Pub Date : 2020-07-01DOI: 10.3966/160792642020072104005
C. Chao, Stephen J. H. Yang
Firewalls are always treated as the core devices for network security to protect networks from being attacked. Still, properly configuring firewall rules is no easy task due to its laboring and time-consuming characteristic. In some cases, firewall rules need to be added, deleted, modified, or order-changed from time to time to fit in the dynamic of network traffic. As a result, firewalls are subject to rule anomalies caused by misconfigurations such that network security holes can be created accordingly, and then damage the managed networks and even worse the firewalls themselves. In this paper, an enhanced firewall rule management approach is proposed where it can not only pinpoint the anomalies among firewall rules effectively and efficiently, but also provide a novel mechanism for correct and speedy removal of rule anomalies. As a demonstration, a visualized firewall rule anomaly removal system has been realized and performance evaluations on anomaly removal have been also conducted, in which our developed mechanism shows its excellence and feasibility.
{"title":"A Novel Mechanism for Anomaly Removal of Firewall Filtering Rules","authors":"C. Chao, Stephen J. H. Yang","doi":"10.3966/160792642020072104005","DOIUrl":"https://doi.org/10.3966/160792642020072104005","url":null,"abstract":"Firewalls are always treated as the core devices for network security to protect networks from being attacked. Still, properly configuring firewall rules is no easy task due to its laboring and time-consuming characteristic. In some cases, firewall rules need to be added, deleted, modified, or order-changed from time to time to fit in the dynamic of network traffic. As a result, firewalls are subject to rule anomalies caused by misconfigurations such that network security holes can be created accordingly, and then damage the managed networks and even worse the firewalls themselves. In this paper, an enhanced firewall rule management approach is proposed where it can not only pinpoint the anomalies among firewall rules effectively and efficiently, but also provide a novel mechanism for correct and speedy removal of rule anomalies. As a demonstration, a visualized firewall rule anomaly removal system has been realized and performance evaluations on anomaly removal have been also conducted, in which our developed mechanism shows its excellence and feasibility.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"949-957"},"PeriodicalIF":1.6,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46997444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.3966/160792642020072104011
G. Gankhuyag, Yoonsik Choe
In this paper, we introduce an image encryption algorithm that can be used in combination with compression algorithms. Existing encryption algorithms focus on either encryption strength or speed without compression, whereas the proposed algorithm improves compression efficiency while ensuring security. Our encryption algorithm decomposes images into pixel values and pixel intensity subsets, and computes the order of permutations. An encrypted image becomes unpredictable after permutation. Order permutation reduces the discontinuity between signals in an image, increasing compression efficiency. The experimental results show that the security strength of the proposed algorithm is similar to that of existing algorithms. Additionally, we tested the algorithm on the JPEG and the JPEG2000 with variable compression ratios. Compared to existing methods applied without encryption, the proposed algorithm significantly increases PSNR and SSIM values.
{"title":"Compression-friendly Image Encryption Algorithm Based on Order Relation","authors":"G. Gankhuyag, Yoonsik Choe","doi":"10.3966/160792642020072104011","DOIUrl":"https://doi.org/10.3966/160792642020072104011","url":null,"abstract":"In this paper, we introduce an image encryption algorithm that can be used in combination with compression algorithms. Existing encryption algorithms focus on either encryption strength or speed without compression, whereas the proposed algorithm improves compression efficiency while ensuring security. Our encryption algorithm decomposes images into pixel values and pixel intensity subsets, and computes the order of permutations. An encrypted image becomes unpredictable after permutation. Order permutation reduces the discontinuity between signals in an image, increasing compression efficiency. The experimental results show that the security strength of the proposed algorithm is similar to that of existing algorithms. Additionally, we tested the algorithm on the JPEG and the JPEG2000 with variable compression ratios. Compared to existing methods applied without encryption, the proposed algorithm significantly increases PSNR and SSIM values.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"1013-1023"},"PeriodicalIF":1.6,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45290399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.3966/160792642020072104017
Noorkholis Luthfil Hakim, T. Shih, Lin Hui
Real-time finger detection and tracking systems have been growing rapidly in the past decade. Among those methods, Appearance-based and Model-based methods have produced excellent results. However, the occlusion issue is one of the main challenges in this field. In this study, we address this issue by considering the repeating-finite gestures of a guitar-strumming or a hand puppet and, represent using a Finite State Machine model. Also we proposed a novel finger pose tracking system using FSM Model combining with the appearance-based method.. The proposed system consists of two parts: FSM-FT builder creates the FSM hand, and the FSM-FT runner controls the FSM-FT system. Empirically, we conducted an experimental study involving one sample repeating hand gesture and our approach achieved a significance recognition rate of 82% in the testing phase.
