bluuebunny/crossref_metadata_2025 huggingface dataset:
Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes
Authors: Yıldız Betül Sultan , Yıldız Ali Rıza
Published: May 2017

In this research, a newly developed moth-flame optimization algorithm (MFO) is presented for solving optimization problems in manufacturing industry. A well-known milling optimization problem is solved to emphasize the effectiveness of the MFO in the optimization of manufacturing problems. In the optimization problem solved in this paper, the main aim is to maximize the profit rate for multi-tool milling operations considering difficult constraints. The results demonstrate that the MFO is an effective optimization method for the solution of manufacturing optimization problems.
Hydrogen assisted cracking in a high strength dual phase steel DP1180HY
Authors: Kröger Benjamin , Holbein Reinhold , Klose Stephan G.
Published: May 2017

Important objectives when using high strength steel materials in the automotive industry are to reduce the weight and therefore the energy consumption of motor vehicles. Suitable test methods must be used to characterize the material specific properties in relation to hydrogen induced effects so that the materials can be used safely in body structures. Strained bend specimens and 4-point loaded specimens were used for this work. Both test methods have specific advantages and disadvantages and provide meaningful results in regard to the time delay in hydrogen assisted brittle fractures. Fundamental insights were obtained regarding the test procedure and the development of test methods for the impact of hydrogen on steel during manufacture and operation of respective components.
A new tool for touch-free patient registration for robot-assisted intracranial surgery: application accuracy from a phantom study and a retrospective surgical series
Authors: Cardinale Francesco , Rizzi Michele , d’Orio Piergiorgio , Casaceli Giuseppe , Arnulfo Gabriele , Narizzano Massimo , Scorza Davide , De Momi Elena , Nichelatti Michele , Redaelli Daniela , Sberna Maurizio , Moscato Alessio , Castana Laura
Published: May 2017

OBJECTIVE

The purpose of this study was to compare the accuracy of Neurolocate frameless registration system and frame-based registration for robotic stereoelectroencephalography (SEEG).

METHODS

The authors performed a 40-trajectory phantom laboratory study and a 127-trajectory retrospective analysis of a surgical series. The laboratory study was aimed at testing the noninferiority of the Neurolocate system. The analysis of the surgical series compared Neurolocate-based SEEG implantations with a frame-based historical control group.

RESULTS

The mean localization errors (LE) ± standard deviations (SD) for Neurolocate-based and frame-based trajectories were 0.67 ± 0.29 mm and 0.76 ± 0.34 mm, respectively, in the phantom study (p = 0.35). The median entry point LE was 0.59 mm (interquartile range [IQR] 0.25–0.88 mm) for Neurolocate-registration–based trajectories and 0.78 mm (IQR 0.49–1.08 mm) for frame-registration–based trajectories (p = 0.00002) in the clinical study. The median target point LE was 1.49 mm (IQR 1.06–2.4 mm) for Neurolocate-registration–based trajectories and 1.77 mm (IQR 1.25–2.5 mm) for frame-registration–based trajectories in the clinical study. All the surgical procedures were successful and uneventful.

CONCLUSIONS

The results of the phantom study demonstrate the noninferiority of Neurolocate frameless registration. The results of the retrospective surgical series analysis suggest that Neurolocate-based procedures can be more accurate than the frame-based ones. The safety profile of Neurolocate-based registration should be similar to that of frame-based registration. The Neurolocate system is comfortable, noninvasive, easy to use, and potentially faster than other registration devices.
Current applications of robotics in spine surgery: a systematic review of the literature
Authors: Joseph Jacob R. , Smith Brandon W. , Liu Xilin , Park Paul
Published: May 2017

OBJECTIVE

Surgical robotics has demonstrated utility across the spectrum of surgery. Robotics in spine surgery, however, remains in its infancy. Here, the authors systematically review the evidence behind robotic applications in spinal instrumentation.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Relevant studies (through October 2016) that reported the use of robotics in spinal instrumentation were identified from a search of the PubMed database. Data regarding the accuracy of screw placement, surgeon learning curve, radiation exposure, and reasons for robotic failure were extracted.

RESULTS

Twenty-five studies describing 2 unique robots met inclusion criteria. Of these, 22 studies evaluated accuracy of spinal instrumentation. Although grading of pedicle screw accuracy was variable, the most commonly used method was the Gertzbein and Robbins system of classification. In the studies using the Gertzbein and Robbins system, accuracy (Grades A and B) ranged from 85% to 100%. Ten studies evaluated radiation exposure during the procedure. In studies that detailed fluoroscopy usage, overall fluoroscopy times ranged from 1.3 to 34 seconds per screw. Nine studies examined the learning curve for the surgeon, and 12 studies described causes of robotic failure, which included registration failure, soft-tissue hindrance, and lateral skiving of the drill guide.

