In this paper, fault detection in HP drum of boilers in Kerman combined cycle power plant is explored by means of support vector machine (SVM) algorithm and principal component analysis (PCA). Initially, SVM classifie...
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ISBN:
(纸本)9781479931170
In this paper, fault detection in HP drum of boilers in Kerman combined cycle power plant is explored by means of support vector machine (SVM) algorithm and principal component analysis (PCA). Initially, SVM classifier algorithm and PCA are discussed and then based on the collecting data on normal and abnormal operating the conditions of boilers, fault detection is carried out via explained methods. Finally, a comparison of these techniques and other routine methods is made to show the superiority with the proposed approaches in Kerman power plant.
In order to reduce software energy consumption, a lot of studies have been carried out focusing on the difference of implementation, such as API and algorithm. However, we hypothesize that there is a strong correlatio...
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ISBN:
(纸本)9781538603673
In order to reduce software energy consumption, a lot of studies have been carried out focusing on the difference of implementation, such as API and algorithm. However, we hypothesize that there is a strong correlation between total energy consumption of a program and duration of its execution. If this hypothesis is correct, reducing energy consumption is equal to decreasing duration. Experimental results reveal that there is a strong positive correlation between them, and its correlation coefficient is higher than 0.9. We also find that memory usage is weakly correlated with total energy consumption. As a result, we conclude that if developers want to reduce software energy consumption, they should firstly decrease duration of execution, and secondly reduce memory usage.
We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Poi...
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ISBN:
(纸本)9798400704314
We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at https://***/ielab/llm-rankers.
The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the use...
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ISBN:
(纸本)0780387422
The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving. In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process. The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the Forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.
Any improvement in packet classification performance is crucial to ensure Internet functions continue to track the ever-increasing link capacities. Packet classification is the foundation of many Internet functions: f...
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ISBN:
(纸本)9781479916337
Any improvement in packet classification performance is crucial to ensure Internet functions continue to track the ever-increasing link capacities. Packet classification is the foundation of many Internet functions: from fundamental packet-forwarding to advanced features such as Quality of Service enforcement, monitoring and security functions. This work proposes a novel trie-based classification algorithm, named Jump-Ahead Trie (JA-trie), utilizing an entropy-based pre-processing phase and a novel approach to wildcard matching. Through extensive experimental tests, we demonstrate that our proposed algorithm is able to outperform a range of state-of-the-art classification algorithms.
Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decisi...
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ISBN:
(纸本)9781424418206
Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decision function. The corresponding IT-distance definition is consequently designed in both input space and feature space. And a classification algorithm based on IT and Nearest Neighbor rule is proposed, named as ITNN. ITNN exploits hyper sphere centers as class prototypes and labels data using a weighted voting strategy. ITNN is of computational ease brought by training dataset reduction and hyper parameter self-tuning. We describe experimental evidence of classification performance improved by ITNN on real datasets over state of the arts.
Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gather...
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ISBN:
(纸本)9780769530451
Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gathered experience of an engineer. Such judgment is time consuming and could be inefficient especially when new products and technologies are rapidly developed for the competing market. If the complexity of a TP increases, the undetected interrelationship among tests in a TP will also increase. In this paper, inferences are performed to a huge and complex TP using different classification algorithms, with the primary goal to discover potential test relationships in a fast and efficient way. The mining output can be used as a reference and basis for test engineers to improve TP setup or to reprogram test machine to replace current exhaustive test policy.
This paper presents two different control strategies to balance the capacitor voltage of sub-modules in modular multi-level converters. In the centralized control, sub-modules receive the switching state defined by a ...
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ISBN:
(纸本)9781509033881
This paper presents two different control strategies to balance the capacitor voltage of sub-modules in modular multi-level converters. In the centralized control, sub-modules receive the switching state defined by a proposed sorting algorithm in order to control the capacitor voltage. The proposed algorithm reduces the number of switching changes in comparison with the classical sorting algorithm used in literature. The local control based on a PI controller is integrated into the sub-modules, for this reason, all the cells in the arm receive the same modulation index, being modified according to voltage references. Both control strategies are evaluated and validated through simulation results.
This paper thoroughly examines the semiconductor power loss characteristics of modular multilevel converters (MMC). Power loss behavior is examined under different pulse width modulation (PWM) methods and operating co...
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ISBN:
(纸本)9781479967353
This paper thoroughly examines the semiconductor power loss characteristics of modular multilevel converters (MMC). Power loss behavior is examined under different pulse width modulation (PWM) methods and operating conditions. The effects of stored energy level, circulating current control utilization, power factor and submodule voltage balancing method on power loss are studied. Furthermore, unbalanced power losses and specific semiconductor stresses within a submodule are visualized by investigating the loss distribution in a submodule. The paper aids in understanding and design of MMC converter modules, selecting PWM and control methods with power loss perspective.
Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) a...
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ISBN:
(纸本)9781479941438
Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence in a user review as plot-related or non-plot-related and hides plot descriptions from user reviews. We tested five common machine-learning algorithms to ascertain the appropriate algorithm to address this problem. We also proposed a method of generalizing people's names, which we think is strongly related to the plot description. We verified its contribution to the classification results. Finally, we implemented a display interface of user reviews in which users can control the level of plot hiding.
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