Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.     

A knowledge assimilation method for logic databases

In this paper we consider a deductive question-answering system for relational databases as a logic database system, and propose a knowledge assimilation method suitable for such a system. The concept of knowledge assimilation for deductive logic is constructed in an implementable form based on the notion of amalgamating object language and metalanguage. This concept calls for checks to be conducted on four subconcepts, provability, contradiction, redundancy, independency, and their corresponding internal database updates. We have implemented this logic database knowledge assimilation program in PROLOG, a logic programming language, and have found PROLOG suitable for knowledge assimilation implementation.     

High rate nitrogen removal by the CANON process at ambient temperature

Completely autotrophic nitrogen removal over nitrite (CANON) is a cost-effective nitrogen removal process. Implementation of the CANON process relies on the cooperation of ammonium-oxidizing and Anammox bacteria, as well as the inhibition of nitrite-oxidizing bacteria. Strict limitations on dissolved oxygen (DO) concentration in the reactor, and the addition of sufficient inorganic carbon in the influent, were adopted as the main operational strategies. The reactor was fed with synthetic inorganic wastewater composed mainly of NH(4)(+)-N, and operated for 106 days. Stable nitrogen removal rates (NRR) of around 1.4 kg N m(-3) d(-1) were obtained at ambient temperature. Morphological characteristics and analysis of bacterial community confirmed the formation of functional outer aerobic and inner anaerobic granular sludge, providing evidence of stable nitrogen removal.     

Treatment of domestic sewage by a metal membrane bioreactor

A submerged flat metal MBR (membrane bioreactor) was used to treat synthetic domestic sewage in this study. The experiment was continued for 270 days and ran under two modes as AMBR (aerobic membrane bioreactor) and A/O-MBR (anoxic/aerobic membrane bioreactor) at a permeate flux of 0.4-1 m3/(m2 d). PVA (polyvinyl alcohol) gel beads were added to the aeration tank with a volume ratio of 10% at the end of the A/O-MBR mode. The mean COD and TN removal efficiencies achieved 96.69 and 32.12% under the AMBR mode, and those were 92.17 and 72.44% under the A/O-MBR mode, respectively. SND (simultaneous nitrification and denitrification) occurred at high MLSS (mixed liquor suspended solids) concentration. The metal membranes reduced effluent COD during filtration. The system ran stably for 115 days at a permeate flux of 0.8-1 m3/(m2 d) without changing membranes under the AMBR mode, but the membrane filterability decreased gradually under high MLSS or A/O-MBR mode, and the addition of PVA worsened the membrane filterability on the contrary. PSD (particle size distribution) and sludge fractions had evident influence on membrane fouling. The main fouling mechanism was cake formation under the AMBR mode, and that was pore blocking under the A/O-MBR mode.     

Agglomeration control of hydroxyapatite nano-crystals grown in phase-separated microenvironments

Materials synthesis processes that require high temperatures consume large quantities of energy that generate an environmental burden. We attempted to synthesize hydroxyapatite (HAp) nano-crystals without firing or melting. “Water in oil” (W/O) emulsions were employed as microreactors for HAp formation. The surfactant-bounded water mediated HAp crystal nucleation, and HAp nano-crystallites were obtained. The obtained particles were aggregates composed of plate-like nano-crystals and monodisperse tiny crystals. Utilization of the W/O emulsions resulted in tunable nucleation frequency and the reactant provision, and yielded HAp nano-crystals with characteristic agglomeration properties.     

Size Distribution of Gold Nanoparticles Used by Small Angle X-ray Scattering

The size distribution of gold nanoparticles was estimated based on the scattering intensity data obtained from small angle X-ray scattering (SAXS) and compared with the result of micrographs by transmission electron microscopy. The slope of the Guinier plot was used to estimate the mean size assuming a narrow particle size distribution. When the size distribution is narrow, the mean size can be easily obtained from slope of a Guinier plot of scattering data within 10% error. Additionally, assuming a lognormal size distribution, the size distribution and the mean size can be calculated using the experimental SAXS data in the fitting analysis. The histogram method, which utilizes the coefficient matrix of scattering intensity, was also applied to the estimation of the size distribution, and this method could be useful for a rough estimate of the size distribution.     

Stochastic Asset Pricing with Seismic Hazard Risk

Since cash flow of asset in the long run is generally subject to economic fluctuation, and also to potential damage due to seismic hazard risk, asset pricing is more relevantly evaluated, provided that uncertainties of both cash flow and seismic risk are properly taken into account. This study will propose a discounted cash flow method to evaluate asset pricing, in which incomes gained from asset are modeled as a Markov process and seismic loss is modeled due to only one earthquake event during the service time. A numerical example is demonstrated for an 11 story steel reinforced commercial building considering the service time of 50 years. Various risk–asset price curves are numerically obtained in the form of probability of excess versus asset price with discount rate as a parameter for cases with and without earthquake loss taken into account, and for a case in which earthquake strengthening measure is implemented. These curves contribute information to decision makers in charge of risk and investment management.     

Multistage bayesian autonomy for high‐precision operation in a large field

This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high‐precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high‐precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real‐world problems. A successful demonstration using the UAV is also presented.     

Development of silicon wafer packaging technology for deep UV LED

This paper reports a deep‐ultraviolet LED (deep‐UV‐LED) package based on silicon MEMS process technology (Si‐PKG). The package consists of a cavity formed by silicon crystalline anisotropic etching, through‐silicon vias (TSVs) filled with electroplated Cu, bonding metals made of electroplated Ni/AuSn and a quartz lid for hermetic sealing. A deep‐UV LED die is directly mounted in the Si‐PKG by AuSn eutectic bonding without a submount. It has advantages in terms of size, heat dissipation, light utilization efficiency, productivity and cost over conventional AlN ceramic packages. We confirmed a light output of 30 mW and effective reflection on Si (111) cavity slopes in the Si‐PKG. Based on simulation, further improvement of the optical output is expected by optimizing DUV‐LED die mount condition.