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1.Monolithic Integration of GaN DC-DC Converters: Technology and Characterization

Author:Miao Cui 2021
Abstract:High-temperature (HT) power converters are increasingly important in extreme environments, such as electric vehicles, aviation, etc. Due to the limited temperature operation beyond 150 °C in Si-based devices, GaN-based power transistors are expected to be excellent candidates for power converters at high temperatures over 200 °C in electric vehicle applications. HT power converters with self-contained functionality (power, driver, microcontroller, sensors, etc.) without external heatsink or cooling systems are increasingly essential owing to reduced size and cost. The lateral AlGaN/GaN-based high electron mobility transistors (HEMTs) have been regarded as promising candidates in high frequency, high power density, and HT applications. GaN smart power integrated circuit (IC) provides an effective solution to achieve a system-on-chip scheme for HT power converters. This thesis uses normally-off GaN transistors with a recessed metal-insulated-semiconductor (MIS) gate, and it focuses on the development of GaN DC-DC converters with integrated gate drivers for HT power converters in extreme environments. To evaluate the recessed MIS gate for high-temperature GaN power converters, the impact of etch depth on the performance of mobility and RON is systematically studied at high temperatures. The mechanisms of carrier scattering are discussed at different etch depths, and full recess with dielectric engineering is proposed to improve the stability of GaN IC. On the lateral GaN smart power IC technology platform, this thesis focuses on three parts for HT power converters including 1) an integrated gate driver for a GaN boost converter; 2) integrated gate drivers with a half-bridge stage for a synchronous GaN buck converter; 3) an integrated technique of deadtime management for a synchronous GaN buck converter. Firstly, the GaN boost converter with the optimized gate driver exhibits a voltage conversion from 5 to 11 V at 100 kHz, and only 11% reduction of output voltage is observed at high temperatures up to 250 °C. Then, the synchronous GaN buck converter with an integrated half-bridge stage achieves a voltage down-conversion with an input voltage of 25 V, and it shows good thermal stability and almost no reduction of output voltage at temperatures up to 250 °C, with a large gate swing of 10 V. Lastly, an integrated GaN buck converter with a no deadtime technique (NDT) exhibits a maximum efficiency of 80 % at high temperatures up to 250 °C , with an input voltage of 30 V at 100 kHz. At high temperatures, the optimized GaN NDT converter shows better performance than a synchronous GaN buck converter with a fixed deadtime technique (FDT) at high load currents, in terms of smaller voltage overshoots and oscillations of gate drivers and better converter efficiency as well. The proposed GaN NDT converter uses one control signal and provides a simple and effective method of deadtime management for high-temperature power converters.

2.Global Optimisation of Multi‐Camera Moving Object Detection

Author:Yuyao Yan 2018
Abstract:An important task in intelligent video surveillance is to detect multiple pedestrians. These pedestrians may be occluded by each other in a camera view. To overcome this problem, multiple cameras can be deployed to provide complementary information, and homography mapping has been widely used for the association and fusion of multi-camera observations. The intersection regions of the foreground projections usually indicate the locations of moving objects. However, many false positives may be generated from the intersections of non-corresponding foreground regions. In this thesis, an algorithm for multi-camera pedestrian detection is proposed. The first stage of this work is to propose pedestrian candidate locations on the top view. Two approaches are proposed in this stage. The first approach is a top-down approach which is based on the probabilistic occupancy map framework. The ground plane is discretized into a grid, and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in all camera views. The second approach is a bottom-up approach, which is based on the multi-plane homography mapping. The foreground regions in all camera views are projected and overlaid in the top view according to the multi-plane homographies and the potential locations of pedestrians are estimated from the intersection regions. In the second stage, where we borrowed the idea from the Quine-McCluskey (QM) method for logic function minimisation, essential candidates are initially identified, each of which covers at least a significant part of the foreground that is not covered by the other candidates. Then non-essential candidates are selected to cover the remaining foregrounds by following a repeated process, which alternates between merging redundant candidates and finding emerging essential candidates. Then, an alternative approach to the QM method, the Petrick’s method, is used for finding the minimum set of pedestrian candidates to cover all the foreground regions. These two methods are non-iterative and can greatly increase the computational speed. No similar work has been proposed before. Experiments on benchmark video datasets have demonstrated the good performance of the proposed algorithm in comparison with other state-of-the-art methods for pedestrian detection.

3.Occupancy-driven intelligent control of HVAC based on thermal comfort

Author:Mehdi Pazhoohesh 2017
Abstract:Nowadays, the building sector is a substantial consumer of world’s energy. The dominant energy share of Heating, Ventilation and Air-Conditioning (HVAC) systems, makes it the focus of research for saving energy. Current air conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain sufficient comfort level. Having information regarding occupancy situation may lead to significant energy-savings. On the other hand, focusing on the reduction of energy only, may lead to sacrificing the thermal comfort of the occupants in a building. Moreover, due to the difference of preference of thermal comfort of individuals, particularly in a shared space, a fixed set point for HVAC systems, can cause discomfort. Therefore, a comprehensive technique is required to save energy while maintaining thermal comfort. The present research proposes an occupancy-driven HVAC control system based on thermal comfort analysis. A ZigBee-based indoor localization system is developed to monitor the location of occupants inside the buildings. Algorithms are used to improve the accuracy of positioning system, which include Near Neighbour Area (NNA), Principle Component Analysis (PCA) and Exponential Moving Average algorithms (EMA). Computational Fluid Dynamics (CFD) is used to simulate the thermal comfort through modelling the indoor air distribution and flows. Wind velocity and temperature are simulated in several scenarios and the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) are computed. The simulation results are verified through a survey asking for occupants’ real feelings and consequently thermal comfort zones are identified with associated occupants, which are used for possible energy saving while providing satisfied level to all the occupants. To investigate different satisfaction feeling of occupants, a personalized thermal profile is created for individuals inside the test bed area. A fuzzy based approach is used to develop a fuzzy map of each occupant and as a result, a personal thermal preference profile is created. Based on the present occupants in the room, the minimum and maximum preferred temperatures are estimated and used for controlling the HVAC system. The Semi-hidden Markov chain method is used to create the occupants’ behavioural pattern which can reduce the frequencies of turning ON or OFF the HVAC systems. The real-time locations of the persons, estimated based on the NNA and MA localization method, are combined with their behavioural patterns and thermal preference profiles and their comfort zones to control the corresponding HVACs. The proposed method has been implemented to a shared office occupied by nine users and equipped with two individual air conditioners. The comparison of different control strategies show that the proposed intelligent control has a significant potential of saving energy and at the same time maintaining occupants in a reasonable thermal comfort range.