CONCLUSIONS

Robotics in spine surgery is an emerging technology that holds promise for future applications. Surgical accuracy in instrumentation implanted using robotics appears to be high. However, the impact of robotics on radiation exposure is not clear and seems to be dependent on technique and robot type.
Intelligent control of neurosurgical robot MM-3 using dynamic motion scaling
Authors: Ko Sunho , Nakazawa Atsushi , Kurose Yusuke , Harada Kanako , Mitsuishi Mamoru , Sora Shigeo , Shono Naoyuki , Nakatomi Hirofumi , Saito Nobuhito , Morita Akio
Published: May 2017

OBJECTIVE

Advanced and intelligent robotic control is necessary for neurosurgical robots, which require great accuracy and precision. In this article, the authors propose methods for dynamically and automatically controlling the motion-scaling ratio of a master-slave neurosurgical robotic system to reduce the task completion time.

METHODS

Three dynamic motion-scaling modes were proposed and compared with the conventional fixed motion-scaling mode. These 3 modes were defined as follows: 1) the distance between a target point and the tip of the slave manipulator, 2) the distance between the tips of the slave manipulators, and 3) the velocity of the master manipulator. Five test subjects, 2 of whom were neurosurgeons, sutured 0.3-mm artificial blood vessels using the MM-3 neurosurgical robot in each mode.

RESULTS

The task time, total path length, and helpfulness score were evaluated. Although no statistically significant differences were observed, the mode using the distance between the tips of the slave manipulators improves the suturing performance.

CONCLUSIONS

Dynamic motion scaling has great potential for the intelligent and accurate control of neurosurgical robots.
Augmented-reality integrated robotics in neurosurgery: are we there yet?
Authors: Madhavan Karthik , Kolcun John Paul G. , Chieng Lee Onn , Wang Michael Y.
Published: May 2017

Surgical robots have captured the interest—if not the widespread acceptance—of spinal neurosurgeons. But successful innovation, scientific or commercial, requires the majority to adopt a new practice. “Faster, better, cheaper” products should in theory conquer the market, but often fail. The psychology of change is complex, and the “follow the leader” mentality, common in the field today, lends little trust to the process of disseminating new technology. Beyond product quality, timing has proven to be a key factor in the inception, design, and execution of new technologies. Although the first robotic surgery was performed in 1985, scant progress was seen until the era of minimally invasive surgery. This movement increased neurosurgeons’ dependence on navigation and fluoroscopy, intensifying the drive for enhanced precision. Outside the field of medicine, various technology companies have made great progress in popularizing co-robots (“cobots”), augmented reality, and processor chips. This has helped to ease practicing surgeons into familiarity with and acceptance of these technologies. The adoption among neurosurgeons in training is a “follow the leader” phenomenon, wherein new surgeons tend to adopt the technology used during residency. In neurosurgery today, robots are limited to computers functioning between the surgeon and patient. Their functions are confined to establishing a trajectory for navigation, with task execution solely in the surgeon’s hands. In this review, the authors discuss significant untapped technologies waiting to be used for more meaningful applications. They explore the history and current manifestations of various modern technologies, and project what innovations may lie ahead.
Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network
Authors: Bouzenad Khaled , Ramdani Messaoud
Published: May 2017

Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.
Exploration Feeding and Higher Space Allocation Improve Welfare of Growing-Finishing Pigs
Authors: Vermeer Herman , Dirx-Kuijken Nienke , Bracke Marc
Published: May 2017

Lack of environmental enrichment and high stocking densities in growing-finishing pigs can lead to adverse social behaviors directed to pen mates, resulting in skin lesions, lameness, and tail biting. The objective of the study was to improve animal welfare and prevent biting behavior in an experiment with a 2 × 2 × 2 factorial design on exploration feeding, stocking density, and sex. We kept 550 pigs in 69 pens from 63 days to 171 days of life. Pigs were supplemented with or without exploration feeding, kept in groups of seven (1.0 m2/pig) or nine animals (0.8 m2/pig) and separated per sex. Exploration feeding provided small amounts of feed periodically on the solid floor. Skin lesion scores were significantly lower in pens with exploration feeding (p = 0.028, p < 0.001, p < 0.001 for front, middle, and hind body), in pens with high compared to low space allowance (p = 0.005, p = 0.006, p < 0.001 for front, middle and hind body), and in pens with females compared to males (p < 0.001, p = 0.005, p < 0.001 for front, middle and hind body). Males with exploration feeding had fewer front skin lesions than females with exploration feeding (p = 0.022). Pigs with 1.0 m2 compared to 0.8 m2 per pig had a higher daily gain of 27 g per pig per day (p = 0.04) and males compared to females had a higher daily gain of 39 g per pig per day (p = 0.01). These results indicate that exploration feeding might contribute to the development of a more welfare-friendly pig husbandry with intact tails in the near future.
Voltage-Mode and Current-Mode Resistorless Third-Order Quadrature Oscillator
Authors: Chen Hua-Pin , Hwang Yuh-Shyan , Ku Yi-Tsen
Published: May 2017

