Unified linear time-invariant model predictive control for strong nonlinear chaotic systems

It is well known that an alone linear controller is difficult to control a chaotic system, because intensive nonlinearities exist in such system. Meanwhile, depending closely on a precise mathematical modeling of the system and high computational complexity, model predictive control has its inherent drawback in controlling nonlinear systems. In this paper, a unified linear time-invariant model predictive control for intensive nonlinear chaotic systems is presented. The presented model predictive control algorithm is based on an extended state observer, and the precise mathematical modeling is not required. Through this method, not only the required coefficient matrix of impulse response can be derived analytically, but also the future output prediction is explicitly calculated by only using the current output sample. Therefore, the computational complexity can be reduced sufficiently. The merits of this method include, the Diophantine equation needing no calculation, and independence of precise mathematical modeling. According to the variation of the cost function, the order of the controller can be reduced, and the system stability is enhanced. Finally, numerical simulations of three kinds of chaotic systems confirm the effectiveness of the proposed method

Essays on stock return forecasting, trend-following trading strategy and empirical asset pricing

The first two essays in this thesis discuss stock return forecast (prediction), a thrilling endeavor of both practitioners and academics of finance with a long history. The practitioners forecast the stock return in real-time to optimize asset allocation and seek an alpha return. In the meantime, recognizing the underlying reason of return predictability may help academic researchers identify what variables explain/drive the stock returns, and thus help them produce improved asset pricing theory. Most of the existing literature on stock return prediction focus on the macroeconomic variables, including the dividend-price ratio, inflation rate, interest rate, volatility, et cetera (e.g., Campbell & Thompson 2008; Welch & Goyal 2008). However, little attention has been paid to the technical indicator (technical analysis) which is extensively used by practitioners (Burghardt & Walls 2011; Covel 2009; Lo & Hasanhodzic 2010, 2011; Menkhoff 2010; Park & Irwin 2007; Schwager 2012). Meanwhile, most of the literature on technical indicator exclusively investigate the profitability but do not investigate the ability of technical indicator in directly predicting the equity risk premium, while predicting equity premium is the focus of vast literature on macroeconomic variables. The only exception is Neely et al. (2014) and they find that technical indicator provides vast complementary information to macroeconomic variables in predicting equity risk premium in the U.S. The first essay extends the playground to China, and investigates the predictability of technical indicator together with macroeconomic variables in China. We choose China for several reasons. Firstly, the Chinese stock market hase become increasingly relevant to not only the academics but also the investment industry. Since 2015, Shanghai and Shenzhen stock exchange together has become the second largest stock market by market capitalization (the largest is NYSE). Secondly, a high level of information friction due to non-transparency and short-sell restriction, and the prevalence of individual investors causing more server behaviour biases (underreaction and overreaction) can boost the predictive power of technical indicators. Lastly, no study has examined the predictability of technical analysis in China, so my first essay filled the gap. We find that technical indicators outperform macroeconomic variables in China and capture ample complementary information. We also find that weekly-level technical indicators outperform monthly-level ones, implying a short-term trending feature of the Chinese stock market. The second essay shifts the focus to the U.S. and other international markets, and is the first study to investigate the predictability of technical indicator in a cross-sectional view. We find that the predictive power of intermediate-term technical indicator identified by Neely et al. (2014) is only useful in predicting the top 10% U.S. companies by market cap, it appears to be a calendar effect, and it does not work well in many other countries. In contrast, the short-term technical indicator can well predict much more U.S. companies, it is not a calendar phenomenon, and it can well predict Japan and other Asia-pacific markets. Finally, contradict to the vast literature on the profitability of technical analysis, we find no positive correlation between volatility and the performance of technical indicators. On the foundation of the Fama and French (2015) five-factor asset pricing model, the third essay proposes three additional risk factors in China based on: 1.) substantial daily-level short-term reversal; 2.) state ownership; 3.) institutional ownership, all of which are unique features of the Chinese stock market. We identify vast useful information provided by our proposed factors and we suggest that the five-factor asset pricing model is not a complete description of expected return in the Chinese stock market.Thesis (Ph.D.) -- University of Adelaide, Business School, 201