4.Robust Moving Object Detection by Information Fusion from Multiple Cameras

Author:Jie REN 2014
Abstract:Moving object detection is an essential process before tracking and event recognition in video surveillance can take place. To monitor a wider field of view and avoid occlusions in pedestrian tracking, multiple cameras are usually used and homography can be employed to associate multiple camera views. Foreground regions detected from each of the multiple camera views are projected into a virtual top view according to the homography for a plane. The intersection regions of the foreground projections indicate the locations of moving objects on that plane. The homography mapping for a set of parallel planes at different heights can increase the robustness of the detection. However, homography mapping is very time consuming and the intersections of non-corresponding foreground regions can cause false-positive detections. In this thesis, a real-time moving object detection algorithm using multiple cameras is proposed. Unlike the pixelwise homography mapping which projects binary foreground images, the approach used in the research described in this thesis was to approximate the contour of each foreground region with a polygon and only transmit and project the polygon vertices. The foreground projections are rebuilt from the projected polygons in the reference view. The experimental results have shown that this method can be run in real time and generate results similar to those using foreground images. To identify the false-positive detections, both geometrical information and colour cues are utilized. The former is a height matching algorithm based on the geometry between the camera views. The latter is a colour matching algorithm based on the Mahalanobis distance of the colour distributions of two foreground regions. Since the height matching is uncertain in the scenarios with the adjacent pedestrian and colour matching cannot handle occluded pedestrians, the two algorithms are combined to improve the robustness of the foreground intersection classification. The robustness of the proposed algorithm is demonstrated in real-world image sequences.

5.Compressive Sensing Based Grant-Free Communication

Author:Yuanchen Wang 2022
Abstract:Grant-free communication, where each user can transmit data without following the strict access grant process, is a promising technique to reduce latency and support massive users. In this thesis, compressive sensing (CS), which exploits signal sparsity to recover data from a small sample, is investigated for user activity detection (UAD), channel estimation, and signal detection in grant-free communication, in order to extract information from the signals received by base station (BS). First, CS aided UAD is investigated by utilizing the property of quasi-time-invariant channel tap delays as the prior information for the burst users in internet of things (IoT). Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. In particular, g-TIDCS and m-TIDCS do not require any prior knowledge of the number of active users like the existing approaches and therefore are more practical. Second, periodic communication as one of the salient features of IoT is considered. Two schemes, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), are proposed to exploit the non-continuous temporal correlation of the received signal for joint UAD, channel estimation, and signal detection. The theoretical analysis and simulation results show that the PBOMP and PBSBL outperform the existing schemes in terms of the success rate of UAD, bit error rate (BER), and accuracy in period estimation and channel estimation. Third, UAD and channel estimation for grant-free communication in the presence of massive users that are actively connected to the BS is studied. An iteratively UAD and signal detection approach for the burst users is proposed, where the interference of the connected users on the burst users is reduced by applying a preconditioning matrix to the received signals at the BS. The proposed approach is capable of providing significant performance gains over the existing algorithms in terms of the success of UAD and BER. Last but not least, since the physical layer security becomes a critical issue for grant-free communication, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The proposed EA-pilots based approach possesses a high level of security by scrambling the eavesdropper’s normalized mean square error performance of channel estimation.

6.Exchange Rate, Exports, and Product Complexity: An Analysis of China's Export of Products with Different Sophistication Levels

Author:Chen Chen 2022
Abstract:The industrialization and development path of East Asia is well explained by the flying geese paradigm (Akamatsu, 1962) in which Japan as the leader has become the technological frontier in the region. The development path of Japan has been followed by other economies in the region, including China. We show that this pattern can be elucidated by using the concept of product space (Hidalgo and Hausmann, 2009). Product space analysis indicates that China’s export basket is transformed from primary industries, such as natural resources and agricultural products, to simple manufacturing industries, such as textiles and footwear. It is finally elevated to relatively more sophisticated products such as electronic products. This successful upgrade of China’s export basket facilitates China’s exports to become less vulnerable to exchange rate fluctuations. With this background, we investigate whether changes in exchange rate exert heterogeneous effects on China’s exports, depending on their sophistication levels, as measured by the product complexity index (PCI). We estimate the exchange rate effects and exchange rate elasticities for 1,242 export categories disaggregated at the HS 4-digit level from 1995 to 2018 by using bilateral trade data between China and 190 partner economies. Results indicate that exchange rate changes affect the most sophisticated exports the least and least sophisticated exports the most. We offer exchange rate effect estimations for different time periods of China’s export basket. The evidence also indicates that as China has upgraded its export basket, the effect of exchange rates on exports has become trivial after 2013. Therefore, exports can be more stable if China’s export basket contains more high-technology goods. Further, we estimate the exchange rate effects and exchange rate elasticities of 44 Information and Communication Technology (ICT) HS 4-digit export categories in China, which are included in Attachment A for the World Trade Organization (WTO) Information Technology Agreement (ITA) by employing high-dimensional fixed effects. Our findings regarding the diminished effects of exchange rate changes on more sophisticated products are robust for ICT products. We also offer an estimate of the exchange rate effect for individual ICT products and an in-depth analysis of the telecommunications industry in China. The result indicates that the exchange rate effect for individual ICT products is not only affected by its sophistication level but also by other factors that require further examination in future research. We contend that our findings and conclusions provide important policy implications in terms of exchange rate practices and industrial policies in China.

7.Temperature-based Weather Derivatives Modeling and Contract Design in Mainland China

Author:Lu ZONG 2015
Abstract:In the presented thesis, we build the theoretical framework for the development of temperature-based weather derivatives market in China. Our research is divided into two separate studies due to their di erent scopes. In the rst study, we focus on the determination of the most precise model for temperature-based weather derivative modeling and pricing in China. To achieve this objective, a heuristic comparison of the new stochastic seasonal variation (SSV) model with three established empirical temperature and pricing models, i.e. the Ala-ton model [1], the CAR model [2] and the Spline model [3] is conducted. Comparison criteria include residual normality, residual auto-correlation function (ACF), Akaike information criterion (AIC), relative errors, and stability of price behaviors. The re- sults show that the SSV model dominates the other three models by providing both a more precise tting of the temperature process and more stable price behaviors. In the second study, novel forms of temperature indices are proposed and an- alyzed both on the city level and the climatic zone level, with the aim to provide a contract-selecting scheme that increases the risk management e ciency in the agricultural sector of China. Performances of the newly-introduced indices are in-vestigated via an e ciency test which considers the root mean square loss (RMSL),the value at risk (VaR) and the certainty-equivalent revenues (CERs). According to the results, agricultural risk management on the city scale can be optimized by using the absolute-deviation growth degree-day (GDD) index. On the other hand, it is suggested that climatic zone-based contracts can be more e cient compared with city-based contracts. The recommended contract-selection scheme is to purchase climatic zone-based average GDD contracts in climatic zone II, and to purchase climatic zone-based optimal-weighted GDD contracts in climatic zone I or III.

8.The impacts of supply chain design on firm performance: perspectives from leadership, network structure, and resource dependency

Author:Taiyu Li 2022
Abstract: Supply chain management has an important role to play in business. To keep a company competitive and survive in international competition, it is important to optimise the processes of production, supply and sales. Today, modern operating companies are faced with an ever-increasing amount of information and problems, and the relationships between supply chain participants are becoming increasingly complex. These problems are even more acute in developing countries. This calls for advanced supply chain design to manage the entire operational processes of the business. Especially in the post-epidemic era, business development in many factories has become unpredictable. Transport logistics and costs are difficult to manage and control accurately. The importance of supply chain resilience is becoming more and more evident. The field of supply chain research needs to break out of its old boundaries and seek more diverse development models and supply chain designs. The results of this thesis first reveal how supply chain leadership affects supply chain performance, providing a theoretical basis for building efficient supply chain. After that, it further reveals the relationship between the risk contagion efficiency of the supply chain network and the individual competitiveness. After that, this thesis emphasizes the impact of resource dependence and operational slack on improving supply chain resilience, and further provides guidance and suggestions for designing supply chain structures in the post-COVID era. Finally, the discussion of supplier relationship and innovation performance provides suggestions on how to improve competitiveness and innovation ability when designing supply chain structure. This thesis conducts a rigorous quantitative analysis of the factors that need to be considered in supply chain design from multiple perspectives and provides a sufficient discussion of their impact, which contributes to research in the field of supply chain management. This thesis also provides policy and corporate operational management guidance for practitioners related to supply chains.

9.Statistical Feature Ordering for Neural-based Incremental Attribute Learning

Author:Ting WANG 2013
Abstract:In pattern recognition, better classification or regression results usually depend on highly discriminative features (also known as attributes) of datasets. Machine learning plays a significant role in the performance improvement for classification and regression. Different from the conventional machine learning approaches which train all features in one batch by some predictive algorithms like neural networks and genetic algorithms, Incremental Attribute Learning (IAL) is a novel supervised machine learning approach which gradually trains one or more features step by step. Such a strategy enables features with greater discrimination abilities to be trained in an earlier step, and avoids interference among relevant features. Previous studies have confirmed that IAL is able to generate accurate results with lower error rates. If features with different discrimination abilities are sorted in different training order, the final results may be strongly influenced. Therefore, the way to sequentially sort features with some orderings and simultaneously reduce the pattern recognition error rates based on IAL inevitably becomes an important issue in this study. Compared with the applicable yet time-consuming contribution-based feature ordering methods which were derived in previous studies, more efficient feature ordering approaches for IAL are presented to tackle classification problems in this study. In the first approach, feature orderings are calculated by statistical correlations between input and output. The second approach is based on mutual information, which employs minimal-redundancy-maximal- relevance criterion (mRMR), a well-known feature selection method, for feature ordering. The third method is improved by Fisher's Linear Discriminant (FLD). Firstly, Single Discriminability (SD) of features is presented based on FLD, which can cope with both univariate and multivariate output classification problems. Secondly, a new feature ordering metric called Accumulative Discriminability (AD) is developed based on SD. This metric is designed for IAL classification with dynamic feature dimensions. It computes the multidimensional feature discrimination ability in each step for all imported features including those imported in previous steps during the IAL training. AD can be treated as a metric for accumulative effect, while SD only measures the one-dimensional feature discrimination ability in each step. Experimental results show that all these three approaches can exhibit better performance than the conventional one-batch training method. Furthermore, the results of AD are the best of the three, because AD is much fitter for the properties of IAL, where feature number in IAL is increasing. Moreover, studies on the combination use of feature ordering and selection in IAL is also presented in this thesis. As a pre-process of machine learning for pattern recognition, sometimes feature orderings are inevitably employed together with feature selection. Experimental results show that at times these integrated approaches can obtain a better performance than non-integrated approaches yet sometimes not. Additionally, feature ordering approaches for solving regression problems are also demonstrated in this study. Experimental results show that a proper feature ordering is also one of the key elements to enhance the accuracy of the results obtained.