This paper presents the realization of a resistorless third-order quadrature oscillator using two multiple-output current controlled current conveyor transconductance amplifiers (MO-CCCCTAs) and three grounded capacitors. The proposed circuit provides two quadrature voltage outputs, two high-impedance quadrature current outputs, and one high-impedance amplitude-controllable current output. The proposed oscillator satisfies the industrial requirement for amplifier and modulation signals and phase shift keying signals, when the input bias current of the second MO-CCCCTA is used as a modulating signal. Its oscillation condition and oscillation frequency can be adjusted independently by two different bias currents of MO-CCCCTAs. The structure of resistorless and only grounded capacitors suggests that the proposed oscillator can be easily implemented in an integrated circuit.
Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments
Authors: Huang Rulin , Liang Huawei , Zhao Pan , Yu Biao , Geng Xinli
Published: May 2017

Existing collision avoidance methods for autonomous vehicles, which ignore the driving intent of detected vehicles, thus, cannot satisfy the requirements for autonomous driving in urban environments because of their high false detection rates of collisions with vehicles on winding roads and the missed detection rate of collisions with maneuvering vehicles. This study introduces an intent-estimation- and motion-model-based (IEMMB) method to address these disadvantages. First, a state vector is constructed by combining the road structure and the moving state of detected vehicles. A Gaussian mixture model is used to learn the maneuvering patterns of vehicles from collected data, and the patterns are used to estimate the driving intent of the detected vehicles. Then, a desirable long-term trajectory is obtained by weighting time and comfort. The long-term trajectory and the short-term trajectory, which are predicted using a constant yaw rate motion model, are fused to achieve an accurate trajectory. Finally, considering the moving state of the autonomous vehicle, collisions can be detected and avoided. Experiments have shown that the intent estimation method performed well, achieving an accuracy of 91.7% on straight roads and an accuracy of 90.5% on winding roads, which is much higher than that achieved by the method that ignores the road structure. The average collision detection distance is increased by more than 8 m. In addition, the maximum yaw rate and acceleration during an evasive maneuver are decreased, indicating an improvement in the driving comfort.
Perfect Light Absorbers Made of Tungsten-Ceramic Membranes
Authors: Iwanaga Masanobu
Published: May 2017

Plasmonic materials are expanding their concept; in addition to noble metals that are good conductors even at optical frequencies and support surface plasmon polaritons at the interface, other metals and refractory materials are now being used as plasmonic materials. In terms of complex permittivity at optical frequencies, these new plasmonic materials are, though not ideal, quite good to support surface plasmons. Numerical investigations of the optical properties have been revealing new capabilities of the plasmonic materials. On the basis of the precise computations for electromagnetic waves in artificially designed nanostructures, in this article, we address membrane structures made of tungsten and silicon nitride that are a typical metal and ceramic, respectively, with high-temperature melting points. The membranes are applicable to low-power-consuming thermal emitters operating at and near the visible range. We numerically substantiate that the membranes serve as perfect light absorbers, in spite of the subwavelength thickness, that is, 200–250 nm thickness. Furthermore, we clarify that the underlying physical mechanism for the unconventional perfect absorption is ascribed to robust impedance matching at the interface between air and the membranes.
Automated Diatom Classification (Part B): A Deep Learning Approach
Authors: Pedraza Anibal , Bueno Gloria , Deniz Oscar , Cristóbal Gabriel , Blanco Saúl , Borrego-Ramos María
Published: May 2017

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.
Control of Corner Separation with Plasma Actuation in a High-Speed Compressor Cascade
Authors: Zhang Haideng , Yu Xianjun , Liu Baojie , Wu Yun , Li Yinghong
Published: May 2017

