Visual navigation based on deep reinforcement learning requires a large amount of interaction with the environment, and due to the reward sparsity, it requires a large amount of training time and computational resources. In this paper, we focus on sample efficiency and navigation performance and propose a framework for visual navigation based on multiple self-supervised auxiliary tasks. Specifically, we present an LSTM-based dynamics model and an attention-based image-reconstruction model as auxiliary tasks. These self-supervised auxiliary tasks enable agents to learn navigation strategies directly from the original high-dimensional images without relying on ResNet features by constructing latent representation learning. Experimental results show that without manually designed features and prior demonstrations, our method significantly improves the training efficiency and outperforms the baseline algorithms on the simulator and real-world image datasets. Autonomous navigation is a prerequisite for mobile robots to accomplish complex tasks. Visual navigation is receiving increasing attention due to its advantages of rich information and low cost. Conventional visual-navigation systems incorporate modules for simultaneous localization and mapping (SLAM) ,2, path To enhance training efficiency and improve navigation performance, Du  proposedKulhánek  employed(1)A general DRL framework for target-driven visual navigation is established, in which multiple self-supervised auxiliary tasks can be integrated easily.(2)Use the LSTM-based dynamics model to establish effective representation learning for navigation tasks, which significantly improves the learning efficiency of DRL.(3)An attention-based image reconstruction model is designed to pay more attention to the critical regions in the navigation task, making it easier for the navigation agents to understand the scene and make decisions more accurately.(4)The effectiveness of our method was validated in a simulator. However, due to the closer resemblance of real-world image datasets to the actual environment, we further validated the performance of our method on real-world image datasets.The remaining sections of the paper are organized as follows: DRL-based visual navigation has received significant attention in recent years. Oh  utilizedVisual auxiliary tasks have been explored in visual navigation. Jaderberg  extendedThe proposed approach differs from previous works in several key aspects. This paper focuses on the agent’s direct learning of navigation strategies from visual inputs without relying on ResNet features and semantic information. This paper also employs a series of self-supervised auxiliary tasks, which do not require labeled images and can be trained directly using real-world images. Additionally, instead of using the inverse dynamics model like , this pat, the state of the environment is denoted as RL is a trial-and-error approach that relies on the continuous interaction between an agent and its environment. In visual navigation tasks, the goal of the navigation agent is to find the optimal set of actions from the current location to the target image. The agent receives the observed image and the target image as inputs. Suppose that at time step Continuously performing the above process, the agent attempts to learn action strategies by continuously interacting with a given environment, and the optimization goal of RL agent can be formally described as an objective function that maximizes the expected cumulative reward, as in Equations (1) and (2):There are three primary categories of methods for optimizing optimal strategies The actor-critic algorithm, initially developed for decision problems involving continuous action spaces , consistThe advantage function, denoted as lized by . The finTo fully leverage computational resources, parallel training is commonly employed in DRL-based methods. One popular algorithm for parallel training is A3C , which uThis paper captures the observation and target images from a monocular camera in a first-person view. The inclusion of target images in the state space aims to enable the agent’s learning process by navigating between different targets and enhancing the generalization performance of the navigation system. Furthermore, this paper incorporates the last action and the last reward as part of the state information, which gives the agent complete information.In this paper, the action space for the agent is discrete and defined as follows: A = {move forward, move backward, turn left, turn right, terminate}. Unlike the approaches described in ,41, whicIn our approach, we give the agent a reward of 1 when it completes the navigation task, which encompasses two primary standards: (i) the agent reaches the target, or (ii) the agent stops using the termination action. If the agent stops using the termination action without reaching the target , a negative reward of −0.1 is assigned. In all other cases, the reward is set to 0. The reward function is defined as:In this paper, the LSTM-based dynamics model, the attention-based image reconstruction model, the pixel-control model, the depth map prediction model, and the reward prediction model are jointly trained as auxiliary tasks with the navigation task. The overall framework of the model is shown in the following The PAAC algorithm runs multiple agents in many parallel environments, each generating experience trajectories as it interacts with the environment. The experience trajectories are randomly sampled in the experience buffer and used to update the policy parameters. A replay buffer similar to DQN is used to improve data utilization. Data are sampled from the replay buffer to train the auxiliary tasks. In this paper, the LSTM-based dynamics model, pixel-control model, depth prediction model, reward prediction model, and value replay model are trained offline using data sampled from the replay buffer, and the attention-based image reconstruction model and policy network are trained online using data from the experience buffer. The self-supervised auxiliary tasks and the navigation task can be combined by sharing convolutional layers. Specifically, the first few convolutional layers of the feature-extraction module can be shared. By sharing the convolutional layers, the self-supervised auxiliary tasks can receive a richer representation of the environment and improve learning efficiency. Meanwhile, the navigation task can benefit from the additional supervised signals provided by the self-supervised auxiliary tasks to improve the model’s performance.The network input of the PAAC algorithm is the target image and the observed image, followed by the feature extraction module. The feature extraction module in this paper mainly uses a convolutional neural network. Each image enters a separate stream consisting of a convolutional layer with shared weight parameters. The output of the convolutional layers is stitched together and passed to two additional convolutional layers, followed by fully connected layers, each layer followed by an activation function. The features of the convolutional layers are spanned by the fully connected layers and merged with the last action and the last reward, fed to the LSTM layer, and finally to the actor and critic networks. Among them, the feature extraction module extracts the features of the input image through the convolutional layer, using shared weight parameters to enable the sharing of the parameters of the feature extraction process between different inputs to reduce the number of parameters and improve the training efficiency, as shown in This paper reconstructs the target image from the observed image for the navigation task . The speThe decoder takes Consider the states LSTM is capable of modeling sequences and capturing long-term dependencies within input sequences. Integrating LSTM with dynamic auxiliary tasks allows the model to capture intricate patterns and structures in the input sequence, leading to a more robust feature representation. In this study, we adopt a residual network to fit the forward dynamics model. This choice is motivated by the observation representation’s relatively large dimension and the actions’ discrete nature. Utilizing residual networks allows for the better preservation of the action information during the computation of the forward model, resulting in an improved representation of the environment.The network inputs are adjacent observed images, passing each image into a separate stream consisting of two convolutional layers with shared-weight parameters. The first convolutional layer is shared with RL, followed by a fully connected linear layer, which is then passed to the LSTM layer to obtain the feature vectors The pixel-control auxiliary task defines an additional pseudo-reward function to maximize the absolute pixel change. The additional pseudo-reward function is the pixel-level difference between the current and next states. This additional pseudo-reward function is usually combined with the task’s reward to achieve better training performance. Similar to , in the The input to the pixel-control network is the target image and the observed image. The structure of the feature-extraction module is the same as that of the PAAC algorithm. The output of the LSTM in the feature extraction module is connected to the pixel-control module, which consists mainly of a deconvolution layer that up-samples the low-dimensional features back to the size of the down-sampled observations. For each action, the last layer outputs an estimate of the action value for each pixel.The reward prediction auxiliary task provides additional learning signals by predicting external reward signals in the environment, thus helping the agent better learn the navigation task. In navigation tasks, reward signals in the environment may be sparse or only present when the task is completed, making it difficult for agents to learn information about how to act from the reward signals. Using reward prediction as an auxiliary task can help the agent learn more about the reward signals, thus improving its learning efficiency and performance.Referring to , in thisThe input of the depth prediction model was the target image and the observed image. The convolutional layer part of the feature extraction module has the same structure as the PAAC algorithm, and the output of the convolutional layer part is spanned by a fully connected layer and then reconstructed by decoding the features through a deconvolutional neural network. The loss function for depth prediction is usually calculated using the mean-squared error, and the sum of squares of the pixel-by-pixel difference between the predicted depth image and the actual depth image was found to be the mean value.The navigation performance of our method was evaluated using simulation environments and real-world image datasets. We demonstrated the improvement in training efficiency by incorporating an LSTM-based dynamics model and attention-based image reconstruction model as auxiliary tasks through ablation experiments.The optimizer uses RMSprop, and the training uses a workstation with NVIDIA GTX 3060 GPU with 12 GB of VRAM, an Intel Core processor i5-12600KF (3.90 GHz × 12), and 16 GB of DDR4 RAM. The detailed training parameters are shown in The evaluation metrics in this paper include the navigation success rate, and the mean traveled distance and the mean number of simulation steps.The success rate (SR): The proportion of the number of times an agent successfully arrives at a specified target location within a specified time step to the total number of tasks. The navigation success rate is one of the critical metrics for evaluating the performance of a visual navigation model because it directly reflects the ability of the model to complete the navigation task. A higher success rate usually indicates a better performance.The mean traveled distance (MT): The distance traveled by the agent from the starting position to the target position. For the same navigation task, if the model has better navigation performance, then the path length it travels should be shorter, and therefore its mean traveled distance should be smaller.The mean number