10.The mechanisms to regulate arsenic behaviors in redox transition zones in paddy soils

Author:Zhaofeng Yuan 2020
Abstract:Rice (Oryza sativa L.) is the staple food for people especially in Asia, but rice production is threatened by arsenic (As) contamination in paddy soil. Contamination of As in paddy soil is mainly caused by anthropogenic activities, such as mining and irrigation of high As groundwater. External As firstly enters overlying water, and then accumulates in paddy soil. Soil-water interface (SWI) is the gate controlling As exchange between soil and overlying water, and rhizosphere is the inlet of As from soils into rice root. Under natural conditions, a redox transition occurs along both micro interfaces due to atmospheric O2 diffusion or radial O2 loss from root. Arsenic is sensitive to redox conditions and tends to change over space and time across those micro interfaces. However, a deep understanding of As cycling in paddy water-soil-rice system has been hindered to date by techniques available to sample micro interfaces repeatedly in high-resolution. In order to fill this gap, a novel high-resolution porewater sampler was developed in this study. Using the technique, the spatiotemporal control of As was studied at paddy SWI and rhizosphere. A hollow fiber membrane tube (~ 2 mm diameter) was evaluated to sample dissolved elements with passive diffusion mechanism. The results showed quantification of solutes surrounding the tube can be achieved in every ≥ 24 h regardless of pH, ionic strength, and dissolved organic matter conditions. This technique, called In-situ Porewater Iterative (IPI) sampler, was further validated in soils under an anoxic-oxic transition by bubbling N2 and air into overlying water. The results showed that the IPI sampler is a powerful and robust technique in monitoring dynamics of element profile in soil porewater in high-resolution (mm). Moreover, measurement methods in ICP-MS and IC-ICP-MS were optimized to promote the measurement throughput of multi-element in limited samples (μL level) collected by high-resolution porewater samplers (e.g. IPI samplers). Major elements (e.g. iron (Fe) and manganese (Mn), mg·L-1 level) were measured by ICP-MS in extended dynamic range mode to avoid signal overflow, while trace elements (e.g. As, μg·L-1 level) in dynamic reaction cell (O2) mode to alleviate potential polyatomic interferences. Ammonium bicarbonate mobile phase was further demonstrated to simultaneously measure common species of As, phosphorus (P) and sulfur (S) with IC-ICP-MS analysis. With the optimized analytical methods and IPI samplers, the measurement throughput of multi-element and their species were improved up to 10 times compared to traditional methods. Furthermore, the cycling of As across SWI and rhizosphere was studied with the updated IPI sampler and state-of-art analytical techniques. In SWI, profiles of As, Fe and other associated elements in five paddy soils were mapped. The results showed a close coupling of Fe, Mn, As and P in 4 out of 5 paddy soils. However, decoupling of Fe, Mn and As was observed in the oxic-anoxic transition zone of one paddy soil. The study provided in situ evidence showing decoupling of As with Fe and Mn may happen in the oxic-anoxic transition zone of SWI. For rhizosphere, dynamic profiles of Fe and As were mapped by IPI samplers from days after transplanting 0 to 40. The results showed Fe and As change spatiotemporally in rhizosphere. Interestingly, Fe oxides formed in rhizospheric soil, rather than on rice root (Fe plaque), play the key role for immobilizing mobile As from bulk soil. A model of As transport from soil to rice, linking the temporal and spatial regulation of As in paddy soils, was provided to help better understand As cycling in paddy soils.

11.Intelligent Train Operation with On-Board Energy Storage Device: An Energy-Saving Perspective

