A functional approach for the formalization of the fixture design process

The design of machining fixtures is a highly complex process that relies on designer experience and his/her implicit knowledge to achieve a good design. In order to facilitate its automation by the development of a knowledge-based application, the explicit definition of the fixture design process and the knowledge involved is a prior and a fundamental task to undertake. Additionally, a fundamental and well-known engineering principle should be considered: the functional requirements and their associated constraints should be the first input to any design process. Considering these two main ideas, this paper presents the development undertaken to facilitate the automation of the fixture design process based on a functional approach.In this context, the MOKA methodology has been used to elicit fixtures knowledge. IDEF0 and UML have been used to represent the fixture design process. A methodology based on the function concept and aiming to formalize such design process is proposed. Fixture functional requirements have been defined and formalized. Functional fixtures elements have been used to create a functional design solution, the link of these elements with the functional requirements and with typical commercial fixture components has been defined via tables and rules mapping. And finally, a prototype knowledge-based application has been developed in order to make an initial validation of the proposed methodology.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

COACH: COllaborative certificate stAtus CHecking mechanism for VANETs

Vehicular Ad Hoc Networks (VANETs) require mechanisms to authenticate messages, identify valid vehicles, and remove misbehaving vehicles. A public key infrastructure (PKI) can be used to provide these functionalities using digital certificates. However, if a vehicle is no longer trusted, its certificates have to be revoked and this status information has to be made available to other vehicles as soon as possible. In this paper, we propose a collaborative certificate status checking mechanism called COACH to efficiently distribute certificate revocation information in VANETs. In COACH, we embed a hash tree in each standard Certificate Revocation List (CRL). This dual structure is called extended-CRL. A node possessing an extended-CRL can respond to certificate status requests without having to send the complete CRL. Instead, the node can send a short response (less than 1 kB) that fits in a single UDP message. Obviously, the substructures included in the short responses are authenticated. This means that any node possessing an extended-CRL can produce short responses that can be authenticated (including Road Side Units or intermediate vehicles). We also propose an extension to the COACH mechanism called EvCOACH that is more efficient than COACH in scenarios with relatively low revocation rates per CRL validity period. To build EvCOACH, we embed an additional hash chain in the extended-CRL. Finally, by conducting a detailed performance evaluation, COACH and EvCOACH are proved to be reliable, efficient, and scalable.     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.     

A New Efficiency-Weighted Strategy for Continuous Human/Robot Cooperation in Navigation

Autonomous robots are capable of navigating on their own. Shared control approaches, however, allow humans to make some navigation decisions. This is typically executed either by overriding the human or the robot control at some specific situations. In this paper, we propose a method to allow cooperation between humans and robots at each point of any given trajectory so that both have some weight in the emergent behavior of the mobile robot. This is achieved by evaluating their efficiencies at each time instant and combining their commands into a single order. In order to achieve a seamless combination, this procedure is integrated into a bottom-up architecture via a reactive layer. We have tested the proposed method using a real robot and several volunteers, and results have been satisfactory both from a quantitative and qualitative point of view.     

Mild Binding of Protein to C2N Monolayer Reveals Its Suitable Biocompatibility

The development of biocompatible nanomaterials for smart drug delivery and bioimaging has attracted great interest in recent years in biomedical fields. Here, the interaction between the recently reported nitrogenated graphene (C₂N) and a prototypical protein (villin headpiece HP35) utilizing atomistic molecular dynamics simulations is studied. The simulations reveal that HP35 can form a stable binding with the C₂N monolayer. Although the C₂N–HP35 attractive interactions are constantly preserved, the binding strength between C₂N and the protein is mild and does not cause significant distortion in the protein's structural integrity. This intrinsic biofriendly property of native C₂N is distinct from several widely studied nanomaterials, such as graphene, carbon nanotubes, and MoS₂, which can induce severe protein denaturation. Interestingly, once the protein is adsorbed onto C₂N surface, its transverse migration is highly restricted at the binding sites. This restriction is orchestrated by C₂N's periodic porous structure with negatively charged “holes,” where the basic residues—such as lysine—can form stable interactions, thus functioning as “anchor points” in confining the protein displacement. It is suggested that the mild, immobilized protein attraction and biofriendly aspects of C₂N would make it a prospective candidate in bio‐ and medical‐related applications.     

Analysis of retailers’ coalition stability for supply chain based on LCS and stable set

To attain the general form of stable coalition structure, this paper addressed the problem of retailers’ coalition stability in a two-stage supply chain consisting of one supplier and multiple retailers. A profit gain function was established via introducing market gain coefficient and coalition cost coefficient for different coalition structures. Based on the function, the profit of each retailer in all kinds of coalition structures was analysed, and the general feature of a stable coalition structure was attained by the largest consistent set method and the stable set method. Furthermore, some insights were obtained. For example, stable coalition structures are equidistributed or approximate equidistributed; with supplier’s cost increasing, the size of the retailers’ coalition increases. Finally, the above conclusions are verified by numerical simulation. The results of this paper provide a reference for retailers’ coalition in a supply chain, such as automobile or Information Technology supply chain.     

Desflurane-mediated sympathetic activation occurs in humans despite preventing hypotension and baroreceptor unloading

BACKGROUND: Increasing concentrations of desflurane result in progressive decreases in blood pressure (BP) and, unlike other currently marketed, potent volatile anesthetics, heightened sympathetic nervous system activity. This study aimed to determine whether baroreflex mechanisms are involved in desflurane-mediated sympathetic excitation. METHODS: Healthy volunteers were anesthetized with desflurane (n = 8) or isoflurane (n = 9). Heart rate (HR; measured by electrocardiograph), blood pressure (BP; measured by arterial catheter), and efferent sympathetic nerve activity (SNA; obtained from percutaneous recordings from the peroneal nerve) were monitored. Baroreflex sensitivity was evaluated at baseline while volunteers were conscious and during 0.5, 1, and 1.5 minimum alveolar concentration (MAC) anesthesia via bolus injections of nitroprusside (100 microg) and phenylephrine (150 microg) to decrease and increase BP. To prevent the BP decline with increasing depths of anesthesia, phenylephrine was infused to maintain mean BP at the 0.5 MAC level. RESULTS: The HR, BP, and SNA were similar between the groups at the conscious baseline measurement. Efferent SNA did not change during higher MAC of isoflurane, but it increased progressively as desflurane concentrations were increased beyond 0.5 MAC, despite maintaining BP at the 0.5 MAC value with phenylephrine infusions (P < 0.05). Cardiac baroslopes (based on changes in HR) were progressively and similarly decreased with increasing concentrations of isoflurane and desflurane (P < 0.05). Sympathetic baroslopes (based on SNA) decreased with increasing isoflurane concentrations but were maintained with increasing concentrations of desflurane; the response was significantly different between groups. CONCLUSIONS: The increase in basal levels of SNA with increasing concentrations of desflurane persisted despite "fixing" BP and thus is probably not due to hypotension and unloading of the baroreceptors. Further, the preservation of reflex increases in SNA to nitroprusside during desflurane indicates that desflurane preserves one component of the baroreflex in humans when BP is "fixed."     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.