of simulation steps (MS): The number of simulation steps  is usually the number of steps required for the model to make inferences and decisions. Fewer simulation steps usually indicate a more efficient model. Since the time and computational resources required for model inference and decision-making are usually large, reducing the mean number of simulation steps can reduce the time and computational cost of the navigation task, thus improving the efficiency of the navigation model.In this paper, the performance of the visual navigation was compared with the following models:PAAC : PAAC isUNREAL : UNREAL A2C-VN : A2C-VN Dmhouse simulator: Kulhánek  developeIn this paper, the navigation performance of the proposed method was evaluated in an environment generated by a Dmhouse simulator and compared with related baseline algorithms. We randomly sampled the starting position and the target. The algorithm had trained The evaluation was performed in a 100 randomly generated environment with randomly selected starting positions and targets for 1000 simulations, and the results are shown in the following Results and analysis: As seen in Real-world datasets:  used theIn this paper, the navigation performance of the proposed method was evaluated in the context of real-world datasets and compared with related baseline algorithms. We randomly sampled the starting position and the target. The algorithm trained for τ starts at 0.3 and gradually increases during the training to 1.0.The initial state was sampled from the uniform probability distribution over the set of possible initial states closer to any target than The training performance is shown in The starting position and target were randomly selected. The results of the average number of simulated steps, the average navigation success rate, and the average navigation distance calculated over 1000 simulations are shown in the following Results and analysis: As seen in We also evaluated the navigation performance of the proposed method in this paper in the context of real-world datasets using features trained in Dmhouse as pre-trained features. The same as before, the algorithm trained for The starting position and target were randomly selected. The results of the average number of simulated steps, the average navigation success rate, and the average navigation distance calculated over 1000 simulations are shown in the following Results and analysis: As seen from This subsection performs ablation of our method to understand the results further. “Ours” is the multi-auxiliary tasks visual-navigation algorithm proposed in this paper, “Ours w/o Att” is the attention-based image-reconstruction model removed, and “Ours w/o Att and Dyn” is the attention-based image-reconstruction model and the LSTM-based dynamics model removed. The algorithm was trained for As seen in This paper proposes multiple self-supervised auxiliary tasks for DRL-based target-driven visual navigation. The LSTM-based dynamics auxiliary task utilizes the LSTM network to help the dynamics model better capture the complex patterns and structures in the input sequence, thus providing a more robust feature representation for the navigation task. The attention-based image reconstruction auxiliary task uses attention networks to reconstruct the target image from the observed image. By introducing the attention networks, the model can pay more attention to the critical regions in the navigation task while generating the scene map, making it easier for the navigation agent to understand the scene and thus make more accurate decisions. Experimental results on the simulator and real-world image datasets show that the method proposed in this paper outperforms the robust baseline algorithms.Further research can be conducted based on this paper. Since the usefulness of auxiliary tasks may change during the learning process, the adaptive adjustment of the weights of multiple self-supervised auxiliary tasks can be considered so that the auxiliary tasks can help the performance of the navigation task the most. We can also consider deploying our model on a real robot.


This article tests the hypothesis that “the likelihood that the species will react and level at which they do to the unmanned aerial vehicle (UAV) is related to the altitude, number of passes, sound intensity, type of UAV, takeoff distance, and species.” This paper examined the behavioral responses of a group of free ranging ungulate species  found in an animal reserve in Namibia to the presence of different in-flight UAV models. The study included 397 passes  over 99 flights at altitudes ranging from 15 to 55 meters in three categories of response level: No response, Alert, and Movement. The ungulates were unhabituated to the UAVs and the study was conducted in the presence of stress-inducing events that occur naturally in the environment. Certain species were found to be more reactive than others, in addition to several displaying different response levels in single or mixed herd environments. Zebras were found to be less responsive in mixed herd environments while Oryx were present, as compared to when the Oryx were not; suggesting that some species may respond based on other species perception of threat or their relative fitness levels. The UAVs also produced inconsistent response rates between movement and alert behavior. The reference vehicle, Phantom 3 was much more likely than the Mavic to induce an alert response, while both having similar probabilities of inducing a movement response. Furthermore, the Custom X8 showed significantly more alert and movement responses than the other UAVs. This shows there may be several aspects to the UAVs that affect the responses of the ungulates. For instance, the sound intensity may alert the species more often, but close proximity may induce a movement response. More generally, the data shows that when the UAV is flying above 50 meters and has a measured sound intensity below 50 dB, the likelihood of inducing a movement response on an ungulate species is below 6% regardless of the vehicle on the first pass over the animals. Additionally, with each subsequent pass the likelihood of response dropped by approximately 20 percent. The results suggest a stronger correlation between flight altitude and response across the different ungulates, and the evidence suggests rapid habituation to the UAVs. The highly valued ungulates in Africa’s savanna, making up more than 25% of its landscape, might be effectively monitored by unmanned aerial vehicles (UAV) . The queSeveral papers have already examined the effect of UAV altitude on non-ungulate response . Biologists found that bears have an increased heart rate when the UAV flies 200 meters from the bear, 20 meters above it . ResearcNote that the AWM engineer or designer, when laying out a system of UAVs to monitor a field, would begin by selecting a flight altitude after which vehicles and a communication network would be selected, and in more advanced systems sophisticated data patching methods and graphical interfaces employed . Other pIn general, the level of danger that an ungulate perceives strongly influences its response. The perceived danger that a UAV poses could come from the ungulate-UAV distance, sensed by an auditory signal, or it could come from secondarily observing the responses of neighboring species who have already responded to the UAV. In Africa’s savanna, one finds many ungulates in mixed herds so one could expect neighboring herds to trigger a response. The usual source of the danger to the ungulate comes from the ground, not the air. Therefore, the level of response could be moderate or low depending on flight altitude, while differing from species to species. One also expects the responses to depend strongly on the environment. The time of day (before or after eating), the proximity of nearby animals posing a danger, and the passing by of vehicles or other stress inducing events would be expected to influence the responses.This paper outlines one of the first longitudinal studies of animal response by a UAV. These operations were conducted in a harsh environment, under high altitude and high heat conditions, where maintaining regular operations was difficult. Locating animals to perform flights was difficult and time consuming and often required considerable hiking for even a limited number of passes and in some circumstances no passes due to failed preflight checks after problems in the field. Even under these unfavorable conditions, the team was able to persevere and acquire 397 passes  over 99 flights.how these ungulates located in the Savannah respond to UAV presence. Specifically, we determine how representing the effects of UAV presence with independent variables that are either continuous, ordinal categorical or qualitative nominal classes might impact our understanding of what drives species reaction. We hypothesized that: (i) altitude (or proximity) is a driving factor and that (ii) the animals will undergo habituation because these ungulates are not normally predated by an aerial foe. To test our hypotheses, we focus on monitoring the wildlife under a variety of conditions in their natural environment through a large data set to see if these factors emerge.In this article we examine The method section describes the study site, the wildlife species monitored, the data collection process, the aerial vehicles, and the data collected. The results section gives the ungulate responses, the occurrences of responses and discusses the data and secondary factors. Finally, the paper summarizes the results and draws conclusions.2, located on Namibia’s central plateau, and its elevation is 1600 to 1800 meters. From September to November, the average temperature is 20 to 23 C, and the average rainfall is 10 mm in September, 10 mm in October, and 30 mm in November. The land is a semi-closed eco-system surrounded by an 8-foot game fence that includes free ranging leopards and cheetahs and the species are unhabituated to UAVs. Oryx gazella), Eland (Taurotragus oryx), Giraffe , Springbok , Hartebeest (Alcelaphus buselaphus), Plains Zebra (Equus quagga), and Kudu (Tragelaphus strepsiceros).The study was conducted at a wildlife sanctuary (N/a’an ku se) located about 40 km east of Windhoek, Namibia’s capital city, over a ten-week period in 2017 from September to November. Permits to operate UAVs of this size were not necessary at the time of flight within the country. The landowners are coauthors on the article and helped identify the need to understand UAV and animal interaction for safe monitoring of wildlife for future use of UAVs in Africa’s savannah. North Carolina State Universities Animal Care and Use Committee (IACUC) was contacted to ensure the welfare of the animals was not governed by any rules or regulations given the nature of the study protocol. They determined that no IACUC review was required for this study. The land is 25 kmA field unit of 12 students collected data over a 10-week period (See the acknowledgement section). The field unit formed data collecting sub-units, each consisted of four personnel: (1) a pilot, (2) a primary observer, (3) a ground camera operator, and (4) a data recorder. The pilot was responsible for flying the UAV. When flying in radio controlled (RC) mode, the pilot was also responsible for operating the flight camera and maintaining visual contact with the study species from the air. The primary observer was responsible for observing the species and maintaining visual contact with the ground station. The primary observer worked next to the data recorder and relayed all of their wildlife response observations throughout the flight to the recorder. The ground camera operator was responsible for photographing/video recording the responses. The data recorder was responsible for annotating information relayed from the primary animal observer on a flight-data collection sheet.The purpose of this study was to assess ungulate responses to UAVs flying at different altitudes in Africa’s arid savannah. Several considerations were vital when setting up the study. First, to ensure minimally impacted studies from human interaction, the study should be conducted in a manner as unobtrusive to