Online tuning of derivative order term of variable-order fractional proportional–integral–derivative controllers for the first-order time delay systems

In this study, an online tuning strategy for the fractional derivative order term of the variable-order fractional proportional–integral–derivative (PID) controller is proposed for processes with dead time. The classical step response is divided into regions, and meta-rules are developed for each region in order to improve the control performance. To achieve the goals of the meta-rules, a set of equations that are the functions of absolute error and model parameters are proposed to manipulate the fractional order derivative during the process. These equations can handle the changes in model parameters since the coefficients of these equations are functions of model parameters. On both simulation studies and experimental results on the active suspension system, we show that the proposed method improves the time domain performance criteria both in relation to reference tracking and load disturbance rejection. Moreover, the robustness of the proposed method has also been tested and analyzed for the dead time variation within the process.     

The analysis of architectural languages for the needs of practitioners

Architectural languages (ALs) have attracted much attention as the modeling notations for specifying and reasoning about important design decisions. In this study, 124 different existing ALs have been analyzed for a set of requirements that are crucial for practitioners. These requirements are concerned with language definition, language features, and tool support. Some of the important findings obtained from the analysis are as follows: (1) performance is the top popular nonfunctional requirement supported by ALs; (2) no ALs offer both textual and visual notation sets, one of which could be used independently; (3) process algebras are the top preferred formal method by formal ALs; (4) the physical, deployment, and operational viewpoints are rarely supported by ALs; (5) the top preferred extension mechanism of the extensible ALs is XML for syntax extension; (6) Java is the top preferred programming language in generating software code; (7) the exhaustive model checking is the top preferred automated analysis method; (8) the logic‐based formal techniques are so popular in specifying system requirements; (9) among the analysis properties considered, consistency is the top supported property for the automated checking; and (10) most ALs do not provide any discussion platform (eg, forums). Hence, these findings can be used by the new AL developers in addressing the needs of practitioners and bridging the gaps in the field. Practitioners can also use the findings to find out about the existing ALs and compare them to choose the one(s) that suits their needs best.     

Random subspace method with class separability weighting

The random subspace method (RSM) is one of the ensemble learning algorithms widely used in pattern classification applications. RSM has the advantages of small error rate and improved noise insensitivity due to ensemble construction of the base‐learners. However, randomness may cause a reduction of the final ensemble decision performance because of contributions of classifiers trained by subsets with low class separability. In this study, we present a new and improved version of the RSM by introducing a weighting factor into the combination phase. One of the class separability criteria, J3, is used as a weighting factor to improve the classification performance and eliminate the drawbacks of the standard RSM algorithm. The randomly selected subsets are quantified by computing their J3 measure to determine voting weights in the model combination phase, assigning lower voting weight to classifiers trained by subsets with poor class separability. Two models are presented including J3‐weighted RSM and optimized J3 weighted RSM. In J3 weighted RSM, computed weighting values are directly multiplied by class assignment posteriors, whereas in optimized J3 weighted RSM, computed weighting values are optimized by a pattern search algorithm before multiplying by posteriors. Both models are shown to provide better error rates at lower subset dimensionality.     

Multi-Criteria Analysis of Water Allocation Scenarios in a Water Scarce Basin

In 2015, a “Post-2015 Development Agenda” is defined for future global developments in water management. The proposed goal is referred to as SDG’s or Sustainable Development Goals, which will balance the economic, social and environmental dimensions of sustainable development with a strong linkage between environment and socio-economic goals. One of the major concepts of SDG’s is water security, which is the basic element of the Global Goal on Water, now defined as: “Securing Sustainable Water for All”. The proposal for a Global Goal for Water is associated with targets and indicators to help countries reach the goal by 2030. Among the five main targets specified, two major ones emphasize “water allocation” as an important support of water security, particularly in conditions of water security. This paper focuses on this latter issue in the case of water scarce Gediz Basin along the Aegean coast of Turkey, where irrigation is the major water consuming activity. In addition to allocation of water among other water use sectors and irrigation, it is important in the basin how irrigation water is allocated to various crops, as previous droughts struck crop yields the most. Thus, 16 alternative allocation scenarios are developed to assess the crop yield produced by each scenario. These alternatives are represented by social, economic and environmental indicators as criteria for selection the most favorable alternative. The selection is realized by Multi Criteria Analysis (MCA) methodology, using the Reference Point Approach (RPA). In terms of methodology, presented paper shows that RPA is a powerful tool to guide engineers and decision makers to rationally select among several possible alternatives in water allocation.     

Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry

Design and implementation of holonic manufacturing control systems for the real industrial applications require risky, careful decisions to ensure that the manufacturing system will successfully satisfy the demands of an ever-changing market. This paper suggests a virtual reality (VR)-based methodology for enhancing the design and implementation process of holonic control systems in manufacturing practice. The major focus has been given to the implementation of holonic control into the small to medium size manufacturing enterprises (SMEs).     

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU .     

Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats

We explore the effect of using bagged decision tree (BDT) as an ensemble learning method with proposed time-domain feature extraction methods on electrocardiogram (ECG) arrhythmia beat classification comparing with single decision tree (DT) classifier. RR interval is the main property which defines irregular heart rhythm, and its ratio to the previous value and difference from mean value are used as morphological feature extraction methods. Form factor, its ratio to the previous value and difference from mean value are used to express ECG waveform complexity. In addition, skewness and second-order linear predictive coding coefficients are added to the feature vector of 56,569 ECG heart beats obtained from MIT–BIH arrhythmia database as time-domain feature extraction methods. The quarter of ECG heart beat samples are used as test data for DT and BDT. The performance measures of these classifiers are evaluated using the metrics such as accuracy, sensitivity, specificity and Kappa coefficient for both classifiers, and the performance of BDT classifier is examined for number of base learners up to 75. The BDT results in more predictive performance than DT according to the performance measures. BDT with 69 base learners has 99.51 % of accuracy, 97.50 % of sensitivity, 99.80 % of specificity and 0.989 of Kappa coefficient while DT gives 98.78, 96.05, 99.57 and 0.975 %, respectively. These metrics show that the suggested BDT increases the numbers of successfully identified arrhythmia beats. Moreover, BDT with at least three base learners has higher distinguishing capability than DT.     

Synthesis of β-myrcene-based macroporous nanocomposite foams: Altering the morphological and mechanical properties by using organo-modified nanoclay

Organo-modified nanoclay incorporated high internal phase emulsions (HIPEs) were successfully used for the preparation of macroporous nanocomposite foams. Due to the aim of obtaining mechanically improved foams, HIPEs were prepared by using a monomer mixture composed of β-myrcene and ethylene glycol dimethacrylate. Accordingly, two groups of macroporous nanocomposite foams were synthesized depending on the nanoclay type. The morphological analysis demonstrated that the pore openness of the resulting nanocomposites were significantly improved due to the decrease in the average cavity size and increase in the interconnected pore size. In terms of mechanical properties, it was found that filling 1 wt% of nanoclay which is surface modified by hydrogenated tallow lead to a 33% of increment in the compression modulus, as compared to the neat foam. However, loading 5 wt% of nanoclay having octadecylamine and aminopropyltriethoxysilane surface groups caused only 11% of increment in the compression modulus, as compared to the neat foam.     

Mechanical test and friction-mapping on recycled polypropylene beads using atomic force microscopy

Mechanical tests at sub-micron scales using force microscopy are often used for the characterization of materials. Here we report the mechanical, tribologic, and morphological characterization of recycled polypropylene beads using force spectroscopy and lateral-force microscopy. The compression-elastic moduli calculated using the Hertzian model for polypropylene beads was between 0.448 ± 0.010 and 1.044 ± 0.057 GPa. The grain size analysis revealed a significant correlation between the grain size and measured compression-elastic moduli. Friction-maps of recycled polypropylene beads obtained using lateral-force microscopy were also reported for 25 μm² scanning areas.