Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning

Due to the increasing quantity and variety of generated and stored data, the manual and automatic analysis becomes a more and more challenging task in many modern applications, like biometric identification and content-based image retrieval. In this thesis, we consider two very typical, related inherent structures of objects: Multiple-Instance (MI) objects and Gaussian Mixture Models (GMM). In both approaches, each object is represented by a set. For MI, each object is a set of vectors from a multi-dimensional space. For GMM, each object is a set of multi-variate Gaussian distribution functions, providing the ability to approximate arbitrary distributions in a concise way. Both approaches are very powerful and natural as they allow to express (1) that an object is additively composed from several components or (2) that an object may have several different, alternative kinds of behavior. Thus we can model e.g. an image which may depict a set of different things (1). Likewise, we can model a sports player who has performed differently at different games (2). We can use GMM to approximate MI objects and vice versa. Both ways of approximation can be appealing because GMM are more concise whereas for MI objects the single components are less complex. A similarity measure quantifies similarities between two objects to assess how much alike these objects are. On this basis, indexing and similarity search play essential roles in data mining, providing efficient and/or indispensable supports for a variety of algorithms such as classification and clustering. This thesis aims to solve challenges in the indexing and knowledge discovery of complex data using MI objects and GMM. For the indexing of GMM, there are several techniques available, including universal index structures and GMM-specific methods. However, the well-known approaches either suffer from poor performance or have too many limitations. To make use of the parameterized properties of GMM and tackle the problem of potential unequal length of components, we propose the Gaussian Components based Index (GCI) for efficient queries on GMM. GCI decomposes GMM into their components, and stores the n-lets of Gaussian combinations that have uniform length of parameter vectors in traditional index structures. We introduce an efficient pruning strategy to filter unqualified GMM using the so-called Matching Probability (MP) as the similarity measure. MP sums up the joint probabilities of two objects all over the space. GCI achieves better performance than its competitors on both synthetic and real-world data. To further increase its efficiency, we propose a strategy to store GMM components in a normalized way. This strategy improves the ability of filtering unqualified GMM. Based on the normalized transformation, we derive a set of novel similarity measures for GMM. Since MP is not a metric (i.e., a symmetric, positive definite distance function guaranteeing the triangle inequality), which would be essential for the application of various analysis techniques, we introduce Infinite Euclidean Distance (IED) for probability distribution functions, a metric with a closed-form expression for GMM. IED allows us to store GMM in well-known metric trees like the Vantage-Point tree or M-tree, which facilitate similarity search in sublinear time by exploiting the triangle inequality. Moreover, analysis techniques that require the properties of a metric (e.g. Multidimensional Scaling) can be applied on GMM with IED. For MI objects which are not well-approximated by GMM, we introduce the potential densities of instances for the representation of MI objects. Based on that, two joint Gaussian based measures are proposed for MI objects and we extend GCI on MI objects for efficient queries as well. To sum up, we propose in this thesis a number of novel similarity measures and novel indexing techniques for GMM and MI objects, enabling efficient queries and knowledge discovery on complex data. In a thorough theoretic analysis as well as extensive experiments we demonstrate the superiority of our approaches over the state-of-the-art with respect to the run-time efficiency and the quality of the result.Angesichts der steigenden Quantität und Vielfalt der generierten und gespeicherten Daten werden manuelle und automatisierte Analysen in vielen modernen Anwendungen eine zunehmend anspruchsvolle Aufgabe, wie z.B. biometrische Identifikation und