Prediction of vertical bearing capacity of waveform micropile

This study proposes a predictive equation for bearing capacity considering the behaviour characteristics of a waveform micropile that can enhance the bearing capacity of a conventional micropile. The bearing capacity of the waveform micropile was analysed by a three-dimensional numerical model with soil and pile conditions obtained from the field and centrifuge tests. The load-transfer mechanism of the waveform micropile was revealed by the numerical analyses, and a new predictive equation for the bearing capacity was proposed. The bearing capacities of the waveform micropile calculated by the new equation were comparable with those measured from the field and centrifuge tests. This validated a prediction potential of the new equation for bearing capacity of waveform micropiles

Global Hilbert Expansion for the Vlasov-Poisson-Boltzmann System

We study the Hilbert expansion for small Knudsen number $\varepsilon$ for the Vlasov-Boltzmann-Poisson system for an electron gas. The zeroth order term takes the form of local Maxwellian: $ F_{0}(t,x,v)=\frac{\rho_{0}(t,x)}{(2\pi \theta_{0}(t,x))^{3/2}} e^{-|v-u_{0}(t,x)|^{2}/2\theta_{0}(t,x)},\text{\ }\theta_{0}(t,x)=K\rho_{0}^{2/3}(t,x).$Our main result states that if the Hilbert expansion is valid at$t=0$for well-prepared small initial data with irrotational velocity$u_0$, then it is valid for$0\leq t\leq \varepsilon ^{-{1/2}\frac{2k-3}{2k-2}},$where$\rho_{0}(t,x)$and$ u_{0}(t,x)$satisfy the Euler-Poisson system for monatomic gas$\gamma=5/3$

Identifying cross country skiing techniques using power meters in ski poles

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.Comment: Presented at the Norwegian Artificial Intelligence Symposium 201

Structure-specified H∞ loop shaping control for balancing of bicycle robots: A particle swarm optimization approach

In this paper, the particle swarm optimization (PSO) algorithm was used to design the structure-specified H∞ loop shaping controllers for balancing of bicycle robots. The structure-specified H∞ loop shaping controller design normally leads to a complex optimization problem. PSO is an efficient meta-heuristic search which is used to solve multi-objectives and non-convex optimizations. A model-based systematic procedure for designing the particle swarm optimization-based structure-specified H∞ loop shaping controllers was proposed in this research. The structure of the obtained controllers are therefore simpler. The simulation and experimental results showed that the robustness and efficiency of the proposed controllers was gained when compared with the proportional plus derivative (PD) as well as conventional H∞ loop shaping controller. The simulation results also showed a better efficiency of the developed control algorithm compared to the Genetic Algorithm based one

A Case Study of Assessing Button Bits Failure through Wavelet Transform Using Rock Drilling Induced Noise Signals

Finding the precise moment of button breakage of bits during drilling, with the experience of drill rig operators is a serious concern for modern vibrant mining industry. This research proposed a new methodology to find the failure of button using the sound generated by rock-bit interactions. The experiment is conducted by the video and sound data recorded during a drilling process in an underground mine, that uses a Sandvik AXERA7 twin boom jumbo drill rig and Polycrystalline diamond (PCD) tapered button bits. Signal analysis techniques such as Fourier transform and Wavelet transform are utilised to analyse the hectic noise signal recorded. The analysed results are shown that Wavelet Transform is much more effective in finding singularity points such as chipping or breakage of a button in compared to the Fourier Transform. The outcome of this analysis, which is the peak intensity at the breakage point, was correlated to the average intensity of the sound wave using moving average method. The results suggest that the noise generated during the drilling process can be used to detect the condition of the drill bit

Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

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