The performances of modern highly loaded compressors are limited by the corner separations. Plasma actuation is a typical active flow control methodology, which has been proven to be capable of controlling the corner separations in low-speed compressor cascades. The main purpose of this paper is to uncover the flow control law and the mechanism of high-speed compressor cascade corner separation control with plasma actuations. The control effects of the suction surface as well as the endwall plasma actuations in suppressing the high-speed compressor cascade flow separations are investigated with numerical methods. The main flow structures within the high-speed compressor cascade corner separation and the development of the corresponding flow loss are investigated firstly. Next, the performances of plasma actuations in suppressing the high-speed compressor cascade corner separation are studied. At last, the mechanisms behind the control effects of the suction surface and the endwall plasma actuations are discussed. Both the suction surface and the endwall plasma actuations can improve the high-speed compressor cascade static pressure rise coefficient, while reducing the corresponding total pressure loss and blockage coefficients. The suction surface plasma actuation can suppress not only the high-speed compressor cascade corner separation vortex but also the airfoil separation, so, compared to the endwall plasma actuation, the suction surface plasma actuation is more efficient in reducing the total pressure loss of the high-speed compressor cascade. However, through suppressing the development of the passage vortex, the endwall plasma actuation is more efficient in reducing the flow blockage and improving the static pressure rise of the high-speed compressor cascade.
Optimal System Frequency Response Model and UFLS Schemes for a Small Receiving-End Power System after Islanding
Authors: Yang Deyou , Liu Shiyu , Cai Guowei
Published: May 2017

Large frequency deviations after islanding are exceedingly critical in small receiving-end power systems. The under-frequency load shedding (UFLS) scheme is an efficient protection step for preventing system black outs. It is very important to get an exact model to design the UFLS schemes. In this paper, an optimization model to achieve the system frequency response (SFR) model either from the full-scale power system or from test records was proposed. The optimized SFR model took into account the response of governors-prime movers and the dynamic characteristics of loads developed in the modern power system. Then the UFLS schemes were designed via the optimized SFR model and particle swarm optimization (PSO) method. The time-domain simulation with the actual small receiving-end power system was presented to investigate the validity of the presented model and the developed technique.
Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
Authors: Liu Xiaowei , Wang Lili , Sun Zhe , Shao Yu , Yu Tingchao
Published: May 2017

Bromate ( BrO 3 − ) contamination in drinking water is a growing concern. Advanced reduction processes (ARPs) are reportedly promising in relieving this concern. In this work, UV/superparamagnetic BiOCl (BiOCl loaded onto superparamagnetic hydroxyapatite) assisted with small molecule carboxylic acid (formate, citrate, and acetate), a carboxyl anion radical ( CO 2 • − )-based ARP, was proposed to eliminate aqueous BrO 3 − . Formate and citrate were found to be ideal CO 2 • − precursor, and the latter was found to be safe for practical use. BrO 3 − (10 μg·L−1, WHO guideline for drinking water) can be completely degraded within 3 min under oxygen-free conditions. In this process, BrO 3 − degradation was realized by the reduction of CO 2 • − (major role) and formyloxyl radical (minor role) in bulk solution. The formation mechanism of radicals and the transformation pathway of BrO 3 − were proposed based on data on electron paramagnetic resonance monitoring, competitive kinetics, and degradation product analysis. The process provided a sustainable decontamination performance (<5% deterioration for 10 cycles) and appeared to be more resistant to common electron acceptors (O2, NO 3 − , and Fe3+) than hydrated electron based-ARPs. Phosphate based-superparamagnetic hydroxyapatite, used to support BiOCl in this work, was believed to be applicable for resolving the recycling problem of other metal-containing catalyst.
Microstructure and Dielectric Properties of PTFE-Based Composites Filled by Micron/Submicron-Blended CCTO
Authors: Xie Chao , Liang Fei , Ma Min , Chen Xizi , Lu Wenzhong , Jia Yunxiang
Published: May 2017

This paper investigated a polymer-based composite by homogeneously embedding calcium copper titanate (CaCu3Ti4O12; CCTO) fillers into a polytetrafluoroethylene matrix. We observed the composite filled by CCTO powder at different sizes. The particle size effects of the CCTO filling, including single-size particle filling and co-blending filling, on the microstructure and dielectric properties of the composite were discussed. The dielectric performance of the composite was investigated within the frequency range of 100 Hz to 1 MHz. Results showed that the composite filled by micron/submicron-blended CCTO particles had the highest dielectric constant (εr = 25.6 at 100 Hz) and almost the same dielectric loss (tanδ = 0.1 at 100 Hz) as the composite filled by submicron CCTO particles at the same volume percentage content. We researched the theoretical reason of the high permittivity and low dielectric loss. We proved that it was effective in improving the dielectric property of the polymer-based composite by co-blending filling in this experiment.
Utility, Revealed Preferences Theory, and Strategic Ambiguity in Iterated Games
Authors: Harré Michael
Published: May 2017