inhaltbasierter Bildzugriff. In dieser Arbeit werden zwei sehr typische und relevante inhärente Strukturen von Objekten behandelt: Multiple-Instance-Objects (MI) und Gaussian Mixture Models (GMM). In beiden Anwendungsfällen wird das Objekt in Form einer Menge dargestellt. Bei MI besteht jedes Objekt aus einer Menge von Vektoren aus einem multidimensionalen Raum. Bei GMM wird jedes Objekt durch eine Menge von multivariaten normalverteilten Dichtefunktionen repräsentiert. Dies bietet die Möglichkeit, beliebige Wahrscheinlichkeitsverteilungen in kompakter Form zu approximieren. Beide Ansätze sind sehr leistungsfähig, denn sie basieren auf einfachsten Ideen: (1) entweder besteht ein Objekt additiv aus mehreren Komponenten oder (2) ein Objekt hat unterschiedliche alternative Verhaltensarten. Dies ermöglicht es uns z.B. ein Bild zu repräsentieren, welches unterschiedliche Objekte und Szenen zeigt (1). In gleicher Weise können wir einen Sportler modellieren, der bei verschiedenen Wettkämpfen unterschiedliche Leistungen gezeigt hat (2). Wir können MI-Objekte durch GMM approximieren und auch der umgekehrte Weg ist möglich. Beide Vorgehensweisen können sehr ansprechend sein, da GMM im Vergleich zu MI kompakter sind, wogegen in MI-Objekten die einzelnen Komponenten weniger Komplexität aufweisen. Ein ähnlichkeitsmaß dient der Quantifikation der Gemeinsamkeit zwischen zwei Objekten. Darauf basierend spielen Indizierung und ähnlichkeitssuche eine wesentliche Rolle für die effiziente Implementierung von einer Vielzahl von Klassifikations- und Clustering-Algorithmen im Bereich des Data Minings. Ziel dieser Arbeit ist es, die Herausforderungen bei Indizierung und Wissensextraktion von komplexen Daten unter Verwendung von MI Objekten und GMM zu bewältigen. Für die Indizierung der GMM stehen verschiedene universelle und GMM-spezifische Indexstrukuren zur Verfügung. Jedoch leiden solche bekannten Ansätze unter schwacher Leistung oder zu vielen Einschränkungen. Um die parametrisieren Eigenschaften der GMM auszunutzen und dem Problem der möglichen ungleichen Komponentenlänge entgegenzuwirken, präsentieren wir das Verfahren Gaussian Components based Index (GCI), welches effizienten Abfrage auf GMM ermöglicht. GCI zerlegt dabei ein GMM in Parameterkomponenten und speichert alle möglichen Kombinationen mit einheitlicher Vektorlänge in traditionellen Indexstrukturen. Wir stellen ein effizientes Pruningverfahren vor, um ungeeignete GMM unter Verwendung der sogenannten Matching Probability (MP) als ähnlichkeitsma\ss auszufiltern. MP errechnet die Summe der gemeinsamen Wahrscheinlichkeit zweier Objekte aus dem gesamten Raum. CGI erzielt bessere Leistung als konkurrierende Verfahren, sowohl in Bezug auf synthetische, als auch auf reale Datensätze. Um ihre Effizienz weiter zu verbessern, stellen wir eine Strategie zur Speicherung der GMM-Komponenten in normalisierter Form vor. Diese Strategie verbessert die Fähigkeit zum Ausfiltern ungeeigneter GMM. Darüber hinaus leiten wir, basierend auf dieser Transformation, neuartige ähnlichkeitsmaße für GMM her. Da MP keine Metrik (d.h. eine symmetrische, positiv definite Distanzfunktion, die die Dreiecksungleichung garantiert) ist, dies jedoch unentbehrlich für die Anwendung mehrerer Analysetechniken ist, führen wir Infinite Euclidean Distance (IED) ein, ein Metrik mit geschlossener Ausdrucksform für GMM. IED erlaubt die Speicherung der GMM in Metrik-Bäumen wie z.B. Vantage-Point Trees oder M-Trees, die die ähnlichkeitssuche in sublinear Zeit mit Hilfe der Dreiecksungleichung erleichtert. Außerdem können Analysetechniken, die die Eigenschaften einer Metrik erfordern (z.B. Multidimensional Scaling), auf GMM mit IED angewandt werden. Für MI-Objekte, die mit GMM nicht in außreichender Qualität approximiert werden können, stellen wir Potential Densities of Instances vor, um MI-Objekte zu repräsentieren. Darauf beruhend werden zwei auf multivariater Gaußverteilungen basierende Maße für MI-Objekte eingeführt. Außerdem erweitern wir GCI für MI-Objekte zur effizienten Abfragen. Zusammenfassend haben wir in dieser Arbeit mehrere neuartige ähnlichkeitsmaße und Indizierungstechniken für GMM- und MI-Objekte vorgestellt. Diese ermöglichen effiziente Abfragen und die Wissensentdeckung in komplexen Daten. Durch eine gründliche theoretische Analyse und durch umfangreiche Experimente demonstrieren wir die überlegenheit unseres Ansatzes gegenüber anderen modernen Ansätzen bezüglich ihrer Laufzeit und Qualität der Resultate