{"title":"Enhanced appearance-based finger detection and tracking using finite state machine control","authors":"Noorkholis Luthfil Hakim, T. Shih, Lin Hui","doi":"10.3966/160792642020072104017","DOIUrl":"https://doi.org/10.3966/160792642020072104017","url":null,"abstract":"Real-time finger detection and tracking systems have been growing rapidly in the past decade. Among those methods, Appearance-based and Model-based methods have produced excellent results. However, the occlusion issue is one of the main challenges in this field. In this study, we address this issue by considering the repeating-finite gestures of a guitar-strumming or a hand puppet and, represent using a Finite State Machine model. Also we proposed a novel finger pose tracking system using FSM Model combining with the appearance-based method.. The proposed system consists of two parts: FSM-FT builder creates the FSM hand, and the FSM-FT runner controls the FSM-FT system. Empirically, we conducted an experimental study involving one sample repeating hand gesture and our approach achieved a significance recognition rate of 82% in the testing phase.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"1087-1096"},"PeriodicalIF":1.6,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41937390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.3966/160792642020052103014
Chung-Ming Huang, Yi Guo
This work designed and developed an innovative Mobile Digital Culture Heritage (M-DCH) platform called Demodulating and Encoding Heritage (DEH) to allow users to have the on-site exploring of the culture heritage using handheld devices and wireless mobile networks. Since most of the current M-DCH platforms (i) view a heritage object as a point and (ii) do not have the group-like authoring scenario, main innovations of the DEH platform are twofold. (1) DEH interprets M-DCH’s contents from the viewpoint of Point of Interest (POI), Line of Interest (LOI), Area of Interest (AOI), and Story/Site of Interest (SOI). The authors’ roles of POIs/LOIs/AOIs/SOIs can be classified as player (regular users), expert and narrator. The work defines the logic rules of composing some POIs (POIs, LOIs and/or AOIs) into a LOI/AOI (SOI) according to (i) the author’s role and (ii) the status, which can be (i) public or private and (ii) successfully verified, unsuccessfully verified or un-verified of the corresponding LOI/AOI (SOI). (2) DEH defines a new grouping function that allows the group creator to have the privilege of modifying contents created by group members. Details of the functional specification, system design and usage examples of DEH’s APPs and web are presented.
{"title":"The demodulating and encoding heritage (DEH) platform for mobile digital culture heritage (M-DCH)","authors":"Chung-Ming Huang, Yi Guo","doi":"10.3966/160792642020052103014","DOIUrl":"https://doi.org/10.3966/160792642020052103014","url":null,"abstract":"This work designed and developed an innovative Mobile Digital Culture Heritage (M-DCH) platform called Demodulating and Encoding Heritage (DEH) to allow users to have the on-site exploring of the culture heritage using handheld devices and wireless mobile networks. Since most of the current M-DCH platforms (i) view a heritage object as a point and (ii) do not have the group-like authoring scenario, main innovations of the DEH platform are twofold. (1) DEH interprets M-DCH’s contents from the viewpoint of Point of Interest (POI), Line of Interest (LOI), Area of Interest (AOI), and Story/Site of Interest (SOI). The authors’ roles of POIs/LOIs/AOIs/SOIs can be classified as player (regular users), expert and narrator. The work defines the logic rules of composing some POIs (POIs, LOIs and/or AOIs) into a LOI/AOI (SOI) according to (i) the author’s role and (ii) the status, which can be (i) public or private and (ii) successfully verified, unsuccessfully verified or un-verified of the corresponding LOI/AOI (SOI). (2) DEH defines a new grouping function that allows the group creator to have the privilege of modifying contents created by group members. Details of the functional specification, system design and usage examples of DEH’s APPs and web are presented.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"765-781"},"PeriodicalIF":1.6,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47874248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.3966/160792642020052103001
Huilin Ge, Yahui Sun, Yueh Min Huang, S. Lim
Synthetic aperture radar (SAR) plays an important role in Satellite IoT, due to its remarkable capability of all-weather monitoring and information acquisition under complicated conditions. It is well-known that SAR image interpretation usually requires accurate segmentation. However, SAR image segmentation inevitably encounters speckle noise because of the unique imaging mechanism of SAR. In order to address the problem, we proposed SAR images segmentation method by combined a hierarchical Student’s t-mixture model (HSMM) with an anisotropic mean template, which can divide the global SAR image segmentation into several sub-clustering-issues efficiently resolved using classical algorithm. With the aid of a non-linear structure tensor for image contents analysis, the adaptive template can explore more spatial correlations between pixels for the purpose of improving HSMM robustness and segmentation accuracy. Experiments results both synthetic and real SAR images demonstrate that our proposed HSMM is more robust to speckle noise and obtains more accurate segmented images.