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date, very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent’s next action by introducing two new classes of games: “invertible games” and “cyclical games”. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances the explicit values of the utility are irrelevant for iterated games and this is then related to revealed preferences theory of classical economics. These information measures are then applied to the Traveler’s Dilemma game and the Prisoner’s Dilemma game, the Prisoner’s Dilemma being invertible, to illustrate their use. In the Prisoner’s Dilemma, a novel connection is made between the computational principles of logic gates and both the structure of games and the agents’ decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: “Tit-for-Tat” and “Win-Stay, Lose-Switch”.
I Thought This Was a Study on Math Games: Attribute Modification in Children’s Interactions with Mathematics Apps
Authors: Tucker Stephen I. , Johnson Teri N.
Published: May 2017

Technology is an increasingly important component of education. Children’s mathematical interactions with technology have become a focus of mathematics education research, but less research has investigated constructs that contribute to these mathematical interactions. Attributes of children and technology play a key role in mathematical interactions and both children and technology can modify attributes during these interactions. Grounded in the Artifact-Centric Activity Theory and linked to recent developments in research on technology in mathematics education, this qualitative study extended an earlier exploratory study to investigate attribute modification. In particular, this study examined patterns of attribute modification evident during fifth grade students’ mathematical interactions with two mathematics virtual manipulative touchscreen tablet apps. Results included three categories related to attribute modification: (1) reactive attribute modification (linear progression or repeated repetition); (2) unperceived attributes and opportunities for proactive modification; and (3) proactive modification (seeking equilibrium, seeking disequilibrium, or seeking equilibrium and disequilibrium). Findings have implications for designers, teachers, and researchers of educational technology.
An Energy-Based Control Strategy for Battery Energy Storage Systems: A Case Study on Microgrid Applications
Authors: Hou Rui , Nguyen Thai-Thanh , Kim Hak-Man , Song Huihui , Qu Yanbin
Published: May 2017

Battery energy storage systems (BESSs) with proportional-integral (PI) control methods have been widely studied in microgrids (MGs). However, the performance of PI control methods might be unsatisfactory for BESSs due to the nonlinear characteristics of the system. To overcome this problem, an energy-based (EB) control method is applied to control the converter of a BESS in this study. The EB method is a robust nonlinear control method based on passivity theory with good performance in both transient and steady states. The detailed design process of the EB method in the BESS by adopting an interconnection and damping assignment (IDA) strategy is described. The design process comprises three steps: the construction of the port-controlled Hamiltonian model, the determination of the equilibrium point and the solution of the undetermined matrix. In addition, integral action is combined to eliminate the steady state error generated by the model mismatch. To establish the correctness and validity of the proposed method, we implement several case simulation studies based on a test MG system and compare the control performance of the EB and PI methods carefully. The case simulation results demonstrate that the EB method has better tracking and anti-disturbance performance compared with the classic PI method. Moreover, the proposed EB method shows stronger robustness to the uncertainty of system parameters.
A Novel Electric Vehicle Powertrain System Supporting Multi-Path Power Flows: Its Architecture, Parameter Determination and System Simulation
Authors: Shi Yujun , Wei Jin , Deng Zhengxing , Jian Linni
Published: May 2017

In this paper, a novel electric vehicle powertrain system is proposed. In the system, a coaxial magnetic gear (CMG), an electromagnetic clutch, a lock, and two electric machines (EMs) are adopted to achieve the power-split by controlling the states of the clutch and the lock, which enables electric vehicles (EVs) to work in four operation modes. The configuration, power flow paths and operation modes are depicted in detail. A dynamic model is established to help determine the parameters and build simulation models. The simple control strategy is adopted to achieve flexible power-splits. How to determine the relevant parameters to meet the drive requirements in the powertrain system is also elaborated. A dynamic simulation using MATLAB/Simulink is performed to take into account the control strategy and New European Drive Cycle. Finally, the simulation results demonstrate, in theory, the rationality of the determined parameters and the feasibility of the operation modes as well as the control strategy.
High-Gain Disturbance Observer-Based Robust Load Frequency Control of Power Systems with Multiple Areas
Authors: Rosyiana Fitri Ismi , Kim Jung-Su , Song Hwachang
Published: May 2017

This paper proposes a high-gain disturbance observer (HDOB)-based controller for load frequency control (LFC) of power systems with multiple areas. The main goal of LFC problem is to maintain the frequency to its nominal value. The objective of this paper is to reject frequency variations due to abrupt load changes and diverse uncertainties (e.g., inertia and damping parameters, and interconnection topology, etc.) by employing the HDOB for the LFC. The simulation results demonstrate the effectiveness of the proposed HDOB-based LFC by showing that it successfully rejects frequency variations owing to load changes and frequency variations occurring in various locations in interconnected power systems. Besides, it is shown that the proposed LFC can eliminate frequency deviations although there are delays in transmission among the power systems with multiple areas.
A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries
Authors: Liu Yuechen , Zhang Linjing , Jiang Jiuchun , Wei Shaoyuan , Liu Sijia , Zhang Weige
Published: May 2017

Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical data collected in the experiments are used to train the BP network model, which reveals a test error of 10−4. With the input of continuous SOC regions and discharge currents, continuous-time efficiency can be estimated by the trained BP network model. The estimated and simulated result is proven to be consistent with the experimental results.
The Contribution of Non-CO2 Greenhouse Gas Mitigation to Achieving Long-Term Temperature Goals
Authors: Gambhir Ajay , Napp Tamaryn , Hawkes Adam , Höglund-Isaksson Lena , Winiwarter Wilfried , Purohit Pallav , Wagner Fabian , Bernie Dan , Lowe Jason
Published: May 2017

This paper analyses the emissions and cost impacts of mitigation of non-CO2 greenhouse gases (GHGs) at a global level, in scenarios aimed at meeting a range of long-term temperature goals (LTTGs). The study combines an integrated assessment model (TIAM-Grantham) representing CO2 emissions (and their mitigation) from the fossil fuel combustion and industrial sectors, coupled with a model covering non-CO2 emissions (GAINS), using the latest global warming potentials from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. We illustrate that in general non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005$100/tCO2e or less throughout the 21st century (compared to a marginal CO2 mitigation cost which is already greater than this by 2030 in the most stringent mitigation scenario). As a result, the total cumulative discounted cost over the period 2010–2100 (at a 5% discount rate) of limiting global average temperature change to 2.5 °C by 2100 is $48 trillion (about 1.6% of cumulative discounted GDP over the period 2010–2100) if only CO2 from the fossil fuel and industrial sectors is targeted, whereas the cost falls to $17 trillion (0.6% of GDP) by including non-CO2 GHG mitigation in the portfolio of options—a cost reduction of about 65%. The criticality of non-CO2 mitigation recommends further research, given its relatively less well-explored nature when compared to CO2 mitigation.
Volume and Mass Measurement of a Burning Wood Pellet by Image Processing
Authors: Kang Sae , Sim Bong , Kim Jong
Published: May 2017

Wood pellets are a form of solid biomass energy and a renewable energy source. In 2015, the new and renewable energy (NRE) portion of wood pellets was 4.6% of the total primary energy in Korea. Wood pellets account for 6.2% of renewable energy consumption in Korea, the equivalent of 824,000 TOE (ton of oil equivalent, 10 million kcal). The burning phases of a wood pellet can be classified into three modes: (1) gasification; (2) flame burning and (3) charcoal burning. At each wood pellet burning mode, the volume and weight of the burning wood pellet can drastically change; these parameters are important to understand the wood pellet burning mechanism. We developed a new method for measuring the volume of a burning wood pellet that involves no contact. To measure the volume of a wood pellet, we take pictures of the wood pellet in each burning mode. The volume of a burning wood pellet can then be calculated by image processing. The difference between the calculation method using image processing and the direct measurement of a burning wood pellet in gasification mode is less than 8.8%. In gasification mode in this research, mass reduction of the wood pellet is 37% and volume reduction of the wood pellet is 7%. Whereas in charcoal burning mode, mass reduction of the wood pellet is 10% and volume reduction of the wood pellet is 41%. By measuring volume using image processing, continuous and non-interruptive volume measurements for various solid fuels are possible and can provide more detailed information for CFD (computational fluid dynamics) analysis.
Sliding Mode Control of a Variable- Speed Wind Energy Conversion System Using a Squirrel Cage Induction Generator
Authors: Zribi Mohamed , Alrifai Muthana , Rayan Mohamed
Published: May 2017

This paper deals with the control of a variable-speed wind energy conversion (WEC) system using a squirrel cage induction generator (SCIG) connected to the grid through a back-to-back three phase (AC-DC-AC) power converter. The sliding mode control technique is used to control the WEC system. The objective of the controllers is to force the states of the system to track their desired states. One controller is used to regulate the generator speed and the flux so that maximum power is extracted from the wind. Another controller is used to control the grid side converter, which controls the DC bus voltage and the active and reactive powers injected into the grid. The performance of the controlled wind energy conversion system is verified through MATLAB simulations, which show that the controlled system performs well.
A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics
Authors: Song Dongran , Yang Jian , Su Mei , Liu Anfeng , Liu Yao , Joo Young
Published: May 2017

