Aspects of the Russo-Japanese War

On 6 February 1998 a symposium was held in STICERD on Aspects of the Russo-Japanese War at which two papers were presented: 'The Japanese Military during the Russo-Japanese War, 1904-05: A Reconsideration of Command Politics and Public Images', by Dr Lone, and 'British Observers of the Russo-Japanese War', by Dr Towle.

Analysis of Nitrogen Loading Reductions for Wastewater Treatment Facilities and Non-Point Sources in the Great Bay Estuary Watershed

In 2009, the New Hampshire Department of Environmental Services (DES) published a proposal for numeric nutrient criteria for the Great Bay Estuary. The report found that total nitrogen concentrations in most of the estuary needed to be less than 0.3 mg N/L to prevent loss of eelgrass habitat and less than 0.45 mg N/L to prevent occurrences of low dissolved oxygen. Based on these criteria and an analysis of a compilation of data from at least seven different sources, DES concluded that 11 of the 18 subestuaries in the Great Bay Estuary were impaired for nitrogen. Under the Clean Water Act, if a water body is determined to be impaired, a study must be completed to determine the existing loads of the pollutant and the load reductions that would be needed to meet the water quality standard. Therefore, DES developed models to determine existing nitrogen loads and nitrogen loading thresholds for the subestuaries to comply with the numeric nutrient criteria. DES also evaluated the effects of different permitting scenarios for wastewater treatment facilities on nitrogen loads and the costs for wastewater treatment facility upgrades. This modeling exercise showed that: Nitrogen loads to the Great Bay, Little Bay, and the Upper Piscataqua River need to be reduced by 30 to 45 percent to attain the numeric nutrient criteria. Both wastewater treatment facilities and non-point sources will need to reduce nitrogen loads to attain the numeric nutrient criteria. The percent reduction targets for nitrogen loads only change minimally between wet and dry years. Wastewater treatment facility upgrades to remove nitrogen will be costly; however, the average cost per pound of nitrogen removed from the estuary due to wastewater facility upgrades is lower than for non-point source controls. The permitting options for some wastewater treatment facilities will be limited by requirements to not increase pollutant loads to impaired waterbodies. The numeric nutrient criteria and models used by DES are sufficiently accurate for calculating nitrogen loading thresholds for the Great Bay watershed. Additional monitoring and modeling is needed to better characterize conditions and nitrogen loading thresholds for the Lower Piscataqua River. This nitrogen loading analysis for Great Bay may provide a framework for setting nitrogen permit limits for wastewater treatment facilities and developing watershed implementation plans to reduce nitrogen loads

Classification of partial discharge EMI conditions using permutation entropy-based features

In this paper we investigate the application of feature extraction and machine learning techniques to fault identification in power systems. Specifically we implement the novel application of Permutation Entropy-based measures known as Weighted Permutation and Dispersion Entropy to field Electro- Magnetic Interference (EMI) signals for classification of discharge sources, also called conditions, such as partial discharge, arcing and corona which arise from various assets of different power sites. This work introduces two main contributions: the application of entropy measures in condition monitoring and the classification of real field EMI captured signals. The two simple and low dimension features are fed to a Multi-Class Support Vector Machine for the classification of different discharge sources contained in the EMI signals. Classification was performed to distinguish between the conditions observed within each site and between all sites. Results demonstrate that the proposed approach separated and identified the discharge sources successfully

Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert’s data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI

Naive bayes multi-label classification approach for high-voltage condition monitoring

This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMI time signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sources contained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI discharge sources opens a new research topic in HV condition monitoring

Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

Deep residual neural network for EMI event classification using bispectrum representation

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task

Imaging time series for the classification of EMI discharge sources

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome

Collisional stripping of planetary crusts

Geochemical studies of planetary accretion and evolution have invoked various degrees of collisional erosion to explain differences in bulk composition between planets and chondrites. Here we undertake a full, dynamical evaluation of 'crustal stripping' during accretion and its key geochemical consequences. We present smoothed particle hydrodynamics simulations of collisions between differentiated rocky planetesimals and planetary embryos. We find that the crust is preferentially lost relative to the mantle during impacts, and we have developed a scaling law that approximates the mass of crust that remains in the largest remnant. Using this scaling law and a recent set of N-body simulations, we have estimated the maximum effect of crustal stripping on incompatible element abundances during the accretion of planetary embryos. We find that on average one third of the initial crust is stripped from embryos as they accrete, which leads to a reduction of ~20% in the budgets of the heat producing elements if the stripped crust does not reaccrete. Erosion of crusts can lead to non-chondritic ratios of incompatible elements, but the magnitude of this effect depends sensitively on the details of the crust-forming melting process. The Lu/Hf system is fractionated for a wide range of crustal formation scenarios. Using eucrites (the products of planetesimal silicate melting, thought to represent the crust of Vesta) as a guide to the Lu/Hf of planetesimal crust partially lost during accretion, we predict the Earth could evolve to a superchondritic 176-Hf/177-Hf (3-5 parts per ten thousand) at present day. Such values are in keeping with compositional estimates of the bulk Earth. Stripping of planetary crusts during accretion can lead to detectable changes in bulk composition of lithophile elements, but the fractionation is relatively subtle, and sensitive to the efficiency of reaccretion.Comment: 15 pages, 9 figures. Accepted for publication in EPSL. Abstract shortened. Accompanying animations can be found at http://www.star.bris.ac.uk/pcarter/crust_strip