{"title":"SAR Image Segmentation with Structure Tensor Based Hierarchical Student’s t-Mixture Model","authors":"Huilin Ge, Yahui Sun, Yueh Min Huang, S. Lim","doi":"10.3966/160792642020052103001","DOIUrl":"https://doi.org/10.3966/160792642020052103001","url":null,"abstract":"Synthetic aperture radar (SAR) plays an important role in Satellite IoT, due to its remarkable capability of all-weather monitoring and information acquisition under complicated conditions. It is well-known that SAR image interpretation usually requires accurate segmentation. However, SAR image segmentation inevitably encounters speckle noise because of the unique imaging mechanism of SAR. In order to address the problem, we proposed SAR images segmentation method by combined a hierarchical Student’s t-mixture model (HSMM) with an anisotropic mean template, which can divide the global SAR image segmentation into several sub-clustering-issues efficiently resolved using classical algorithm. With the aid of a non-linear structure tensor for image contents analysis, the adaptive template can explore more spatial correlations between pixels for the purpose of improving HSMM robustness and segmentation accuracy. Experiments results both synthetic and real SAR images demonstrate that our proposed HSMM is more robust to speckle noise and obtains more accurate segmented images.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"615-628"},"PeriodicalIF":1.6,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47749215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.3966/160792642020052103015
Yuan Chang, W. Gunarathne, T. Shih
Industrial product defect detection has been known for a while to make sure the released products meet the expected requirements. Earlier, product defect detection was commonly done manually by humans; they have detected whether the products consist of defects or not by using their human senses based on the standard. In this industrial era, product defect detection is expected to be faster and more accurate, while humans could be exhausted and become slower and less reliable. Deep learning technology is very famous in the field of image processing, such as image classification, object detection, object tracking, and of course the defect detection. In this study, we propose a novel automated solution system to identify the good and defective products on a production line using deep learning technology. In the experiment, we have compared several algorithms of defect detections using a data set, which comprises 20 categories of objects and 50 images in each category. The experimental results demonstrated that the proposed system had produced effective results within a short time.
{"title":"Deep Learning Approaches for Dynamic Object Understanding and Defect Detection","authors":"Yuan Chang, W. Gunarathne, T. Shih","doi":"10.3966/160792642020052103015","DOIUrl":"https://doi.org/10.3966/160792642020052103015","url":null,"abstract":"Industrial product defect detection has been known for a while to make sure the released products meet the expected requirements. Earlier, product defect detection was commonly done manually by humans; they have detected whether the products consist of defects or not by using their human senses based on the standard. In this industrial era, product defect detection is expected to be faster and more accurate, while humans could be exhausted and become slower and less reliable. Deep learning technology is very famous in the field of image processing, such as image classification, object detection, object tracking, and of course the defect detection. In this study, we propose a novel automated solution system to identify the good and defective products on a production line using deep learning technology. In the experiment, we have compared several algorithms of defect detections using a data set, which comprises 20 categories of objects and 50 images in each category. The experimental results demonstrated that the proposed system had produced effective results within a short time.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"783-790"},"PeriodicalIF":1.6,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46578363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.3966/160792642020032102026
I-Hsiung Chang, H. Keh, Bhargavi Dande, D. S. Roy
Internet of Things (IoT) and Artificial Intelligence (AI) have received much attention in recent years. Embedded with sensors and connected to the Internet, the IoT device can collect massive data and interact with a human. The data collected by IoT can be further analyzed by applying AI mechanisms for exploring the information behind the data and then have impacts on the interactions between human and things. This paper aims to design and implement a Smart Hat, which is a wearable device and majorly applies the IoT and AI technologies, aiming to help a kid for exploring knowledge in a manner of easy, active, and aggressive. The designed Smart Hat can identify objects in the outside environment and give output as an audio format, which adopts the IoT and AI technologies. The learning Smart Hat intends to help kids aid them in the primary learning task of identifying objects without the supervision of the third party (parents, teachers, others etc.,) in real life. This Smart Hat device provides a sophisticated technology to kids for easy, active, and aggressive learning in daily life. Performance studies show that the obtained results are promising and very satisfactory.