Variable speed wind turbines (VSWTs) usually adopt a maximum power point tracking (MPPT) method to optimize energy capture performance. Nevertheless, obtained performance offered by different MPPT methods may be affected by the impact of wind turbine (WT)’s inertia and wind speed characteristics and it needs to be clarified. In this paper, the tip speed ratio (TSR) and optimal torque (OT) methods are investigated in terms of their performance under different wind speed characteristics on a 1.5 MW wind turbine model. To this end, the TSR control method based on an effective wind speed estimator and the OT control method are firstly presented. Then, their performance is investigated and compared through simulation test results under different wind speeds using Bladed software. Comparison results show that the TSR control method can capture slightly more wind energy at the cost of high component loads than the other one under all wind conditions. Furthermore, it is found that both control methods present similar trends of power reduction that is relevant to mean wind speed and turbulence intensity. From the obtained results, we demonstrate that, to further improve MPPT capability of large VSWTs, other advanced control methods using wind speed prediction information need to be addressed.
Laccases as a Potential Tool for the Efficient Conversion of Lignocellulosic Biomass: A Review
Authors: Fillat Úrsula , Ibarra David , Eugenio María , Moreno Antonio , Tomás-Pejó Elia , Martín-Sampedro Raquel
Published: May 2017

The continuous increase in the world energy and chemicals demand requires the development of sustainable alternatives to non-renewable sources of energy. Biomass facilities and biorefineries represent interesting options to gradually replace the present industry based on fossil fuels. Lignocellulose is the most promising feedstock to be used in biorefineries. From a sugar platform perspective, a wide range of fuels and chemicals can be obtained via microbial fermentation processes, being ethanol the most significant lignocellulose-derived fuel. Before fermentation, lignocellulose must be pretreated to overcome its inherent recalcitrant structure and obtain the fermentable sugars. Usually, harsh conditions are required for pretreatment of lignocellulose, producing biomass degradation and releasing different compounds that are inhibitors of the hydrolytic enzymes and fermenting microorganisms. Moreover, the lignin polymer that remains in pretreated materials also affects biomass conversion by limiting the enzymatic hydrolysis. The use of laccases has been considered as a very powerful tool for delignification and detoxification of pretreated lignocellulosic materials, boosting subsequent saccharification and fermentation processes. This review compiles the latest studies about the application of laccases as useful and environmentally friendly delignification and detoxification technology, highlighting the main challenges and possible ways to make possible the integration of these enzymes in future lignocellulose-based industries.
Diagnosing Post-Polio Syndrome in the Elderly, a Case Report
Authors: Amole Morolake , Khouzam-Skelton Nadine
Published: May 2017

Poliomyelitis is a disorder of the nervous system caused by an enterovirus. There are many survivors who, years later, develop a little-understood condition called Post-polio syndrome. Post-polio syndrome is a group of delayed sequalae of polio infection that can cause paralysis and bulbar symptoms in patients with a history of polio infection who have had a prolonged symptom-free period, often greater than two decades. Diagnosis of post-polio syndrome is difficult in the geriatric population because many of the symptoms overlap with other disease processes affecting older individuals. An extensive workup is necessary to exclude more concerning etiologies. Furthermore, several symptoms can be attributed to normal ageing. We present the case of an elderly patient with a history of poliomyelitis and multiple comorbidities who presented with complaints of weakness and fatigue.
Interventions for Preventing Childhood Obesity with Smartphones and Wearable Device: A Protocol for a Non-Randomized Controlled Trial
Authors: Yang Hye , Kang Jae-Heon , Kim Ok , Choi Mona , Oh Myungju , Nam Jihyun , Sung Eunju
Published: May 2017

Background: Childhood obesity is a critical health issue, both currently and for the foreseeable future. To prevent obesity, behavior changes are essential. Smartphones can be a good tool, as the number of child smartphone users is rapidly increasing. We have developed a mobile platform system named “HAPPY ME,” which is a smartphone application coupled with a wearable device, designed to improve healthy behaviors to prevent childhood obesity. This study aimed to evaluate the effectiveness of obesity prevention among children 10–12 years of age using HAPPY ME. Methods: A total of 1000 participants, all fifth and sixth graders from four schools, were assigned to either control or intervention groups by school. Students in the intervention group used HAPPY ME. The study comprises a safety test, a 12-week efficacy test, and a six-month follow-up test to determine the long-term effects of preventive intervention via the integrated service platform. The integrated service platform aims to facilitate child-parent-school participation, involving the child-parent mobile application, a child-teacher mobile web, and a school website. Primary outcome measures are behavioral changes, including healthy eating, increased physical activity, and fitness. Secondary outcome measures are changes in anthropometric parameters (body weight, height, body mass index z-score, and waist circumference), body mass index (BMI) percentiles (obesity rate), and psychological perceptions among participants. Conclusions: The results of this study will offer evidence of the effectiveness of a mobile platform service with a multi-component intervention program based on a comprehensive approach.
The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China
Authors: Zhao Hujia , Che Huizheng , Ma Yanjun , Wang Yangfeng , Yang Hongbin , Liu Yuche , Wang Yaqiang , Wang Hong , Zhang Xiaoye
Published: May 2017

