Pub Date : 2018-03-01DOI: 10.1109/INFOCT.2018.8356859
Kotchakorn Jetinai
With the increasing of sharing learning resources to enable the resources discovering published on the e-learning systems. The finding suitable learning resource takes too much time because a system retrieves similar resources for all users (or learners) without considering the needs of individual users. This paper proposes a resource recommendation approach for the personalized e-learning based on reasoning rules. The proposed approach designs ontology as a reference ontology which concentrates on describing the learning style appropriate to each learner. The Personalization Rules are defined to support personalized semantic search for heterogeneous learning resources, which deduced by a reasoning engine. Experimental results demonstrate that the proposed approach enables the resource recommendation to individual users, which is originated from multiple sources.
{"title":"Rule-based reasoning for resource recommendation in personalized e-learning","authors":"Kotchakorn Jetinai","doi":"10.1109/INFOCT.2018.8356859","DOIUrl":"https://doi.org/10.1109/INFOCT.2018.8356859","url":null,"abstract":"With the increasing of sharing learning resources to enable the resources discovering published on the e-learning systems. The finding suitable learning resource takes too much time because a system retrieves similar resources for all users (or learners) without considering the needs of individual users. This paper proposes a resource recommendation approach for the personalized e-learning based on reasoning rules. The proposed approach designs ontology as a reference ontology which concentrates on describing the learning style appropriate to each learner. The Personalization Rules are defined to support personalized semantic search for heterogeneous learning resources, which deduced by a reasoning engine. Experimental results demonstrate that the proposed approach enables the resource recommendation to individual users, which is originated from multiple sources.","PeriodicalId":376443,"journal":{"name":"2018 International Conference on Information and Computer Technologies (ICICT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125306968","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 : 2018-03-01DOI: 10.1109/INFOCT.2018.8356857
Koichi Tanaka, M. Nishigaki, Miki Sode, T. Mizuno
In order to reduce the burden of agricultural work, rice cultivation management systems using sensor nodes, aka field servers, that monitor the environment are studied. It is difficult to install large power generation devices, such as solar panels, in rice fields as they obstruct farm work. In addition, it is also difficult to install the wiring for power supplies. For these reasons, each sensor node must operate on independent power with batteries or other power sources. Furthermore, given that they continue to operate during the farming season, low power consumption is required. It is difficult to use a communication network that requires a line use fee such as a 3G line from the viewpoint of cost. Therefore, in this paper, we report on a transmission method using LoRa, which is a communication standard for IoT, which does not require a line usage fee. In particular, we propose a data collection method that makes it possible to collect the data of all sensor nodes in a short amount of time. This method is effective for low power consumption. The comparison results with a conventional method using a simulation demonstrate that the proposed method can collect all of the data in the shortest amount of time. Moreover, it was confirmed that the power consumption is also lower than the conventional method. Furthermore, it was confirmed that the rate of increase in time necessary for the parent node to collect data, due to the increase in the number of sensor nodes, is lower than that of the other methods. We believe that the proposed method is useful and can efficiently transmit the situation of a rice field at a low cost.
