Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network.

Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods.

Encoder-decoder with densely convolutional networks for monocular depth estimation.

We propose an encoder-decoder with densely convolutional networks model to recover the depth information from a single RGB image without the need for depth sensors. The encoder part serves to extract the most representative information from the original data through a series of convolution operations and to reduce the resolution of the spatial input feature. We use the decoder section to produce an upsampling structure that improves the output resolution. Our model is trained from scratch, without any special tuning process, and uses a new optimization function to adaptively learn the rate. We demonstrate the effectiveness of the method by evaluating both indoor and outdoor scenes, and the experimental results show that our proposed approach is more accurate than competing methods.

Study on field experiments of forest soil thermoelectric power generation devices.

As a new strategy to power forest wireless sensors in remote areas, an environmental microenergy collection device has been improved, and field experiments were carried out under natural conditions for the first time. The thermoelectric power generation devices used a gravity-assisted heat pipe to transmit heat from shallow soil to ground level, and a thermoelectric generator (TEG) was employed to generate electric power from the temperature difference between soil and air. Over the 6-month experimental period at two natural sites, approximately 128.74 J of energy could be harvested in a single day, and 5 209.92 J of energy could be harvested in a generation cycle. The results showed the feasibility of using this green energy to power wireless sensors in remote forests or other environments, This work is relevant to the current acute energy shortages and environmental pollution problems.

Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment.

Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented.

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization.

Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

Predicting Depth from Single RGB Images with Pyramidal Three-Streamed Networks.

Predicting depth from a monocular image is an ill-posed and inherently ambiguous issue in computer vision. In this paper, we propose a pyramidal third-streamed network (PTSN) that recovers the depth information using a single given RGB image. PTSN uses pyramidal structure images, which can extract multiresolution features to improve the robustness of the network as the network input. The full connection layer is changed into fully convolutional layers with a new upconvolution structure, which reduces the network parameters and computational complexity. We propose a new loss function including scale-invariant, horizontal and vertical gradient loss that not only helps predict the depth values, but also clearly obtains local contours. We evaluate PTSN on the NYU Depth v2 dataset and the experimental results show that our depth predictions have better accuracy than competing methods.

Fine-grained diabetic wound depth and granulation tissue amount assessment using bilinear convolutional neural network.

Diabetes mellitus is a serious chronic disease that affects millions of people worldwide. In patients with diabetes, ulcers occur frequently and heal slowly. Grading and staging of diabetic ulcers is the first step of effective treatment and wound depth and granulation tissue amount are two important indicators of wound healing progress. However, wound depths and granulation tissue amount of different severities can visually appear quite similar, making accurate machine learning classification challenging. In this paper, we innovatively adopted the fine-grained classification idea for diabetic wound grading by using a Bilinear CNN (Bi-CNN) architecture to deal with highly similar images of five grades. Wound area extraction, sharpening, resizing and augmentation were used to pre-process images before being input to the Bi-CNN. Innovative modifications of the generic Bi-CNN network architecture are explored to improve its performance. Our research generated a valuable wound dataset. In collaboration with wound experts from University of Massachusetts Medical School, we collected a diabetic wound dataset of 1639 images and annotated them with wound depth and granulation tissue grades as labels for classification. Deep learning experiments were conducted using holdout validation on this diabetic wound dataset. Comparisons with widely used CNN classification architectures demonstrated that our Bi-CNN fine-grained classification approach outperformed prior work for the task of grading diabetic wounds.

Effects of Electrode Material on the Voltage of a Tree-Based Energy Generator.

The voltage between a standing tree and its surrounding soil is regarded as an innovative renewable energy source. This source is expected to provide a new power generation system for the low-power electrical equipment used in forestry. However, the voltage is weak, which has caused great difficulty in application. Consequently, the development of a method to increase the voltage is a key issue that must be addressed in this area of applied research. As the front-end component for energy harvesting, a metal electrode has a material effect on the level and stability of the voltage obtained. This study aimed to preliminarily ascertain the rules and mechanisms that underlie the effects of electrode material on voltage. Electrodes of different materials were used to measure the tree-source voltage, and the data were employed in a comparative analysis. The results indicate that the conductivity of the metal electrode significantly affects the contact resistance of the electrode-soil and electrode-trunk contact surfaces, thereby influencing the voltage level. The metal reactivity of the electrode has no significant effect on the voltage. However, passivation of the electrode materials markedly reduces the voltage. Suitable electrode materials are demonstrated and recommended.