Monetary Value Evaluation of Linghe River Estuarine Wetland Ecosystem Service Function

AbstractIn order to achieve sustainable use of Linghe estuarine wetland ecosystems resources, this article calculated and analysed the monetary value of service functions for Linghe river estuarine wetland. The wetland's monetary value of service functions equals to direct use value plus indirect use value and non-use value. Using method of map data visual interpretation and the classification of functional zones, we identified nine main functions of wetland ecosystem services, classified the wetland's functional zones. And the monetary value is calculated based on the functional zones with map data after visual interpretation. Conclusions can be made that the ecosystem service function's monetary value of Linghe river estuarine wetland is a large amount, increasingly awareness on scientific conservation and development of resources should be taken into account

Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning

Due to the increasing quantity and variety of generated and stored data, the manual and automatic analysis becomes a more and more challenging task in many modern applications, like biometric identification and content-based image retrieval. In this thesis, we consider two very typical, related inherent structures of objects: Multiple-Instance (MI) objects and Gaussian Mixture Models (GMM). In both approaches, each object is represented by a set. For MI, each object is a set of vectors from a multi-dimensional space. For GMM, each object is a set of multi-variate Gaussian distribution functions, providing the ability to approximate arbitrary distributions in a concise way. Both approaches are very powerful and natural as they allow to express (1) that an object is additively composed from several components or (2) that an object may have several different, alternative kinds of behavior. Thus we can model e.g. an image which may depict a set of different things (1). Likewise, we can model a sports player who has performed differently at different games (2). We can use GMM to approximate MI objects and vice versa. Both ways of approximation can be appealing because GMM are more concise whereas for MI objects the single components are less complex. A similarity measure quantifies similarities between two objects to assess how much alike these objects are. On this basis, indexing and similarity search play essential roles in data mining, providing efficient and/or indispensable supports for a variety of algorithms such as classification and clustering. This thesis aims to solve challenges in the indexing and knowledge discovery of complex data using MI objects and GMM. For the indexing of GMM, there are several techniques available, including universal index structures and GMM-specific methods. However, the well-known approaches either suffer from poor performance or have too many limitations. To make use of the parameterized properties of GMM and tackle the problem of potential unequal length of components, we propose the Gaussian Components based Index (GCI) for efficient queries on GMM. GCI decomposes GMM into their components, and stores the n-lets of Gaussian combinations that have uniform length of parameter vectors in traditional index structures. We introduce an efficient pruning strategy to filter unqualified GMM using the so-called Matching Probability (MP) as the similarity measure. MP sums up the joint probabilities of two objects all over the space. GCI achieves better performance than its competitors on both synthetic and real-world data. To further increase its efficiency, we propose a strategy to store GMM components in a normalized way. This strategy improves the ability of filtering unqualified GMM. Based on the normalized transformation, we derive a set of novel similarity measures for GMM. Since MP is not a metric (i.e., a symmetric, positive definite distance function guaranteeing the triangle inequality), which would be essential for the application of various analysis techniques, we introduce Infinite Euclidean Distance (IED) for probability distribution functions, a metric with a closed-form expression for GMM. IED allows us to store GMM in well-known metric trees like the Vantage-Point tree or M-tree, which facilitate similarity search in sublinear time by exploiting the triangle inequality. Moreover, analysis techniques that require the properties of a metric (e.g. Multidimensional Scaling) can be applied on GMM with IED. For MI objects which are not well-approximated by GMM, we introduce the potential densities of instances for the representation of MI objects. Based on that, two joint Gaussian based measures are proposed for MI objects and we extend GCI on MI objects for efficient queries as well. To sum up, we propose in this thesis a number of novel similarity measures and novel indexing techniques for GMM and MI objects, enabling efficient queries and knowledge