Combination of convex theories: Modularity,deduction completeness,and explanation

Decision procedures are key components of theorem provers and constraint satisfaction systems. Their modular combination is of prime interest for building efficient systems, but their effective use is often limited by poor interface capabilities, when such procedures only provide a simple “sat/unsat” answer. In this paper, we develop a framework to design cooperation schemas between such procedures while maintaining modularity of their interfaces. First, we use the framework to specify and prove the correctness of classic combination schemas by Nelson–Oppen and Shostak. Second, we introduce the concept of deduction complete satisfiability procedures, we show how to build them for large classes of theories, then we provide a schema to modularly combine them. Third, we consider the problem of modularly constructing explanations for combinations by re-using available proof-producing procedures for the component theories.     

Spin-valve planar Hall sensor for single bead detection

The planar Hall effect (PHE) sensor with a junction size of 3 μm × 3 μm for a single micro-bead detection has been fabricated successfully using a typical spin-valve thin film Ta(5)/NiFe(16)/Cu(1.2)/NiFe(2)/IrMn(15)/Ta(5) nm. The PHE sensor exhibits a sensitivity of about 7.2 μV Oe^?1 in the magnetic field range of ±7 Oe approximately. We have performed an experiment to illustrated the possibility of single micro-bead detection by using a PHE sensor. A single micro-bead of 2.8 μm diameter size is secluded from 0.1% dilute solution of the Dynabeads^® M-280 dropped on the sensor surface and is located on the sensor junction by using a micro magnetic needle. The comparison of the PHE voltage profiles in the field range from 0 to 20 Oe in the absence and presence of a single micro-bead identifies a single Dynabeads^® M-280, the maximal signal change as large as ΔV ～ 1.1 μV can be obtained at the field ～6.6 Oe. The results are well described in terms of the reversal of a basic single domain structure.     

A study of Q-learning considering negative rewards

In the reinforcement learning system, the agent obtains a positive reward, such as 1, when it achieves its goal. Positive rewards are propagated around the goal area, and the agent gradually succeeds in reaching its goal. If you want to avoid certain situations, such as dangerous places or poison, you might want to give a negative reward to the agent. However, in conventional Q-learning, negative rewards are not propagated in more than one state. In this article, we propose a new way to propagate negative rewards. This is a very simple and efficient technique for Q-learning. Finally, we show the results of computer simulations and the effectiveness of the proposed method.     

TRUST BY ASSOCIATION: A META‐REPUTATION SYSTEM FOR PEER‐TO‐PEER NETWORKS

Trust mechanisms are used in peer‐to‐peer (P2P) networks to help well‐behaving peers find other well‐behaving peers with which to trade. Unfortunately, these trust mechanisms often do little to keep badly behaving peers from entering and taking advantage of the network, which makes the resulting network difficult or impossible to use for legitimate purposes such as e‐commerce. We propose trust by association, a way of tying peers together in invitation‐only P2P networks in such a way as to encourage the removal of badly behaving peers. We use invitations to create a structure within the otherwise ad hoc P2P network. Using this structure, we create a meta‐reputation system where we measure a peer’s trustworthiness not only by its own behavior, but also by the behavior of the peers it has invited to join. The connection created between the peers takes advantage of the external social relationship that must exist before a peer can be invited into the network. The result is a P2P network where, rather than just trying to marginalize badly behaving peers, there is incentive to kick them out of the network. We present results from a simple simulation showing that our approach works well in general when combined with and compared to an existing trust mechanism.     

Improving traversability of quadruped walking robots using body movement in 3D rough terrains

This paper presents a study on improving the traversability of a quadruped walking robot in 3D rough terrains. The key idea is to exploit body movement of the robot. The position and orientation of the robot are systematically adjusted and the possibility of finding a valid foothold for the next swing is maximized, which makes the robot have more chances to overcome the rough terrains. In addition, a foothold search algorithm that provides the valid foothold while maintaining a high traversability of the robot, is investigated and a gait selection algorithm is developed to help the robot avoid deadlock situations. To explain the algorithms, new concepts such as reachable area, stable area, potential search direction, and complementary kinematic margin are introduced, and the effectiveness of the algorithms is validated via simulations and experiments.     

A dual K-Na selective Prussian blue nanotubes sensor

A strategy for dual sensing of Na⁺ and K⁺ ions using Prussian blue nanotubes via selective inter/deintercalation of K⁺ ion and competitive inhibition by Na⁺ ion, is reported. The analytical signal is derived from the cyclic voltammetry cathodic peak position E_pc of Prussian blue nanotubes. Na⁺ and K⁺ levels in a sample solution can be determined conveniently using one Prussian blue nanotubes sensor. In addition, this versatile method can be applied for the analysis of single type of either Na⁺ or K⁺ ions. The dual-ion sensor response towards Na⁺ and K⁺ can be described using a model based on the competitive inhibition effects of Na⁺ on K⁺ inter/deintercalation in Prussian blue nanotubes. Successful application of the Prussian blue nanotubes sensor for Na⁺ and K⁺ determination is demonstrated in artificial saliva.     

Integrated micro Pirani gauge based hermetical package monitoring for uncooled VO x bolometer FPAs

This paper presents the design and fabrication of a micro Pirani gauge using VO _x as the sensitive material for monitoring the pressure inside a hermetical package for micro bolometer focal plane arrays (FPAs). The designed Pirani gauge working in heat dissipating mode was intentionally fabricated using standard MEMS processing which is highly compatible with the FPAs fabrication. The functional layer of the micro Pirani gauge is a VO _x thin film designed as a 100 × 200 μm pixel, suspended 2 μm above the substrate. By modeling of rarefied gas heat conduction using the Extended Fourier’s law, finite element analysis is used to investigate the sensitivity of the pressure gauge. Also the thermal interactions between the micro Pirani gauge and bolometer FPAs are verified. From the fabricated prototype, the measured device TCR is about −0.8% K⁻¹ and the sensitivity about 1.84 × 10⁻³ W K⁻¹ mbar⁻¹.     

A dispatching method for automated lifting vehicles in automated port container terminals

In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Nanowire structured SnOx–SWNT composites: High performance sensor for NOx detection

A nanowire structured nanocomposite of tin oxide (SnO_x) and a single-walled carbon nanotube (SWNT) are fabricated using rheotaxial growth and thermal oxidation method for gas sensor application. The morphology, gas sensing properties, as well as the chemical and electrical properties are investigated. The oxidation temperature for Sn mainly determines the stoichiometry of the SnO_x nano-beads, and consequently the electrical and gas sensing properties of the nanocomposite sensors. The gas sensing to nitrogen oxide, hydrogen, oxygen, xylene, acetone, carbon monoxide, and ammonia are also examined to determine the gas selectivity of the sensor. The high sensitivity and selectivity towards NO_x of the nanocomposite sensor is realized via the porous structure of the SWNT template. The gas sensing mechanism of the nanocomposite structure is also discussed.