Author:Chaoxian Wu 2022
Abstract:Railway transportation is applied extensively in urban transportation to satisfy the increasing travel demand as well as reduce CO2 emission from the road transportation. However, energy consumption of the railway transportation is observed to increase due to its boosting construction and usage. In this case, energy efficiency of the railway transportation has become a popular research topic in the past few decades. At the same time, as an emerging technology, the on-board energy storage device (OESD) is utilised in many modern railway systems to help improve the energy efficiency. This thesis focuses on the investigation of the intelligent train operation with OESD, in which the optimal train operation strategy, OESD discharging/charging strategy, mutual in influence between the train operation strategy and OESD, and study on different types of energy storage as OESD in train operation are given. First, the intelligent train operation with OESD in single inter-station section is discussed. An integrated mathematical model to optimise the train speed trajectory with OESD to minimise the net energy consumption of the system for a single inter-station section is established. The in influence of the OESD capacity, energy status of OESD and OESD degradation on optimal train operation solution is revealed based on the proposed model. The results show that with a general railway case, the net energy consumption can be signifficantly reduced by the intelligent management of both the train operation mode and OESD discharging/charging process. Second, the intelligent train operation with OESD in a service cycle is discussed. The OESD is allowed to discharge/be charged when the train dwells at each station. A two-step method, which overcomes the drawbacks of the originally proposed mathematical programming model, is proposed to locate the optimal train speed trajectory, timetable and OESD discharging/charging strategy for both inter-station running and dwelling at stations with high computation efficiency. Beijing Yizhuang line as numerical experiment is given in this thesis, where the reduced energy consumption is observed when compared to other scenarios by using the optimised solution, showing the effectiveness of the proposed method. Third, the intelligent train operation with OESD in a network is investigated. By considering the train network and power network as the environment information, an agent-environment model for the train with OESD is proposed. In the model, the available regenerative braking energy from other trains in the railway power network is formulated into the time-variant expectation based on the stochastic running time distribution of the train network, which can be used by train and OESD during the running. This further utilises the OESD to receive as much as the regenerative braking energy in the environment to reduce the energy waste. The results shows that the proposed method is able to improve the utilisation of the regenerative braking energy in the power network, and also lead to the significantly reduced energy consumption. Fourth, impact of the different types of energy storage as OESD on the optimal train operation strategy is studied. The dynamic discharging/charging power limits with respect to the energy status of each type of OESD, supercapacitors, ywheels and Li-ion batteries, are taken into account. In addition, the optimal sizing problems of the above three types of OESD are also investigated. The results show that the introduction of different type of OESD will lead to the change of the optimal train operation strategy and the resulted energy-saving performance. Using Beijing Changping line as numerical experiment, it is found that choosing the right type and right sized OESD are important due to the significantly different engineering characteristics, energy cost and monetary cost brought to the system when introducing different OESD. In summary, this thesis gives a systematic discussion and exploration on the intelligent train operation with OESD in a perspective of energy saving from small operation scale to large operation scale, and the OESD is also studied as general/no specific type to multiple/specific type to ensure both of the academic and industrial value of the thesis.

12.Large-scale functional annotation of individual RNA methylation sites by mining complex biological networks

Author:Xiangyu Wu 2021
Abstract:Increasing evidences suggest that post-transcriptional RNA modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation limited by laborious experimental procedures. However, there is limited investigation of the functional relevance of individual m6A RNA methylation sites. To address this, we annotated human m6A sites in large-scale based on the guilt-by-association principle from complex biological networks. In the first chapter, the network was constructed based on public human MeRIP-Seq datasets profiling the m6A epitranscriptome under independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m6A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m6A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. In the second chapter, another approach was applied to annotate the individual human m6A sites by integrating the methylation profile, gene expression profile and protein-protein interaction network with guilt-by-association principle. The consensus signals on sites were amplified by multiplying the co-methylation network and the methylation-expression network. The PPI network smoothed the correlation for a query site to gene expression for furthering GSEA functional annotation. In the third chapter, we functionally annotated 18,886 m6A sites that are conserved between human and mouse from a larger epitranscriptome datasets using method previously described. Besides, we also completed two side projects related to SARS-CoV-2 viral m6A site prediction and m6A site prediction from Nanopore sequencing technology.

13.Optimization Approaches for Parameter Estimation and Maximum Power Point Tracking (MPPT) of Photovoltaic Systems

Author:Jieming MA 2014
Abstract:Optimization techniques are widely applied in various engineering areas, such as model-ing, identi?cation, optimization, prediction, forecasting and control of complex systems. This thesis presents the novel optimization methods that are used to control Photo-voltaic (PV) generation systems. PV power systems are electrical power systems energized by PV modules or cells. This thesis starts with the introduction of PV modeling methods, on which our re-search is based. Parameter estimation is used to extract the parameters of the PV models characterizing the utilized PV devices. To improve e?ciency and accuracy, we proposed sequential Cuckoo Search (CS) and Parallel Particle Swarm Optimization (PPSO) methods to extract the parameters for di?erent PV electrical models. Simu-lation results show the CS has a faster convergence rate than the traditional Genetic Algorithm (GA), Pattern Search (PS) and Particle Swarm Optimization (PSO) in se-quential processing. The PPSO, with an accurate estimation capability, can reduce at least 50% of the elapsed time for an Intel i7 quad-core processor. A major challenge in the utilization of PV generation is posed by its non linear Current-Voltage (I-V ) relations, which result in the unique Maximum Power Point (MPP) varying with di?erent atmospheric conditions. Maximum Power Point Tracking (MPPT) is a technique employed to gain maximum power available from PV devices. It tracks operating voltage corresponding to the MPP and constrains the operating point at the MPP. A novel model-based two-stage MPPT strategy is proposed in this thesis to combine the o?ine maximum power point estimation using the Weightless Swarm Algorithm (WSA) with an online Adaptive Perturb & Observe (APO) method. In addition, an Approximate Single Diode Model (ASDM) is developed for the fast evaluations of the output power. The feasibility of the proposed method is veri?ed in an MPPT system implemented with a Single-Ended Primary-Inductor Converter (SEPIC). Simulation results show the proposed MPPT method is capable of locating the operating point to the MPP under various environmental conditions.

