• Energy Research

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

Abstract Z-scheme composites are considered as desirable photocatalysts. However, most of them are heterostructure and Z-scheme homojunction have been rarely reported. In this paper, for the first time, Z-scheme anatase/rutile homojunction was obtained by Ag nanoparticles (NPs) induction. Polystyrene spheres modified by Ag NPs were used as sacrificial templates to prepare three dimensionally ordered macroporous (3DOM) anatase. Incredibly, Ag NPs could induce the formation of rutile from anatase and anatase/rutile homojunction was created. High-resolution transmission electron microscopy clearly revealed the structure of anatase-Ag-rutile. Both experimental and theoretical evidences were utilized to study the induction of Ag NPs. Compared to commercial P25 and control samples, anatase-Ag-rutile composites exhibited obviously enhanced photocatalytic hydrogen evolution and disinfection under simulated solar light. The enhanced photocatalytic activities could be mainly attributed to the Z-scheme electron migration mechanism in ternary composites, which was confirmed via active species trapping experiment, electron spin resonance spectra and surface redox reactions.

Abstract Z-scheme composites are considered as desirable photocatalysts. However, most of them are heterostructure and Z-scheme homojunction have been rarely reported. In this paper, for the first time, Z-scheme anatase/rutile homojunction was obtained by Ag nanoparticles (NPs) induction. Polystyrene spheres modified by Ag NPs were used as sacrificial templates to prepare three dimensionally ordered macroporous (3DOM) anatase. Incredibly, Ag NPs could induce the formation of rutile from anatase and anatase/rutile homojunction was created. High-resolution transmission electron microscopy clearly revealed the structure of anatase-Ag-rutile. Both experimental and theoretical evidences were utilized to study the induction of Ag NPs. Compared to commercial P25 and control samples, anatase-Ag-rutile composites exhibited obviously enhanced photocatalytic hydrogen evolution and disinfection under simulated solar light. The enhanced photocatalytic activities could be mainly attributed to the Z-scheme electron migration mechanism in ternary composites, which was confirmed via active species trapping experiment, electron spin resonance spectra and surface redox reactions.

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.