discovery on complex data. In a thorough theoretic analysis as well as extensive experiments we demonstrate the superiority of our approaches over the state-of-the-art with respect to the run-time efficiency and the quality of the result.Angesichts der steigenden Quantität und Vielfalt der generierten und gespeicherten Daten werden manuelle und automatisierte Analysen in vielen modernen Anwendungen eine zunehmend anspruchsvolle Aufgabe, wie z.B. biometrische Identifikation und inhaltbasierter Bildzugriff. In dieser Arbeit werden zwei sehr typische und relevante inhärente Strukturen von Objekten behandelt: Multiple-Instance-Objects (MI) und Gaussian Mixture Models (GMM). In beiden Anwendungsfällen wird das Objekt in Form einer Menge dargestellt. Bei MI besteht jedes Objekt aus einer Menge von Vektoren aus einem multidimensionalen Raum. Bei GMM wird jedes Objekt durch eine Menge von multivariaten normalverteilten Dichtefunktionen repräsentiert. Dies bietet die Möglichkeit, beliebige Wahrscheinlichkeitsverteilungen in kompakter Form zu approximieren. Beide Ansätze sind sehr leistungsfähig, denn sie basieren auf einfachsten Ideen: (1) entweder besteht ein Objekt additiv aus mehreren Komponenten oder (2) ein Objekt hat unterschiedliche alternative Verhaltensarten. Dies ermöglicht es uns z.B. ein Bild zu repräsentieren, welches unterschiedliche Objekte und Szenen zeigt (1). In gleicher Weise können wir einen Sportler modellieren, der bei verschiedenen Wettkämpfen unterschiedliche Leistungen gezeigt hat (2). Wir können MI-Objekte durch GMM approximieren und auch der umgekehrte Weg ist möglich. Beide Vorgehensweisen können sehr ansprechend sein, da GMM im Vergleich zu MI kompakter sind, wogegen in MI-Objekten die einzelnen Komponenten weniger Komplexität aufweisen. Ein ähnlichkeitsmaß dient der Quantifikation der Gemeinsamkeit zwischen zwei Objekten. Darauf basierend spielen Indizierung und ähnlichkeitssuche eine wesentliche Rolle für die effiziente Implementierung von einer Vielzahl von Klassifikations- und Clustering-Algorithmen im Bereich des Data Minings. Ziel dieser Arbeit ist es, die Herausforderungen bei Indizierung und Wissensextraktion von komplexen Daten unter Verwendung von MI Objekten und GMM zu bewältigen. Für die Indizierung der GMM stehen verschiedene universelle und GMM-spezifische Indexstrukuren zur Verfügung. Jedoch leiden solche bekannten Ansätze unter schwacher Leistung oder zu vielen Einschränkungen. Um die parametrisieren Eigenschaften der GMM auszunutzen und dem Problem der möglichen ungleichen Komponentenlänge entgegenzuwirken, präsentieren wir das Verfahren Gaussian Components based Index (GCI), welches effizienten Abfrage auf GMM ermöglicht. GCI zerlegt dabei ein GMM in Parameterkomponenten und speichert alle möglichen Kombinationen mit einheitlicher Vektorlänge in traditionellen Indexstrukturen. Wir stellen ein effizientes Pruningverfahren vor, um ungeeignete GMM unter Verwendung der sogenannten Matching Probability (MP) als ähnlichkeitsma\ss auszufiltern. MP errechnet die Summe der gemeinsamen Wahrscheinlichkeit zweier Objekte aus dem gesamten Raum. CGI erzielt bessere Leistung als konkurrierende Verfahren, sowohl in Bezug auf synthetische, als auch auf reale Datensätze. Um ihre Effizienz weiter zu verbessern, stellen wir eine Strategie zur Speicherung der GMM-Komponenten in normalisierter Form vor. Diese Strategie verbessert die Fähigkeit zum Ausfiltern ungeeigneter GMM. Darüber hinaus leiten wir, basierend auf dieser Transformation, neuartige ähnlichkeitsmaße für GMM her. Da MP keine Metrik (d.h. eine symmetrische, positiv definite Distanzfunktion, die die Dreiecksungleichung garantiert) ist, dies jedoch unentbehrlich für die Anwendung mehrerer Analysetechniken ist, führen wir Infinite Euclidean Distance (IED) ein, ein Metrik mit geschlossener Ausdrucksform für GMM. IED erlaubt die Speicherung der GMM in Metrik-Bäumen wie z.B. Vantage-Point Trees oder M-Trees, die die ähnlichkeitssuche in sublinear Zeit mit Hilfe der Dreiecksungleichung erleichtert. Außerdem können Analysetechniken, die die Eigenschaften einer Metrik erfordern (z.B. Multidimensional Scaling), auf GMM mit IED angewandt werden. Für MI-Objekte, die mit GMM nicht in außreichender Qualität approximiert werden können, stellen wir Potential Densities of Instances vor, um MI-Objekte zu repräsentieren. Darauf beruhend werden zwei auf multivariater Gaußverteilungen basierende Maße für MI-Objekte eingeführt. Außerdem erweitern wir GCI für MI-Objekte zur effizienten Abfragen. Zusammenfassend haben wir in dieser Arbeit mehrere neuartige ähnlichkeitsmaße und Indizierungstechniken für GMM- und MI-Objekte vorgestellt. Diese ermöglichen effiziente Abfragen und die Wissensentdeckung in komplexen Daten. Durch eine gründliche theoretische Analyse und durch umfangreiche Experimente demonstrieren wir die überlegenheit unseres Ansatzes gegenüber anderen modernen Ansätzen bezüglich ihrer Laufzeit und Qualität der Resultate