Trajectory tracking control of a quadrotor UAV based on sliding mode active disturbance rejection control

This paper proposes a sliding mode active disturbance rejection control scheme to deal with trajectory tracking control problems for the quadrotor unmanned aerial vehicle (UAV). Firstly, the differential signal of the reference trajectory can be obtained directly by using the tracking differentiator (TD), then the design processes of the controller can be simplified. Secondly, the estimated values of the UAV's velocities, angular velocities, total disturbance can be acquired by using extended state observer (ESO), and the total disturbance of the system can be compensated in the controller in real time, then the robustness and anti-interference capability of the system can be improved. Finally, the sliding mode controller based on TD and ESO is designed, the stability of the closed-loop system is proved by Lyapunov method. Simulation results show that the control scheme proposed in this paper can make the quadrotor track the desired trajectory quickly and accurately. &nbsp

Is I-Voting I-Llegal?

The Voting Rights Act was passed to prevent racial discrimination in all voting booths. Does the existence of a racial digital divide make Internet elections for public office merely a computer geek\u27s pipe dream? Or can i-voting withstand scrutiny under the current state of the law? This i-Brief will consider the current state of the law, and whether disproportionate benefits will be enough to stop this extension of technology dead in its tracks

EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

Thanks to the advancement of deep learning technology, vision transformer has demonstrated competitive performance in various computer vision tasks. Unfortunately, vision transformer still faces some challenges such as high computational complexity and absence of desirable inductive bias. To alleviate these problems, a novel Bi-Fovea Self-Attention (BFSA) is proposed, inspired by the physiological structure and characteristics of bi-fovea vision in eagle eyes. This BFSA can simulate the shallow fovea and deep fovea functions of eagle vision, enable the network to extract feature representations of targets from coarse to fine, facilitate the interaction of multi-scale feature representations. Additionally, a Bionic Eagle Vision (BEV) block based on BFSA is designed in this study. It combines the advantages of CNNs and Vision Transformers to enhance the ability of global and local feature representations of networks. Furthermore, a unified and efficient general pyramid backbone network family is developed by stacking the BEV blocks in this study, called Eagle Vision Transformers (EViTs). Experimental results on various computer vision tasks including image classification, object detection, instance segmentation and other transfer learning tasks show that the proposed EViTs perform effectively by comparing with the baselines under same model size and exhibit higher speed on graphics processing unit than other models. Code is available at https://github.com/nkusyl/EViT.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Receding Horizon Trajectory Optimization with Terminal Impact Specifications

The trajectory optimization problem subject to terminal impact time and angle specifications can be reformulated as a nonlinear programming problem using the Gauss pseudospectral method. The cost function of the trajectory optimization problem is modified to reduce the terminal control energy. A receding horizon optimization strategy is implemented to reject the errors caused by the motion of a surface target. Several simulations were performed to validate the proposed method via the C programming language. The simulation results demonstrate the effectiveness of the proposed algorithm and that the real-time requirement can be easily achieved if the C programming language is used to realize it

Food adulteration and traceability tests using stable carbon isotope technologies

Due to the fractionation of stable carbon isotopes in plant photosynthesis, bio-decomposition processes, environmental factors, plant physiology, geographical factors, climatic conditions and agricultural practices, different foods exhibit significant differences in stable carbon isotope ratios. Therefore, stable carbon isotope ratio analysis (SCIRA) presents an effective tool for detecting food adulteration and food traceability control. In addition, stable carbon isotopes can frequently be used as markers to identify veterinary drug residues, pesticide residues and toxic substances remaining in foods by isotope dilution mass spectrometry (IDMS). The emphasis of this review, which will help readers to modify stable carbon isotope technologies more easily and extend their application in adulteration and traceability for foods, is on the characteristics of various instruments and the data processing methods in SCIRA and IDMS technologies. The latest research is also reviewed and highlighted. This paper reviews potential applications of these technologies to improve current food detection and protect consumers’ rights

