Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study

Quantitative analysis of coastal marine benthic communities enables to adequately estimate the state of coastal marine environment, provide better evidence for environmental changes and describe processes that are conditioned by anthropogenic forces. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra of green (Cladophora glomerata), red (Furcellaria lumbricalis), and brown (Fucus vesiculosus) macroalgae and used a bio-optical model in estimating whether these algae distinguish optically from each other, from sandy bottom or deep water in turbid water conditions of the Baltic Sea. The simulation was carried out for three different water types: (1) CDOM-rich coastal water, (2) coastal waters not directly impacted by high CDOM discharge from rivers but with high concentration of cyanobacteria, (3) open Baltic waters. Our modelling results indicate that the reflectance spectra of C. glomerata, F. lumbricalis, F. vesiculosus differ from each other and also from sand and deep water reflectance spectra. The differences are detectable by remote sensing instruments at spectral resolution of 10 nm and SNR better than 1000:1. Thus, the lowest depth limits where the studied macroalgae grow do not exceed the depth where such remote sensing instruments could potentially detect the spectral differences between the studied species.     

Characterization and analytical validation of a microcantilever-based sensor for the determination of total carbonate in soil samples

A microcantilever device has been used as a miniaturized sensor for the determination of total carbonate in soil samples. The method is based on the selective generation of CO₂ (g) from samples and the measure of the pressure effect on the microcantilever sensor located in a closed system. The experimental set-up uses a manifold including the sample reaction minichamber and a dedicated flow-cell designed and built for housing the microcantilever. The response of this sensor was electronically and analytically characterized by using conductance signals. The analytical performance of the sensor was compared to that provided by the conventional quartz crystal microbalance (QCM), demonstrating advantages in terms of sensitivity, linear range of response and resolution. After the optimization of the method, it was validated for its use for direct determination of carbonate in soil samples. The method allowed the determination of carbonate in the 3–75 mg range, with a precision of 1.7%, expressed as relative standard deviation. It was applied to the analysis of different soil samples, obtaining results in agreement with those produced by the official method.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

蚂蚁算法在面向零件的网络化制造中的应用研究*

为了有效地实现网络制造环境下资源共享与优化配置,提出了协同制造链的概念,指出协同制造链是一种围绕零件制造过程的网络化制造动态联盟.在此基础上给出了协同制造链的定义和形式化描述,并以某航空发动机关键零件为具体应用研究对象,详细分析了协同制造链的构建和演化过程;同时,对协同制造链构建和演化过程中的关键问题--协同制造链排序进行了重点研究,建立了该问题的数学描述模型,设计并实现了一个基于蚂蚁算法的协同制造链排序策略.最后通过实例验证了该策略的有效性.     

A flexible ANN-GA-multivariate algorithm for assessment and optimization of machinery productivity in complex production units

This paper presents a flexible algorithm based on artificial neural networks (ANNs), genetic algorithms (GAs), and multivariate analysis for performance assessment and optimization of complex production units (CPUs) with respect to machinery productivity indicators (MPIs). Multivariate techniques include data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT). Two case studies are considered to show the applicability of the proposed approach. In the first case, the machinery productivity indicators are categorized into four standard classes as availability, machinery stoppage, random failure and value added and production value. In the second case, the productivity of production units in terms of health, safety, environment and ergonomics indicators is evaluated. The flexible algorithm is capable of handling both linearity and complexity of data sets. Moreover, ANN and GA are efficiently applied to cover nonlinearity and complexity of CPUs. The results are also validated and verified by the internal mechanism of the algorithm. The algorithm is applied to a large set of production units to show its superiority and applicability over conventional approaches. Results show that, in the case of having non-linear data sets, ANN outperforms GA and conventional approaches. The flexible algorithm of this study may be easily extended to other units for assessment and optimization of CPUs with respect to machinery indicators.     

An n-gram hash and skip algorithm for finding large numbers of keywords in continuous text streams

A method of full-text scanning for matches in a large dictionary of keywords is described, suitable for Selective Dissemination of Information (SDI). The method is applicable to large dictionaries (say 10⁴ to 10⁵ entries), and to arbitrary byte streams for both patterns and data samples. The approach involves a sequence of tests, beginning with Boyer–Moore–Horspool skipping on digrams, followed by a succession of hash tests, and completed by trie searching, the combination of which is quite fast. Background information is provided, the algorithm and its implementation are described in detail, and experimental results are presented. In particular, tests suggest that the proposed method outperforms the algorithms of Aho–Corasick and Commentz–Walter when implementing large dictionaries. © 1998 John Wiley & Sons, Ltd.     

Improvement strategies for the tools and techniques of quality improvement: Utilization of a performance evaluation matrix in the taiwanese high‐tech industry

This study integrates importance–performance analysis (IPA) and Slack's IPA matrix to provide a more comprehensive performance‐evaluation matrix for prioritizing improvement actions with regard to the tools and techniques of quality improvement. The study undertakes a review of the literature to identify the tools and techniques most widely used by high‐tech businesses in Taiwan. Following a survey of 394 managers and technicians, these tools and techniques are then placed on the integrated performance‐evaluation matrix to determine appropriate priorities for improvement actions. The study finds that “new seven” total quality management tools are used less often than “old seven” total quality management tools, and that education, training, and the efficient use of corporate resources are required to improve the frequency of utilization and the performance of the tools and techniques of quality improvement. © 2011Wiley Periodicals, Inc.     

Super‐Twisting Observer‐Based Integral Sliding Mode Control for Tracking the Rapid Acceleration of a Piston in a Hybrid Electro‐Hydraulic and Pneumatic System

A new hybrid electro‐hydraulic and pneumatic actuator system and its dynamic model for high‐performance control are presented. This work focuses on tracking control of rapidly changing acceleration that is an advanced area with various practical applications in industries. The impact motion control of the actuator is one of challenging task due to the system instability during the transition state. Since composite disturbances derived from the inaccurate and unmodeled dynamics considerably reduce the control performance. A novel structure of variable integral sliding mode controls integrated with a sliding mode disturbance observer is proposed based on the super‐twisting algorithm. With the control strategy, not only does the controller overcome the extreme sensitivity of the system during rapid movements, but it also eliminates the internal parameter uncertainties and external load disturbance while tracking rapid gain‐scheduled acceleration. The results of the numerical simulation and field experiment are presented to assess the effectiveness of the proposed control scheme.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.