Influences of graphene oxide support on the electrochemical performances of graphene oxide-MnO2 nanocomposites

MnO2 supported on graphene oxide (GO) made from different graphite materials has been synthesized and further investigated as electrode materials for supercapacitors. The structure and morphology of MnO2-GO nanocomposites are characterized by X-ray diffraction, X-ray photoemission spectroscopy, scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, and Nitrogen adsorption-desorption. As demonstrated, the GO fabricated from commercial expanded graphite (denoted as GO(1)) possesses more functional groups and larger interplane gap compared to the GO from commercial graphite powder (denoted as GO(2)). The surface area and functionalities of GO have significant effects on the morphology and electrochemical activity of MnO2, which lead to the fact that the loading amount of MnO2 on GO(1) is much higher than that on GO(2). Elemental analysis performed via inductively coupled plasma optical emission spectroscopy confirmed higher amounts of MnO2 loading on GO(1). As the electrode of supercapacitor, MnO2-GO(1) nanocomposites show larger capacitance (307.7 F g-1) and better electrochemical activity than MnO2-GO(2) possibly due to the high loading, good uniformity, and homogeneous distribution of MnO2 on GO(1) support

High-fat diets enhance and delay ursodeoxycholic acid absorption but elevate circulating hydrophobic bile salts

Background: Ursodeoxycholic acid (UDCA) is a natural drug essential for the treatment of cholestatic liver diseases. The food effects on the absorption of UDCA and the disposition of circulating bile salts remain unclear despite its widespread global uses. This study aims to investigate the effects of high-fat (HF) diets on the pharmacokinetics of UDCA and disclose how the circulated bile salts were simultaneously perturbed.Methods: After an overnight fast, a cohort of 36 healthy subjects received a single oral dose (500 mg) of UDCA capsules, and another cohort of 31 healthy subjects received the same dose after consuming a 900 kcal HF meal. Blood samples were collected from 48 h pre-dose up to 72 h post-dose for pharmacokinetic assessment and bile acid profiling analysis.Results: The HF diets significantly delayed the absorption of UDCA, with the Tmax of UDCA and its major metabolite, glycoursodeoxycholic acid (GUDCA), changing from 3.3 h and 8.0 h in the fasting study to 4.5 h and 10.0 h in the fed study, respectively. The HF diets did not alter the Cmax of UDCA and GUDCA but immediately led to a sharp increase in the plasma levels of endogenous bile salts including those hydrophobic ones. The AUC0–72h of UDCA significantly increased from 25.4 μg h/mL in the fasting study to 30.8 μg h/mL in the fed study, while the AUC0–72h of GUDCA showed no difference in both studies. As a result, the Cmax of total UDCA (the sum of UDCA, GUDCA, and TUDCA) showed a significant elevation, while the AUC0–72h of total UDCA showed a slight increase without significance in the fed study compared to the fasting study.Conclusion: The HF diets delay UDCA absorption due to the extension of gastric empty time. Although UDCA absorption was slightly enhanced by the HF diets, the beneficial effect may be limited in consideration of the simultaneous elevation of circulating hydrophobic bile salts

