Trajectory Design of Laser-Powered Multi-Drone Enabled Data Collection System for Smart Cities

This paper considers a multi-drone enabled data collection system for smart cities, where there are two kinds of drones, i.e., Low Altitude Platforms (LAPs) and a High Altitude Platform (HAP). In the proposed system, the LAPs perform data collection tasks for smart cities and the solar-powered HAP provides energy to the LAPs using wireless laser beams. We aim to minimize the total laser charging energy of the HAP, by jointly optimizing the LAPs’ trajectory and the laser charging duration for each LAP, subject to the energy capacity constraints of the LAPs. This problem is formulated as a mixed-integer and non-convex Drones Traveling Problem (DTP), which is a combinatorial optimization problem and NP-hard. We propose an efficient and novel search algorithm named DronesTraveling Algorithm (DTA) to obtain a near-optimal solution. Simulation results show that DTA can deal with the large scale DTP (i.e., more than 400 data collection points) efficiently. Moreover, the DTA only uses 5 iterations to obtain the nearoptimal solution whereas the normal Genetic Algorithm needs nearly 10000 iterations and still fails to obtain an acceptable solution

Sequential labeling with structural SVM under non-decomposable loss functions

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis mainly focuses on the sequential labeling problem. Sequential labeling is a fundamental problem in computer vision and machine learning areas and has been researched in many applications. The most popular model for sequential labeling is the hidden Markov model where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, other structural models, in particular, the extension of SVM to the classification of sequences and other structures have benefited from minimum-loss training approaches which in many cases lead to greater classification accuracy. However, SVM training requires the choice of a suitable loss function. Common loss functions available for training are restricted to decomposable cases such as the zero-one loss and the Hamming loss. Other useful losses such as the F₁ loss, average precision (AP) loss, equal error rates and others are not available for sequential labeling. For the average precision, some results have been proposed in the past, but our results are more general. On the other hand, classification accuracy often suffers from the uncertainty of ground truth labeling and traditional structural SVM only ensures that the ground-truth labeling of each sample receives a score higher than that of any other labeling. However, no specific score ranking is imposed among the other labelings. For the loss functions problem, we propose a training algorithm that can cater for the F₁ loss and any other loss function based on the contingency table. In our thesis, we propose exact solutions for the F₁ loss, precision/recall at fixed value of recall/precision, precision for a fixed value of predicted positives ("precision at k"), precision/recall Break-Even Point and a formulation of the Average Precision (AP loss). For further experiments, we not only apply the AP loss in the training, but also in testing. For the uncertainty in the ground-truth labeling problem, we extend the standard constraint set of structural SVM with constraints between "almost-correct" labelings and less desirable ones to obtain a partial ranking structural SVM (PR-SSVM) approach. We choose different datasets to verify our approaches: human activity datasets including the challenging TUM Kitchen dataset and CMU-MMAC dataset, and the Ozone Level Detection dataset. The experimental results show the efficiency of our approaches on different performance measurements, such as detection rate, false alarm rate and F₁ measure, compared to the conventional SVM, HMM and structural SVM with decomposable losses such as the 0-1 loss and Hamming loss

THE APPLIED OF KINITECH ISOKINETIC REHABILITATION AND TESTING UNIT IN THE STRENGTH TRAI ING OF ELITE ATHLETES AFTER KNEE JOINT INJURY

Knee joint injury is one of common injuries in sports, it affects the improvement of sports performance, reduce the number of years for sports, even ends athlete's sports career. This study, which aims to apply the isokinetic training in the most excellent Chinese female athletes of softball after knee joint injury, verifies that isokinetic training not only improves muscle strength of athletes but also is a very effective way in the rehabilitation after knee joint injury

Coordinated Speed Control Strategy for Minimizing Energy Consumption of a Shearer in Fully Mechanized Mining

As one of the major pieces of equipment in fully mechanized coal mining, the drum shearer plays a critical role in improving the efficiency and energy utilization in the coal mining production process. In this paper, an energy consumption model of a shearer, derived from the analysis of the cutting and traction resistances on the shearer during different processes within a working cycle, is established. Based on the derived model, control and coordination strategies between the two speeds are proposed to minimize the shearer’s energy consumption in unidirectional mining. The case study of a real coal mine shows that the proposed models are valid, and the optimal control of shearer speeds can effectively reduce the energy consumption by 5.16% in a working cycle. To gain further insights into the impact of traction speed and drum rotational speed on the shearer’s energy consumption, several speed coordination cases are employed to further compare with the optimized one. Our study results show that the energy consumption of a shearer can be decreased with the increase of traction speed while decreasing drum rotational speed in coordination

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework

LGMD and DSNs neural networks integration for collision predication

An ability to predict collisions is essential for current vehicles and autonomous robots. In this paper, an integrated collision predication system is proposed based on neural subsystems inspired from Lobula giant movement detector (LGMD) and directional selective neurons (DSNs) which focus on different part of the visual field separately. The two type of neurons found in the visual pathways of insects respond most strongly to moving objects with preferred motion patterns, i.e., the LGMD prefers looming stimuli and DSNs prefer specific lateral movements. We fuse the extracted information by each type of neurons to make final decision. By dividing the whole field of view into four regions for each subsystem to process, the proposed approaches can detect hazardous situations that had been difficult for single subsystem only. Our experiments show that the integrated system works in most of the hazardous scenarios

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer non-convex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems

Performance Analysis of RIS-Assisted Wireless Communications With Energy Harvesting

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted wireless communication system with energy harvesting. In the single information user (IU) scenario, we consider the power control of base station (BS) and the random deployment of energy users (EUs). To this end, we first characterize the statistical features of the channel gains over BS-RIS-IU and BS-RIS-EU cascaded links. Then, we derive a closed-form expression of the information outage probability (IOP) of the IU and show an upper bound of the energy outage probability (EOP) of EUs by invoking the Jensen's inequality. Furthermore, we consider two more general extensions, namely, the existence of imperfect phase alignment and multiple IUs. Finally, the correctness of the analysis results is verified by Monte-Carlo simulation

IRS-Assisted Short Packet Wireless Energy Transfer and Communications

In this letter, we analyse and optimize an intelligent reflecting surface (IRS)-assisted ultra-reliable and low-latency communications (uRLLC) system supported by wireless energy transfer (WET) technology, in which short packets are used in both the WET and wireless information transfer (WIT) phases. We first present the statistical features of the signal-to-noise ratio (SNR) of the system. Then, we derive an approximate closed-form expression of the average packet error probability (APEP). Additionally, we optimize the channel uses in the WET and WIT phases to maximize the effective throughput (ET) of the system. Finally, the effectiveness of the proposed solution is verified by Monte-Carlo simulation