the ungulate wildlife such that little or no levels of habituation are required. Furthermore, when none of the conditioning is negative, habituation would only tend to decrease the altitude that an ungulate tolerates. Therefore, for the purposes of this study, it was reasonable to focus on unhabituated ungulates. Secondly, in order for the results to apply to the natural habitat of the savannah, it was also important to perform the study in the mixed herd environment in the presence of its naturally occurring stress inducing events. The variable conditions of the natural environment introduce noise into the study which increases the difficulty to discern the effect of altitude on the response data. However, it can also help determine whether or not UAV altitude is a dominant factor against the other factors, as hypothesized.The ungulate response was gathered at four levels. The levels were designated by the following: No Response (no discernable movement), Alert (eye/head movement and/or turning of the upper body), Relocation (moving out of the way of the UAV but still in sight), and Flee (significant movement from UAV location). Due to the very low number of flee responses and the similarity of the movement aspect, the two location movement responses (Relocation and Flee) were combined into one category (Movement), which was categorized as relocation away from the UAV. Therefore, the data were analyzed by three categories .The suite of UAVs consisted of the DJI Phantom 3 (the reference vehicle), DJI Mavic Pro, Custom X8 (Also referred to as an Octocopter), and Sky Eye. As shown, the loudest to quietest vehicles were the Custom X8, Phantom 3, Skyeye and Mavic Pro. Noise disturbance is not only influenced by the noise which can be qualified by sound intensity (dB) and frequency (Hz), but also by its duration and pattern. The age and physiological state of the animal at the time of exposure, the exposure history of the animal, and the predictability of the acoustic stimulus can also play a role . The effThe design of the experiment began with determining a range of flight altitudes. The range of flight altitudes were determined through 23 preliminary flights, flying at altitudes from 20 meters to 100 meters in 10-meter increments. We determined that 15 meters was a suitable lower bound because of safety concerns and that 55 meters was a suitable upper bound because negative response had already significantly dropped off by that altitude. The data collection process began every morning with locating animal herds and setting up a field unit nearby. Local personnel understood the best locations to set up field units and launch the UAVs. As shown in The horizontal distances between the animals and the field unit points varied widely–between 50 and 600 meters (measured in 25 m increments). The total data set consisted of 397 passes  over 99 flights, however, the analyzed dataset not including preliminary flights was 346 passes over 76 flights that ranged in altitude from 15 to 55 meters. Each pass began with taking off from a launch point, flying about 200 m away from a spotted animal group, changing altitude as appropriate, and then flying 400 m, passing over the animal group. The UAV was flown over the animal or herd at 10 m/s. The next pass was flown at another randomly selected altitude. If a movement response was invoked (relocation or flee), the UAV would wait at a distance while the animals would settle back to a sedentary behavior. Passes were continued until battery levels were too low to continue or the animals were no longer within sight. An ordinal logistical regression was chosen due to the ordered nature of the response between No Response, Alert, and Movement. These models were developed using R 4.0.5 (R Development Core Team 2021). Several model generating techniques were used from three packages, the polr function from the MASS package , vglm frThe first model was started by using the polr function, which comes from proportional odds logistical regression, in the MASS package . Considering the large number of variables, the final models’ variables were chosen using the Akaike information criterion (AIC). The AIC is an estimator of the relative quality of a model for a given data set by dealing with the tradeoff between goodness of fit and simplicity. The variables were first tested for independence before entering the model using the chi-squared test for independence. The best model was chosen by minimizing AIC to an extent and in close situations allowing the discretion of the researchers. Several iterations using the step function were run in forward, backward, and both direction configuration to confirm the best fitting model. An initial model containing all variables was fitted and run with an ANOVA type II test for significance of variables. This all-variable model was compared to the best fitting model to evaluate variable contributions. The variables that were left out were found to not have meaningful influence or significance. The final model was then chosen after analyzing that there were sufficient data to create a good fitting model given the current data set. Furthermore, the model was then tested to meet the proportional odds assumption and ensured that the model was an accurate representation of the data through various methods detailed in the results.The data set used in analysis did not include preliminary flights for testing and was comprised of 346 passes over 76 flights. The data consisted of species reactions . Of these reactions, 164 (47.4%) were No Response, 115 (33.2%) were Alert, and 67 (19.4%) were Movement. The research hypothesis posed to the data was that “the likelihood that the species will react and level at which they do to the UAV is related to the altitude, number of passes, sound intensity, type of UAV, takeoff distance, and species.” Thus, the outcome variable, Response, was the herds reaction to the UAV after a pass taken overhead. The predictors were Altitude , Pass (the pass number over the herd during a given flight), Drone , Sound Intensity , takeoff distance (herd distance from ground crew at takeoff measuring in meters), as well as each animal species with sufficient samples which were Oryx, Giraffe, Springbok, and Zebra in both single species and mixed group.The relative abundance of the different species varies from site to site and by season. As previously stated, the data were broken up by distinguishing between a single species encounter and a mixed herd encounter. Eland, Hartebeest, Impala, and Waterbuck were also found on the property; however, they were not passes over in enough flights to include in the species analysis. Therefore, the species predictors used were Oryx Single, Zebra Single, Springbok Single, Giraffe Single, Oryx Mixed, Zebra Mixed, Springbok Mixed, and Giraffe Mixed.A 13-predictor ordinal logistical regression was fitted to the data to test the hypothesis relationship regarding the likelihood that a species will react based on the flight altitude, number of passes, sound intensity, type of UAV, takeoff distance, and species. The Akaike Information Criterion (AIC) was used to find the best fitting model. This was done by eliminating predictors to minimize the models AIC score. This process was done in a backwards , forward (Starting with no predictors and adding), and both directions (starting with a number of predictors and adding and eliminating based on AIC score) to find the best fitting model. Two models were found with similar AIC, both with the same predictors except one contained altitude and the other dB. Interestingly, the sound intensity predictor was calculated from the altitude but did not show any correlation when tested. This was likely due to the large difference in the level of sound produced by the different UAVs.The parallel lines test was done to test proportional odds assumption by comparing it to a general model . The comparison was done between the general model and using the Chi-Squared test to the multinomial logistical regression using the multinom function from the nnet package . The tesThe models were then developed as partial proportional odds models relaxing the proportional odds assumption for ZebraMixed and Drone. Finally, the model was tested again against the more general model and the general model was not found to be a significantly (p<0.05) better fit. The following table is the summary of the model. Evaluation of the models were done by taking an overall model evaluation, statistical tests of the individual predictors, goodness of fit statistics, and validation of predicted probabilities.The first models developed were those without interaction effects. The two best fitting models are listed in Model 1: Response ~ Altitude + Pass + Drone + GiraffeSingle + ZebraMixedModel 2: Response ~ dB + Pass + Drone + GiraffeSingle + ZebraMixedThe overall model evaluation was done by comparing the model to an intercept only model . Consequently, the significant chi-squared statistic  = 0) for model 1 and  = 0) for model 2 indicates the models containing predictors give a significant improvement over the baseline intercept-only model.The statistical significance of individual regression coefficients  is tested using the Wald chi-squared statistic . As seen2 (df = 9) of 7.6884, model2 = X2 (df = 9) of 11.965) and was insignificant (p>0.05), suggesting the models fit well to the data confirming the null hypothesis. Similar findings were concluded with the PR Chi-Squared test and the PR deviance test both yielding significant values (p>0.05) and confirming the null hypothesis that the model is a good fit to the data. Using both the PR test and Lipsitz test helps fully grasp the goodness of fit as PR tests work best when the lack of fit is associated with categorical variables, whereas the Lipsitz test works best when continuous covariates drive the lack of fit [Goodness-of-fit statistics assess the fit of logistical model against actual outcomes. Three inferential goodness-of-fit tests were performed on the model: the Lipsitz goodness of fit test, the Pulkstenis-Robinson chi-squared test, and Pulkstenis-Robinson deviance test. k of fit , 19.The resultant predicted probabilities were then compared with the actual outcome to determine if high probabilities are indeed associated with events and low probabilities with non-events. The degree to which predicted probabilities agree with actual outcomes is expressed in the Interaction effects were added based on the underlying main effects. For both models, the interaction effects between Altitude or dB (model 1 or model 2 respectively), Pass, and Drone were tested as well as the ZebraMixed with other mixed group species. Based on this analysis significant interaction effects were found in model 1 between Altitude and Pass as well as ZebraMixed and OryxMixed and similarly with model 2 between dB and Pass and ZebraMixed and OryxMixed. There was significance found between Drone and (Altitude and dB) but given the low number of samples with the Sky Eye and Custom X8, the model seemed to be saturated with some of the limited amounts of data. Similar to the initial model evaluation, the models containing interaction effects had an overall evaluation, statistical tests for individual predictors, goodness-of-fit tests, and validation of the predicted probabilities.The second set of analyzed were those with interaction effects. The two best fitting models are listed in Model 3: Response ~ Altitude + Pass + Drone + GiraffeSingle + ZebraM*OryxM + Altitude:PassModel 4: Response ~ dB + Pass + Drone + GiraffeSingle + ZebraM*OryxM + dB:PassThe overall model evaluation was done by comparing the model to an intercept only model . Consequently, the significant chi-squared statistic  = 0) for model 3 and  = 0) for model 4 indicates the models containing predictors give a significant improvement over the baseline intercept-only model. This was expected since the models were only adding interaction effects.The statistical significance of individual regression coefficients  is tested using the Wald chi-squared statistic . As seen2 (df = 9) of 10.852, model2 = X2 (df = 9) of 12.442) and was insignificant (p>0.05), suggesting the models fit well to the data