Ubiquitin ligase RNF125 targets PD-L1 for ubiquitination and degradation

As a critical immune checkpoint molecule, PD-L1 is expressed at significantly higher levels in multiple neoplastic tissues compared to normal ones. PD-L1/PD-1 axis is a critical target for tumor immunotherapy, blocking the PD-L1/PD-1 axis is recognized and has achieved unprecedented success in clinical applications. However, the clinical efficacy of therapies targeting the PD-1/PD-L1 pathway remains limited, emphasizing the need for the mechanistic elucidation of PD-1/PD-L1 expression. In this study, we found that RNF125 interacted with PD-L1 and regulated PD-L1 protein expression. Mechanistically, RNF125 promoted K48-linked polyubiquitination of PD-L1 and mediated its degradation. Notably, MC-38 and H22 cell lines with RNF125 knockout, transplanted in C57BL/6 mice, exhibited a higher PD-L1 level and faster tumor growth than their parental cell lines. In contrast, overexpression of RNF125 in MC-38 and H22 cells had the opposite effect, resulting in lower PD-L1 levels and delayed tumor growth compared with parental cell lines. In addition, immunohistochemical analysis of MC-38 tumors with RNF125 overexpression showed significantly increased infiltration of CD4+, CD8+ T cells and macrophages. Consistent with these findings, analyses using The Cancer Genome Atlas (TCGA) public database revealed a positive correlation of RNF125 expression with CD4+, CD8+ T cell and macrophage tumor infiltration. Moreover, RNF125 expression was significantly downregulated in several human cancer tissues, and was negatively correlated with the clinical stage of these tumors, and patients with higher RNF125 expression had better clinical outcomes. Our findings identify a novel mechanism for regulating PD-L1 expression and may provide a new strategy to increase the efficacy of immunotherapy

Improved Extension Neural Network and Its Applications

Extension neural network (ENN) is a new neural network that is a combination of extension theory and artificial neural network (ANN). The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data) and the borders between clusters (boundary data) are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN) has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work

Transformational Leadership and Emotional Labor: The Mediation Effects of Psychological Empowerment

In order to survive the fiercer competition, more and more service firms emphasize front-line employees’ role of creating excellent customer experience by displaying positive emotions during the service interactions. However, the underlying mechanisms for the relationship between transformational leadership and front-line employees’ emotional labor remain unclear. Drawing upon the conservation of resources (COR) theory, this study develops a conceptual model in which transformational leadership influences front-line employees’ emotional labor through the mediator of psychological empowerment. By collecting data from 436 employees in five call centers, we tested our model and hypotheses through PROCESS 3.3 macro for SPSS developed by Hayes. The results show that transformational leadership shows positive and negative effects on deep acting and surface acting, respectively. The positive effect on deep acting is partially mediated by psychological empowerment, while the negative effect on surface acting is fully mediated by psychological empowerment. Specifically, two dimensions of psychological empowerment (impact, self-efficacy) play negative mediating roles between transformational leadership and surface acting, while impact, self-determination, and self-efficacy play positive mediating roles of transformational leadership and deep acting. The findings advance our understanding about how transformational leadership influences front-line employees’ emotional labor by introducing psychological empowerment as a mediator

A calcium looping process for simultaneous CO2 capture and peak shaving in a coal-fired power plant

CO2 capture and peak shaving are two of the main challenges for coal-fired power plants in China. This paper proposed a calcium looping (CaL) combustion system with cryogenic O2 storage for simultaneous flue gas decarbonization and peak shaving for a 1000 MWe coal-fired power plant. The philosophy of this concept is that: (1) the boiler always operates at maximum continuous rating (MCR) to ensure the highest boiler efficiency; (2) during off-peak times, the excess energy output from coal combustion is used to provide heat for the calciner and produce pure oxygen for energy storage; (3) at peak times, the O2 produced is used to capture CO2 in the flue gas via the CaL process and reduce the CO2 abatement penalty; and (4) any excess O2 is treated as a by-product for commercial utilization. The whole system was simulated in Aspen Plus® which shows that the net electric efficiency of the proposed system without cryogenic O2 storage system is 35.52%LHV (LHV, low heating value), while that of the conventional CaL system is 34.54%LHV. The proposed system can reduce the methane consumption rate by 38.5 t/h when methane is used as fuel in the calciner. Including the cryogenic O2 storage system, the peaking capability of the proposed system can range from 534.6 MWe to 1041 MWe. Correspondingly, the net electric efficiency is improved from 18.98%LHV to 36.97%LHV. Increasing the rate of oxygen production can reduce the minimum net power output to lower than 534.6 MWe. The peaking capability can be regulated by the rate of oxygen production where excess oxygen serves as a byproduct