The variations of visibility, PM-mass concentration and mixing-layer height (MLH) in four major urban/industry regions (Shenyang, Anshan, Benxi and Fushun) of central Liaoning in Northeast China are evaluated from 2009 to 2012 to characterize their dynamic effect on air pollution. The annual mean visibilities are about 13.7 ± 7.8, 13.5 ± 6.5, 12.8 ± 6.1 and 11.5 ± 6.8 km in Shenyang, Anshan, Benxi and Fushun, respectively. The pollution load (PM × MLH) shows a weaker vertical diffusion in Anshan, with a higher PM concentration near the surface. High concentrations of fine-mode particles may be partially attributed to the biomass-burning emissions from September in Liaoning Province and surrounding regions in Northeast China as well as the coal burning during the heating period with lower MLH in winter. The visibility on non-hazy fog days is about 2.5–3.0 times higher than that on hazy and foggy days. The fine-particle concentrations of PM2.5 and PM1.0 on hazy and foggy days are ~1.8–1.9 times and ~1.5 times higher than those on non-hazy foggy days. The MLH declined more severely during fog pollution than in haze pollution. The results of this study can provide useful information to better recognize the effects of vertical pollutant diffusion on air quality in the multi-cities of central Liaoning Province in Northeast China.
Stress in School. Some Empirical Hints on the Circadian Cortisol Rhythm of Children in Outdoor and Indoor Classes
Authors: Dettweiler Ulrich , Becker Christoph , Auestad Bjørn H. , Simon Perikles , Kirsch Peter
Published: May 2017

This prospective longitudinal survey compared the stress levels of students taught using an outdoor curriculum in a forest, with children in a normal school setting. We were especially interested in the effect outdoor teaching might have on the children’s normal diurnal cortisol rhythm. 48 children (mean age = 11.23; standard deviation (SD) = 0.46) were enrolled, with 37 in the intervention group (IG), and 11 in the control group (CG). The intervention consisted of one full school day per week in the forest over the school year. Stress levels were measured in cortisol with three samples of saliva per day. Furthermore, the data allowed for statistical control of physical activity (PA) values. For data analysis, we used a linear mixed-effects model (LMM) with random intercept and general correlation matrix for the within-unit residuals. The LMM yields that IG have expected greater decline of cortisol compared to CG; rate 0.069 µg/L vs. 0.0102 µg/L (log-units/2 h), p = 0.009. PA does not show a statistically significant interaction with cortisol (p = 0.857), despite being higher in the intervention group (p < 0.001). The main effect in our measures was that the IG had a steady decline of cortisol during the school day. This is in accordance with a healthy child’s diurnal rhythm, with a significant decline of cortisol from morning to noon. This effect is constant over the school year. The CG does not show this decline during either measurement day. Further research is needed to fully explain this interesting phenomenon.
Individuals’ Acceptance to Free-Floating Electric Carsharing Mode: A Web-Based Survey in China
Authors: Wang Yun , Yan Xuedong , Zhou Yu , Xue Qingwan , Sun Li
Published: May 2017

Carsharing is growing rapidly in popularity worldwide. When the vehicles involved are Battery Electric Vehicles (BEV), carsharing has been proven to remarkably contribute to easing energy and environment crises. In this study, individuals’ acceptance to carsharing in China was measured from three aspects: carsharing mode choice behavior, highest acceptable price to use carsharing, and willingness to forgo car purchases. The data were collected by a web-based survey. The hierarchical tree-based regression (HTBR) method was applied to explore the effects of potential influencing factors on individuals’ acceptance, and some interesting findings were obtained: participants who know about carsharing were more likely to use carsharing, pay higher prices and forgo car purchases; the most competitive trip purpose and trip distance for choosing carsharing were, respectively, business activities and 11–20 km; most participants (47.1%) were willing to pay 1–2 Yuan per minute to use carsharing, and males or participants with higher income-level could accept higher price; and when car purchase restrain policy (CPRP) was carried out in a city or the urban public transport service level (UPTSL) was high, participants were more willing to forgo car purchases. Based on the above findings, corresponding policies were proposed to provide guidance for successful establishment of carsharing in China.