14.Intelligent Planning for Refractive Surgeries: A Modelling and Visualisation-based Approach

Author:Wei Wang 2020
Abstract:Laser refractive surgeries have been commonly used in ophthalmic operations. Considerable research has been carried out and encouraging progress made in recent years. It covers properties of the cornea and behaviour of tissue in different parts of the eye, topography and material expression of individual patient's eyes, prediction using finite element (FE) analysis to estimate the corneal shape change and the change in refractive power. Further effort is still required to advance the research to aid the decision making for laser refractive surgeries. This study comprehensively reviews the latest techniques of refractive surgery and research on computational analysis and modelling techniques and their applications, especially the current prediction and planning techniques for laser refractive surgeries. The aim of this study is to develop an intelligent assistant tool for the laser refractive surgeries with prediction and visualisation functions. For this aim, two objectives will be achieved: prediction with the clinical dataset and human vision simulation. Due to clinical statistics, the clinical dataset is often incomplete, imbalanced, and sparse. Three methods are proposed to predict surgery parameters and outcomes using the clinical dataset. A multiple imputation method, with multiple regression, is proposed for imputing the missing data. For the imbalance of data distribution in the clinical dataset, an over-sampling of the minority data method is proposed. The accuracy of predicted minority data is close to the accuracy of predicted majority data. Finally an ensemble learning method which is optimised by the genetic algorithm is proposed to improve the accuracy of the prediction results with a sparse dataset. According to the distribution of the sample in the clinical data, the percentage of unacceptable results is 23.02%. The methods in this study could provide an accuracy of 79.02% to find the possible unacceptable cases, that is, the method could reduce the percentage of unacceptable results from 23.02% to 4.82%. In human vision simulation, the study focuses on how the human vision simulation could be determined and obtained accurately within a required timeframe. The ray tracing technique can provide more precise results than the rasterisation technique, especially for the simulation of light reflection and refraction in the human eyeball. However, the thin lens assumption affects the accuracy of the pathological vision simulation with the ray tracing technique. An improved schematic human eye model is proposed to obtain a numerical model predicting the size of the defocus blur for the pathological vision, which wraps the shape of the ray intersection area. In order to generalise this model to other healthy and pathological vision, an intelligent blur range derivation method is proposed. On the other hand, ray tracing scene rendering requires repeated iterative computing which takes a significant amount of computation time. A GPU-based ray tracing computing method is proposed to accelerate and optimise the rendering of scenes. With this method, the scene rendering speed is about 75 times faster than using the CPU.

15.Three essays on the corporate social responsibility and firm outcomes

Author:Xiao Pan 2020
Abstract:This thesis builds upon three separate papers that focus on how socially responsible behavior, mainly corporate social responsibility (CSR), affects firm-level strategies and outcomes. I first investigate how a firm’s corporate philanthropy (CP)–corporate financial performance (CFP) relationship changes as firms move through their life-cycle stages. Drawing from dynamic capability-life-cycle perspective, I find that the CP–CFP relationship is not monotonically linear throughout the firm’s life cycle, but changes from negative to positive from development to mature life-cycle stage. I then investigate the benefits of CSR in helping a firm increase its investment from an integrated information-asymmetry and stakeholder perspective. Different from previous literature that takes internal aspects such as financial capability as its starting point to investigate corporate investment, we argue that the external social-acceptance perspective is also crucial in determining a firm’s investment behavior. We find that the firm’s CSR behavior will help a firm achieve more investment opportunities and, thus, lead to a higher level of corporate investment. Finally, I link the firm’s CSR activities at home with foreign-market location choices. According to the springboard and dynamic learning perspective, we find that EM-MNEs need to accumulate the experience of doing CSR to enter host countries with advanced know-how knowledge. Using Chinese listed companies from Shanghai and Shenzhen stock market, we find strong evidence to support our argument.

16.Information Visualizaion Interfaces for Large Display Multi-device Colocated Synchronous Collaboration

Author:Yu Liu 2022
Abstract:The objective of this project is to investigate the use of information-visualisation interfaces to support co-located synchronous collaboration in large display multi-device environments. This is interface for people working together, at the same time in the same place, using interactive graphics with multiple connected computer devices, including large-displays as well as handheld devices such as smartphones and tablets. The technology to support this type of working is already commonplace and research indicates there are a significant advantages to this mode of working. There are however some significant challenges for the designers and developers of these types of system. Firstly, these interfaces need to deal with the inherent complexity of human-human as well as human-computer interaction. We have a limited understanding of how users work together in multi device environments, and need to manage a shared display space and shared control to accommodate different types of collaborative working. For example, at different times users can be working together closely, working separately, or even in disagreement. This introduces an additional level of complicity to the design of the interface that makes it in-practical to simply adapt traditional visualisation interfaces or apply established visual interface design methodologies for multi device collaboration. The work in this thesis investigates the processes of co-located synchronous collaboration in large display multi-device environments and how we can improve this type of activity through better interface design. par The first stage in our study, to improve the design of large display multi-device environment interfaces for co-located collaboration, was to observe how people tend to work in this type of environment using a simple interface with basic view coordination on a large display connected to two mobile devices. This revealed that the users would tend to move between patterns of individual and closer collaborative working. The next stage of our study was to look at ways to better support this type of working by making the transition between individual and collaborative working smoother for the user. This included looking at different ways of coordinating views between mobile devices and the large display, managing different user selections, and showing different users. Finally we used the results of these studies to develop an interface for a real-world collaborative visualisation activity. This validated some of the design decisions generated and demonstrates the potential of large display multi-device collaborative visualisation. Our study demonstrated the potential of a large-display to act as a shared space for users to view the context of their selections and share their insights into the data. We also present preliminary design guidelines on how to coordinate selections among the different displays for different types of collaborative activities. This includes the finding that users preferred a single shared view on a large display for close collaboration or activities that lend themselves naturally to turn taking and a more predictable split screen with separate views for activities that involve a large navigable area and more independent working. Users also preferred to have selections automatically sent to the large screen and interact to retrieve the selections of other users. It was also found that weak presentation of non-essential information was the most effective method for highlighting different user selections. These findings can help guide interface designers and developers for this sort of environment in the future. 

