Jiage dataset.

The trade-off between feature representation capability and spatial positioning accuracy is crucial to dense classification or semantic segmentation of remote sensing images. In order to better balance the low-level spatial details in the shallow network and the high-level abstract semantics in the deep network, the bilateral attention refinement lightweight network BARNet is introduced. In this way, we can use the fine-grained features in the shallow layer to further supplement and capture the deeper information of the high-level semantic features. The network employs an asymmetric encoder decoder architecture for the task of real-time semantic segmentation. Encoder part proposes a lightweight network residual unit with the split, concatenate and split bottleneck structure to achieve more light weighted, effificient and powerful feature extraction. In the decoding section, we propose an adaptive method to enhance feature representation in local attention enhancement module. In addition, the global context embedding module is introduced to divide the high-level features into two branches. One branch gets the weight vector to guide the low-level learning, and the other branch will get a semantic vector, which is used to calculate the multi-label category loss and further introduce into the overall loss function to regulate the training process better. The effectiveness and efficiency of the network are verified on ISPRS Potsdam data set and CCF data set, respectively. The results show that the models using these strategies outperform the baseline network on MIoU, PA and F1, which increase by 18.86%, 16.21% and 15.64% on the Potsdam dataset; 10.51%, 6.53% and 8.19% on the CCF dataset.

With the widespread application of semantic segmentation in remote sensing images with high-resolution, how to improve the accuracy of segmentation becomes a research goal in the remote sensing field. An innovative Fully Convolutional Network (FCN) is proposed based on regional attention for improving the performance of the semantic segmentation framework for remote sensing images. The proposed network follows the encoder-decoder architecture of semantic segmentation and includes the following three strategies to improve segmentation accuracy. The enhanced GCN module is applied to capture the semantic features of remote sensing images. MGFM is proposed to capture different contexts by sampling at different densities. Furthermore, RAM is offered to assign large weights to high-value information in different regions of the feature map. Our method is assessed on two datasets: ISPRS Potsdam dataset and CCF dataset. The results indicate that our model with those strategies outperforms baseline models (DCED50) concerning F1, mean IoU and PA, 10.81%,19.11%, and 11.36% on the Potsdam dataset and 29.26%, 27.64% and 13.57% on the CCF dataset.

At present, the barrier to HIV-1 functional cure is the persistence of HIV-1 reservoirs. The “shock (reversing latency) and kill (antiretroviral therapy)” strategy sheds light on reducing or eliminating the latent reservoir of HIV-1. However, the current limits of latency-reversing agents (LRAs) are their toxicity or side effects, which limit their practicability pharmacologically and immunologically. Our previous research found that HSF1 is a key transcriptional regulatory factor in the reversion of HIV-1 latency. We then constructed the in vitro HSF1-knockout (HSF1-KO) HIV-1 latency models and found that HSF1 depletion inhibited the reactivation ability of LRAs including salubrinal, carfizomib, bortezomib, PR-957 and resveratrol, respectively. Furthermore, bortezomib/carfizomib treatment induced the increase of heat shock elements (HSEs) activity after HSF1-KO, suggesting that HSEs participated in reversing the latent HIV-1. Subsequent investigation showed that latent HIV-1-reversal by H2O2-induced DNA damage was inhibited by PARP1 inhibitors, while PARP1 was unable to down-regulate HSF1-depleted HSE activity, indicating that PARP1 could serve as a replaceable protein for HSF1 in HIV-1 latent cells. In summary, we succeeded in finding the mechanisms by which HSF1 reactivates the latent HIV-1, which also provides a theoretical basis for the further development of LRAs that specifically target HSF1.

The hydrodynamic interaction between an AUV (Autonomous Underwater Vehicle) and a recovery device, such as a real-scale submarine, is a crucial factor affecting the safe recovery of the AUV. This paper employs the CFD (Computational Fluid Dynamics) method to investigate the hydrodynamic interaction of the AUV and the submarine during the recovery process. Both the AUV and the submarine are considered to be relatively stationary. The results indicate that the submarine has a significant impact on the AUV during the recovery process, with sailing speed and relative positions identified as key influential factors. Due to the influence of the submarine, it can be difficult for the AUV to approach the submarine and be recovered safely. This study provides valuable insights into the hydrodynamic interaction between the AUV and the recovery device, and offers guidance for future submarine recovery operations involving AUVs. By considering the influence of the submarine’s position and motion, as well as other relevant factors, it may be possible to improve the stability, safety, and efficiency of AUV recovery operations.

The motion stability of the Unmanned Surface Vessel (USV) is threatened by the action of waves under a rough sea state. In the present paper, the motion of a large-scale USV is numerically simulated under high sea state of level 5 and 7. The overset grid method and Reynolds Averaged Navier–Stokes (RANS) approach are employed to solve Navier–Stokes (N-S) equations. For the case of wave incident angle 0° and 30°, the heave, pitch and roll motion response of a large scale USV are investigated by using the six Degrees of Freedom (6-DOF) numerical model. The effects of different sea states, as well as different wave directions, on the motion of USV are compared. The comparative results indicate that the response of this USV in waves is the periodic free-motion according to the corresponding amplitude, which does not exceed the stable range, and there are no overturning and other situations that may affect the safety, in the case of level 5 and 7 sea states. The corresponding pressure at the bottom of this USV meets the range of material strength, and no structural damage or injury to the hull occurs, although the pressure varies at different wave periods. For the case of different wave directions, the analysis of the boundary layer thickness shows that the wave direction is of great importance to the boundary layer thickness distribution, both in the level 5 and level 7 sea states.