confirming the null hypothesis. Similar findings were concluded with the PR Chi-Squared test and the PR deviance test both yielding significant values (p>0.05) and confirming the null hypothesis that the model is a good fit to the data.Goodness-of-fit statistics assess the fit of logistical model against actual outcomes. Three inferential goodness-of-fit tests were performed on the model: the Lipsitz goodness of fit test, the Pulkstenis-Robinson chi-squared test, and Pulkstenis-Robinson deviance test. As stated previously, the 4 models were chosen because of their Akaike information criterion (AIC) score relative to other models containing different sets of predictors. Model 1 (619.59) and Model 2 (618.95) had the best AIC scores and were very close in value for the models without interaction effects. Both models were retained for analysis and from which to evaluate the key predictors. It was important to first analyze the predictors without interactions to understand how the predictors alone effected the Response, after which the interaction effects were added and their influence on the models evaluated. When the models were developed with interaction effects the scores were not quite as close and the score for the model containing altitude instead of dB scoring lower . Model 3 had the best AIC score and the highest overall prediction at 64.6%. All four models consistently predicted more false positives than false negatives. This was to be expected as there were a lot of other factors in the natural habitat to create response. It was also expected as a species will generally react in most circumstances over not reacting due to the consequences. It is in the animal’s best interest to react to an undetermined threat as the consequence could be grave for not reacting, while the opposite is not true. And on the same note, these models were not meant for predictive purposes, but to understand the influence of UAVs more generally on ungulate species.The field unit observed the animal’s response with the effect of their proximity to the UAV, the time of day, and whether the UAV appeared to be competing with attention of a larger source of danger. These other sources include vehicles that drive by, nearby baboon/cheetah walks, and weather, as well as general species behavioral patterns. Generally, most ungulates graze or browse in the early morning and late afternoon/early evening, allowing them to avoid the heat during midday by hiding under the shade of trees. Some ungulates are browsers (eat leaves) and are found in thick bush, making them more difficult to spot and observe. Others are grazers, found in open grassland, and are easier to observe. Impala and Kudu numbers were lower simply because fewer of them are free ranging on the property. Kudus are very cryptic/elusive animals, often hiding in bush cover and in small herds. Most of the data were collected from larger open areas where the animals were easier to locate, and where they could be observed from a distance without disturbing them prior to take-off.More generally, pertaining to animal habituation to humans and traffic, we used vehicles to transport ourselves and the vehicle traffic appeared to affect the responses. We observed the animals to be flightier in areas that are closer to more traveled roads, and it was difficult to collect data close to neighboring farms that allow game hunting since the animals there were flighty when sensing human presence. Male responses may have also differed from female responses. For example, some male springboks may have suppressed their flight responses to keep hold of their territory while the females and maternity herds exhibited a flight response.From the models we see that both Giraffe in single species herd were significant as well as Zebra in mixed herd environments. The Giraffe did not violate the proportional odds assumption. Therefore, the odds of inducing a No Response reaction over an Alert reaction is equal to inducing an Alert reaction over a Movement reaction. The given values single species giraffe had an odds ratio of 3.05 with a 95% confidence interval ranging from 1.581 to 5.871 in the no interaction model and 3.62 with a confidence interval ranging from 1.802–7.266 in the model including interaction terms. Both terms do overlap and are found within each other’s confidence intervals. More interestingly, the Zebra was found to have a significant effect in mixed herd environments and the interaction term was significant between the Zebra and Oryx in mixed herd environments. The response is found to be similar when Oryx is in the mixed herd environment as well as the Zebra. This is not found to be the case when Oryx is not present. The Zebra in the mixed herd environments without Oryx create a much higher probability of response than without them. This indicates that some species do react more than others, just like Giraffe in single species environments, and also implies that some species (Oryx in this case) can mitigate the reaction. While Zebra may normally be a more reactive species, when Oryx is present in the mixed herd, they become less reactive or may look to the Oryx as to their perception of the threat. This may also be attributed to different species comparative fitness levels in the mixed herd.There were considerable differences within the suite of UAVs from size, speed, sound profile, to mode of flight. Through these differences the predictor was tested to see if any UAV created a higher response compared to other UAVs. The Phantom 3 was considered the reference UAV in most of the previous calculations, but we can see how the species response would vary considerably based on the UAV flown. The Custom X8 (Octo Copter) has a significantly higher probability of inducing a response. The proportional odds assumption was not met for the predictor Drone so the odds of inducing a response vary between the two interaction interfaces. The Custom X8 has a 90% predicted chance of inducing an alert response with the 95% confidence interval encompassing up to 96%. That is considerably higher than the other UAVs. Additionally, the UAVs do not have consistent response rates. While the Phantom 3 is much more likely than the Mavic to induce an alert response, they have very similar probabilities of inducing a movement response. This suggests there may be several aspects to the UAVs that affect the response of the ungulates. For instance, the sound intensity may alert the species more often, but close proximity may induce a movement response.Pass was considered to understand the reaction based on the UAVs continued presence in the area. The UAV would continue passes until the battery was too low or the herd left visual range. As seen in model 3, there was a significant interaction effect between Altitude and Pass. As seen in While the sound intensity models fit well with the data, the researchers believe the evidence is not compelling enough to ascribe the animal response to sound intensity over altitude. Sound intensity was added as a predictor because of the much higher responses to the Custom X8, which was a much louder vehicle. This led the researchers to believe that sound intensity could be the contributing factor over altitude alone. Therefore, the sound profiles of the vehicle were measured in an anechoic chamber under similar conditions to attempt to differentiate between the different levels of inducing responses.Several factors led the researchers to believe that altitude was a better predictor than sound intensity. First, and very apparently, in the model associated with sound intensity, the Beta’s for each individual UAV were different. If sound intensity were the dominating factor, then these would be the same or close. These Beta’s showed significance between the Phantom 3 and Mavic. Similarly, the differences in response levels from the varying UAVs at similar altitudes appear to illuminate multiple factors at play associated with the UAVs. Therefore, altitude as a predictor likely encompasses more of these factors because proximity (intensity) to these varying characteristics likely plays a role as well.Second, the interaction effect between sound intensity and pass was not significant. If the animal is becoming habituated to the vehicle flying overhead, it would be anticipated that they would also become capable of enduring slightly more of the effector  and having the same response, like we see with altitude. For instance, if the UAV passes over a second time at the same altitude, the results show it will induce less of a response. Similarly, if passed at a lower altitude on the second pass, it would induce the same probability as the first pass, which we see in the data for altitude. This same interaction would be expected from sound intensity. If sound intensity where the leading factor over altitude, there would be a higher sound intensity on the second pass that would create an equal probability of response as the first pass, which is not present.This paper reports on the behavioral responses of free ranging ungulate species  housed in an animal reserve in Namibia to the presence of different UAV models. The study included 397 passes over 99 flights; however, the dataset only used 346 passes over 76 flights negating preliminary flights and only including properly performed flights at altitudes ranging from 15 m to 55 m in three categories of response level: No response, Alert, and Movement. The results suggest a stronger correlation between flight altitude and response across the different ungulates, and the evidence suggests rapid habituation to the UAVs.The single herd and mixed herd observations conducted in the study provide evidence in support of the feasibility of aerial wildlife monitoring (AWM) by UAVs. The highest percentage of responses that did not invoke movement occurred at the highest altitudes. The results suggest that the longer the UAV was in the presence of the animals the less likely they were to negatively respond. Certain species were found to be more reactive than others, in addition to several displaying different response levels in single or mixed herd environments. Zebras were found to be less responsive in mixed herd environments while Oryx were present, as compared to when the Oryx were not; suggesting that some species may respond based on other species perception of threat or their relative fitness levels.The UAVs also produced inconsistent response rates between movement and alert behavior. The reference vehicle, Phantom 3 was much more likely than the Mavic to induce an alert response, while both having similar probabilities of inducing a movement response. Furthermore, the Custom X8 showed significantly more alert and movement responses than the other UAVs. This shows there may be several aspects to the UAVs that affect the responses of the ungulates. For instance, the sound intensity may alert the species more often, but close proximity may induce a movement response. As seen with the reference vehicle, Phantom 3, there was a less than 7.5% chance of inducing a movement response at 55 meters. Additionally, after the average number of passes (4.5) while flying at 55 meters reduced the predicted probabilities of inducing an alert response from 46% to 10% and a movement response from 6% to 1% or an average reduction of approximately 80%. More generally, the data shows that when the UAV is flying above 50 meters and has a measured sound intensity below 50 dB, the likelihood of inducing a movement response on an ungulate species is below 6% regardless of the vehicle on the first pass over the animals.The factors that contribute the most are not exactly known; however, sound profile , size, and style of flight  likely play a roll. The study did not conclude any long-term effects of the habituation of the UAVs because it was very difficult to confirm that the same animals were being flown over on each subsequent day. This was due to the large size of the property and substantial number of animals. Further study of habituation to the presence of UAVs could further strengthen the results including studies monitoring the same animals over extended periods of time.S1 Questionnaire(DOCX)Click here for additional data file.


Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system. Global warming causes the occurrence of extreme weather and the rapid melting of glaciers, affecting the environment and the safety of humans ,2. As thIt is feasible to detect whether a dammed lake event is caused by observing the river water level because variations in water level are typically associated with river obstacles. Currently, numerous efforts have been made to monitor the variation in water level, such as pressure sensors ,9,10,11,Ground-based monitoring systems are well suited for long-term fixed-point automatic monitoring of specific small areas, such as wild rivers, urban rivers, drainage ditches, and so on. Optical satellite and aerial monitoring systems are often used for large-scale geographical analysis because their accuracy is not as high as that of ground monitoring due to long observation ranges, and their monitoring of fixed sites is not continuous. Water level identification methods for automatic monitoring are classified into two types based on whether or not manual marking is used. Using artificial markers, such as a water gauge, can determine the variation in water level, and recognizing the variation in the river boundary also can determine the variation in water level. When the monitoring site has a water gauge marker, the intensity information on both sides of the water level will be different, and the variation of the water level can be obtained by extracting the reflection parameter of the river water bodies from the image ,21. In aCanny edge detection is a simple and effective image processing technology proposed for application in real-world environments ,29. HoweThe remainder of this paper is organized as follows. Lhasa Earth System Multi-Dimension Observatory Network established a long-term automatic monitoring and early warning system focused on glacier collapse disaster chains in the Grand Canyon on the YTR . In the The proposed algorithm is a combination of RG and k-means clustering algorithms. The goal of RG is to separate the greenish river pixels, and the goal of k-means clustering is to classify all pixels, so green channel gray image and full-channel gray image are used, respectively. The input RGB images are divided into two types of gray images. The gray value of full-channel gray images adopts 30% red channel, 59% green channel, and 11% blue channel. The gray value of green channel gray images adopts 100% green channel. Step 1 shows the process of selecting the region of interest (ROI) and the detection line. Step 2 shows the process of using the RG algorithm to obtain the RG line as the auxiliary line on the green channel of an image. Step 3 shows the process of using the k-means clustering algorithm on denoised images. Step 4 shows the process of obtaining water level by combining the skeleton thinning algorithm based on k-means clustering with the RG-line. The region of interest (ROI) is a manually selected rectangular area of the image. Step 5 shows the process of making alarm judgments by using pixel water level data of the waterline.The proposed method’s flow diagram is shown below, Step 1:Region of interest (ROI) is selected to avoid unneeded information interference. It reduces the influence of low-resolution regions and improves the performance of image segmentation. The detection line gives additional scene information helping the RG algorithm to segment the scene.The details of the processing are shown in The water level is low in April and high in August according to the hydrological characteristics of the central basin of the YTR. As shown in the a priori knowledge image in Step 2:RG algorithm is the process of aggregating pixels or subregions into larger areas according to predefined criteria. The location of the seed point and the growth criterion are the parameters involved in the RG algorithm. The algorithm evaluates the relationship between each seed point and its eight neighboring areas. Similar points are used as the seed points for the next growth. The algorithm stops growing when no similar seed points exist.In this study, the location of the seed point is obtained by automatically spreading seed points in a square grid. Each seed point produces an RG image. Overlay all RG images that do not overlap the detection line. The max-connected domain is derived from the overlay graph, while RG-line is derived from the water level-side boundary information of the max-connected domain graph. The growth criterion is the gray value difference on both sides of the water level. The RG image in Step 3:(1)Initialize class gravity center (2)Assign each pixel to the nearest center of class (3)The update center is the mean of the gray value of pixels that are assigned to a particular class;(4)Repeat (2) and (3) until the algorithm converges.K-means clustering is a classification algorithm that classifies pixels in an image into K classes, while the Non-local mean (NLM) algorithm is used to obtain the denoised image. The K-mean clustering algorithm is achieved by iteratively updating the initial estimate of the class center, using the procedures below:The K-means algorithm minimizes as much as possible the variance The K-means clustering image in Step 4:The principle of the traditional skeleton thinning algorithm is to extract the center contour of the object on the image. The algorithm uses 3 × 3 pixel windows to thin out the target from the target boundary to the target center until it is corroded to the point where it cannot be corroded (width of a single-layer pixel) in binary images. Then the image skeleton is obtained.Our proposed boundary-based skeleton thinning algorithm is to extract the boundary skeleton instead of the center skeleton. Image skeleton extraction based on the boundary is divided into two steps: (1) Extract the contour of the target and record these contour points. (2) Detect whether x pixels below these contour points are all non-target pixels in turn . When all pixels are non-target pixels, the contour point is a river boundary point pixel. When all pixels are not non-target pixels, the contour point is not a river boundary point pixel. Where, x is related to the estimated average river width, generally one-tenth of the average river width is appropriate. We use the RG-line as a supplementary line to help remove noisy pixels from the skeleton thinning graph. The waterline is derived from the image boundary of the processed skeleton thinning graph on the water level side.The intermediate process image in The skeleton pixel image in The blue pixel image in The resulting image in Step 5:The monitoring system analyzes the difference in pixel water level to determine whether a dammed lake event has happened. The user set the water level alarm threshold, and the system alarm when the difference exceeds the threshold. The water level threshold used in this paper is 2 pixels. The pixel water level is the number of pixels within the image at the water level height under a fixed field of view. The difference in pixel water level equates to the difference in water level between two images captured at adjacent monitoring times.The proposed AMAS was used in Nyingchi, Yarlung Tsangpo River Basin, Tibet Autonomous Region, China. The river is frequently blocked by glacial debris flows, and causing a significant risk of the dammed lake. The format of the image is png, and the capture rate of the image is one frame per hour. The AMAS software interface consists of reference images, input images, and parameter settings. The reference image is the initial image of the water level, and the input image is the image that needs to be evaluated. When water levels exceed a predetermined threshold, the AMAS shows an alarm screen.The river region is saturated with water vapor and sunlight, providing a stable temperature inversion. Hence, fog is common in the morning. Due to the viewing angle, the camera tends to produce overexposed images during midday. An hour after noon, when the sunlight shifts to the right angle is a good window for observation. Notably, noon in Tibet is two to three hours later than Beijing time because of the difference in longitude. In conclusion, we choose 4 pm as the viewing window. In order to estimate the boundary recognition performance, the images identified by the proposed algorithm were compared with the RG algorithm. The results show that the proposed algorithm has a higher accurate ability for recognizing boundaries than the RG algorithm.Significant gray differences created by white water waves will have a negative effect on the RG algorithm, which uses gray differences as a growth criterion, causing the algorithm to terminate growth before the region of the target is completely detected. In addition, noise interference still presents in the image after denoising, resulting in the identification of the non-river region as a river region. K-means clustering employs Euclidean distance based on pixel gray value as the judging criteria to classify pixels across the whole image. Compared to the local gray difference comparison of the RG algorithm, the global gray difference comparison is able to distinguish between the regions of white water waves and rivers more accurately. The proposed method is able to recognize river border pixels accurately. When all samples were evaluated to be negative, the accuracy index remained extremely high if the majority of samples were negative. The precision index suffers from the same issue, when a False positive (FP) is low, the precision index will be high. Therefore, we measured the recall (TPR) of statistical results. In this paper, a hybrid algorithm based on RG and k-means clustering is proposed for an automatic monitoring alarm sub-system for dammed lake events. Firstly, ROI and detection lines are used to provide prior knowledge of the scenario. Then, a hybrid segmentation algorithm is proposed to identify river boundaries. The results of the visual comparison show that the proposed algorithm has more accurate boundary recognition ability than the RG algorithm. Lastly, the identified boundaries are analyzed numerically. The results show that the proposed algorithm performs well in quantitative measurement compared with other methods. Regarding accurate alarms, the performance of the proposed method is better than that of the RG method in accuracy (89.29%), precision (93.75%), and recall (88.23%). In terms of its ability to reduce errors, the proposed method is also superior to the RG method in false alarm (9.09%) and miss rate (11.76%), hence decreasing resource waste caused by false alarms. In the future, the proposed method will be tested in different geographical regions to strengthen the algorithm’s robustness, and at the same time, image enhancement will be carried out in image preprocessing to filter halo and fog and obtain clear images to realize better performance of the proposed method. This research proposes an automatic monitoring alarm sub-system as a solution to the problem of dammed lake monitoring. Future applications of this method include flood warnings and seasonal river state judgment.


Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods. As one of the most important and expensive piece of equipment in a power system, the power transformer plays a vital role in power conversion and delivery . Power tGenerally, transformer fault diagnosis methods can be classified as offline and online according to the working state of the transformer. The offline methods, due to their simple principle and accurate results, are commonly used for annual maintenance and fault analysis. For instance, frequency response analysis (FRA) can determine the condition of the winding by measuring the impedance or admittance of the winding ,5,6. ShoBy contrast, the online methods can be implemented while the transformer is in operation. Dissolved gas analysis (DGA) can be used to diagnose latent transformer faults by continuously detecting and analyzing the components of different gases dissolved in the insulating oil ,9. SimilAlternatively, vibration analysis provides a new online diagnosis method for transformers with easy and low-cost implementation, which has attracted increasing attention in the recent years. The authors of  proved tRecent research has shown that fault diagnosis methods with deep learning (DL) can overcome the expertise dependence issue ; furtherThe main contributions of this study are summarized in the following.(1)An intelligent fault diagnosis method for dry-type transformers using vibration signals is proposed, which can quickly identify different faults under various loads of the transformer with high accuracy.(2)A CWT method is adopted to convert the raw vibration signals of the transformer to RGB images, which could adequately extract fault features from the different conditions.(3)An improved CNN model is designed to accurately classify the RGB images for transformer fault diagnosis, and its optimal structure and parameters are determined.The rest of this article is organized as follows. U is the voltage.The transformer vibrates all the time in service with or without load, and the vibrations are mainly caused by core vibration and winding vibration. Core vibrations are mainly generated by magnetostriction since the geometry of magnetic material changes slightly when it is in a magnetic field, and the vibration occurs when the strength of the magnetic field varies considerably . The funI is the current.The winding vibrations are mainly generated by electromagnetic forces due to the interaction between the current in winding and the leakage flux field. Those electromagnetic forces are proportional to the current squared ; since tThe vibration of a transformer is highly correlated with its condition ; therefoWavelet transform is a popular tool for extracting time–frequency information from time-domain signals . It inhea is the scale of the mother wavelet, and b is the translation of the mother wavelet. DWT can transform the discrete input data sequence N wavelet coefficients, and CWT is defined by the wavelet coefficients which are produced by the convolution of the original signal Wavelet decomposition is implemented through two filters: the low-pass filter  and the high-pass filter (wavelet filter) . They shCNN is a typical deep learning algorithm, inspired by the concept of the visual nervous system , which cThe typical CNN structure consists of three types of layers, which are the convolutional layer, pooling layer, and fully connected layer. The process of pooling operation is illustrated in The transformer under study is a customized 50 kVA dry-type transformer with two terminals A and B, which can easily simulate turn-to-turn short circuit faults. Its main parameters are shown in Two accelerometers with the sensitivity of 500 mV/g of type CA-YD-188T were used to collect vibration signals of the transformer. Then, the collected raw signals are processed by the SIRIUSm-4xACC data acquisition instrument with a sampling rate of 8000 Hz, and saved by the Devesoft X3 software. Considering the structural characteristics and insulation safety of the studied transformer, as shown in The loosening faults of the core, winding, and connection bar were simulated by adjusting the tightness of the clamp bolts from 50 to 80 Nm using a torque wrench, the turn-to-turn short circuit fault was simulated by connecting a resistor between terminals A and B. It is worth mentioning that all fault types have multiple load levels to represent changing loads.As shown in In order to train the proposed diagnosis model, 400 segments of the vibration signal were collected for each working condition, which eventually constituted a total dataset of 4000 samples, of which 70% were selected as the training dataset, 20% as the validation dataset, and the remaining 10% as the test dataset. It should be noted that each sample can only be assigned to one dataset, which means that the samples of the testing dataset are completely different from the training dataset and validation dataset.The proposed transformer fault diagnosis method is presented in this section. After the vibration signals are acquired from the transformer, they are converted into RGB images by the CWT method described in Vibration signals are collected by the high-frequency accelerometers. In order to fully collect transformer vibration characteristics, the sampling rate is usually around 10 kHz. The collected time-domain signals contain rich characteristic information; however, it can hardly be used directly for fault diagnosis. Therefore, a proper feature extraction method is essential.For the purpose of extracting sufficient feature information from the original vibration signal, CWT is used to process the vibration signal in this study. The length of the selected raw signal segment is 1280 , and the cmor3-3 (Morlet wavelet) is employed as the mother wavelet with a total scale of 256. It is worth mentioning that the sampling rate is set to 8000 Hz since the vibration frequency of the transformer in this case is basically below 4000 Hz. As shown in n classes of images corresponding to n transformer working conditions. The RGB image can be divided into 3 monochrome layers to meet the requirements of the input format. In order to improve the accuracy of image recognition, the input size of proposed model is set to 64 × 64 in this study.After converting the raw signals to RGB images, there are Based on experience and comparison, the proposed CNN structure was finally determined as shown in Some other initial hyperparameters of the structure are set as follows: learning rate = 0.015, batch size = 12. The optimal combination of the above parameters will be discussed in In this section, an experimental setup was designed to simulate different faults, and the corresponding vibration signals were collected to train and test the proposed diagnosis model. Moreover, the performances of different parameters in the proposed model were compared to select the optimal combination. The CNN model is written in Python 3.7 with PyTorch and runs on windows 10 with two Nvidia RTX 2080Ti GPUs.x-y-z means that there are x, y, and z neurons in the first, second, and third fully connected layer, respectively. For example, CNN-2704-126 means that there are 2704 neurons in the first layer, 126 neurons in the second layer, and there is no third layer in this structure.The structure of the proposed model has a crucial impact on diagnosis accuracy. In order to find the best combination of structures, the performances of different structures were compared, and the results are shown in Each model was run ten times, and the maximum, minimum, mean, and standard deviation (SD) of the testing accuracy were employed as criteria to evaluate the performance of diagnostic models. From the results shown in The batch size (BS) is one of the most important hyperparameters in deep learning, which represents the number of samples picked for a training session. It affects the degree of model optimization as well as the speed of optimization by changing the GPU memory usage. In order to select the most suitable BS, the diagnosis performances of different BS are compared, which are shown in The learning rate (LR) determines whether and when the objective function can converge to a local minimum. A suitable LR can make the objective function converge fast and efficiently. To this end, the diagnostic performances of different LR are compared, and the results are shown in Based on the above comparison and analysis, the hyperparameters of the proposed diagnosis model are finally determined as BS = 20 and LR = 0.02. The confusion matrix of diagnosis results is illustrated in To verify the superiority of the proposed diagnosis method in this study, the performances of different methods are compared, including ANN , DBN 4141, 1D-CNThis study proposed a deep learning-based fault diagnosis method for transformers, which converted vibration signals into RGB images to extract the corresponding fault features using CWT and then achieved fault diagnosis through an improved CNN model. In order to train and validate the proposed model, an experimental setup was designed to simulate transformer faults, including core clamp looseness, winding clamp looseness, connection bar looseness, and turn-to-turn short circuit. The optimal structural and hyperparameters of the proposed model were determined by comparing their diagnostic performances. Compared with other methods, the proposed diagnosis method can achieve the highest mean accuracy of 99.95% and the lowest SD of 0.32%. Moreover, due to the offline training strategy, the feature extraction and diagnosis process took less than 7 s, which can provide fast and accurate online fault diagnosis for the transformer. This study can expand the field of transformer fault diagnosis and offer technical support for condition-based maintenance of operating transformers.


