Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity. Accurately locating the mobile robots in such a distributed scenario is an essential requirement, such that the mobile robots can be instructed to coordinate with the appropriate sensor nodes. Packet loss is one of the prevailing issues on such wireless sensor network-based mobile robot localization applications. The packet loss might result from node failure, data transmission delay, and communication channel instability, which could significantly affect the transmission quality of the wireless signals. Such issues affect the localization accuracy of the mobile robot applications to an overwhelming margin, causing localization failures. To this end, this paper proposes an improved Unscented Kalman Filter-based localization algorithm to reduce the impacts of packet loss in the localization process. Rather than ignoring the missing measurements caused by packet loss, the proposed algorithm exploits the calculated measurement errors to estimate and compensate for the missing measurements. Some simulation experiments are conducted by subjecting the proposed algorithm with various packet loss rates, to evaluate its localization accuracy. The simulations demonstrate that the average localization error of the robot is 0.39 m when the packet loss rate is less than 90%, and the average running time of each iteration is 0.295 ms. The achieved results show that the proposed algorithm exhibits significant tolerance to packet loss while locating mobile robots in real-time, to achieve reliable localization accuracy and outperforms the existing UKF algorithm

DNA modified MSN-films as versatile biointerfaces to study stem cell adhesion processes

A significant bottleneck in the clinical translation of stem cells remains eliciting the desired stem cell behavior once transplanted in the body. In their natural environment, stem cell fate is regulated by their interaction with extracellular matrix (ECM), mainly through integrin-mediated cell adhesion. 2D biointerfaces that selectively present ECM-derived ligands can be used as valuable tools to study and improve our understanding on how stem cells interact with their environment. Here we developed a new type of biointerface based on mesoporous silica nanoparticles (MSN) which are interesting nanomaterials for biointerface engineering because they allow close control over surface physiochemical properties. To create the platform, DNA functionalized MSN (MSN-ssDNA) with varying PEG linker length were developed. Cell adhesion tripeptide RGD was conjugated to a complementary DNA strand, which could specifically bind to MSN-ssDNA to create MSN-dsDNA-RGD films. We showed that MSN-dsDNA-RGD films could promote hMSCs adhesion and spreading, whereas MSN-dsDNA films without RGD resulted in poor cell spreading with round morphology, and low cell adhesion. In addition, we showed that cell adhesion to the films is PEG length-dependent. The design of the platform allows easy incorporation of other and multiple ECM ligands, as well as soluble cues, making MSN-ssDNA based biointerfaces a novel tool to study ligand-stem cell interactions

Micro grid fault diagnosis based on redundant embedding Petri net

On account of the variable topology and multi-terminal power supply in micro-grid, the fault diagnosis faces more and more challenges. Traditional fault location criteria are unsuitable and fault diagnosis modelling is complex or poor versatility. Further on, the fault reasoning operation is time-consuming. A high transplantable fault diagnosis model aiming at the fault features in micro-grid is established in this paper, and a simple inference algorithm with good error-detecting capability is proposed. Firstly, the fault location criterion based on current magnitude, current phase and Distributed Generation’s current direction information is proposed, and the fault transient component is adopted as a supplementary criterion. Secondly, a hierarchical Petri net model utilizing the electrical information, relays’ and circuit breakers’ state information is accomplished. The model consists of fault location layer and fault clearance layer. In order to increase the portability of the model, the collective processing for the breakers is implemented. Moreover, ‘bidirectional arrowhead arc’ is introduced to reduce the number of places to optimize the Petri net model well. An improved redundant coding Petri net reasoning algorithm is proposed based on the fault clearance layer of the Petri net model. Finally, the validity of the method is verified through case analysis and comparison

Set-membership filtering for generator dynamic state estimation with delayed measurements

In this paper, the set-membership filtering problem is investigated for the dynamic state estimation (DSE) of the synchronous generator with delayed measurements. The process noise and measurement noise are assumed to be unknown, bounded and confined to a specified ellipsoidal set. The measurement delay is modeled by a special matrix composed of a delay-driven variable taking values of 1 or 0. Taking into explicit consideration the estimation uncertainty due to the linearization, the constrained Quasi-Newton method is adopted to minimize the linearization errors. The aim of this paper is to design a set-membership filter capable of confining the state estimate of the system to a certain ellipsoidal region, and the ellipsoidal set including all possible states is obtained by the convex optimization approach. Finally, the proposed algorithm is verified on a single machine infinite bus system to further demonstrate its effectiveness

Study on Optimal Loading of Zero Load Cargo

In recent years, thanks to the transformation and upgrading of domestic consumption and the continuous improvement of logistics network, the total freight volume of China’s part-load logistics market is also increasing, among which urban logistics network plays an important role in part-load logistics enterprises. Reasonable and perfect urban network is helpful to reduce the total cost of market logistics and improve distribution efficiency. This paper introduces the problems existing in part-load logistics, studies the urban logistics model of part-load logistics enterprises, introduces three ways of part-load transportation, and analyzes the urban logistics model of part-load logistics enterprises. At the end of this paper, the above contents are briefly summarized

Modeling and Verification of Stress Relaxation Behavior of Ti-6Al-4V

The phenomenon of gradual decrease of internal stress with the deformation of material maintained under the precondition of certain temperature and initial stress or pre-strain is called stress relaxation. Due to that, the flow stress of the metal material falls rapidly when the hot forming process pauses, and then the required forming load. In this paper, the experiment was carried out to study the stress relaxation property of Ti-6Al-4V, in the temperature range of 1023K~1123K and with the pre-tension strain 0.7%, 4% and 10%. The quartic delay function was used to describe the stress relaxation behavior. The predicted value of stress relaxation equation is in good agreement with the experimental data, and the correlation coefficient is above 0.99. Arrhenius creep constitutive equation embedded in CAE software was derived. The finite element model of stress relaxation process of test bar was builded, and the tensile-relaxation experiment was performed under the loading condition of 1:1 part forming process. The forming force results agree well, and the validity and accuracy of the constitutive model are verified, laying a foundation for the subsequent process simulation and optimization

“English Disease”: Historical Notes on Rickets, the Bone–Lung Link and Child Neglect Issues

Nutritional or classical rickets (here labeled as “rickets”) is a worldwide disease involving mostly infants and young children having inadequate sunlight exposure, often associated with a low dietary intake of Vitamin D. Rickets targets all layers of society independently of economic status with historical information spanning more than two millennia. Vitamin D is critical for the absorption of calcium and prevention of rickets in children as well as osteomalacia in adults. The initial and misleading paradigm of the 19th and 20th centuries that rickets may have been the consequence of infection has been, indeed, reversed following the identification of the Vitamin D molecule’s important role in the function of the immune system. Although traditionally considered limited to osteopathology, Vitamin D deficiency is now known to be linked to infection, inflammation, and carcinogenesis. In this review, we consider the key historical (Whistler, pre-Whistler and post-Whistler descriptors) and social facts around rickets; highlight the osteo-pathological features of rickets and the pathology of the upper and lower respiratory tract, stressing the fact that lungs remain the main secondary organ affected by Vitamin D deficiency; and emphasize the public health role in identifying the cases of child neglect or abuse based on the evaluation of the costochondral region

Optimal Capacity Allocation of Energy Storage System considering Uncertainty of Load and Wind Generation

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses