In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification. Residential household customers’ electricity consumption continues to rise, and improving residential electricity consumption is of great significance to improving the efficiency of electrical energy utilization . ProvidiPower fingerprint identification is a hot issue in the field of Non-intrusive load monitoring (NILM), which relies on power fingerprint features and classifiers to identify different types of devices. Common power fingerprint characteristics typically include voltage, current, harmonics, power, V-I trajectory, etc –9. The VNowadays, scientists and researchers used machine learning (ML) and deep learning (DL) models in several applications including agriculture , 16, envFor V-I trajectory classification, the small amount of load data for specific user results in a poorly trained model with low accuracy or low generalization capability . NILD doIn addition, there is a class imbalance in the NILM dataset, which can have an impact on the classification performance of the classifier. When the classifier is more biased toward majority class samples, then the identification accuracy of minority samples decreases and the model lacks generalization . UsuallyTo solve these problems, this paper proposes a power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet34. Experimental results on the PLAID  dataset Use color encoding to add more feature information to the V-I trajectory and improve the identification of similar electrical appliances. It solves the problem of insufficient feature information in single feature recognition devices.The transferred CBAM-ResNet34 model was constructed to fully extract V-I features and was applied to small samples of V-I trajectory samples. The CBAM and model transferred methods effectively improve the extraction capability of V-I trajectories and sufficiently reduce the training time of the model.For the first time, image-level CB loss is introduced to reweight the loss function to obtain better V-I trajectory identification in unbalanced datasets.The remainder of this study is organized into the following sections: The second part outlines the overall power fingerprint identification process and introduces the method of acquiring color V-I trajectory. Section 3 explains the construction and improvement process of the proposed model. The validity of the method is verified by the PLAID dataset in Sections 4 and 5. The last section summarizes the work of this paper.The power fingerprint identification process includes data acquisition, data pre-processing, feature extraction, and load identification. The specific process of power fingerprint identification in this study is shown in The primary function of data acquisition is to sample and store the voltage and current waveforms of various household appliances, including  using a collection system . The colThe original V-I trajectory image is a single-channel, two-dimensional pixel matrix. Much valuable information is lost in the original V-I trajectory images. In this section, the original V-I trajectory image is combined with the color coding technique to construct a unique power fingerprint label.First, The V-I traces of different cycles vary slightly due to load fluctuations or noise. When only one waveform cycle is used, this phenomenon inevitably leads to misclassification . Therefovk and ik represent the kth data point of v and i respectively. Tk = floor(Dk) is the number of interpolation points to be added between the kth and (k+1)th sampling points, floor(⋅) represents rounding down.tth interpolation point of the fill t = 1,2,⋯,Tm.Then, considering that pixel discontinuity may occur in the mapping process of V-I trajectory images, it is not conducive to subsequent load identification. For this purpose, the traditional mapping method is improved using a bilinear interpolation technique in Finally, To cover the load identification information as much as possible with its characteristic parameters, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image in the RGB color space. Each channel corresponds to a two-dimensional matrix, and each matrix element can vary continuously from zero to one. V-I trajectories were plotted for 11 categories of electrical equipment randomly selected from the PLAID dataset as shown in imax, imin are the maximum and minimum values of the current sampling values, respectively; vmax, vmin are the maximum and minimum values of the voltage sampling value, respectively; ⌊ ⌋ represents rounding down.Red channel: The shape of the V-I trajectory depends heavily on the load current that reflects the physical characteristics of the device. The average current of the device during stable operation is populated in the R channel, aiming to extract a more stable V-I trajectory and ensure the classification effect.K represents the number of cycles of the load in the transient state. Wk represents the corresponding pixel value in the V-I trajectory feature at the kth instantaneous state point value.Green channel: The V-I trajectory characteristics in the transient state are somewhat different from those after the stable operation. Therefore, in this paper, the average instantaneous power of the device in the transient state is filled in the G channel. The transient V-I trajectory characteristics are also an important characteristic for different loads.Blue channel: the rate of change of voltage and current during the stabilization cycle varies from device to device. V-I trajectory has a loop direction, reflecting the phase relationship between current and voltage. To capture the motion information the of V-I trajectory, the blue channel is plotted by the voltage and current of the appliance for continuous 20 cycles in steady-state operation.The ResNet model effectively mitigates