Computational Object-Wrapping Rope Nets

Wrapping objects using ropes is a common practice in our daily life. However, it is difficult to design and tie ropes on a 3D object with complex topology and geometry features while ensuring wrapping security and easy operation. In this article, we propose to compute a rope net that can tightly wrap around various 3D shapes. Our computed rope net not only immobilizes the object but also maintains the load balance during lifting. Based on the key observation that if every knot of the net has four adjacent curve edges, then only a single rope is needed to construct the entire net. We reformulate the rope net computation problem into a constrained curve network optimization. We propose a discrete-continuous optimization approach, where the topological constraints are satisfied in the discrete phase and the geometrical goals are achieved in the continuous stage. We also develop a hoist planning to pick anchor points so that the rope net equally distributes the load during hoisting. Furthermore, we simulate the wrapping process and use it to guide the physical rope net construction process. We demonstrate the effectiveness of our method on 3D objects with varying geometric and topological complexity. In addition, we conduct physical experiments to demonstrate the practicability of our method.

Detection of Aerobics Action Based on Convolutional Neural Network

To further improve the accuracy of aerobics action detection, a method of aerobics action detection based on improving multiscale characteristics is proposed. In this method, based on faster R-CNN and aiming at the problems existing in faster R-CNN, the feature pyramid network (FPN) is used to extract aerobics action image features. So, the low-level semantic information in the images can be extracted, and it can be converted into high-resolution deep-level semantic information. Finally, the target detector is constructed by the above-extracted anchor points so as to realize the detection of aerobics action. The results show that the loss function of the neural network is reduced to 0.2 by using the proposed method, and the accuracy of the proposed method can reach 96.5% compared with other methods, which proves the feasibility of this study.

Research on Ultra-Wideband (UWB) Accurate Positioning under Signal Interference Based on Deep Learning

UWB (Ultra-Wideband) technology is also called "Ultra-Wideband", also known as pulse radio technology. UWB-based positioning technology has real-time indoor and outdoor accurate tracking capabilities, with high positioning accuracy, which can achieve centimeter-level or even millimeter-level positioning. Based on the provided anchor point ranging information, this paper analyzes normal and abnormal data and establishes an accurate positioning model. For task 1, preprocess the data, export the file, delete invalid data, and fill in missing values. For task 2, it is required to establish models for normal and abnormal data respectively. For task 3, it is consistent with the model obtained in task 2. The only difference is that the coordinates of the four anchor points used for the test data have changed. When the target coordinates are calculated, the anchor point coordinates can be replaced to obtain the model required by task 3. For task 4, use the processed data in task one to establish a mathematical model, and train the model through the integrated learning method to determine whether the collected signal is interference. For task 5, first use the integrated learning model in task four to eliminate the interference data in the data, import the eliminated data into the positioning model of task two for positioning, and add Kalman based on interference recognition to the static estimation algorithm.

Effect of Molecular Weight and Nanoarchitecture of Chitosan and Polycaprolactone Electrospun Membranes on Physicochemical and Hemocompatible Properties for Possible Wound Dressing

Tissue engineering has focused on the development of biomaterials that emulate the native extracellular matrix. Therefore, the purpose of this research was oriented to the development of nanofibrillar bilayer membranes composed of polycaprolactone with low and medium molecular weight chitosan, evaluating their physicochemical and biological properties. Two-bilayer membranes were developed by an electrospinning technique considering the effect of chitosan molecular weight and parameter changes in the technique. Subsequently, the membranes were evaluated by scanning electron microscopy, Fourier transform spectroscopy, stress tests, permeability, contact angle, hemolysis evaluation, and an MTT test. From the results, it was found that changes in the electrospinning parameters and the molecular weight of chitosan influence the formation, fiber orientation, and nanoarchitecture of the membranes. Likewise, it was evidenced that a higher molecular weight of chitosan in the bilayer membranes increases the stiffness and favors polar anchor points. This increased Young’s modulus, wettability, and permeability, which, in turn, influenced the reduction in the percentage of cell viability and hemolysis. It is concluded that the development of biomimetic bilayer nanofibrillar membranes modulate the physicochemical properties and improve the hemolytic behavior so they can be used as a hemocompatible biomaterial.

