Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029222
Jianhua Chen, Jiaohua Qin, Xuyu Xiang, Yun Tan
The traditional image retrieval has high requirements on the computing power and storage capacity of the platform, so the cloud server has become the preferred choice for outsourcing image retrieval. However, the cloud server is not completely reliable, and outsourcing image retrieval may bring many security, low retrieval accuracy, and privacy problems. In this paper, an encrypted image retrieval method based on feature fusion is proposed. Firstly, the images are encrypted by the encryption operator. Then, the feature extractor is designed, and the enhanced RGB feature and HSV histogram feature are extracted. Finally, the feature extractor and encrypted images are uploaded to the cloud server. The computation of similarity between images is done in the cloud. The experiments and security analysis show that the proposed method has good security and accuracy.
{"title":"A new encrypted image retrieval method based on feature fusion in cloud computing","authors":"Jianhua Chen, Jiaohua Qin, Xuyu Xiang, Yun Tan","doi":"10.1504/ijcse.2020.10029222","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029222","url":null,"abstract":"The traditional image retrieval has high requirements on the computing power and storage capacity of the platform, so the cloud server has become the preferred choice for outsourcing image retrieval. However, the cloud server is not completely reliable, and outsourcing image retrieval may bring many security, low retrieval accuracy, and privacy problems. In this paper, an encrypted image retrieval method based on feature fusion is proposed. Firstly, the images are encrypted by the encryption operator. Then, the feature extractor is designed, and the enhanced RGB feature and HSV histogram feature are extracted. Finally, the feature extractor and encrypted images are uploaded to the cloud server. The computation of similarity between images is done in the cloud. The experiments and security analysis show that the proposed method has good security and accuracy.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115722863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029227
Shu Chen, Nanxi Chen, Jiayi Tang, Xu Wang
The internet of things (IoT) connects numerous physical devices in urban areas to implement smart cities, and health monitoring has emerged as the most promising application area in such cities. However, the current health monitoring solution heavily relies on cloud-based data centres to integrate data, which put the city's confidential data and user's private information at risk of leakage. Fog computing as an extension of cloud computing has attracted lots of attention in the IoT community, for its safety to local data and friendliness to time-sensitive applications. This article adopts the paradigm of fog computing and proposes the cognitive fog for health (CFH) to address the requirement of estimating urban-level health impact in the real-world scenario.
{"title":"Cognitive fog for health: a distributed solution for smart city","authors":"Shu Chen, Nanxi Chen, Jiayi Tang, Xu Wang","doi":"10.1504/ijcse.2020.10029227","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029227","url":null,"abstract":"The internet of things (IoT) connects numerous physical devices in urban areas to implement smart cities, and health monitoring has emerged as the most promising application area in such cities. However, the current health monitoring solution heavily relies on cloud-based data centres to integrate data, which put the city's confidential data and user's private information at risk of leakage. Fog computing as an extension of cloud computing has attracted lots of attention in the IoT community, for its safety to local data and friendliness to time-sensitive applications. This article adopts the paradigm of fog computing and proposes the cognitive fog for health (CFH) to address the requirement of estimating urban-level health impact in the real-world scenario.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029229
Neena Aloysius, M. Geetha
A sign language recognition system facilitates communication between the deaf community and the hearing majority. This paper proposes a novel specialised convolutional neural network (CNN) model, SignNet, to recognise hand gesture signs by incorporating scale space theory to deep learning framework. The proposed model is a weighted average ensemble of CNNs – a low resolution network (LRN), an intermediate resolution network (IRN) and a high resolution network (HRN). Augmented versions of VGG-16 are used as LRN, IRN and HRN. The ensemble works at different spatial resolutions and at varying depths of CNN. The SignNet model was assessed with static signs of American Sign Language – alphabets and digits. Since there exists no sign dataset for deep learning, the ensemble performance is evaluated on the synthetic dataset which we have collected for this task. Assessment of the synthetic dataset by SignNet reported an impressive accuracy of over 92%, notably superior to the other existing models.
