The Construction of Female Images in Zero Focus

Seicho Matsumoto is a famous Japanese detective fiction writer, and one of the three masters of detective fiction in the world. The subject of investigation was not just the crime but also the society affected. By reading his works, readers can feel as if they are immersed in the social context of that era. In terms of character setting, the role of the detective is usually not a professional such as a police officer or a lawyer, but an ordinary woman. Secondly, female criminals often appear in Matsumoto’s novels. Analyzing the construction of female images is of great significance for studying Matsumoto’s novels.This paper takes Zero Focus as the research object, focuses on the issue of female image construction, and makes a detailed interpretation of the three female images in Zero Focus, aiming to discover the light and shadow on them, and to summarize and analyze the causes of their female images

Composite nonlinear feedback control for systems with actuator saturation - Towards improved tracking performance

Ph.DDOCTOR OF PHILOSOPH

Data hovering algorithm for improving data retention and data quality in energy-constrained mobile wireless sensor networks

A Wireless Sensor Network (WSN) is composed of numerous spatially distributed, low cost, low power and multifunctional sensor nodes which can be used to monitor the surrounding environment. In mobile networks, the sensed data collected by the sensor nodes may move out of the area where it has been gathered (area of origin) with its carrying node. A problem may arise in this situation: when requesting the historical information of a specific area, it is possible that none of the nodes currently located in such area can provide the required information. This thesis addresses the issue of retaining data it its area of origin in an energy-constrained, infrastructure-less mobile Wireless Sensor Network. The concept of this “Data Hovering” has been defined in which the location-based data hovers in its area of origin by transmission between network nodes. Based on this concept, several policies need to be defined as well as considering the constraints of WSN including limited energy and limited transmission bandwidth. The existing related work has then been investigated by examining how it proposed to define the Data Hovering policies, in order to explore the limitations. It has been found that the existing approaches are not well suited to mobile WSN, due to the unique characteristics of WSN. In this thesis, an autonomous Data Hovering algorithm consisting of defined policies has been designed to improve the data retention (data availability) and the quality of the retained data which ensures that the retained data represents different information. The defined Data Hovering algorithm has been implemented in a network simulator and a baseline with simple policies has also been selected in order to be compared with the defined policies. The evaluation in terms of data availability, data quality and energy consumption has then been carried out to analyze the behaviours of the defined algorithm. Finally, the potential future work has been suggested.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.Comment: 6 pages, 4 figures, accepted by ICME202

Research on the equivalent virtual space vector modulation output of diode clamped n-level converter under multi-modulation carrier modulation

Diode-clamped multi-level converters have DC-side capacitors in series, which will lead to the unbalance of DC-side capacitor voltage, the distortion of the output waveform, the increase of total harmonic distortion (THD), and even the damage of switching devices, which will make the system inoperable. The proposal of virtual space vector pulse-width modulation (VSVPWM) realizes the balanced control of the capacitor voltage, but when the output level of converter increases, the implementation of VSVPWM becomes very complicated, and the amount of calculation also increases greatly, thus hindering its application in the multi-level circuit. Compared with VSVPWM, the carrier-based pulse-width modulation (CBPWM) is simple to operate and easy to implement. If the equivalent relationship between CBPWM and VSVPWM can be found, the application of VSVPWM can be generalized to any level, and the advantages of VSVPWM can be fully utilized. This paper aims to study the inner relationship of VSVPWM and the multi-modulation carrier CBPWM (MCBPWM). After strict theoretical analysis, the equivalent relationship of VSVPWM and MCBPWM in the three-level and four-level and converter is realized by injecting the zero-sequence component into the modulation waves. Furthermore, the equivalent relationship between VSVPWM and MCBPWM is deduced to the N-level converter. Finally, the correctness of the relevant theoretical analysis is verified by the experiment

Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.Comment: 10 pages, 3 figure

Robust PDE Identification from Noisy Data

We propose robust methods to identify underlying Partial Differential Equation (PDE) from a given set of noisy time dependent data. We assume that the governing equation is a linear combination of a few linear and nonlinear differential terms in a prescribed dictionary. Noisy data make such identification particularly challenging. Our objective is to develop methods which are robust against a high level of noise, and to approximate the underlying noise-free dynamics well. We first introduce a Successively Denoised Differentiation (SDD) scheme to stabilize the amplified noise in numerical differentiation. SDD effectively denoises the given data and the corresponding derivatives. Secondly, we present two algorithms for PDE identification: Subspace pursuit Time evolution error (ST) and Subspace pursuit Cross-validation (SC). Our general strategy is to first find a candidate set using the Subspace Pursuit (SP) greedy algorithm, then choose the best one via time evolution or cross validation. ST uses multi-shooting numerical time evolution and selects the PDE which yields the least evolution error. SC evaluates the cross-validation error in the least squares fitting and picks the PDE that gives the smallest validation error. We present a unified notion of PDE identification error to compare the objectives of related approaches. We present various numerical experiments to validate our methods. Both methods are efficient and robust to noise