{"title":"Smart Hat: Design and Implementation of a Wearable Learning Device for Kids Using AI and IoTs Techniques","authors":"I-Hsiung Chang, H. Keh, Bhargavi Dande, D. S. Roy","doi":"10.3966/160792642020032102026","DOIUrl":"https://doi.org/10.3966/160792642020032102026","url":null,"abstract":"Internet of Things (IoT) and Artificial Intelligence (AI) have received much attention in recent years. Embedded with sensors and connected to the Internet, the IoT device can collect massive data and interact with a human. The data collected by IoT can be further analyzed by applying AI mechanisms for exploring the information behind the data and then have impacts on the interactions between human and things. This paper aims to design and implement a Smart Hat, which is a wearable device and majorly applies the IoT and AI technologies, aiming to help a kid for exploring knowledge in a manner of easy, active, and aggressive. The designed Smart Hat can identify objects in the outside environment and give output as an audio format, which adopts the IoT and AI technologies. The learning Smart Hat intends to help kids aid them in the primary learning task of identifying objects without the supervision of the third party (parents, teachers, others etc.,) in real life. This Smart Hat device provides a sophisticated technology to kids for easy, active, and aggressive learning in daily life. Performance studies show that the obtained results are promising and very satisfactory.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"593-604"},"PeriodicalIF":1.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44616873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.3966/160792642020032102003
J. Balen, Denis Vajak, K. Salah
Virtual Private Server (VPS) enables user access to an operating system instance with a fine-grained control of private software and hardware resources. Many various factors can affect VPS performance and they primarily include physical server hardware specifications, installed operating system, virtualization layer, and the underlying network infrastructure. Therefore, it is very important to properly select a VPS host that meets users, applications and services resource and performance requirements. This paper presents a performance evaluation of three popular VPS hosts; namely Digital Ocean, Linode and VULTR. Performance measurement was conducted under the same controlled conditions for all three VPS hosts using a popular benchmark application for Unix operating systems - UnixBench. Three performance evaluation experiments with a focus on examining and studying key performance metrics which include CPU scheduling, memory management, hard disk drive management and Unix operating system task scheduling, were conducted. Performance easurement results show that VULTR achieves the best results under most of the tests in the first two experiments making it the best choice for low demand users, while DigitalOcean achieves the best results in the third experiment making it the best solution for high demand users who are looking for a high performance VPS.
{"title":"Comparative Performance Evaluation of Popular Virtual Private Servers","authors":"J. Balen, Denis Vajak, K. Salah","doi":"10.3966/160792642020032102003","DOIUrl":"https://doi.org/10.3966/160792642020032102003","url":null,"abstract":"Virtual Private Server (VPS) enables user access to an operating system instance with a fine-grained control of private software and hardware resources. Many various factors can affect VPS performance and they primarily include physical server hardware specifications, installed operating system, virtualization layer, and the underlying network infrastructure. Therefore, it is very important to properly select a VPS host that meets users, applications and services resource and performance requirements. This paper presents a performance evaluation of three popular VPS hosts; namely Digital Ocean, Linode and VULTR. Performance measurement was conducted under the same controlled conditions for all three VPS hosts using a popular benchmark application for Unix operating systems - UnixBench. Three performance evaluation experiments with a focus on examining and studying key performance metrics which include CPU scheduling, memory management, hard disk drive management and Unix operating system task scheduling, were conducted. Performance easurement results show that VULTR achieves the best results under most of the tests in the first two experiments making it the best choice for low demand users, while DigitalOcean achieves the best results in the third experiment making it the best solution for high demand users who are looking for a high performance VPS.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"343-356"},"PeriodicalIF":1.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45210160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3966/160792642020012101026
Lu-Xian Wu, Shin-Jye Lee
With the high industrialization at a rapid pace, the demand for energy increases exponentially, but it is difficult to meet the balance of demand and supply. Therefore, how to effectively meet the balance of demand and supply intelligently has become a popular issue in this century. In recent years, Taiwan Power Company (Taipower), the biggest electric company in Taiwan, is committed to the construction of Advanced Metering Infrastructure (AMI), which provides communication channels and enables demand-side users to participate in load dispatch. On the other hand, the construction of AMI is expected to generate a tremendous number of valuable data on electricity consumption, but it is not easy to convert these data into effective information by the conventional quantitative methods. In as much as the rapid progression of AI technology in the industrial field, the application of AI technology in the technology management has become an increasing issue as an interdisciplinary study. To address this task, this work applies the recurrent neural network based on deep learning to predict low-voltage usage shortly by the electricity information of low-voltage user and meteorological data. After many vicissitudes, the electricity consumption per hour can be predicted and a sound energy arrangement can be therefore planned. Through introducing the proposed model, Taipower Company will have an effective capability that schedules power, reduces unnecessary backup power, and provides time-consuming electricity prices for industrial enterprises accurately among high usage of Taiwan industries.