Box2Poly: Memory-Efficient Polygon Prediction of Arbitrarily Shaped and Rotated Text

Recently, Transformer-based text detection techniques have sought to predict polygons by encoding the coordinates of individual boundary vertices using distinct query features. However, this approach incurs a significant memory overhead and struggles to effectively capture the intricate relationships between vertices belonging to the same instance. Consequently, irregular text layouts often lead to the prediction of outlined vertices, diminishing the quality of results. To address these challenges, we present an innovative approach rooted in Sparse R-CNN: a cascade decoding pipeline for polygon prediction. Our method ensures precision by iteratively refining polygon predictions, considering both the scale and location of preceding results. Leveraging this stabilized regression pipeline, even employing just a single feature vector to guide polygon instance regression yields promising detection results. Simultaneously, the leverage of instance-level feature proposal substantially enhances memory efficiency (>50% less vs. the state-of-the-art method DPText-DETR) and reduces inference speed (>40% less vs. DPText-DETR) with minor performance drop on benchmarks

Serum soluble triggering receptor levels expressed on myeloid cells2 identify early acute kidney injury in infants and young children after pediatric cardiopulmonary bypass

BackgroundAcute kidney injury (AKI) is a potential complication after cardiopulmonary bypass (CPB) of pediatric cardiac surgery and contributes to a certain amount of perioperative mortality. Serum soluble triggering receptor expressed on myeloid cells2 (sTREM2) is an inflammation-associated cytokine in circulation. Alterations of sTREM2 level have been reported in Alzheimer's disease, sepsis, and some other pathologic conditions. This study aimed to investigate the role of sTREM2 as a forecasting factor for AKI in infants and young children and other factors associated with early renal injury after pediatric CPB.MethodsA prospective cohort study with consecutive infants and young children ≤ 3 years old undergoing CPB from September 2021 to August 2022 was conducted in an affiliated university children's hospital. These patients were divided into an AKI group (n = 10) and a non-AKI group (n = 60). Children′s characteristics and clinical data were measured. Perioperative sTREM2 levels were analyzed with enzyme-linked immunosorbent assay (ELISA).ResultsIn children developing AKI, the sTREM2 levels significantly decreased at the beginning of CPB compared to the non-AKI group. Based on binary logistic regression analysis and multivariable regression analysis, risk-adjusted classification for congenital heart surgery (RACHS-1), operation time, and the s-TREM2 level at the beginning of CPB (AUC = 0.839, p = 0.001, optimal cut-off value: 716.0 pg/ml) had predictive value for post-CPB AKI. When combining the sTREM2 level at the beginning of CPB and other indicators together, the area under the ROC curve enlarged.ConclusionsOperation time, RACHS-1 score, and sTREM2 level at the beginning of CPB were independent prognosis factors of post-CPB AKI in infants and young children ≤ 3 years old. Decreased sTREM2 identified post-CPB AKI, and ultimately hampered the outcomes. Our findings indicated that sTREM2 may be a protective factor for AKI after CPB in infants and young children ≤ 3 years old

Identity Authentication Security Management in Mobile Payment Systems

Mobile payment is a new payment method offering users mobility, reachability, compatibility, and convenience. But mobile payment involves great uncertainty and risk given its electronic and wireless nature. Therefore, biometric authentication has been adopted widely in mobile payment in recent years. However, although technology requirements for secure mobile payment have been met, standards and consistent requirements of user authentication in mobile payment are not available. The flow management of user authentication in mobile payment is still at its early stage. Accordingly, this paper proposes an anonymous authentication and management flow for mobile payment to support secure transaction to prevent the disclosure of users\u27 information and to reduce identity theft. The proposed management flow integrates transaction key generation, encryption and decryption, and matching to process users\u27 personal information and biometric characteristics based on mobile equipment authentication carrier