{"title":"A scale space model of weighted average CNN ensemble for ASL fingerspelling recognition","authors":"Neena Aloysius, M. Geetha","doi":"10.1504/ijcse.2020.10029229","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029229","url":null,"abstract":"A sign language recognition system facilitates communication between the deaf community and the hearing majority. This paper proposes a novel specialised convolutional neural network (CNN) model, SignNet, to recognise hand gesture signs by incorporating scale space theory to deep learning framework. The proposed model is a weighted average ensemble of CNNs – a low resolution network (LRN), an intermediate resolution network (IRN) and a high resolution network (HRN). Augmented versions of VGG-16 are used as LRN, IRN and HRN. The ensemble works at different spatial resolutions and at varying depths of CNN. The SignNet model was assessed with static signs of American Sign Language – alphabets and digits. Since there exists no sign dataset for deep learning, the ensemble performance is evaluated on the synthetic dataset which we have collected for this task. Assessment of the synthetic dataset by SignNet reported an impressive accuracy of over 92%, notably superior to the other existing models.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114685690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029214
Min Yang, Shibin Zhang, Yang Zhao, Qirun Wang
With the increasing number of network systems and users, there are a myriad of data generated by users' daily activities, then comes the big data era. The focus of this paper tries to realise user trust negotiation with the help of blockchain technology. Therefore, in this paper, how to not only detect a malicious user but also negotiate the users' trust value among network systems are discussed. The dominating work is as follows: firstly, a variety of decentralised systems form a federated system. Secondly, every user behaviour profile is anchored on the user behaviour blockchain among the federated system, which concludes the trajectory of a user's overall behaviours, on the basis of user behaviour profile, the trust negotiation model based on practical byzantine fault tolerance (PBFT) is proposed. Finally, a scenario based on the model is proposed and its safety problems are analysed, which makes a new try in dynamic negotiation of user trust.
{"title":"Dynamic negotiation of user behaviour via blockchain technology in federated system","authors":"Min Yang, Shibin Zhang, Yang Zhao, Qirun Wang","doi":"10.1504/ijcse.2020.10029214","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029214","url":null,"abstract":"With the increasing number of network systems and users, there are a myriad of data generated by users' daily activities, then comes the big data era. The focus of this paper tries to realise user trust negotiation with the help of blockchain technology. Therefore, in this paper, how to not only detect a malicious user but also negotiate the users' trust value among network systems are discussed. The dominating work is as follows: firstly, a variety of decentralised systems form a federated system. Secondly, every user behaviour profile is anchored on the user behaviour blockchain among the federated system, which concludes the trajectory of a user's overall behaviours, on the basis of user behaviour profile, the trust negotiation model based on practical byzantine fault tolerance (PBFT) is proposed. Finally, a scenario based on the model is proposed and its safety problems are analysed, which makes a new try in dynamic negotiation of user trust.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123600574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029230
Jiao Yao, Yaxuan Dai, Yiling Ni, Jin Wang, J. Zhao
Owing to the rapid development of emergency rescue transportation in cities and the frequent emergencies, demand for emergency rescue is increasing drastically. How to select an emergency rescue route quickly and shorten the rescue travel time under the condition of limited urban road resources is of great significance. Based on the characteristics analysis of emergency rescue, this paper classifies priority levels of different emergency traffic, moreover, the travel times are also analysed with three scenarios: 1) emergency rescue vehicles encountering no queues; 2) encountered queues but lanes available; 3) encountered queues with no available lanes. Related case study shows that model in this paper can effectively shorten travel time of emergency traffic in the route and improve its efficiency.