{"title":"Low delay data gathering method for rice cultivation management system: IoT specialized outdoor communication procedure","authors":"Koichi Tanaka, M. Nishigaki, Miki Sode, T. Mizuno","doi":"10.1109/INFOCT.2018.8356857","DOIUrl":"https://doi.org/10.1109/INFOCT.2018.8356857","url":null,"abstract":"In order to reduce the burden of agricultural work, rice cultivation management systems using sensor nodes, aka field servers, that monitor the environment are studied. It is difficult to install large power generation devices, such as solar panels, in rice fields as they obstruct farm work. In addition, it is also difficult to install the wiring for power supplies. For these reasons, each sensor node must operate on independent power with batteries or other power sources. Furthermore, given that they continue to operate during the farming season, low power consumption is required. It is difficult to use a communication network that requires a line use fee such as a 3G line from the viewpoint of cost. Therefore, in this paper, we report on a transmission method using LoRa, which is a communication standard for IoT, which does not require a line usage fee. In particular, we propose a data collection method that makes it possible to collect the data of all sensor nodes in a short amount of time. This method is effective for low power consumption. The comparison results with a conventional method using a simulation demonstrate that the proposed method can collect all of the data in the shortest amount of time. Moreover, it was confirmed that the power consumption is also lower than the conventional method. Furthermore, it was confirmed that the rate of increase in time necessary for the parent node to collect data, due to the increase in the number of sensor nodes, is lower than that of the other methods. We believe that the proposed method is useful and can efficiently transmit the situation of a rice field at a low cost.","PeriodicalId":376443,"journal":{"name":"2018 International Conference on Information and Computer Technologies (ICICT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125452943","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 : 2017-12-18DOI: 10.1109/INFOCT.2018.8356842
Dohyung Kim, Hyochang Yang, Minki Chung, Sungzoon Cho
In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.
{"title":"Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things","authors":"Dohyung Kim, Hyochang Yang, Minki Chung, Sungzoon Cho","doi":"10.1109/INFOCT.2018.8356842","DOIUrl":"https://doi.org/10.1109/INFOCT.2018.8356842","url":null,"abstract":"In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.","PeriodicalId":376443,"journal":{"name":"2018 International Conference on Information and Computer Technologies (ICICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115971890","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 : 1900-01-01DOI: 10.1109/INFOCT.2018.8356830
Yeh-Cheng Chen, Yu-Teng Chang, Y-S Kan, R. Chen, S. F. Wu
The new billing approaches are manly to apply the integrated concept of data warehouse with relevant billing data; in addition, use the methods of mining association rule to sort out the Billing Quantities Pattern and then figure out the billing quantities. Moreover, employ the Decision Tree algorithm of data mining to find out the unit billing price. As a result, the new billing approach is made of the methods of data warehouse and date mining. This study is mainly focused on improving the operation of current billing system to establish the new functionality of the Billing quantities and Billing price. As for the benefit of these two new functions, it is not only able to lead into clients' billing systems, but it is also capable of upgrading the efficiency in rapid setup; especially for the enterprises that already possessed billing system internally but not yet implemented. In addition, it can also reduce the difference in revenue, shorten the process of issuing invoice, speed up the export operation, increase the export efficiency and provide the revenue data for integrating into the Executive Data System (EIS), Decision Support System (DSS) and Business Intelligent System (BIS) to allow enterprises making the right decisions promptly.
{"title":"Using data mining technique to improve billing system performance in semiconductor industry","authors":"Yeh-Cheng Chen, Yu-Teng Chang, Y-S Kan, R. Chen, S. F. Wu","doi":"10.1109/INFOCT.2018.8356830","DOIUrl":"https://doi.org/10.1109/INFOCT.2018.8356830","url":null,"abstract":"The new billing approaches are manly to apply the integrated concept of data warehouse with relevant billing data; in addition, use the methods of mining association rule to sort out the Billing Quantities Pattern and then figure out the billing quantities. Moreover, employ the Decision Tree algorithm of data mining to find out the unit billing price. As a result, the new billing approach is made of the methods of data warehouse and date mining. This study is mainly focused on improving the operation of current billing system to establish the new functionality of the Billing quantities and Billing price. As for the benefit of these two new functions, it is not only able to lead into clients' billing systems, but it is also capable of upgrading the efficiency in rapid setup; especially for the enterprises that already possessed billing system internally but not yet implemented. In addition, it can also reduce the difference in revenue, shorten the process of issuing invoice, speed up the export operation, increase the export efficiency and provide the revenue data for integrating into the Executive Data System (EIS), Decision Support System (DSS) and Business Intelligent System (BIS) to allow enterprises making the right decisions promptly.","PeriodicalId":376443,"journal":{"name":"2018 International Conference on Information and Computer Technologies (ICICT)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116260215","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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