Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility

The paper proposes a new class of continuous-time asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating self-exciting co-jumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and in-sample over-fitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and non-nested models, but also generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as negative jumps in asset returns mainly through jumps in diffusion volatility. We find substantial evidence for jump clustering, in particular, after the recent financial crisis in 2008, even though parameters driving dynamics of the jump intensity remain difficult to identify.Self-Excitation, Volatility Jump, Jump Clustering, Extreme Events, Parameter Learning, Particle Filters, Sequential Bayes Factor, Risk Management

Experimental study on dynamic performance of pneumatic flexible manipulator with single degree of freedom

Aiming at the problem of insufficient transverse stiffness of flexible manipulator, a kind of pneumatic flexible manipulator with single degree of freedom is developed by using elongated pneumatic artificial muscle joint as the actuator. The structure and functional principle of the manipulator are introduced. The dynamic experiment of the manipulator was carried out by using the three-dimensional motion measurement system, and the dynamic characteristics of the manipulator under three different incentive signals (step, pulse and ramp ) were analyzed, which provided a basis for the establishment of the manipulator control model in the later stage

Smart WiFi Sensing for Network and Environmental Awareness

The rapid growth of WiFi-capable Internet-of-Things (IoT) devices has connected the world. With the ubiquity of WiFi transmissions around, numerous research has shown that these WiFi-capable IoT devices can be leveraged like radars to sense the environment through the use of WiFi Channel State Information (CSI). However, our literature survey reveals that existing attempts at WiFi CSI sensing primarily focus on detecting changes caused by direct human or object movements, while little attention has been given to changes caused by the underlying network and environmental factors. To address this gap, this thesis explores the potential of WiFi sensing techniques for network traffic, environmental occupancy and thermal environment monitoring by CSI extracted from the same WiFi stream, integrated onto an autonomous WiFi sensing architecture. As WiFi CSI are extracted from users' network activities, this thesis begins by demonstrating that CSI can be used to distinguish the underlying network traffic types that generate WiFi signals originally. Notably, we propose a novel WiFi CSI processing technique for Network Traffic Classification (NTC), which is evaluated under different environmental setups and wireless interference scenarios to verify its robustness. After demonstrating WiFi CSI-based network awareness, we pivot to environmental occupancy sensing as the foundation for our subsequent physical environment sensing attempts. Building on existing CSI-based human activity monitoring efforts, we show that CSI can be used to monitor occupancy levels in an effective yet lightweight fashion, timely for COVID-19 containment efforts. Uniquely, we show the possibility to implement an occupancy threshold detection using binary classifications. We also show that CSI-based occupancy monitoring can be extended to challenging outdoor scenarios. Thereafter, we focus on expanding the capabilities of WiFi CSI-based environmental sensing. In a pioneering attempt, we show that WiFi CSI can be used to detect temperature changes in the ambient environment, and thus, detecting the onset of anomalous temperature changes caused by fire incidents. Lastly, we aim to integrate the versatile network and environmental awareness applications of WiFi CSI-based sensing into a single, autonomous platform that can support the versatile CSI sensing using commercially off-the-shelf IoT devices with the potential for scalability. In summary, this thesis significantly expanded the depth of WiFi CSI-based sensing capabilities, addressing the gap in existing WiFi CSI sensing research

A Novice Method for Calibrating the Transient Model of an Automotive HVAC System

A novice method for calibrating the transient model of an automotive HVAC system is presented in this paper. Transient models can be of great importance in the development process of automotive HVAC control algorithms, especially model based ones, as it saves both time and effort. However, the calibration process is usually difficult and relies heavily on experience due to the complexity of the model. A set of customized measurement tools, which consists of several wireless temperature and humidity sensors and an OBD dongle, is used to capture time series data related to the HVAC system during normal driving. Parts of the time series data are then fed into an optimization algorithm to generate a cost function, which can be minimized when the measured data correspond to the simulation data generated by the transient model, while other parts of the data are remained for the validation step. A sensitivity analysis is then performed to find out which parameters in the HVAC transient model need to be optimized to calibrate the model. As the transient model is a physical network model which can be generally considered as a set of differential and algebraic equations, this presented method reduces the calibration process of a complex physical model into solving a common optimization problem. Therefore, various optimization algorithms and tools can be applied. The method is developed and tested during the modeling process of an automotive HVAC system. The efficiency of the modelling process is improved while the calibration results fit better with the measured data.

