A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay

LAUN Improved StarGAN for Facial Emotion Recognition

In the field of facial expression recognition, deep learning is extensively used. However, insufficient and unbalanced facial training data in available public databases is a major challenge for improving the expression recognition rate. Generative Adversarial Networks (GANs) can produce more one-to-one faces with different expressions, which can be used to enhance databases. StarGAN can perform one-to-many translations for multiple expressions. Compared with original GANs, StarGAN can increase the efficiency of sample generation. Nevertheless, there are some defects in essential areas of the generated face, such as the mouth and the fuzzy side face image generation. To address these limitations, we improved StarGAN to alleviate the defects of images generation by modifying the reconstruction loss and adding the Contextual loss. Meanwhile, we added the Attention U-Net to StarGAN's generator, replacing StarGAN's original generator. Therefore, we proposed the Contextual loss and Attention U-Net (LAUN) improved StarGAN. The U-shape structure and skip connection in Attention U-Net can effectively integrate the details and semantic features of images. The network's attention structure can pay attention to the essential areas of the human face. The experimental results demonstrate that the improved model can alleviate some flaws in the face generated by the original StarGAN. Therefore, it can generate person images with better quality with different poses and expressions. The experiments were conducted on the Karolinska Directed Emotional Faces database, and the accuracy of facial expression recognition is 95.97%, 2.19% higher than that by using StarGAN. Meanwhile, the experiments were carried out on the MMI Facial Expression Database, and the accuracy of expression is 98.30%, 1.21% higher than that by using StarGAN. Moreover, experiment results have better performance based on the LAUN improved StarGAN enhanced databases than those without enhancement

Design on the Winter Jujubes Harvesting and Sorting Device

According to the existing problems of winter jujube harvesting, such as the intensive labor of manual picking, damage to the surface of winter jujubes, a winter jujube harvesting and sorting device was developed. This device consisted of vibration mechanism, collection mechanism, and sorting mechanism. The eccentric vibration mechanism made the winter jujubes fall, and the umbrella collecting mechanism can collect winter jujube and avoid the impact of winter jujube on the ground, and the sorting mechanism removed jujube leaves and divided the jujube into two types, and the automatic leveling mechanism made the device run smoothly in the field. Through finite element analysis and BP (Back Propagation) neural network analysis, the results show that: The vibration displacement of jujube tree is related to the trunk diameter and vibration position; the impact force of winter jujubes falling is related to the elastic modulus of umbrella material; the collecting area can be increased four times for each additional step of the collection mechanism; jujube leaves can be effectively removed when blower wind speed reaches 45.64 m/s. According to the evaluation standard grades of the jujubes harvesting and sorting, the device has good effects and the excellent rate up to 90%, which has good practicability and economy

Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure

FER Based on Fusion Features of CS-LSMP

Local feature descriptors play a fundamental and important role in facial expression recognition. This paper presents a new descriptor, Center-Symmetric Local Signal Magnitude Pattern (CS-LSMP), which is used for extracting texture features from facial images. CS-LSMP operator takes signal and magnitude information of local regions into account compared to conventional LBP-based operators. Additionally, due to the limitation of single feature extraction method and in order to make full advantages of different features, this paper employs CS-LSMP operator to extract features from Orientational Magnitude Feature Maps (OMFMs), Positive-and-Negative Magnitude Feature Maps (PNMFMs), Gabor Feature Maps (GFMs) and facial patches (eyebrows-eyes, mouths) for obtaining fused features. Unlike HOG, which only retains horizontal and vertical magnitudes, our work generates Orientational Magnitude Feature Maps (OMFMs) by expanding multi-orientations. This paper build two distinct feature maps by dividing local magnitudes into two groups, i.e., positive and negative magnitude feature maps. The generated Gabor Feature Maps (GFMs) are also grouped to reduce the computational complexity. Experiments on the JAFFE and CK+ facial expression datasets showed that the proposed framework achieved significant improvement and outperformed some state-of-the-art methods

Longitudinal Ultrasonic Guided Waves for Monitoring the Minor Crack of Rotating Shaft with Galfenol Transducer

The safety of rotating machinery is important for the manufacture industry. As the core component of the rotating machinery, the rotating shaft with racks or damages will lead to the terrifically disaster. Compare with the vibration signal diagnosis of the rotating shaft, it presents a novel method to detect crack on rotating shaft with longitudinal ultrasonic guided wave. In the view of elastic wave propagating mechanism in the cylindrical waveguide, it discuss the influence of rotor dynamics effect on the system, from the govern equation of wave propagating in rod, it consider the lateral effect of the rotating shaft, the reflection and transmission of the guided wave in the waveguide. It takes the Galfenol transducer as a pulse-echo transducer to detect the crack on the rotating shaft. Finally, it gives the simulation of experiment results, the results indicated the method can locate the crack accurately after conducting the wavelet transformation on the difference between cracked and non-cracked rotating shaft experiment waveform

Gallium-assisted diffusion bonding of stainless steel to titanium; microstructural evolution and bond strength

Strong joints between stainless steel 304L and pure titanium (grade-2) were made using the novel method of “gallium-assisted diffusion bonding”. The microstructural evolution and interfacial reactions were investigated in detail. The possible mechanisms of phase changes at the joint interface when bonding with and without a nickel interlayer were identified. Layers of FeTi and (Fe,Cr)2Ti intermetallic compounds were found at the reaction zone in the case of direct bonding, whereas (Fe,Ni)Ti and Fe2Ti phases were identified in the reaction zone of the samples bonded using nickel interlayers. A layer of αFe was observed on the steel side of the reaction zone in both the cases, probably due to the enrichment of Cr at the interface. The diffusion of gallium led to formation of a layer of αTi, while the diffusion of Fe and Ni assisted in the formation of a duplex (α+β)Ti phase in the inter-diffusion zone. The joints fractured along the intermetallic layers at the interface, during tensile testing, with limited ductility. The maximum tensile strengths of the bonded samples were 280 and 313 MPa with and without nickel interlayer, respectively. The latter equals 92% of the tensile strength of the pure grade-2 titanium used in this work (i.e. 340 MPa)

Effects of inkjet printed toughener on delamination suppression in drilling of carbon fibre reinforced plastics (CFRPs)

Delamination has been recognised as the predominant damage induced during the drilling of carbon fibre reinforced plastics (CFRPs). It could significantly reduce the bearing capacity and shorten the service life of the designed component. To enhance the delamination resistance of CFRPs for different applications, great affords have been done to improve their interlaminar fracture toughness. However, due to the difficulty in accurately controlling the amount of the toughener applied in the interface, effect of the toughener content on the toughening efficiency is rarely studied. In this work, an experimental research was developed to investigate the performance of the toughener on the improvement of delamination resistance in the drilling of CFRPs and parametrically optimise the toughener content with the consideration of different feed rates. Specifically, poly(methyl methacrylate) (PMMA) solutions with various concentrations were selected to add on the CFRP prepreg, and co-cured together with layups. The inkjet printing technology was adopted to deposit the PMMA solutions for precisely controlled toughener contents. Through drilling experiments on the toughened CFRPs, it was found that the optimal content of the PMMA solution was 10 wt% to offer the least delamination, in particular, for the situation under the highest feed rate condition. The toughing mechanisms were also concluded by analysing the histories of the thrust force and torque in the drilling process. The results of this study is significantly contribute to the locally toughening of the composite interfaces and the improvement of the drilling quality, which is specifically helpful to strengthen the joint property for the structural design stage for the aircraft