{"title":"Deep characteristics analysis on travel time of emergency traffic","authors":"Jiao Yao, Yaxuan Dai, Yiling Ni, Jin Wang, J. Zhao","doi":"10.1504/ijcse.2020.10029230","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029230","url":null,"abstract":"Owing to the rapid development of emergency rescue transportation in cities and the frequent emergencies, demand for emergency rescue is increasing drastically. How to select an emergency rescue route quickly and shorten the rescue travel time under the condition of limited urban road resources is of great significance. Based on the characteristics analysis of emergency rescue, this paper classifies priority levels of different emergency traffic, moreover, the travel times are also analysed with three scenarios: 1) emergency rescue vehicles encountering no queues; 2) encountered queues but lanes available; 3) encountered queues with no available lanes. Related case study shows that model in this paper can effectively shorten travel time of emergency traffic in the route and improve its efficiency.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133554360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-04DOI: 10.1504/ijcse.2020.10029216
Ce Li, Tan He, Yingheng Wang, Liguo Zhang, Ruili Liu, Jing Zheng
Pipeline fault detection is very important application of pipeline robots for the security of underground drainage pipeline facilities. The detection performance of existing systems is closely related to the image definition in the complex pipeline environment in terms of darkness, water fog, haze, etc. In this paper, the techniques of dark channel prior and cloud processing are combined into the framework of pipeline image haze removal system. In the system, including the user management module, system sitting module, cloud-based image management module and image processing module, we transmit the image data with the secure cloud data control mechanism, and remove the haze in each image using dark channel prior. The experimental results show that the system has good effects on haze removal of pipe images, especially for the larger reflection area. The system can be applied to engineering practice.
{"title":"Pipeline image haze removal system using dark channel prior on cloud processing platform","authors":"Ce Li, Tan He, Yingheng Wang, Liguo Zhang, Ruili Liu, Jing Zheng","doi":"10.1504/ijcse.2020.10029216","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10029216","url":null,"abstract":"Pipeline fault detection is very important application of pipeline robots for the security of underground drainage pipeline facilities. The detection performance of existing systems is closely related to the image definition in the complex pipeline environment in terms of darkness, water fog, haze, etc. In this paper, the techniques of dark channel prior and cloud processing are combined into the framework of pipeline image haze removal system. In the system, including the user management module, system sitting module, cloud-based image management module and image processing module, we transmit the image data with the secure cloud data control mechanism, and remove the haze in each image using dark channel prior. The experimental results show that the system has good effects on haze removal of pipe images, especially for the larger reflection area. The system can be applied to engineering practice.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128690263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-21DOI: 10.1504/ijcse.2020.10024788
Rupak Chakraborty, R. Sushil, M. L. Garg
This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.
{"title":"Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function","authors":"Rupak Chakraborty, R. Sushil, M. L. Garg","doi":"10.1504/ijcse.2020.10024788","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10024788","url":null,"abstract":"This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124175243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-17DOI: 10.1504/ijcse.2020.10028621
Saboora M. Roshan, A. Karsaz, A. Vejdani, Yaser M. Roshan
Diabetic retinopathy is a serious complication of diabetes, and if not controlled, may cause blindness. Automated screening of diabetic retinopathy helps physicians to diagnose and control the disease in early stages. In this paper, two case studies are proposed, each on a different dataset. Firstly, automatic screening of diabetic retinopathy utilising pre-trained convolutional neural networks was employed on the Kaggle dataset. The reason for using pre-trained networks is to save time and resources during training compared to fully training a convolutional neural network. The proposed networks were fine-tuned for the pre-processed dataset, and the selectable parameters of the fine-tuning approach were optimised. At the end, the performance of the fine-tuned network was evaluated using a clinical dataset comprising 101 images. The clinical dataset is completely independent from the fine-tuning dataset and is taken by a different device with different image quality and size.
