Multi-AGV Path Planning for Indoor Factory by Using Prioritized Planning and Improved Ant Algorithm

Multiple automated guided vehicle (multi-AGV) path planning in manufacturing workshops has always been technically difficult for industrial applications. This paper presents a multi-AGV path planning method based on prioritized planning and improved ant colony algorithms. Firstly, in dealing with the problem of path coordination between AGVs, an improved priority algorithm is introduced, where priority is assigned based on the remaining battery charge of the AGVs, which improves the power usage efficiency of the AGVs. Secondly, an improved ant colony algorithm (IAC) is proposed to calculate the optimal path for the AGVs. In the algorithm, a random amount of pheromone is distributed in the map and the amount of pheromone is updated according to a fitness value. As a result, the computational efficiency of the ant colony algorithm is improved. Moreover, a mutation operation is introduced to mutate the amount of pheromone in randomly selected locations of the map, by which the problem of local optimum is well overcome. Simulation results and a comparative analysis showed the validity of the proposed method

Influence of impeller clearance structure on volume loss of centrifugal pump

Abstract In this paper, the three-dimensional full flow field model of double volute structure centrifugal pump is established, and the ring seal, teeth seal and interlocking seal structures are set up. The K-e turbulence model of CFX software is used to simulate and analyse the fluid flow state at the gap of different mouth ring structures of centrifugal pump. The results show that the staggered ring structure can effectively reduce the leakage and improve the volumetric efficiency of centrifugal pump.</jats:p

Deep Feature Extraction for <i>Cymbidium</i> Species Classification Using Global–Local CNN

Cymbidium is the most famous and widely distributed type of plant in the Orchidaceae family. It has extremely high ornamental and economic value. With the continuous development of the Cymbidium industry in recent years, it has become increasingly difficult to classify, identify, develop, and utilize orchids. In this study, a classification model GL-CNN based on a convolutional neural network was proposed to solve the problem of Cymbidium classification. First, the image set was expanded by four methods (mirror rotation, salt-and-pepper noise, image sharpening, and random angle flip), and then a cascade fusion strategy was used to fit the multiscale features obtained from the two branches. Comparing the performance of GL-CNN with other four classic models (AlexNet, ResNet50, GoogleNet, and VGG16), the results showed that GL-CNN achieves the highest classification prediction accuracy with a value of 94.13%. This model can effectively detect different species of Cymbidium and provide a reference for the identification of Cymbidium germplasm resources

Deep Feature Extraction for Cymbidium Species Classification Using Global–Local CNN

Cymbidium is the most famous and widely distributed type of plant in the Orchidaceae family. It has extremely high ornamental and economic value. With the continuous development of the Cymbidium industry in recent years, it has become increasingly difficult to classify, identify, develop, and utilize orchids. In this study, a classification model GL-CNN based on a convolutional neural network was proposed to solve the problem of Cymbidium classification. First, the image set was expanded by four methods (mirror rotation, salt-and-pepper noise, image sharpening, and random angle flip), and then a cascade fusion strategy was used to fit the multiscale features obtained from the two branches. Comparing the performance of GL-CNN with other four classic models (AlexNet, ResNet50, GoogleNet, and VGG16), the results showed that GL-CNN achieves the highest classification prediction accuracy with a value of 94.13%. This model can effectively detect different species of Cymbidium and provide a reference for the identification of Cymbidium germplasm resources.</jats:p

Thrombolysis Combined Therapy Using CuS@SiO2-PEG/uPA Nanoparticles

Massive hemorrhage caused by the uncontrolled release of thrombolysis drugs is a key issue of thrombolysis therapy in clinical practice. In this study, we report a near-infrared (NIR) light-triggered drug delivery system, i.e., CuS@mSiO2-PEG (CSP) nanoparticles, for the loading of a thrombolytic drug (urokinase plasminogen activators, uPA). CSP nanoparticles with the CuS nanoparticles as photothermal agents and mesoporous SiO2 for the loading of uPA were synthesized using a facile hydrothermal method. The CSP core-shell nanoparticles were demonstrated to possess excellent photothermal performance, exhibiting a photothermal conversion efficiency of up to 52.8%. Due to the mesoporous SiO2 coating, the CSP core-shell nanoparticles exhibited appropriate pore size, high pore volume, and large surface area; thus, they showed great potential to be used as drug carriers. Importantly, the release of uPA from CuS@mSiO2-PEG/uPA (CSPA) carriers can be promoted by the NIR laser irradiation. The drug loading content of uPA for the as-prepared NIR-triggered drug delivery system was calculated to be 8.2%, and the loading efficiency can be determined to be as high as 89.6%. Due to the excellent photothermal effect of CSP nanocarriers, the NIR-triggered drug delivery system can be used for infrared thermal imaging in vivo. The in vivo thrombolysis assessment demonstrated that the NIR-triggered drug delivery system showed excellent thrombolytic ability under the irradiation of an 808 nm laser, showing the combined therapy for thrombolysis. As far as we know, the CSPA core-shell nanoparticles used as NIR-triggered drug delivery systems for thrombolysis have not been reported.</jats:p