{"title":"A Deep Learning-Based Strategy to the Energy Management-Advice for Time-of-Use Rate of Household Electricity Consumption","authors":"Lu-Xian Wu, Shin-Jye Lee","doi":"10.3966/160792642020012101026","DOIUrl":"https://doi.org/10.3966/160792642020012101026","url":null,"abstract":"With the high industrialization at a rapid pace, the demand for energy increases exponentially, but it is difficult to meet the balance of demand and supply. Therefore, how to effectively meet the balance of demand and supply intelligently has become a popular issue in this century. In recent years, Taiwan Power Company (Taipower), the biggest electric company in Taiwan, is committed to the construction of Advanced Metering Infrastructure (AMI), which provides communication channels and enables demand-side users to participate in load dispatch. On the other hand, the construction of AMI is expected to generate a tremendous number of valuable data on electricity consumption, but it is not easy to convert these data into effective information by the conventional quantitative methods. In as much as the rapid progression of AI technology in the industrial field, the application of AI technology in the technology management has become an increasing issue as an interdisciplinary study. To address this task, this work applies the recurrent neural network based on deep learning to predict low-voltage usage shortly by the electricity information of low-voltage user and meteorological data. After many vicissitudes, the electricity consumption per hour can be predicted and a sound energy arrangement can be therefore planned. Through introducing the proposed model, Taipower Company will have an effective capability that schedules power, reduces unnecessary backup power, and provides time-consuming electricity prices for industrial enterprises accurately among high usage of Taiwan industries.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"305-311"},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70039349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3966/160792642020012101002
S. Chu, Xingsi Xue, Jeng-Shyang Pan, Xiaojing Wu
Ontology matching technique aims at determining the identical entities, which can effectively solve the ontology heterogeneity problem and implement the collaborations among ontology-based intelligent systems. Typically, an ontology consists of a set of concepts which are described by various properties, and they define a space such that each distinct concept and property represents one dimension in that space. Therefore, it is an effective way to model an ontology in a vector space, and use the vector space based similarity measure to calculate two entities’ similarity. In this work, the entities’ structure information is utilized to model an ontology in a vector space, and then, their linguistic information is used to reduce the number of dimensions, which can improve the efficiency of the similarity calculation and entity matching process. After that, a discrete optimization model is constructed for the ontology matching problem, and a compact Evolutionary Algorithm (cEA) based ontology matching technique is proposed to efficiently address it. The experiment uses the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the comparing results with state-of-the-art ontology matching systems show that our approach can efficiently determine high-quality ontology alignments.
{"title":"Optimizing Ontology Alignment in Vector Space","authors":"S. Chu, Xingsi Xue, Jeng-Shyang Pan, Xiaojing Wu","doi":"10.3966/160792642020012101002","DOIUrl":"https://doi.org/10.3966/160792642020012101002","url":null,"abstract":"Ontology matching technique aims at determining the identical entities, which can effectively solve the ontology heterogeneity problem and implement the collaborations among ontology-based intelligent systems. Typically, an ontology consists of a set of concepts which are described by various properties, and they define a space such that each distinct concept and property represents one dimension in that space. Therefore, it is an effective way to model an ontology in a vector space, and use the vector space based similarity measure to calculate two entities’ similarity. In this work, the entities’ structure information is utilized to model an ontology in a vector space, and then, their linguistic information is used to reduce the number of dimensions, which can improve the efficiency of the similarity calculation and entity matching process. After that, a discrete optimization model is constructed for the ontology matching problem, and a compact Evolutionary Algorithm (cEA) based ontology matching technique is proposed to efficiently address it. The experiment uses the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the comparing results with state-of-the-art ontology matching systems show that our approach can efficiently determine high-quality ontology alignments.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"21 1","pages":"15-22"},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70039296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：