degradation and gradient disappearance during model training by stacking the residual structures . NetworkThe CBAM module represents the attention mechanism module of the convolutional module, which is an attention mechanism module that combines spatial and channel . CompareThe details of the channel attention module (CAM) and spatial attention module (SAM) are shown in W0 and W1 are learnable weights and σ is an S-shaped function.The CAM can be expressed as follows.f denotes the convolution operation.SAM is a complement to CAM and its main purpose is to discover the most meaningful information after CAM processing. First, the input features are processed serially through the mean and maximum pools. This information is then forwarded by the convolutional layer. The final mathematical representation is as follows.In this paper, the position of CBAM is added to each residual block to reduce the influence of redundant features with the help of an attention mechanism and improve the accuracy and timeliness of subsequent recognition. As in The ResNet model was initially designed to identify the ImageNet datasets. However, the learned high-level abstract features can still be used to assist in identifying the V-I trajectories of different types of appliances. We used a model-transferring method and added the CBAM attention module for the ResNet networks, as shown in The ResNet34 neural network was pre-trained using the ImageNet dataset, and all layers except the last fully connected layer were extracted from the pre-trained ResNet34.The Simple Attention module of CBAM was introduced, which was added to each residual structure module of ResNet34. The last full connection layer was replaced with a new one, and the layers were transferred to the new electrical equipment identification task.Input color V-I trajectory image. The color V-I trajectory image size was adjusted to the same size as the input neuron of ResNet34.Output the categories of the electric loads. The new full connection layer is adjusted to the same number of electrical equipment identification categories.All the parameters are retained until the termination condition is satisfied.In power fingerprint identification, data set imbalance is another issue of concern. The usage frequency of household appliances varies greatly, which is reflected in the data set as a significant variation in the sample sizes of different categories. To solve this problem, it is usually necessary to expand some samples so that the number in different categories is the same. The class-balanced loss is an image-level class-balanced loss function that can be implemented by inverse weighting the loss function by the number of effective classes.Therefore, to improve the identification accuracy of color V-I trajectory images, we rebalance the loss by weighting the loss function using the number of valid samples per class to improve the loss. A weighting factor is introduced in the class-balanced loss . We applz = T, where C is the total number of classes. The softmax function treats each class as mutually exclusive and calculates the probability distribution of all classes asAssume that the predicted output of the model for all classes is y, the SM-CE loss for this sample is written as:Given a sample with the class label y has ny training samples, the class-balanced SM-CE loss is:Suppose the class The class-balanced cross-entropy loss uses a balancing factor ω to increase the weight of minority samples in the target loss and decrease the weight of majority samples in the target loss, which makes the classifier focus more on the features of minority categories in training, thus improving the category imbalance problem in the dataset and increasing the accuracy of recognition.The 2 appliances in the PLAID dataset, Microwave (majority class) and Washing (minority class), have different sample sizes as shown in In the actual example, Python 3.8 and Pytorch 1.3.0 deep learning frameworks are used in the actual arithmetic example. The hardware platform was GeForceRTX3090, and the software platform was Linux OS Ubuntu 18.04. Hardware acceleration using GPUs when training deep learning models. The proposed load identification method was tested using the PLAID dataset. The dataset recorded 1793 sets of voltage and current data of 312 appliance loads in 11 categories from 55 households at a sampling frequency of 30 kHz . The num−4 to reduce the learning rate of the transferred layer. Meanwhile, the learning rate of the new fully connected layer was increased to 1 × 10−3.The number of iterations and pixels of the color V-I trajectory image was optimized to obtain the best parameters for load identification. When analyzing the influence of one parameter on the identification result, the other parameter was fixed. After determining the input and output of the model transfer, the new network model was trained until the termination condition was reached. The cross-entropy function was chosen as the minimum loss function, and the optimizer was chosen as Adam. The initial learning rate was configured as 1 × 10To quantitatively analyze the effect of the number of iterations on the method, the variation curves of the identification accuracy and loss value with the number of iterations during a specific test were plotted, as shown in N, the identification accuracy gradually decreased. When the image resolution is small, the color V-I trajectory image is not clear enough, and there is insufficient information about the features. When the resolution of the image is increased to a specific range, more feature information is contained, and CBAM-ResNet34 can extract the feature information. In addition, an image resolution that is too high can also increase the effect of random noise and perturbations, resulting in sharp color V-I trajectory images that are not