In this paper, we give for the first time a general approach for implementing risk in production, adapted from economic Portfolio Theory, in a new theory, which we will then refer to as Production Portfolio Theory and use it with basic illustrative examples of the non-mathematical type. By this, we can measure risk, optimise it concerning production goals, and compare it with extrinsic optimisation. A follow-up work shall give then mathematical applications.
{"title":"First Elements of Production Portfolio Theory: A New Industrial Engineering Scientific Method","authors":"Bernhard Heiden, Bianca Tonino-Heiden","doi":"10.1145/3594692.3594705","DOIUrl":"https://doi.org/10.1145/3594692.3594705","url":null,"abstract":"In this paper, we give for the first time a general approach for implementing risk in production, adapted from economic Portfolio Theory, in a new theory, which we will then refer to as Production Portfolio Theory and use it with basic illustrative examples of the non-mathematical type. By this, we can measure risk, optimise it concerning production goals, and compare it with extrinsic optimisation. A follow-up work shall give then mathematical applications.","PeriodicalId":207141,"journal":{"name":"Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132249443","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}
Multiple temporal anomaly detection algorithms have important research significance in many application fields, such as system state estimation, fault prediction and diagnosis, network behavior anomaly detection and so on. Aiming at the problems of abnormal noise, high dimensionality, lack of labeling, and difficulty in learning abnormal features of various temporal data, an anomaly detection model TRAD based on Transformer reconstruction was proposed, which used self-conditioning to extract robust multi-modal features to obtain the stability of training. At the same time, the adversarial training process is used to amplify the reconstruction error. Experiments on three public datasets show that the proposed model not only has excellent detection performance, but also has strong applicability and generalization ability for unknown heterogeneous time series data.
{"title":"Anomaly Detection Method for Time Series Data Based on Transformer Reconstruction","authors":"Yuwei Wang, Jing Li","doi":"10.1145/3594692.3594702","DOIUrl":"https://doi.org/10.1145/3594692.3594702","url":null,"abstract":"Multiple temporal anomaly detection algorithms have important research significance in many application fields, such as system state estimation, fault prediction and diagnosis, network behavior anomaly detection and so on. Aiming at the problems of abnormal noise, high dimensionality, lack of labeling, and difficulty in learning abnormal features of various temporal data, an anomaly detection model TRAD based on Transformer reconstruction was proposed, which used self-conditioning to extract robust multi-modal features to obtain the stability of training. At the same time, the adversarial training process is used to amplify the reconstruction error. Experiments on three public datasets show that the proposed model not only has excellent detection performance, but also has strong applicability and generalization ability for unknown heterogeneous time series data.","PeriodicalId":207141,"journal":{"name":"Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124930507","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}
Remote sensing technology has great potential in monitoring chlorophyll a (Chl-a), which is an important indicator of eutrophication in water bodies. However, the spatial and temporal continuity of remote sensing data are inevitably influenced by the limitation of sensor resolution and cloud contamination, which prevent the highly dynamic monitoring of water quality in inland median and small water bodies. Spatial and temporal fusion (STF) provides an effective way to address this issue. However, the errors might be introduced into the remote sensing reflectance () in the pre-processing and fusion process, which might bring large uncertainties in the derived Chla datasets. In this paper, an analytical study was designed to understand the influence of using different atmospheric correction processors for generating the images in STF, and the accuracy of the estimated Chla using the corresponding fusion images was validated with the in-situ samples. The experimental results show that ACOLITE DSF processor achieved the best performance for processing Multi-spectral Instrument (MSI) and Ocean and Land Color Instrument (OLCI) images in the atmospheric correction tests. Moreover, the machine-learning based Chla inversion accuracy of fusion images was comparable with that of real MSI images.
{"title":"The Impact of Atmospheric Correction Processors in Spatio-Temporal Fusion for Monitoring Chlorophyll-A Concentration in Inland Lakes","authors":"Lei Zhang, Linwei Yue","doi":"10.1145/3594692.3594694","DOIUrl":"https://doi.org/10.1145/3594692.3594694","url":null,"abstract":"Remote sensing technology has great potential in monitoring chlorophyll a (Chl-a), which is an important indicator of eutrophication in water bodies. However, the spatial and temporal continuity of remote sensing data are inevitably influenced by the limitation of sensor resolution and cloud contamination, which prevent the highly dynamic monitoring of water quality in inland median and small water bodies. Spatial and temporal fusion (STF) provides an effective way to address this issue. However, the errors might be introduced into the remote sensing reflectance () in the pre-processing and fusion process, which might bring large uncertainties in the derived Chla datasets. In this paper, an analytical study was designed to understand the influence of using different atmospheric correction processors for generating the images in STF, and the accuracy of the estimated Chla using the corresponding fusion images was validated with the in-situ samples. The experimental results show that ACOLITE DSF processor achieved the best performance for processing Multi-spectral Instrument (MSI) and Ocean and Land Color Instrument (OLCI) images in the atmospheric correction tests. Moreover, the machine-learning based Chla inversion accuracy of fusion images was comparable with that of real MSI images.","PeriodicalId":207141,"journal":{"name":"Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127793071","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}
Time of Flight Mass Spectrometry is a well-known tool for the analysis of substances in a great number of scientific disciplines, including environmental sciences. The information obtained is the molecular mass to charge ratio of analytes. To reach a high accuracy and resolving power, time of flight devices need to be large so that the ions can fly for long times before they are detected. Using a reflector at the end of the flight distance allows using at least part of this distance twice, thereby increasing the resolving power. These reflectors should reflect ion packages without changing them. Furthermore, modern reflectors allow compensating differences of kinetic energy that the ions of same mass might possess. In 2016, a patent has been published proposing a reflector for time of flight mass spectrometry based electric fields with the shape of Cassinian Ovals, similarly to a Cassinian Ion Trap. In this paper we have used finite elements method simulations in order to characterize such a reflector, thereby showing how well it can fulfill its purpose regarding the previously mentioned points, i.e. not changing the ion packets and allowing for energy difference compensation.
{"title":"Evaluation of the Concept of a Cassinian Ion Trap Based Reflector for Time of Flight Mass Spectrometry","authors":"F. Gunzer","doi":"10.1145/3594692.3594701","DOIUrl":"https://doi.org/10.1145/3594692.3594701","url":null,"abstract":"Time of Flight Mass Spectrometry is a well-known tool for the analysis of substances in a great number of scientific disciplines, including environmental sciences. The information obtained is the molecular mass to charge ratio of analytes. To reach a high accuracy and resolving power, time of flight devices need to be large so that the ions can fly for long times before they are detected. Using a reflector at the end of the flight distance allows using at least part of this distance twice, thereby increasing the resolving power. These reflectors should reflect ion packages without changing them. Furthermore, modern reflectors allow compensating differences of kinetic energy that the ions of same mass might possess. In 2016, a patent has been published proposing a reflector for time of flight mass spectrometry based electric fields with the shape of Cassinian Ovals, similarly to a Cassinian Ion Trap. In this paper we have used finite elements method simulations in order to characterize such a reflector, thereby showing how well it can fulfill its purpose regarding the previously mentioned points, i.e. not changing the ion packets and allowing for energy difference compensation.","PeriodicalId":207141,"journal":{"name":"Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679599","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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