PeerJ Computer Science最新文献

{"title":"Abnormal handling mechanism and improvement measures of optical DC current transformer in smart grid environment.","authors":"Shihao Zhou,&nbsp;Hansong Tang,&nbsp;Benren Pan,&nbsp;Wei Zhang","doi":"10.7717/peerj-cs.1132","DOIUrl":"https://doi.org/10.7717/peerj-cs.1132","url":null,"abstract":"<p><p>An optical DC current transformer anomaly handling mechanism is proposed to address the problem that the conventional DC current transformer anomaly handling mechanism cannot compensate for the defect of capacitor anomaly blocking. First, the implementation principle, modulation loop, demodulation method and its anomaly warning mechanism of the sine-wave modulated all-fibre-optic current transformer (FOCT) are investigated, and the effects of light source intensity and modulation voltage on current decoding are explained. The modulation loop is then simulated and modelled and a FOCT anomaly handling mechanism is proposed based on the Bessel function with real-time dynamic current compensation for small changes in modulation depth. Finally, an integrated dynamic test system for DC current transformers and DC protection is designed, and the actual system operation and fault model is established using the RTDS simulation system. The experiments demonstrate that the proposed FOCT anomaly handling and improvement measures can effectively improve the transient performance of FOCT, and at the same time provide a complete set of testing means for the engineering application and later upgrade and replacement of FOCT.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1132"},"PeriodicalIF":3.8,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality assuring the quality assurance tool: applying safety-critical concepts to test framework development.","authors":"Jonathan Thörn,&nbsp;Per Erik Strandberg,&nbsp;Daniel Sundmark,&nbsp;Wasif Afzal","doi":"10.7717/peerj-cs.1131","DOIUrl":"https://doi.org/10.7717/peerj-cs.1131","url":null,"abstract":"<p><p>The quality of embedded systems is demonstrated by the performed tests. The quality of such tests is often dependent on the quality of one or more testing tools, especially in automated testing. Test automation is also central to the success of agile development. It is thus critical to ensure the quality of testing tools. This work explores how industries with agile processes can learn from safety-critical system development with regards to the quality assurance of the test framework development. Safety-critical systems typically need adherence to safety standards that often suggests substantial upfront documentation, plans and a long-term perspective on several development aspects. In contrast, agile approaches focus on quick adaptation, evolving software and incremental deliveries. This article identifies several approaches of quality assurance of software development tools in functional safety development and agile development. The extracted approaches are further analyzed and processed into candidate solutions, <i>i.e.</i>, principles and practices for the test framework quality assurance applicable in an industrial context. An industrial focus group with experienced practitioners further validated the candidate solutions through moderated group discussions. The two main contributions from this study are: (i) 48 approaches and 25 derived candidate solutions for test framework quality assurance in four categories (development, analysis, run-time measures, and validation and verification) with related insights, <i>e.g.</i>, a test framework should be perceived as a tool-chain and not a single tool, (ii) the perceived value of the candidate solutions in industry as collected from the focus group.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1131"},"PeriodicalIF":3.8,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards survival prediction of cancer patients using medical images.","authors":"Nazeef Ul Haq,&nbsp;Bilal Tahir,&nbsp;Samar Firdous,&nbsp;Muhammad Amir Mehmood","doi":"10.7717/peerj-cs.1090","DOIUrl":"https://doi.org/10.7717/peerj-cs.1090","url":null,"abstract":"<p><p>Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients. Recently, state-of-the-art medical imaging and analysis techniques have paved the way for survival prediction of cancer patients by understanding key features extracted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanned images with the help of image processing and machine learning techniques. However, survival prediction is a challenging task due to the complexity in benchmarking of image features, feature selection methods, and machine learning models. In this article, we evaluate the performance of 156 visual features from radiomic and hand-crafted feature classes, six feature selection methods, and 10 machine learning models to benchmark their performance. In addition, MRI scanned Brain Tumor Segmentation (BraTS) and CT scanned non-small cell lung cancer (NSCLC) datasets are used to train classification and regression models. Our results highlight that logistic regression outperforms for the classification with 66 and 54% accuracy for BraTS and NSCLC datasets, respectively. Moreover, our analysis of best-performing features shows that age is a common and significant feature for survival prediction. Also, gray level and shape-based features play a vital role in regression. We believe that the study can be helpful for oncologists, radiologists, and medical imaging researchers to understand and automate the procedure of decision-making and prognosis of cancer patients.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1090"},"PeriodicalIF":3.8,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strip thickness prediction method based on improved border collie optimizing LSTM.","authors":"Lijie Sun,&nbsp;Lin Zeng,&nbsp;Hongjuan Zhou,&nbsp;Lei Zhang","doi":"10.7717/peerj-cs.1114","DOIUrl":"https://doi.org/10.7717/peerj-cs.1114","url":null,"abstract":"<p><strong>Background: </strong>The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry.</p><p><strong>Methods: </strong>A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What's more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established.</p><p><strong>Results: </strong>The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1114"},"PeriodicalIF":3.8,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oil spill identification in X-band marine radar image using K-means and texture feature.","authors":"Rong Chen,&nbsp;Bo Li,&nbsp;Baozhu Jia,&nbsp;Jin Xu,&nbsp;Long Ma,&nbsp;Hongbo Yang,&nbsp;Haixia Wang","doi":"10.7717/peerj-cs.1133","DOIUrl":"https://doi.org/10.7717/peerj-cs.1133","url":null,"abstract":"<p><p>Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by \"Sperry Marine\" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1133"},"PeriodicalIF":3.8,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of routing attacks detection in wireless sensor networks.","authors":"Zainab Alansari,&nbsp;Nor Badrul Anuar,&nbsp;Amirrudin Kamsin,&nbsp;Mohammad Riyaz Belgaum","doi":"10.7717/peerj-cs.1135","DOIUrl":"https://doi.org/10.7717/peerj-cs.1135","url":null,"abstract":"<p><p>Wireless sensor networks (WSNs) consist of hundreds, or thousands of sensor nodes distributed over a wide area and used as the Internet of Things (IoT) devices to benefit many home users and autonomous systems industries. With many users adopting WSN-based IoT technology, ensuring that the sensor's information is protected from attacks is essential. Many attacks interrupt WSNs, such as Quality of Service (QoS) attacks, malicious nodes, and routing attacks. To combat these attacks, especially on the routing attacks, we need to detect the attacker nodes and prevent them from any access to WSN. Although some survey studies on routing attacks have been published, a lack of systematic studies on detecting WSN routing attacks can be seen in the literature. This study enhances the topic with a taxonomy of current and emerging detection techniques for routing attacks in wireless sensor networks to improve QoS. This article uses a PRISMA flow diagram for a systematic review of 87 articles from 2016 to 2022 based on eight routing attacks: wormhole, sybil, Grayhole/selective forwarding, blackhole, sinkhole, replay, spoofing, and hello flood attacks. The review also includes an evaluation of the metrics and criteria used to evaluate performance. Researchers can use this article to fill in any information gaps within the WSN routing attack detection domain.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1135"},"PeriodicalIF":3.8,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40722409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deepfake attack prevention using steganography GANs.","authors":"Iram Noreen,&nbsp;Muhammad Shahid Muneer,&nbsp;Saira Gillani","doi":"10.7717/peerj-cs.1125","DOIUrl":"https://doi.org/10.7717/peerj-cs.1125","url":null,"abstract":"<p><strong>Background: </strong>Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techniques are not effective in the future due to continuous improvements in GANs. Style GANs can create fake videos with high accuracy that cannot be easily detected. Hence, deepfake prevention is the need of the hour rather than just mere detection.</p><p><strong>Methods: </strong>Recently, blockchain-based ownership methods, image tags, and watermarks in video frames have been used to prevent deepfake. However, this process is not fully functional. An image frame could be faked by copying watermarks and reusing them to create a deepfake. In this research, an enhanced modified version of the steganography technique RivaGAN is used to address the issue. The proposed approach encodes watermarks into features of the video frames by training an \"attention model\" with the ReLU activation function to achieve a fast learning rate.</p><p><strong>Results: </strong>The proposed attention-generating approach has been validated with multiple activation functions and learning rates. It achieved 99.7% accuracy in embedding watermarks into the frames of the video. After generating the attention model, the generative adversarial network has trained using DeepFaceLab 2.0 and has tested the prevention of deepfake attacks using watermark embedded videos comprising 8,074 frames from different benchmark datasets. The proposed approach has acquired a 100% success rate in preventing deepfake attacks. Our code is available at https://github.com/shahidmuneer/deepfakes-watermarking-technique.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1125"},"PeriodicalIF":3.8,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity.","authors":"Hanan Hardan,&nbsp;Ali Alawneh,&nbsp;Nameer N El-Emam","doi":"10.7717/peerj-cs.1115","DOIUrl":"https://doi.org/10.7717/peerj-cs.1115","url":null,"abstract":"<p><p>The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm's performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1115"},"PeriodicalIF":3.8,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing.","authors":"Jianbin Wu,&nbsp;Sami Ahmed Haider,&nbsp;Mukesh Soni,&nbsp;Ashima Kalra,&nbsp;Nabamita Deb","doi":"10.7717/peerj-cs.1118","DOIUrl":"https://doi.org/10.7717/peerj-cs.1118","url":null,"abstract":"<p><p>Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1118"},"PeriodicalIF":3.8,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning.","authors":"Minjae Jung,&nbsp;Hyondong Oh","doi":"10.7717/peerj-cs.1119","DOIUrl":"https://doi.org/10.7717/peerj-cs.1119","url":null,"abstract":"<p><p>Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (<i>e.g</i>., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1119"},"PeriodicalIF":3.8,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}