In this study, particle image velocimetry was applied to measure the flow field around the bow region of a trimaran with different appendages. The dimensionless axial velocity u/U in test planes 1 and 2 of the testing model was measured by using a towed underwater stereoscopic particle image velocimetry (SPIV) system. Based on the measured flow field data, the local sinkage values in test planes 1 and 2 of the testing model with different appendages at speeds of 1.766 and 2.943 m/s were presented. In addition, the effects of speed, bulbous bow type, T foils, and bow wave on the axial velocity u/U were studied in detail. The acquired experimental data help in understanding the distribution of the flow field around the ship bow, and the data can also act as a reference to verify computational fluid dynamics (CFD) results.

The scale effects of an aft hull wake field pose a great challenge to propeller design and its performance prediction. Research into the characteristics of the scale effects and the subsequent correction of the errors caused by such effects play an important role in improving a ship’s energy conservation and propulsion performance. For this research, using a KCS ship as the research target, the aft shape of an original ship model has been modified based on the smart dummy model (SDM) to change its nominal wake field. The present study explores the aft hull deformation of a KCS ship through a series of numerical calculations and validates the results using a similar ship model. In addition, wake field PIV-measurements are performed using particle image velocimetry to verify the corrected effects of the SDM. The SDM correction method offers a new pathway for correcting the errors associated with the scale effects in the nominal wake field measurements of a ship model.

Experimental and numerical analyses performed on a scaled-down model of a 1900TEU container-ship are reported herein. Wind-tunnel and towing-tank experiments along with computational-fluid-dynamic simulations were performed to obtain (1) wind-load coefficients for superstructure of container ship at different wind angles under full-load operating conditions; (2) wave resistance of the model sans the superstructure under different wave conditions; and (3) combined wind-wave resistance of the model in the head waves coupled with a fluctuating wind. Wind-tunnel experiments were first performed to determine wind-load coefficients concerning of the superstructure at different wind angles. Subsequently, the obtained wind-load coefficients from the wind tunnel test were compared against numerical and empirically obtained results to validate the applicability of the applied numerical methods. Next, the wave-induced resistance to ship motion was investigated via a series of towing-tank experiments and numerical simulations to analyze the resistance and motion of ship under wavy conditions. Finally, characteristics of the added resistance to ship motion under conditions of combined wind-wave load were analyzed, and the coupling between ship motion and combined wind-wave load was used to investigate the changes in added resistance under different load scenarios. The results reveal that combined wind-wave load causes the resistance to ship motion to exceed the algebraic sum of the corresponding resistances under standalone wind- and wave-load conditions. The additional resistance was observed to be a combined manifestation of resistances induced by ship motion and wave-parameter alterations.

The effect of waves on ice sheet is critical in the marginal ice zone (MIZ). Waves break large sea ice into small pieces and cause them to collide with each other. Simultaneously, the interaction between sea ice and waves attenuates these waves. In this study, a numerical research is conducted based on a computational fluid dynamics (CFD) method to investigate the response of single ice floe to wave action. The obtained results demonstrate that the sea ice has a violent six degree of freedom (6DoF) motion in waves. Ice floes with different sizes, thicknesses, and shapes exhibit different 6DoF motions under the action of waves. The heave and surge response amplitude operator (RAO) of the sea ice are related to wavelength. Furthermore, the overwash phenomenon can be observed in the simulation. The obtained results are compared with the model test in the towing tank based on artificial ice, and they agree well with test results.

Particle image velocimetry is applied in this study to measure the wake flow field of a Panamax Bulker ship model under the ballast condition. This investigation revealed that the Froude number is 0.167. The time-averaged velocity, turbulent fluctuations, turbulent kinetic energy (TKE), Reynolds stresses, and vorticity information were measured to perform a comparison with the design condition. The time-averaged velocity contours indicated that the ballast and design conditions have distinct hook-like axial velocity contours; however, they appeared at different positions. The big difference under the ballast condition is that the top of the propeller disk area is near the free surface and a region with strong root mean square velocity fluctuation is formed near the free surface. The TKE, the Reynolds stresses, and the hub cap vortex (Hcv) are all affected by the turbulent velocity fluctuation region under the ballast condition. A strong bilge vortex (Bv) is produced when the water flows through the U-shaped stern for the design and ballast conditions.

The molar ratio between methanol and isobutylene (MRMI) is very important to the operation of methyl tertiary butyl ether (MTBE) production units. A new on-line near-infrared (NIR) analytical system was integrated for monitoring the MRMI in real time and was successfully applied in a rubber plant. Calibration models for methanol and isobutylene were established using partial least squares (PLS). The sample temperature effect on the performance of the models is discussed. The MRMI is calculated by methanol content and isobutylene content predicted by NIR. A large benefit has been obtained by the user through controlling the operation of the unit according to the monitoring of the MRMI of the feedstock in real time. [ABSTRACT FROM AUTHOR]