On the cosimulation of multibody systems and hydraulic dynamics

Multibody System Dynamics - The simulation of mechanical devices using multibody system dynamics (MBS) algorithms frequently requires the consideration of their interaction with components of a...     

Using regression makes extraction of shared variation in multiple datasets easy

In many data analysis tasks it is important to understand the relationships between different datasets. Several methods exist for this task but many of them are limited to two datasets and linear relationships. In this paper, we propose a new efficient algorithm, termed cocoreg, for the extraction of variation common to all datasets in a given collection of arbitrary size. cocoreg extends redundancy analysis to more than two datasets, utilizing chains of regression functions to extract the shared variation in the original data space. The algorithm can be used with any linear or non-linear regression function, which makes it robust, straightforward, fast, and easy to implement and use. We empirically demonstrate the efficacy of shared variation extraction using the cocoreg algorithm on five artificial and three real datasets.     

Novel Packet Switching for Green IP Networks

A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy‐efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing‐related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using well‐known real network topologies and achieved a more energy‐ efficient performance than that achieved in previous studies.     

Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7-7.0%, with an overestimation of 3.1-4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0-15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1-11.3%, bias -6.1-+4.6%). The dependency of LiDAR metrics and ACC at 0-75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy.     

Wastewater treatment: New insight provided by interactive multiobjective optimization

In this paper, we describe a new interactive tool developed for wastewater treatment plant design. The tool is aimed at supporting the designer in designing new wastewater treatment plants as well as optimizing the performance of already available plants. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is more important than ever because the requirements for wastewater treatment plants are getting tighter and tighter from both environmental and economical reasons. By combining a process simulator to simulate wastewater treatment and an interactive multiobjective optimization software to aid the designer during the design process, we obtain a practically useful tool for decision support. The applicability of our tool is illustrated with a case study related to municipal wastewater treatment where three conflicting evaluation criteria are considered.     

Photoresist-based integration of enzyme functionality into MEMS

We demonstrate the direct immobilization of glucose oxidase and lactate oxidase onto photoresists commonly used in microfabrication. The method allows for a cost-effective, facile inclusion of enzyme functionality into novel MEMS devices because it does not require any chemical or physical pre-treatment of surfaces, and it is largely compatible with existing fabrication technologies. We used fluorescence imaging and absorbance spectrometry to confirm attachment of the enzymes onto the photoresists and to determine their activity. In addition, the procedure was used to successfully integrate enzyme functionality into a photoresist-based biosensor. This further demonstrates the effectiveness of the approach, and opens a path towards other novel applications in MEMS research.     

Variation and directional anisotropy of reflectance at the crown scale — Implications for tree species classification in digital aerial images

Tree species classification is still solved at insufficient reliability in airborne optical data. The variation caused by directional reflectance anisotropy hampers image-based solutions. In addition, trees show considerable within-species variation in reflectance properties. We examined these phenomena at the single-tree level, using the Leica ADS40 line sensor and XPro software, which constitute the first photogrammetric large-format multispectral system to provide target reflectance images. To analyze the influence of illumination conditions in the canopy, we developed a method in which the crown shape as well as between-tree occlusions and shading were modeled, using dense LiDAR data. The precision of the ADS40 reflectance images in well-defined surfaces was 5% as coefficient of variation when 1−4-km image data were fused. The range of reflectance anisotropy was ± 30% for trees near the solar principal plane, with differences between bands and species. Because of the anisotropy differences observed, the spectral separability of the tree species in different bands is dependent on the view-illumination geometry. The within-species variation was high; the coefficient of variation was 13−31%. The contribution of tree and stand variables to anisotropy-normalized reflectance variation was examined. The effects of the species composition of adjacent trees were substantial in NIR and this variation hampers spectral classification in mixed stands. We also studied species- and band-specific intracrown brightness patterns, and we suggest their use as high-order image features in species classification. A species classification accuracy of up to 80% was obtained using 4-km data, which showed the high potential of the ADS40.     

Seasonality: Nature,People's Preferences and Urban Planning in Oulunsalo,Finland

Seasonal changes are particularly strong in northern Europe. Daylight, temperature, flora, fauna and landscape change from day to day. We discuss how seasonality exists in nature, is felt by people and is reflected in urban planning. The case study regards Oulunsalo municipality with a population of 9,000 at the northern most Baltic Sea coast in Finland. In 2003 the mean temperature of the coldest month in Oulunsalo was ?16.4 and the warmest +19.5 centigrade. The longest day was 22 hours 3 minutes and the shortest 3 hours and 35 minutes. A survey among the adult Oulunsalo population shows that summer is the best season for the majority of people (72%)—more often for men (78%) than for women (66%). Almost everybody (96%) likes the change of seasons. Seasons and seasonal preferences should be considered more in urban planning processes and land-use plans.     

Jitter characteristics of an on-chip voltage reference-locked time-to-digital converter

The noise and jitter characteristics of an on-chip voltage reference-locked ring oscillator used in the time-to-digital converter (TDC) of the integrated receiver of a pulsed time-of-flight laser rangefinder are presented. The frequency of the ring oscillator, 683 MHz, was locked to the on-chip voltage reference by means of a frequency-to-voltage converter, resulting in better than 90 ppm/°C stability. The noise and jitter transfer characteristics of the loop were derived, and simulations were performed to see the effects of different noise types (white and 1/f noise) on the cumulative jitter of the locked ring oscillator. Finally, these results were verified by jitter measurements performed using an integrated time-to-digital converter (TDC) fabricated on the same die (0.18 μm CMOS process). The cumulative jitter of the on-chip reference-locked ring oscillator was less than 30 ps (sigma value) over a time range of 70 ns, which made it possible to use this oscillator as the heart of a TDC when aiming at centimetre-level precision (1 cm = 67 ps) in laser ranging.     

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat – a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modeling and optimization studies at large.