Vacancy expansion in alpha-Ti under tensile loads at different strain rates with MD simulation

In order to analyze the effect of strain rate under tensile load on microcrack growth in Alpha Titanium, molecular dynamics simulation was used to analyze the results of atomic location, dislocation distribution, lattice phase transition, potential energy distribution and volume strain distribution. It was found that the cracks gradually evolved into holes after unstable propagation, and the holes were occupied by clusters on both sides of the material in the later stage under the necking of the material. The higher the tensile strain rate, the earlier the crack initiation and the larger the evolution of the through-hole. When the same strain value is reached, the lattice transformation ratio is higher under high strain rate loading. HCP is transformed into amorphous structure, BCC lattice type and a small amount of FCC type. Moreover, the larger the strain rate, the less the compatible deformation ability of the lattice is, and the more twins are produced. In addition, it is found that there are volumetric strain wave emission and diffusion in the model at the moment of void birth, and voids play a role in dividing the energy absorption region. Dislocation emission occurs at the crack tip and energy competition exists between dislocation and crack propagation

The research of polishing nozzle quality based on discrete element method

In order to get further study for the effect of abrasive grains to the wall of the workpiece during polishing process, a new method of discrete element that carry out the numerical simulation with DEM is put forward, and the visual calculation is performed for the abrasive grain movement in the nozzle. The interaction of particles-particles or particles-workpiece wall during the polishing process and the tracks of single grain in the workpiece are analyzed by observing the distribution of abrasive grain in the workpiece at different time. The surface removal mechanism of abrasive grains to the workpiece material is discussed by analyzing the collision process of particles to the workpiece wall. The wear level of the abrasive grains to the inner surface of the workpiece is studied through the force of abrasive grain to the workpiece wall consumption, and finally explore the cutting effect of particles to workpiece wall. As a consequence, the abrasive flow processing experiment is carried out. The surface roughness of the large hole and small hole of the nozzle are detected by stylus measurement. The conclusion shows that the surface roughness for the large hole and the small hole before the experiment is1.741 μm and 1.201 μm, its 0.801 μm, 0.651 μm after it. Further roughness tests are performed on the surface of the pores by means of a grating surface measuring instrument. The result indicates that the surface roughness reduces from 2.67 μm to 0.697 μm, 0.728 μm, 0.782 μm. Apparently, the surface roughness of the hole is sharply reduced, which has a smooth and flat inner surface, the effectiveness and reliability of the abrasive flow are verified

Estimating and Testing Long-Run Risk Models: International Evidence

We estimate and test long-run risk models using international macroeconomic and financial data. The benchmark model features a representative agent who has recursive preferences with a time preference shock, a persistent component in expected consumption growth, and stochastic volatility in fundamentals characterized by an autoregressive Gamma process. We construct a comprehensive dataset with quarterly frequency for ten developed countries and employ an efficient likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to make full econometric inference. Our empirical findings provide international evidence in support of long-run risks, time-varying preference shocks, and countercyclicality of the stochastic discount factor. We show the existence of a global long-run consumption factor driving equity returns across individual countries

Effects of different inlet velocity on the polishing quality of abrasive flow machining

In order to study the effect of different inlet velocity on the polishing quality of abrasive flow machining, this paper takes the variable diameter pipe as an example. The fluid dynamic pressure and total energy of abrasive particles under coupling field with different inlet velocities were carried out by using computational fluid dynamics software. The results of numerical analysis show that the polishing quality becomes better with the increase of the inlet velocity. At the same inlet velocity, the smaller the pipe diameter is, the higher the polishing quality will be. Therefore, the optimum inlet velocity can be selected by numerical simulation according to the size of the aperture of workpiece in the actual processing, which can provide technical support for the production

Numerical analysis of spiral curved tube of the solid liquid two phase abrasive flow polishing

In order to solve the problem that the inner surface of heterosexual surface parts is difficult to be machined, a solid-liquid two-phase abrasive flow machining method is proposed. The standard model and the pressure-coupled SIMPLEC algorithm are used. The dynamic characteristics of the dynamic pressure, turbulent kinetic energy and turbulence intensity of the polygonal spiral surface flow channel were obtained by numerical analysis of the solid-liquid two-phase abrasive grain polished polygonal spiral curved pipe under different outlet pressure conditions. The numerical simulation results show that the grinding effect is inversely proportional to the outlet pressure. We can achieve better polishing effect by appropriately reducing the outlet pressure

Experimental Study of Fouling Performance of Air Conditioning System with Microchannel Heat Exchanger

Microchannel heat exchanger (MCHX) has many advantages compared with traditional heat exchanger. The research and application of it also develop fast in recent years. However, in the domestic air conditioning field, it has not been widely used yet. One of the obstacles is the dust fouling of the outside condenser. The affect of dust fouling problem on air conditioning systems with louver fin and wavy fin was researched in this article. The results show the fouling problem of the MCHX can cause drastically performance degradation of the system, the cooling capacity decreased by 23% and 17.2%, the energy consumption increased by 52.5% and 28.4%, and COP decreased by 49.5% and 35.6% for air condition systems with louver fin and wavy fin MCHX respectively after they run for 3 months in the factory environment. Based on this situation, a fan blowback control strategy was introduced into the air conditioning system. After adding the control strategy, the cooling capacity only decreased by 5.5% and 2%, the energy consumption increased by 7.8% and 2.3%, and COP decreased by 12.3% and 4.3% for the systems with louver fin and wavy fin MCHX respectively 3 months later