conducive to subsequent feature extraction. This causes the accuracy of the load identification to decrease rather than increase. Given this, the resolution N of the color V-I image was set to 128.A box plot of the load identification accuracy at different resolutions is shown in Pre represents the precision . The fridge, air conditioner, and washing machine are multi-state appliances with limited working modes Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: N/AReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Summary:In this work, a fingerprint identification based on voltage-current (V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. To obtain a color V-I trajectory image, the current, instantaneous power, and trajectory momentum information are added first to the original V-I trajectory image using color coding.Then, the ResNet34 model was pretrained using the ImageNet dataset and a fully-connected layer. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, to solve the problem of data imbalance in V-I trajectory identification, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function.The manuscript is interesting; however, the following comment should be addressed :Abstract :- - - - - - - - - - -1 – Results in terms of improvement ratio between the proposed and existing work need to be included .Introduction Section :- - - - - - - - - - - - - - - - - - - - - -2 – This section is fine. No comments .Materials and methods Section :- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -3 – This section is fine. No comments .Power fingerprint identification based on transferred CBAM-ResNet34 Section :- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -4 – This section is fine. No comments .Experiments Section :- - - - - - - - - - - - - - - - - - - - - - - - - -5 – This section is fine. No comments .Result and discussion Section :- - - - - - - - - - - - - - - - - - - - - - - - - - -6 – This section is fine. No comments .Conclusion Section :- - - - - - - - - - - - - - - - - - - - - -7 – The limitation of this work should be clearly included in the conclusion section .General Comments:- - - - - - - - - - - - - - - - -8 - There are some grammatical errors should be corrected .- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Reviewer #2: Dear AuthorsThe paper titled “Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet” proposed a power fingerprint identificationbased on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pretrained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets.The paper is interesting however it needs improvements.1. There are various types of ResNet models are available including ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-110, ResNet-152, ResNet-164, ResNet-1202, why authors choose ResNet-34.2. Line 371-372: the SMOTE is not explained, I suggest the author to explain it with proper work e.g. [1].3. Line 36: Power fingerprint identification is a hot issue in the field of NILM, include the full form when any abbreviation appears first time in the entire manuscript.4. This paper used ResNet however the role of ML/DL is not discussed to improve the need to utilize the ML/DL.5. Introduction needs a new paragraph which discuss the AI/DL applications with different domains at once. This paragraph should be added as “Nowadays, scientists and researchers used the machine learning (ML) and Deep learning (DL) models in several applications including agriculture , environment [3-6], and power fingerprint identification. 1. Planetscope Nanosatellites Image Classification Using Machine Learning; 2. CNN Based Automated Weed Detection System Using UAV Imagery; 3. SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification; 4. CDLSTM: A Novel Model for Climate Change Forecasting; 5. Deep Learning Based Modeling of Groundwater Storage Change; 6. Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification.6. Figure 1-13 needs improvement, all these figures looks blur.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  22 Jan 2023Response to Reviewer 1 CommentsPoint 1: Abstract: Results in terms of improvement ratio between the proposed and existing work need to be included.Response 1: Thank you for underlining this deficiency. In the revised manuscript (page 1), we have added more details about the contribution of the paper in the abstract as the following: ‘’The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 min compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.’’Point 2: Conclusion Section: The limitation of this work should be clearly included in the conclusion section.Response 2: Thank you for underlining this deficiency. In the revised manuscript (page 23), we have added more details about the limitation of this work in the conclusion section as the following: ‘’However, the method proposed in this paper still has some problems to be solved. The identification effect of multi-state load in this paper can be further improved, and more advanced device identification models are needed to solve the problem in the future. In addition, the actual engineering implementation faces difficulties in constructing sample libraries and high data sampling frequency requirements. Therefore, a lot of related research work is still needed.’’Point 3: General Comments: There are some grammatical errors should be corrected.Response 3: Thank you for underlining this deficiency. In the revised manuscript , we have corrected grammatical errors and made detailed changes throughout the text. In addition, changes suggested by another reviewer and the grammar of the paragraphs added to the introduction were checked. The correction of major grammatical errors as the following: ‘’For V-I trajectory classification, the small amount of load data for specific user results in a poorly trained model with low accuracy or low generalization capability [21].’’