The revenue function is based on the Theil entropy measure, whose factors are in the residual energy of the nodes and their neighbors. By using a primary payoff function that considers all initial network factors and a secondary one that addresses connectivity in case of reduced residual energy, a segmentation function can calculate the model's payoff. Simulation experiments show that compared to the existing game algorithms of 3DK-RNG, DEBA, and EFPC, the proposed algorithm can effectively eliminate redundant links, reduce node degree and network link length, balance node energy consumption, enhance network load, and extend the network life cycle.  Specifications tableMethod detailsWireless sensor networks are self-organizing networks composed of a large number of battery-powered sensor nodes with limited energy This paper also focuses on the performance study of many different network topological settings for mission-critical applications. Lavanya and Shanmugapriya EBTCA algorithm, comprehensively considering both the remaining energy and the link length of nodes to enhance the robustness of the network, balance the network load, and extend the network life.Although the above algorithms considered the uncertainties in wireless sensor networks and their impacts on algorithm performance, they did not consider energy consumption and transmission delay that affect the life cycles of algorithms. Nouir et al. EAC-ECHS) to optimize the performances of wireless sensor networks in terms of network lifetime and enhance energy consumption. The algorithm took into account not only the residual energy of nodes but also the link quality of the network, thus balancing energy consumption and enhancing network robustness. Lee and Yang However, topology control algorithms sometimes overemphasize energy balance at the cost of connectivity, leading to low network robustness in wireless sensor networks. Priya and Mohanraj The paper is organized as follows. In Sec.Ⅱ, the model and related theory are reviewed. A network topology control algorithm based on Theil's entropy measure is designed by improving the revenue function is discussed in Sec. Ⅲ. Sec. Ⅳ is devoted to analyzing the system performance through simulation example and studying the proposed algorithm, which reduced the number of nodes’ deaths and the rate of a network fault. The proposed algorithm has also proved to have done a good performance in terms of the survival number of nodes, network traffic, and the prolongation of network lifetime. Conclusions are summarized in Sec. Ⅴ.N nodes deployed; N nodes within their communication range; i, i.j. To enable experimental simulation analysis, we impose the following constraints on the three-dimensional wireless sensor network:(1)The impact of boundary constraints on wireless sensor network monitoring can be neglected.(2)r(i), also known as the communication radius, and can be adjusted according to its application. The node's transmission range forms a spherical boundary with r(i) as its radius.The sensor node's transmission range is determined by (3)Assuming that each randomly deployed sensor node demonstrates rationality and selfishness.(4)The lifetime of a wireless sensor network is determined by the time that takes the first node to become dead.(5)i) is identical, except for the node ID, which is unique in a wireless sensor network.The initialization process for each node ((6)A randomly selected node can determine the connectivity of a wireless sensor network.(7)i and node j.The three-dimensional wireless sensor network enables half-duplex signal transmission between node Based on the relevant principles of graph theory, the network topology can be represented as an undirected graph in this paper. Where denotes the set of (1)Network connectivity: Network connectivity is a crucial requirement for a complete wireless sensor network topology. After multiple rounds of optimizing the sensor nodes, it is essential to ensure that the network is still connected. Otherwise, any further conducted studies would be meaningless. Supposing the connected function is:(2)Network coverage: A comprehensive network topology must ensure that all nodes are covered. Supposing the coverage function is:(3)j, j.Residual energy: The topology of a wireless sensor network can be regulated by considering the residual energy of the nodes. When nodes communicate with others, it is likely to become hampered if the nodes display varying residual energy levels, leading to energy depletion and eventually resulting in a failure to communicate. Thus, the node with higher energy reserves ought to be utilized for forwarding. To reflect the residual energy balance of nodes more realistically (4)r is the communication radius of the node, l is the size of the data packet, T is the time required for data transmission, a(f) is the medium absorption factor, p is the pressure, Energy consumption: Due to the limited energy of nodes used for communication in wireless sensor networks, the energy consumption model is utilized from the literature The energy consumption (5)I into a total of m groups, this the noise variance, this the communication power, the f is the frequency at which the signal is transmitted.The signal-to-interference-plus-noise ratio (SINR) plays a pivotal role in measuring the quality of data transmission in wireless sensor networks as shown in (6)l is the size of the data packet.Data transmission success rate: When the data packets in the wireless sensor network are large, the transmission success rate is shown in formula Measuring the sensor nodes is difficult due to the unpredictable wireless network environment. Therefore, a systematic evaluation of several factors is necessary to scrutinize network topology, as described below:(1)N: n is the number of participants in the game.Game participants (2)W: i, and denotes the k strategies available for participant i. A particular strategy choice is generally denoted by, Where denotes the strategy of i and denotes the strategies of participants other than participant i.Strategy set (3)U (w): Revenue function By considering sensor nodes as game participants and their available power sets as the strategy set, a wireless sensor network can be regarded as a strategy game. The ordinal potential game is a type of strategy game that comprises three elements: game participants, strategy set, and payoff function. The detailed meanings of each element are as follows:Definition 1 Nash equilibrium: If the game modeli to other participants, andi? Then is a Nash equilibrium of the gameWhere Definition 2 ordinal potential function: For the strategy gameThen the function is the ordinal potential function of theTheorem 1Ifis an ordinal potential game, the function is an ordinal potential function, and if there isthat makes it optimal, then w is a Nash equilibrium of G.Definition 3 Pareto optimal: If no strategy can obtain i is B, then the node degree K is the number of nodes in the set, namely K=|B|. The average node degree is the ratio of the node degree K to the total number of sensor nodes n in the network, namely |B|/n.Definition 4 Node degree: If the set of neighboring nodes in the wireless sensor network that can communicate with sensor node Theil entropy measure is a metric in economics that employs entropy to measure the inequality level among individuals or groups. The aforementioned concept is represented by the expression in ith individual is the average of the total income of the n individuals.Where the income of T into the revenue function. The population share is then understood as the residual energy balance, wherein the residual energy of all nodes in T is substituted with the residual energy of neighbor node j of node i for an accurate output as shown in i,i, and represents the average of the residual energy. The network load is balanced by adding an equation i will prefer neighbor node j with more remaining energy to perform the forwarding task.(1)Improved Return Function Based on Theil Entropy MeasureThe concept of entropy frequently serves as a measure of the degree of energy equilibrium. To achieve a more precise representation of the residual energy balance and attain energy equilibrium, we introduce Theil entropy measure Due to the harsh environment of random deployment of wireless sensor nodes, it's not easy to design realistic models. However, the key part of the model design is the establishment of the gain function; this paper combines the elements mentioned before to design the gain function in line with the game modelT to express the equilibrium of the nodes' residual energy in this paper. If the residual energy of neighboring nodes of node i is more, the smaller the value of T and the larger the gain function. To reduce the energy consumption during the game and prolong the life cycle of the network, when the remaining energy of node i is(2)Improved Model ProofAs the game is executed, the residual energy of a node decreases. We use the Theil entropy measure The improved game model ProofI under different strategies According to The r(i) of each sensor node is initialized to the maximum value and the “Hello” broadcast packet is sent by node i. The 'Hello' broadcast packet is composed of node ID number, location, synchronization packet, and remaining energy. After receiving the packet, neighbor node j returns an ACK packet to transmitting node i. The ACK packet is composed of node ID number, location, synchronization packet, remaining energy, energy consumption, transmission success rate, and profit value. The key part of this phase is to collect the “Hello” broadcast packets sent and the ACK packets received between the nodes. At the same time, the set of neighboring nodes, link sets, and policy sets of node i are determined and the network topology G is established.The communication radius After the topology G is initialized, the set of the remaining energy and optional power is obtained by the current power. Each node selects the node of low power compared with the current node according to the policy and checks whether the revenue obtained increases. If this node is updated as to the current node the comparison continues. Otherwise, this node is left unchanged. In addition, there is only one node for adjustment in each round. When the power of each node is the optimal power, there is no change of any node to make the benefit of the whole sensor network greater, and then the network reaches a Nash equilibrium.T to assess the residual energy consumption and a segmentation function to determine the gain. Designing a thresholdAs the game progresses, each node has different amounts of data to forward and consumes varying levels of energy because of the complex and unpredictable wireless environment, resulting in the network frequently depleting its energy and becoming disconnected. The network topology maintains robustness to adjust itself and maintain normal operation. However, the excessive pursuit of network robustness certainly results in noteworthy energy consumption. The proposed algorithm aims to balance the energy consumption of the nodes in the network and extend the network's lifespan. This is achieved by utilizing Theil entropy measure The pseudo-code for the gaming process is shown in M, where M being the maximum number of strategies of each node), the algorithm complexity for this stage is denoted as 2n). The topology maintenance stage utilizes Theil entropy measure T to assess the residual energy consumption. The algorithm complexity for this stage is denoted as 2n) and The complexity of an algorithm is a standard measure of its calculation complexity. This section analyses the time complexity of