17.Molecular Simulation Studies of Adsorption and Separation of Gases in Nanoporous Molecular Crystals

Author:Siyuan Yang 2020
Abstract:To date, gas adsorption simulations at a molecular level have been usually carried out using the grand canonical Monte Carlo (GCMC) method, most of which ignore the structural flexibility of the adsorbents. This thesis aims to investigate the flexibility effect on the adsorption and separation behaviours of the porous molecular crystals. Computational results showed the importance of flexibility of porous molecular crystals on these applications. Structure analysis methods were applied to understand and quantify this effect. Molecular dynamics (MD) simulations that capture the dynamic evolution of a system require a fixed number of particles in the simulation box. We use a hybrid GCMC/MD scheme to include host flexibility in gas adsorption simulations. We study the adsorption of three gases—CH4, CO2, and SF6—in a porous organic cage (POC) crystal, CC3-R, whose structural flexibility is known by experiment to play an important role in the adsorption of large guest molecules. The results suggested that hybrid GCMC/MD simulations could reproduce experimental adsorption results, without modifying the generic force fields. Hybrid GCMC/MD adsorption simulations can accurately reproduce experimental adsorption isotherm of SF6 in CC3-R. This work also provides a molecular-level understanding of the cooperative adsorption mechanism of SF6 in the CC3-R molecular crystal. Linear and branch alkanes adsorption and separation in CC3-R cage were simulated to investigate the flexibility effects further. The simulation results suggested that cage flexibility showed a negligible effect on the adsorption capacity of linear alkane (C4 to C8) adsorption. However, simulations showed that cage flexibility tended to adapt branched alkanes’ adsorption in POC. It was concluded that cage flexibility showed significant influence on the branched alkanes adsorption. Especially for branched alkanes with long chains, such as 22-dimethylbutane, 23-dimethylbutane, 22-dimethylpentane, 23-dimethylpentane, and 33-dimethylpentane, flexible cage showed the possibility of the uptake, which the rigid model was incapable of. In this section, the flexibility effects were further established. Furthermore, adsorption simulations with both flexible and rigid hosts were applied to screen CH3I and I2 capture in porous molecular crystals at low concentrations. The rigid approach largely overestimated their capture performance, which could compromise the accuracy of the screen result. CC3R, CC3RS were POCs with the best I2 capture material among our studied range. Furthermore, GCMC/MD simulation with host flexibility included showed that 6ET-RCC3, CC3S and the cocrystal possessed the excellent capability to separate H2/D2.

18.Trading Rule and Market Quality: Simulations based on Agent-based Artificial Stock Markets

Author:Xinhui Yang 2021
Abstract:The stock market is one of the most important financial markets in a country. In recent decades, many financial markets have changed their trading rules to achieve higher market quality (e.g. market liquidity, market volatility and price efficiency). This thesis focuses on three important trading rules—tick size, secondary priority rule and price limit—and tests their influence on market quality based on agent-based artificial stock markets (ASMs), which are agent-based order-driven simulated stock market models. Unlike empirical market data, ASMs ensure that the trading rule is the only exogenous variable varying between experiments. Given the lack of a consensus method for determination of the fundamental stock price in real stock markets, previous empirical studies have generally focused on market liquidity and volatility. However, as the fundamental stock price can be set in ASMs, in addition to liquidity and volatility, price efficiency can also be analysed. Therefore, in this thesis, the market quality is investigated from a more comprehensive perspective, including that of market liquidity, volatility and price efficiency. Tick size, the minimum change in stock price in stock markets, is the first trading rule to be investigated in this study. Two types of tick size system are investigated: uniform tick size and stepwise tick size systems. Under the uniform tick size system, the tick size is the same for all stocks in the market. By testing the market quality with tick size 1, 0.1, 0.01 and 0.001, the results show that smaller tick size can improve market quality, while an extremely small tick size would damage it. The price stepwise tick size system—where tick size increases with price—and volume stepwise tick size system—where tick size increases with decreasing trade volume—are then investigated. The results indicate that both price stepwise and volume stepwise systems could promote market quality in different ways. These results might be expected as the price stepwise system is mainly designed to limit noise in markets, while the volume stepwise system is used to balance the benefits for liquidity suppliers and demanders. Based on the performance of the price stepwise and volume stepwise systems, a combination stepwise tick size system is designed and investigated in this study to test whether it combines the advantages of the two systems and further improves market quality. A combination stepwise tick size system as proposed and supported by Goldstein and Kavajecz (2000), but has not been adopted in real stock markets. The tick size in a maximal combination system or minimal combination system is determined by the larger or smaller tick size in the price stepwise system and volume stepwise system, respectively. Consistent with expectation, the results indicate that a combination system, especially a minimal combination system, can further promote market quality. The secondary priority rule, which determines how the quoted order in the market is matched, is the second trading rule investigated here. The impact of various secondary priority rules, including the time priority rule, pro-rata priority rule and equal sharing priority rule, on stock market quality are investigated with consideration given to different investors’ strategies under different secondary priority rules. The time priority, first-come, first-served rule is the most common secondary priority rule in financial markets, and almost all stock markets choose it as their secondary priority rule. The pro-rata and equal sharing priority rules are generally used in other financial markets, such as futures markets. The pro-rata priority rule allocates market orders to limit orders on the best price list based proportionally on limit order sizes, while the equal sharing priority rule allocates market orders equally. Since 2017 the New York Stock Exchange has used the ‘parity’ priority rule, a combination of the time and pro-rata priority rules, which indicates that some stock markets might have realised the importance of the secondary priority rule for market quality and have tried to identify a more effective secondary priority rule than the time priority rule to promote market quality. Taking market quality under the time priority rule as the benchmark, the results show that the pro-rata priority rule can enhance trading activity and price efficiency, but can also increase volatility; the equal sharing priority rule may damage market quality with respect to market liquidity, market volatility and price efficiency. Price limit—that is, setting an established amount by which a price may increase or decrease in any single trading period—is the third trading rule to be investigated in the thesis. In financial markets with a price limit, trade is prevented from occurring outside specified price bands. The results of previous empirical studies have shown that lower limit hits are followed by price reversals, low volatility and lower/stable trade volume, while upper limit hits are followed by price continues, high volatility and higher trade volume price limit (e.g. Kim et al., 2013; Li et al., 2014). This provides evidence that the price limit is beneficial when the lower limit is hit, but harmful when the upper limit is hit. Therefore, a new policy with a lower price limit but no upper price limit (termed the asymmetric limit policy) is proposed here. The market quality under the asymmetric limit policy is tested and compared with that for a market that adopts the symmetric limit policy (with both lower and upper limits) and a market without limits. The experimental results verify the hypothesis that the asymmetric limit policy can promote market quality significantly. The reference price, which is the real-time price used to determine the price band under the price limit policy, is another focus of this study. It is found that, compared with the quoted price, the traded price is more suitable as the reference price under both asymmetric and symmetric limit policies. This finding suggests that the asymmetric price limit with trade price as the reference price might be a feasible policy for stock markets to use to promote market quality. This thesis examines the effects of changes in tick size, secondary priority rule and price limit policy on market quality, including market liquidity, market volatility and price efficiency. The results indicate the effectiveness of the minimal combination tick size system, pro-rata secondary priority rule and asymmetric price limit for promoting market quality, which has important theoretical and management implications for stock markets. Moreover, by investigating trading rules that are still at the theoretical stage, this study indicates that ASMs are an important complement for empirical studies.

