Many challenges have been identified to monitor, manage, process, and store the big data that accumulates from different sources in the IoT concept. The focus of this paper is very significant and limited to solving the problem of monitoring classified big data. Detection of anomalies in a grouping of classified data made it easy to monitor and help to make decisions for action to operate. There is no need to store, process, or manage the redundant data further that is already within the range of the group. So, the main concern is abnormal values in the groups that need to be processed further and require focus. The method proposed in this paper serves as an optimal solution designed to address the visualization challenges associated with dense and high-volume datasets. Our approach involves a strategic process of categorizing data into groups and pinpointing anomalies within these groups. This systematic classification not only enhances data organization but also plays a pivotal role in simplifying the visualization of intricate data patterns. Additionally, this method brings about significant cost efficiencies by strategically optimizing the expenses incurred in processing operations and the allocation of storage space for the equipment.
{"title":"Optimal method to monitor network for IoT devices based on anomaly detection","authors":"Umar Ali̇, Cenk Cali̇s","doi":"10.62189/ci.1260288","DOIUrl":"https://doi.org/10.62189/ci.1260288","url":null,"abstract":"Many challenges have been identified to monitor, manage, process, and store the big data that accumulates from different sources in the IoT concept. The focus of this paper is very significant and limited to solving the problem of monitoring classified big data. Detection of anomalies in a grouping of classified data made it easy to monitor and help to make decisions for action to operate. There is no need to store, process, or manage the redundant data further that is already within the range of the group. So, the main concern is abnormal values in the groups that need to be processed further and require focus. The method proposed in this paper serves as an optimal solution designed to address the visualization challenges associated with dense and high-volume datasets. Our approach involves a strategic process of categorizing data into groups and pinpointing anomalies within these groups. This systematic classification not only enhances data organization but also plays a pivotal role in simplifying the visualization of intricate data patterns. Additionally, this method brings about significant cost efficiencies by strategically optimizing the expenses incurred in processing operations and the allocation of storage space for the equipment.","PeriodicalId":517118,"journal":{"name":"Computers and Informatics","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896441","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 this paper, a vision-based road profile estimation method was studied for the control of semi-active and active suspension systems. For the purpose, a monocular camera was used to collect data from the road tests to develop a logic to convert the camera measurements into the road profile data. For the generation of the road profile, alignment of the different sets of camera measurements and their coherence were expressed. Importance of the sensor and process noise removal were shown in recognition of the high frequency content of the road profile, which was a particular interest of the study. Additionally, a density-based clustering algorithm was taken into account to cluster the measured points vertically, to remove the process and sensor noise. The density-based clustering method reduced the noises and allowed detection of the high and low frequency contents of the road.
{"title":"Vision based road profile estimation for preview-controlled vehicle suspension systems","authors":"Mert Büyükköprü, E. Uzunsoy, X. Mouton","doi":"10.62189/ci.1266211","DOIUrl":"https://doi.org/10.62189/ci.1266211","url":null,"abstract":"In this paper, a vision-based road profile estimation method was studied for the control of semi-active and active suspension systems. For the purpose, a monocular camera was used to collect data from the road tests to develop a logic to convert the camera measurements into the road profile data. For the generation of the road profile, alignment of the different sets of camera measurements and their coherence were expressed. Importance of the sensor and process noise removal were shown in recognition of the high frequency content of the road profile, which was a particular interest of the study. Additionally, a density-based clustering algorithm was taken into account to cluster the measured points vertically, to remove the process and sensor noise. The density-based clustering method reduced the noises and allowed detection of the high and low frequency contents of the road.","PeriodicalId":517118,"journal":{"name":"Computers and Informatics","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518786","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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