the PG-ORTC algorithm used in this paper, which consists of two stages: the topology game stage and the topology maintenance stage. In the topology game stage, the payoff of each node is calculated using PG-ORTC algorithm is simulated by using the PyCharm Integrated Development Environment to assess its effectiveness in terms of robustness, network link length, average remaining energy of nodes, and life cycle. The algorithm is then compared to 3DK-RNG, DEBA, and EFPC within the same simulation environment. These comparisons were conducted to verify the efficacy of PG-ORTC.The parameters of the simulation environment are shown in Before the comparative analysis, the weight factor It can be seen that the average power of nodes decreases with increasing increases with the increasing ofThe sensor nodes were randomly deployed in a monitoring area of 400mAs can be seen from T to effectively reduce the node load. Therefore, the lifetime is the longest relative to comparison algorithms (The comparison of the life cycle changes of 3DK-RNG, DEBA, EFPC, and PG-ORTC algorithms with the increase in the number of nodes is shown in gorithms .Fig. 5CoChanges in link length of 3DK-RNG, DEBA, EFPC, and PG-ORTC algorithms in the same experimental environment are shown in Figure. As can be seen from the figure, the 3DK-RNG algorithm has a higher average link length of the network than the other three algorithms due to more redundant links. What's more, the EFPC algorithm is second only to it. In addition, the DEBA algorithm has the lowest average link length due to the presence of more independent nodes. However, the average link length of the PG-ORTC algorithm proposed in this paper is moderate and the link quality is superior. This shows a comparison of the link lengths of the PG-ORTC algorithm for different numbers of nodes as the number of links changes in This study proposes a solution to the issues related to energy consumption and delay during the transmission process of wireless network signals. The proposed solution integrated the ordinal potential games into wireless sensor network topologies. The Theil entropy measure is used to enhance the revenue function, resulting in an increasing potential function with a unique Nash equilibrium solution that has been theoretically proven. The PG-ORTC algorithm is proposed. The simulation results demonstrate that the algorithm can adjust the weight factor to help the network adapt to different wireless environments and increase the robustness of the network while maintaining wireless sensor network connectivity. This paper comprehensively evaluates the residual energy equilibrium degree between a wireless node and its neighboring nodes. Furthermore, the revenue function is improved by incorporating the Theil entropy measure to assess the performance of the wireless sensor network at different stages. The proposed algorithm effectively reduces the energy consumption of nodes, balances network load and improves the transmission success rate between nodes, as compared to existing gaming algorithms such as 3DK-RNG, DEBA, and EFPC. It achieves high performance by eliminating the need for nodes with low remaining energy to consider comprehensive factors, reducing the incidence of premature deaths, and prolonging the life cycle of the network. Continuous running of nodes will not only lead to high energy consumption but also interfere with the signal transmission within the network. To address this issue, the forthcoming solution will implement the technique of node sleep scheduling within the network topology to enhance the load balancing across the nodes. The algorithm proposed in this paper utilizes an enumerating method to update the node game strategy. However, this leads to a significant amount of redundant game iterations and prolonged processing time, which subsequently increases the computational load and results in a reduced response capability of the algorithm. Therefore, further optimization of the algorithm is required. We will focus on the problem of selecting algorithmic parameters and utilizing machine-learning algorithms to enable an adaptive parameter-tuning mechanism in the future.Wei Lian Suo: Methodology, Validation, Data curation, Writing – original draft. Huan Shen Hao: Supervision. Ma Long Yu: Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object’s surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications. In recent years, thermography-based non-destructive testing and evaluation (NDT&E) techniques have been developed rapidly in industrial applications. With the benefits of contactless and large inspection areas, high testing efficiency can be achieved by using active thermography-based NDT&E methods. However, the emissivity issue is always the most critical challenge of thermography-based technologies. These range from difficulties regarding the evaluation of thermal patterns illustrated; materials with high reflectivity surfaces, materials with low emissivity surfaces, and objects where surfaces are formed from different materials . Thermography also provides relative temperature maps, which can provide false temperature contrasts as a result of different emissivity across the sample under test (SUT). Non-uniform emissivity across sample surfaces poses challenges in showing accurate thermal contrast from the infrared detector. The material information in a thermal image is obscured by false temperature/thermal variations due to non-uniform emissivity.2 μm), at a given temperature (Planck’s law) is captured, producing the surface thermal pattern of the SUT. Since the ECPT material evaluation is based upon evaluations of the temperature contrast within infrared image sequences, if the output signals of an infrared camera differ with varied surface conditions across the same material, even with the same actual temperature in these areas, the evaluation of material is affected. In general, the emissivity correction can involve external apparatus, such as specific optical lenses, special preparation of the SUT, optimized radiation reduction chambers, and the use of surrounding references with known temperatures or emissivity values   is:i is one frame of the infrared video, c is the speed of light in vacuum, h is Planck’s constant, c is Boltzmann’s constant, T is the temperature in Kelvin, and the λ is the wavelength in meters. Since the temperature variations are small in comparison to the pulsed excitation, we assume that emissivity is independent of temperature, and the wavelength does not change significantly during the measurement. We also have to take into account the emissivity ε when the SUT is considered a grey body.The IR detector produces signals which are proportional to the photon flux emitted from position  received by the detector is the combination of four radiance sources: (1) lenses and filter, (2) the capture environment around the SUT, (3) infrared detector itself (Narcissus effect) and (4) the emitted spectral infrared radiance from the SUT during heating.The measured radiance MR  and the general performance of the thermal detector, and are given byThe thermal detector cell consists of an absorber connected to a heat sink (enabling a constant temperature at the cell); as this absorber absorbs radiation, its temperature rises above that of the heat sink. A thermometer can be attached to the absorber, allowing the absorbed power to be calculated from the measured temperature. Finally, the readout circuit converts the analog output signal of the detector to a 12-bit parallel digital level signal at the output of the processing unit of the IR camera. The output thermal patterns of the camera are a function of TP from a non-ideal body is related to the signal BBTP from a blackbody byε ⋯ BBTP is the fourth source of radiance received by the detector, the spectral infrared radiance from the SUT. BGTP  consists of the combined signal from the remaining three sources as explained above.Such that the output signal BGTP can be removed by the subtraction of the first frame from the remainder, producing DTP,DBBTP is proportional to BBTP. A true emissivity map ε can be illustrated as:As one of the advantages of electromagnetic thermography, infrared video recording and eddy current excitation are synchronized. The first frame of the video is under the influence of all four sources of radiance received by the detector. Hence, the DBBTP is independent of  by definition. DBBTP in the denominator can be taken out of the average. This results inHowever, it is not available to acquire the precise temperature of a blackbody in real-world applications. For electromagnetic thermography emissivity correction, it is sufficient to have the relative emissivity across the SUT surface:ATP as the relative emissivity map of DTPATP indicates the coefficient which is proportional to the surface emissivity. The transient response is divided by ATP, namelyHence, we can adapt the average value over all frames of every pixel Equation (11) indicates that the proposed adjustment amounts to normalization by simply dividing the thermal response rise at each pixel by its average temperature rise. Hence, we can have the corrected surface thermal pattern after the emissivity map adjustment. The implementation of emissivity correction can be illustrated in the flow chart in Experimental validations for the proposed methods are conducted in three aspects: (1) reducing the surface-oil-coating influence in carburizing-treated steel samples; (2) case-depth separation in nitriding-treated gear steels; (3) fault detection in gears and (4) fatigue process analysis of gears in realistic working scenarios.We also employed ECPT to separate different case depths of case-hardened steels, as depth information could be retrieved from thermal pattern variations. As illustrated in To validate the proposed method for enhancing the detectability of the faults in real industrial applications, we have applied the method to a gear made of the same material in the case of carburized steels for crack visualization and failure detection. Pressure testing has been performed on this gear, and gear tooth 19 failed due to repeated contact pressing. A crack is generated across the gear tooth root and cannot be seen with the naked eye.As shown in In this paper, we proposed an efficient emissivity correction algorithm and thermal pattern reconstruction method based on the physical process modelling of active thermography. Based on the understanding of the mathematical and physical models, we proposed an emissivity correction method in eddy current pulsed thermography. By using this method, a reduction in the influence of non-uniform emissivity across the surface of a sample can be gained. For the first time, this paper demonstrated the analysis of materials and fault detections of heat-treated steels and gears using thermography with emissivity corrections. Experimental studies have been conducted in three aspects: (1) thermal pattern reconstruction for heat-treated steels based on emissivity correction and correction; (2) case-depth separation of the nitride steels with different times of heat treatment; (3) fault detection and fatigue process identification of heat-treated gears. Thermal pattern reconstruction can be achieved based on the modelling for enhancing the detectability of the faults in heat-treated gears with better contrast and detail textures; moreover, it can analyze the heat-treated materials in both manufacturing and fatigue scales. Although the emissivity-corrected thermal images significantly improved the accuracies of the thermal contract, there are still some highlighted pixels in area 1—as can be seen in