Pub Date : 2021-01-01DOI: 10.32604/jnm.2021.019694
Kaixin Qi, Guiying Wang, Yinggang Huang, P. Fang
: A visual pH indicator through purple cabbage dye is selected to test the freshness of venison. Chitosan and cassava starch of an equal weight were used to prepare the film-forming matrix of the indicator. The crystallization of natural purple cabbage dyes with a weight ratio of 5%, 10%, 20% and 40% were added to the matrix, respectively. The pH color test showed that the natural purple cabbage lyophilized powder with a weight ratio of 40% was the best for the pH indicator, which was used to test the freshness of venison stored at 4℃. Th e total bacterial count, volatile basic nitrogen (TVB-N) and thiobarbituric acid (TBA) of venison were tested through an experiment. When stored at under 4℃, a significant color change of the indicator from pink to blue-green was shown when the total amount of volatile basic nitrogen (TVB-N) (22.78 mg/100 g) exceeded the critical value (20 mg/100 g). The test result showed that the indicator was very sensitive to pH value and it would help customers identify the freshness of venison.
{"title":"A Visual pH Indicator through Purple Cabbage Dye for Freshness Test of Venison","authors":"Kaixin Qi, Guiying Wang, Yinggang Huang, P. Fang","doi":"10.32604/jnm.2021.019694","DOIUrl":"https://doi.org/10.32604/jnm.2021.019694","url":null,"abstract":": A visual pH indicator through purple cabbage dye is selected to test the freshness of venison. Chitosan and cassava starch of an equal weight were used to prepare the film-forming matrix of the indicator. The crystallization of natural purple cabbage dyes with a weight ratio of 5%, 10%, 20% and 40% were added to the matrix, respectively. The pH color test showed that the natural purple cabbage lyophilized powder with a weight ratio of 40% was the best for the pH indicator, which was used to test the freshness of venison stored at 4℃. Th e total bacterial count, volatile basic nitrogen (TVB-N) and thiobarbituric acid (TBA) of venison were tested through an experiment. When stored at under 4℃, a significant color change of the indicator from pink to blue-green was shown when the total amount of volatile basic nitrogen (TVB-N) (22.78 mg/100 g) exceeded the critical value (20 mg/100 g). The test result showed that the indicator was very sensitive to pH value and it would help customers identify the freshness of venison.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69797164","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 : 2021-01-01DOI: 10.32604/JNM.2021.018267
Mingming Yang, Junchuan Yang
: Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods. In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected. This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm. The differences between them and their respective applicable scopes are analyzed. Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features is calculated according to the mutual information distance measure, and the high redundant features are removed to obtain the feature subset with higher quality. Experimental results on six datasets show the effectiveness of our method.
{"title":"Feature Selection Based on Distance Measurement","authors":"Mingming Yang, Junchuan Yang","doi":"10.32604/JNM.2021.018267","DOIUrl":"https://doi.org/10.32604/JNM.2021.018267","url":null,"abstract":": Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods. In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected. This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm. The differences between them and their respective applicable scopes are analyzed. Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features is calculated according to the mutual information distance measure, and the high redundant features are removed to obtain the feature subset with higher quality. Experimental results on six datasets show the effectiveness of our method.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796987","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 : 2021-01-01DOI: 10.32604/jnm.2021.024665
Xiaoying Chen, Jie Kang, Cong Hu
: The texture of ground-based nephogram is abundant and multiplicity. Many cloud textures are not as clear as artificial textures. A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image. Grunwald-Lentikov (G-L) and Grunwald-Lentikov (R-L) fractional differential operators are applied to the enhancement algorithm of ground-based nephogram. An operator mask based on adaptive differential order is designed. The corresponding mask template is used to process each pixel. The results show that this method can extract image texture and edge details and simplify the process of differential order selection.
{"title":"Ground Nephogram Enhancement Algorithm Based on Improved Adaptive Fractional Differentiation","authors":"Xiaoying Chen, Jie Kang, Cong Hu","doi":"10.32604/jnm.2021.024665","DOIUrl":"https://doi.org/10.32604/jnm.2021.024665","url":null,"abstract":": The texture of ground-based nephogram is abundant and multiplicity. Many cloud textures are not as clear as artificial textures. A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image. Grunwald-Lentikov (G-L) and Grunwald-Lentikov (R-L) fractional differential operators are applied to the enhancement algorithm of ground-based nephogram. An operator mask based on adaptive differential order is designed. The corresponding mask template is used to process each pixel. The results show that this method can extract image texture and edge details and simplify the process of differential order selection.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69797501","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}
: Image super resolution is an important field of computer research. The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration. However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images. In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks. This network contains three cascaded FSRCNNs. Due to each sub FSRCNN can process a specific scale image, our network can simultaneously exploit three scale images, and can also use the information of three different scales of images. Experiments on multiple datasets confirmed that the proposed network can achieve better performance for image SR.