{"title":"Fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening: a clinical study","authors":"Saboora M. Roshan, A. Karsaz, A. Vejdani, Yaser M. Roshan","doi":"10.1504/ijcse.2020.10028621","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10028621","url":null,"abstract":"Diabetic retinopathy is a serious complication of diabetes, and if not controlled, may cause blindness. Automated screening of diabetic retinopathy helps physicians to diagnose and control the disease in early stages. In this paper, two case studies are proposed, each on a different dataset. Firstly, automatic screening of diabetic retinopathy utilising pre-trained convolutional neural networks was employed on the Kaggle dataset. The reason for using pre-trained networks is to save time and resources during training compared to fully training a convolutional neural network. The proposed networks were fine-tuned for the pre-processed dataset, and the selectable parameters of the fine-tuning approach were optimised. At the end, the performance of the fine-tuned network was evaluated using a clinical dataset comprising 101 images. The clinical dataset is completely independent from the fine-tuning dataset and is taken by a different device with different image quality and size.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-17DOI: 10.1504/ijcse.2020.10028624
Yu Chen, Jiang-Yi Lin, Chinchen Chang, Yu-Chen Hu
This paper presents a novel greyscale image reversible data hiding scheme based on exploiting modification direction (EMD) method. In this scheme, two 5-ary secret numbers are embedded into each pixel pair in the cover image according to the EMD method to generate two pairs of stego pixels. Two meaningful shadow images are obtained by shifting the generated corresponding pixel pairs, and the original image and the secret data can be accurately recovered when the two shadow images are operated together. Experimental results show that the proposed scheme has a good performance in the shadow image quality and the image embedding ratio.
{"title":"Reversibly hiding data using dual images scheme based on EMD data hiding method","authors":"Yu Chen, Jiang-Yi Lin, Chinchen Chang, Yu-Chen Hu","doi":"10.1504/ijcse.2020.10028624","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10028624","url":null,"abstract":"This paper presents a novel greyscale image reversible data hiding scheme based on exploiting modification direction (EMD) method. In this scheme, two 5-ary secret numbers are embedded into each pixel pair in the cover image according to the EMD method to generate two pairs of stego pixels. Two meaningful shadow images are obtained by shifting the generated corresponding pixel pairs, and the original image and the secret data can be accurately recovered when the two shadow images are operated together. Experimental results show that the proposed scheme has a good performance in the shadow image quality and the image embedding ratio.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121108156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-17DOI: 10.1504/ijcse.2020.10028623
Soumi Ghosh, A. Rana, Vineet Kansal
Prediction of software defects in time improves quality and helps in locating the defect-prone areas accurately. Although earlier considerable methods were applied, actually none of those measures was found to be fool-proof and accurate. Hence, a newer framework includes nonlinear manifold detection model, and its algorithm originated for defect prediction using different techniques of nonlinear manifold detection (nonlinear MDs) along with 14 different machine learning techniques (MLTs) on eight defective software datasets. A critical analysis cum exhaustive comparative estimation revealed that nonlinear manifold detection model has a more accurate and effective impact on defect prediction as compared to feature selection techniques. The outcome of the experiment was statistically tested by Friedman and post hoc analysis using Nemenyi test, which validates that hidden Markov model (HMM) along with nonlinear manifold detection model outperforms and is significantly different from MLTs.
{"title":"A benchmarking framework using nonlinear manifold detection techniques for software defect prediction","authors":"Soumi Ghosh, A. Rana, Vineet Kansal","doi":"10.1504/ijcse.2020.10028623","DOIUrl":"https://doi.org/10.1504/ijcse.2020.10028623","url":null,"abstract":"Prediction of software defects in time improves quality and helps in locating the defect-prone areas accurately. Although earlier considerable methods were applied, actually none of those measures was found to be fool-proof and accurate. Hence, a newer framework includes nonlinear manifold detection model, and its algorithm originated for defect prediction using different techniques of nonlinear manifold detection (nonlinear MDs) along with 14 different machine learning techniques (MLTs) on eight defective software datasets. A critical analysis cum exhaustive comparative estimation revealed that nonlinear manifold detection model has a more accurate and effective impact on defect prediction as compared to feature selection techniques. The outcome of the experiment was statistically tested by Friedman and post hoc analysis using Nemenyi test, which validates that hidden Markov model (HMM) along with nonlinear manifold detection model outperforms and is significantly different from MLTs.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115381747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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