19.Depth Assisted Background Modeling and Super-resolution of Depth Map

Author:Boyuan Sun 2018
Abstract:Background modeling is one of the fundamental tasks in the computer vision, which detects the foreground objects from the images. This is used in many applications such as object tracking, traffic analysis, scene understanding and other video applications. The easiest way to model the background is to obtain background image that does not include any moving objects. However, in some environment, the background may not be available and can be changed by the surrounding conditions like illumination changes (light switch on/off), object removed from the scene and objects with constant moving pattern (waving trees). The robustness and adaptation of the background are essential to this problem. Mixture of Gaussians (MOG) is one of the most widely used methods for background modeling using color information, whereas the depth map provides one more dimensional information of the images that is independent of the color. In this thesis, the color only based methods such as Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Kernel Density Estimation (KDE) are thoroughly reviewed firstly. Then the algorithm that jointly uses color and depth information is proposed, which uses MOG and single Gaussian model (SGM) to represent recent observations of the color and depth respectively. And the color-depth consistency check mechanism is also incorporated into the algorithm to improve the accuracy of the extracted background. The spatial resolution of the depth images captured from consumer depth camera is generally limited due to the element size of the senor. To overcome this limitation, depth image super-resolution is proposed to obtain the high resolution depth image from the low resolution depth image by making the inference on high frequency components. Deep convolution neural network has been widely successfully used in various computer vision tasks like image segmentation, classification and recognitions with remarkable performance. Recently, the residual network configuration has been proposed to further improve the performance. Inspired by this residual network, we redesign the popular deep model Super-Resolution Convolution Neural Network (SRCNN) for depth image super-resolution. Based on the idea of residual network and SRCNN structure, we proposed three neural network based approaches to address the problem of depth image super-resolution. In these approaches, we introduce the deconvolution layer into the network which enables the learning directly from original low resolution image to the desired high resolution image, instead of using conventional method like bicubic to interpolate the image before entering the network. Then in order to minimize the sharpness loss near the boundary regions, we add layers at the end of network to learn the residuals.

20.Enjoyment in VR games: Factors, Challenges, and Simulator Sickness Mitigation Techniques

Author:Diego Vilela Monteiro 2021
Abstract:Although Virtual Reality (VR) has been developed for a while, the last decade has seen a surge in its popularity with the advent of commercial VR Head-Mounted Displays (HMDs), making the technology more accessible. One field that significantly benefits from VR is the entertainment industry, for example, games. Games can be challenging to design as they involve several components that are found in other types of applications as well, such as presentation, navigation, interaction with virtual agents, and in-game measurements. Despite recent advances, the optimal configurations for game applications in VR are still widely unexplored. In this thesis, we propose to fill this gap by a series of studies that analyse different components involved in making VR applications more enjoyable. We propose studying three characteristics that are heavily influential in game enjoyment (1) the aesthetical realism and emotions of virtual agents; (2) viewing perspective (First-Person Perspective and Third-Person Perspective), its influence on subjective feelings and how to measure those feelings; and (3) how to reduce or eliminate VR Sickness without affecting the experience (or affecting it positively). Our results showed that Virtual Agents' facial expressions are one of the most important aspects to be considered. On the second topic, we have observed that viewing perspective is influential on VR Sickness; however, other subjective feelings were challenging to measure in this context. On the last topic, we analysed existing tendencies in Simulator Sickness mitigation techniques that do not affect in-game mechanics and present a novel solution that has a good trade-off between mitigating VR Sickness and maintaining or enhancing immersion and performance. Finally, we propose some guidelines based on our results.
Total 200 results found
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