{"title":"Design of Network Cascade Structure for Image Super-Resolution","authors":"Jianwei Zhang, Zhenxing Wang, Yuhui Zheng, Guoqing Zhang","doi":"10.32604/JNM.2021.018383","DOIUrl":"https://doi.org/10.32604/JNM.2021.018383","url":null,"abstract":": Image super resolution is an important field of computer research. The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration. However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images. In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks. This network contains three cascaded FSRCNNs. Due to each sub FSRCNN can process a specific scale image, our network can simultaneously exploit three scale images, and can also use the information of three different scales of images. Experiments on multiple datasets confirmed that the proposed network can achieve better performance for image SR.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796795","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 : 2021-01-01DOI: 10.32604/JNM.2021.017763
Jiancheng Zou, Na Zhu, Bailin Ge, Don Hong
: We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly. The VGG16 network part of the SSD network is replaced with the MobilenetV2 network. At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not down-sampled at the end were removed, which can make the model structure simpler and faster to detect. The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions. We use Feature Pyramid Network (FPN) for up-sampling, it can fuse low-level feature maps with high resolution and high-level feature maps with rich semantic information. For sampling results, we use the Secure Shell (SSH) module to extract different receptive fields, which improves the detection accuracy. Our model ensures that the accuracy of the elderly fall detection remains unchanged, but it greatly improves the detection speed that only takes 10 milliseconds to detect a picture.
{"title":"Elderly Fall Detection Based on Improved SSD Algorithm","authors":"Jiancheng Zou, Na Zhu, Bailin Ge, Don Hong","doi":"10.32604/JNM.2021.017763","DOIUrl":"https://doi.org/10.32604/JNM.2021.017763","url":null,"abstract":": We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly. The VGG16 network part of the SSD network is replaced with the MobilenetV2 network. At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not down-sampled at the end were removed, which can make the model structure simpler and faster to detect. The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions. We use Feature Pyramid Network (FPN) for up-sampling, it can fuse low-level feature maps with high resolution and high-level feature maps with rich semantic information. For sampling results, we use the Secure Shell (SSH) module to extract different receptive fields, which improves the detection accuracy. Our model ensures that the accuracy of the elderly fall detection remains unchanged, but it greatly improves the detection speed that only takes 10 milliseconds to detect a picture.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796732","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}
In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of these two algorithms, the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services. This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests, and then combine the collaborative filtering algorithm to establish a potential interest model, mix the existing and potential interests, and calculate with the candidate search content set. The similarity gets the recommendation list.
{"title":"Design of Hybrid Recommendation Algorithm in Online Shopping System","authors":"Yingchao Wang, Yuanhao Zhu, Zongtian Zhang, Huihuang Liu, Peng Guo","doi":"10.32604/jnm.2021.016655","DOIUrl":"https://doi.org/10.32604/jnm.2021.016655","url":null,"abstract":"In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of these two algorithms, the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services. This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests, and then combine the collaborative filtering algorithm to establish a potential interest model, mix the existing and potential interests, and calculate with the candidate search content set. The similarity gets the recommendation list.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796964","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 : 2021-01-01DOI: 10.32604/jnm.2021.018923
Zheng Zhang, Shuhua Zhou
{"title":"Research on Feature Extraction Method of Social Network Text","authors":"Zheng Zhang, Shuhua Zhou","doi":"10.32604/jnm.2021.018923","DOIUrl":"https://doi.org/10.32604/jnm.2021.018923","url":null,"abstract":"","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69797011","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}
: Low-power wide area network (LPWAN) has developed rapidly in recent years and is widely used in various Internet of Things (IoT) services. In order to reduce cost and power consumption, wide coverage, LPWAN tends to use simple channel access control protocols, such as the Aloha protocol. This protocol is simple with poor extension capability. In high-density environment, Aloha protocol will lead to low channel utilization, prolonged access and high conflict probability. Therefore, in order to solve the above problems, we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol, that is, a dynamic listening backoff mechanism. We combine the improved “listen first and then talk” (LBT) mechanism with the current state of the channel to adaptively adjust the size of the backoff window. The theoretical analysis and simulation results show that the proposed mechanism have a better performance than the existing mechanism, it can reduce conflicts in dense environments. By comparison, the packet transmission success rate is increased by 17%.
{"title":"A LoRaWAN Access Technology Based on Channel Adaptive Adjustment","authors":"Li Ma, Meng Zhao, Dongchao Ma, Yingxun Fu","doi":"10.32604/jnm.2020.09715","DOIUrl":"https://doi.org/10.32604/jnm.2020.09715","url":null,"abstract":": Low-power wide area network (LPWAN) has developed rapidly in recent years and is widely used in various Internet of Things (IoT) services. In order to reduce cost and power consumption, wide coverage, LPWAN tends to use simple channel access control protocols, such as the Aloha protocol. This protocol is simple with poor extension capability. In high-density environment, Aloha protocol will lead to low channel utilization, prolonged access and high conflict probability. Therefore, in order to solve the above problems, we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol, that is, a dynamic listening backoff mechanism. We combine the improved “listen first and then talk” (LBT) mechanism with the current state of the channel to adaptively adjust the size of the backoff window. The theoretical analysis and simulation results show that the proposed mechanism have a better performance than the existing mechanism, it can reduce conflicts in dense environments. By comparison, the packet transmission success rate is increased by 17%.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796633","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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