‘’(a)The instantaneous voltage of the Fridge over 1 s. (b) The instantaneous current of the Fridge over 1 s. (c) The instantaneous power of the Fridge over 1 s.’’‘’Color-encoded V-I trajectory images and transferred CBAM-ResNet34 were used as the load feature and training model, respectively, which significantly enhanced the identification ability of the algorithm and reduced the model training time.’’Response to Reviewer 2 CommentsPoint 1: There are various types of ResNet models are available including ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-110, ResNet-152, ResNet-164, ResNet-1202, why authors choose ResNet-34?Response 1: We are grateful for the suggestion. According to the reviewer’s comment, we have provided more details to describe the possible reasons. We have made additions in the corresponding sections of the manuscript. The reasons for selecting ResNet-34 to construct the classification model in this paper as the following:The different numbers of ResNet networks represent the number of layers of the network. Compared to ResNet-18, the network with the least number of layers lacks certain feature representation abilities. Compared to ResNet50, ResNet-101, ResNet-110, ResNet-152, ResNet-164, and ResNet-1202, the increase in the number of layers of the network is accompanied by an increase in the number of parameters and computations, which makes the network training slower. Also, with the addition of the CBAM attention module, ResNet34 can achieve not only higher accuracy but also higher accuracy at the beginning of training. This indicates that the network can learn the target features more efficiently and speed up the training of the network. This is since the introduction of the attention mechanism inside the residual block can learn the features of V-I image samples more efficiently.Compared with other layers of ResNet networks, the accuracy improvement of the attention mechanism module on the network is inferior to that of ResNet34. This is because the attention mechanism is more likely to learn effective features in shallow networks, and the improvement effect of the attention mechanism module on the performance of deeper networks is less obvious, and it is difficult for the attention mechanism to learn effective features.Therefore, considering the hardware cost limitation and the difficulty of training V-I images, and to reflect the feature extraction capability and convergence speed of ResNet34-CBAM, ResNet34-CBAM is selected as the classification model for V-I images in this paper.Point 2: Line 371-372: the SMOTE is not explained, I suggest the author explain it with proper work e.g. [1].Response 2: The authors thank the reviewer for pointing out this problem. The required explanation has been made. In the revised manuscript , the revised contents are as follows: ‘’Using transferred CBAM-ResNet34 as the base model, the methods in this paper were compared with the original data, random oversampling, SMOTE [41], and SVMSMOTE [42] data balancing algorithms on the PLAID dataset, respectively.’’https://doi.org/10.1109/ACCESS.2021.3120738[41] Kim K. Noise Avoidance SMOTE in Ensemble Learning for Imbalanced Data. IEEE Access. 2021; 9: 143250–65. https://doi.org/10.1007/s42835-021-00931-1[42] Rajesh L, Satyanarayana P. Evaluation of Machine Learning Algorithms for Detection of Malicious Traffic in SCADA Network. J Electr Eng Technol. 2022 Mar; 17(2): 913–28. Point 3: Line 36: Power fingerprint identification is a hot issue in the field of NILM, include the full form when any abbreviation appears first time in the entire manuscript.Response 3: Thank you for underlining this deficiency. In the revised manuscript , the revised contents are as follows: ‘’Power fingerprint identification is a hot issue in the field of Non-intrusive load monitoring (NILM), which relies on power fingerprint features and classifiers to identify different types of devices. Common power fingerprint characteristics typically include voltage, current, harmonics, power, V-I trajectory, etc [7-9].’’Point 4: This paper used ResNet however the role of ML/DL is not discussed to improve the need to utilize the ML/DL.Response 4: We deeply appreciate the reviewer’s suggestion. According to the reviewer’s comment, we have added more details to describe and discuss the role of ML/DL. In the revised manuscript ( page 3), the revised contents are as follows:‘’Nowadays, scientists and researchers used machine learning (ML) and deep learning (DL) models in several applications including agriculture , environment [17-20], and power fingerprint identification. Machine learning is often applied to feature extraction and classification of power fingerprints, such as k-nearest neighbors, support vector machines, decision trees, and random forests. These methods are less computationally intensive, but the identification correct rate is lower. Recently, deep learning has achieved good results in the field of power fingerprint identification, such as CNN, RNN, etc. Meanwhile, researchers propose to construct V-I trajectory images with the help of color coding methods to convert power fingerprint identification into an image classification task in which deep learning excels. However, compared to machine learning, deep learning-based classifiers rely on large-scale training data and longer training time, which limits the application of deep learning.’’Point 5: Introduction needs a new paragraph which discuss the AI/DL applications with different domains at once. This paragraph should be added as “Nowadays, scientists and researchers used the machine learning (ML) and Deep learning (DL) models in several applications including agriculture , environment [3-6], and power fingerprint identification. 