More than an offender location tool: Geographic profiling and body deposition

In homicide cases, it is difficult to provide resolution for the bereaved or to obtain a successful criminal conviction of the guilty party when no body is found. Since the mid-nineteenth century, geographic and environmental patterns have been used to better understand the relationship between crime and its environment. Now known as geographic profiling, practitioners in this field amalgamate criminological, psychological, and geographical knowledge, as well as aspects of mathematics, statistics, and physics to identify spatial patterns associated with criminal behaviour as a means of locating anchor points of an offender (where they live, or work). The same techniques can also be used to locate the covert body deposition sites of their victims. This paper aims to (1) provide a brief summary of criminal behaviour and the environment and how understanding their relationship can be helpful to geographic profiling, (2) amalgamate the available literature on the application of geographic profiling in locating clandestine graves (as most documented uses are to locate offender residences), and (3) include a geographic profile of Ivan Milat, an Australian serial killer (officially) active from 1989 to 1992, demonstrating how geographic profiling techniques can help to identify additional victims and potential body deposition sites. The information in this review will be helpful to law enforcement and practitioners to improve missing persons investigations and searches for clandestine graves.

Detection Based on Crack Key Point and Deep Convolutional Neural Network

Based on the features of cracks, this research proposes the concept of a crack key point as a method for crack characterization and establishes a model of image crack detection based on the reference anchor points method, named KP-CraNet. Based on ResNet, the last three feature layers are repurposed for the specific task of crack key point feature extraction, named a feature filtration network. The accuracy of the model recognition is controllable and can meet both the pixel-level requirements and the efficiency needs of engineering. In order to verify the rationality and applicability of the image crack detection model in this study, we propose a distribution map of distance. The results for factors of a classical evaluation such as accuracy, recall rate, F1 score, and the distribution map of distance show that the method established in this research can improve crack detection quality and has a strong generalization ability. Our model provides a new method of crack detection based on computer vision technology.

Nodes

Nodes within smart grids play an important role in providing stability—keeping things the same—as well as innovating. The theme of nodes is closely related to that of networks, as nodes are fixed, stable points on networks. But whereas the network metaphor encourages us to think about connections and flows, the concept of nodes focuses our attention on the key organisations, people, and technologies that provide stable anchor points and typically act as brokers at crucial intersections within the energy sector. In this chapter, I examine social and technical nodes using three examples: the digital electricity meter, with a focus on household transitions in the UK and Australia, an energy authority (the Australian Energy Market Operator), and islands, specifically looking at the smart grid on King Island, Australia.

A Single-Copter UWB-Ranging-Based Localization System Extendable to a Swarm of Drones

This paper presents a single-copter localization system as a first step towards a scalable multihop drone swarm localization system. The drone was equipped with ultrawideband (UWB) transceiver modules, which can be used for communication, as well as distance measurement. The location of the drone was detected based on fixed anchor points using a single type of UWB transceiver. Our aim is to create a swarm localization system that enables drones to switch their role between an active swarm member and an anchor node to enhance the localization of the whole swarm. To this end, this paper presents our current baseline localization system and its performance regarding single-drone localization with fixed anchors and its integration into our current modular quadcopters, which was designed to be easily extendable to a swarm localization system. The distance between each drone and the anchors was measured periodically, and a specially tailored gradient descent algorithm was used to solve the resulting nonlinear optimization problem. Additional copter and wireless-specific adaptations were performed to enhance the robustness. The system was tested with a Vicon system as a position reference and showed a high precision of 0.2 m with an update rate of <10 Hz. Additionally, the system was integrated into the FINken copters of the SwarmLab and evaluated in multiple outdoor scenarios. These scenarios showed the generic usability of the approach, even though no accurate precision measurement was possible.