1. Planetscope Nanosatellites Image Classification Using Machine Learning; 2. CNN Based Automated Weed Detection System Using UAV Imagery; 3. SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification; 4. CDLSTM: A Novel Model for Climate Change Forecasting; 5. Deep Learning Based Modeling of Groundwater Storage Change; 6. Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification.Response 5: We are grateful for the suggestion. According to the reviewer’s comment, we have provided more details to discuss the AI/DL applications with different domains at once in the introduction. In the revised manuscript ( page 3), the revised contents are as follows:‘’Nowadays, scientists and researchers used machine learning (ML) and deep learning (DL) models in several applications including agriculture , environment [17-20], and power fingerprint identification. Machine learning is often applied to feature extraction and classification of power fingerprints, such as k-nearest neighbors, support vector machines, decision trees, and random forests. These methods are less computationally intensive, but the identification correct rate is lower. Recently, deep learning has achieved good results in the field of power fingerprint identification, such as CNN, RNN, etc. Meanwhile, researchers propose to construct V-I trajectory images with the help of color coding methods to convert power fingerprint identification into an image classification task in which deep learning excels. However, compared to machine learning, deep learning-based classifiers rely on large-scale training data and longer training time, which limits the application of deep learning.’’https://doi.org/10.32604/csse.2022.023221[15] Anul Haq M. Planetscope Nanosatellites Image Classification Using Machine Learning. Computer Systems Science and Engineering. 2022; 42(3): 1031–46. https://doi.org/10.32604/csse.2022.023016[16] Anul Haq M. CNN Based Automated Weed Detection System Using UAV Imagery. Computer Systems Science and Engineering. 2022; 42(2): 837–49. https://doi.org/10.32604/cmc.2022.021968[17] Attaallah A, Ahmad Khan R. SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification. Computers, Materials & Continua. 2022; 71(1): 1403–25. https://doi.org/10.32604/cmc.2022.023059[18] Anul Haq M. CDLSTM: A Novel Model for Climate Change Forecasting. Computers, Materials & Continua. 2022; 71(2): 2363–81. https://doi.org/10.32604/cmc.2022.020495[19] Anul Haq M, Khadar Jilani A, Prabu P. Deep Learning Based Modeling of Groundwater Storage Change. Computers, Materials & Continua. 2022; 70(3): 4599–617. https://doi.org/10.1007/s12524-020-01231-3[20] Haq MA, Rahaman G, Baral P, Ghosh A. Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification. J Indian Soc Remote Sens. 2021 Mar; 49(3): 601–6. Point 6: Figure 1-13 needs improvement, all these figures looks blur.Response 6: Thank you for underlining this deficiency. In the revised manuscript, we have adjusted the clarity of Figures 1-13.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 25 Jan 2023Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNetPONE-D-22-31416R1Dear Dr. Lin,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Mohamed Hammad, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author  1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: All comments have been addressedReviewer #2: All comments have been addressed**********  2. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  3. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  5. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  6. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Summary:In this work, a fingerprint identification based on voltage-current (V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. To obtain a color V-I trajectory image, the current, instantaneous power, and trajectory momentum information are added first to the original V-I trajectory image using color coding.Then, the ResNet34 model was pretrained using the ImageNet dataset and a fully-connected layer. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, to solve the problem of data imbalance in V-I trajectory identification, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function.The authors have addressed the raised comments .Abstract :- - - - - - - - - - -1 – The abstract is fine. No further comments .Introduction Section :- - - - - - - - - - - - - - - - - - - - - -2 – This section is fine. No further comments .Materials and methods Section :- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -3 – This section is fine. No further comments .Power fingerprint identification based on transferred CBAM-ResNet34 Section :- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -4 – This section is fine. No further comments .Experiments Section :- - - - - - - - - - - - - - - - - - - - - - - - - -5 – This section is fine. No further comments .Result and discussion Section :- - - - - - - - - - - - - - - - - - - - - - - - - - -6 – This section is fine. No further comments .Conclusion Section :- - - - - - - - - - - - - - - - - - - - - -7 – This section is fine. No further comments .General Comments:- - - - - - - - - - - - - - - - -8 – No further comments .- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Reviewer #2: Dear AuthorsI have now completed the review of the revised manuscript, titled " Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet”. I have observed that the authors put in good efforts to address most of the comments satisfactorily. The improvements in the figures is also seems excellent. Ref 17 details are incorrect, please correct.Best wishes**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoYes: Dr Mohd Anul HaqReviewer #2: ********** 30 Jan 2023PONE-D-22-31416R1 Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet Dear Dr. Lin:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Mohamed Hammad Academic EditorPLOS ONE