Performance In Real Environment Research Articles

Electrostatically assisted air coarse filtration for energy efficient ambient particles removal: Long-term performance in real environment and influencing factors

Electrostatically assisted air (EAA) coarse filter can improve the particle filtration performance of low efficiency filters without increasing their pressure drop. In this study, we investigated the long-term single pass filtration efficiency for 0.3–10 μm ambient particles, pressure drop, PM2.5 collecting amount, and energy consumption of an EAA coarse filter during a 51-day operation period. We found that the efficiency of EAA coarse filter varied with average values of 87.6%–97.1% for 0.3–10 μm particles, respectively, while the pressure drop increased from 14.6 Pa to 63.1 Pa at face air velocity of 1 m/s. Using K-means clustering and principal components analysis methods, we discovered that before 33 days, the significant influencing factors of the efficiency for 0.3–0.5 μm particles included air temperature, air moisture content, 3–10 μm particle number concentration, and PM2.5 collecting amount. While after 33 days, only PM2.5 mass collecting amount has a significant negative correlation with the efficiency for 0.3–0.5 μm particles. The efficiency became relatively unsteady and the PM2.5 collecting amount reached 131 g/m2 after 33 days. The total energy consumption of EAA coarse filter rose more slowly as PM2.5 collecting amount increased when compared with that of a conventional electret filter (KAFPO78A4, Daikin Inc., Japan). We suggest the high efficiency EAA coarse filtration as an energy efficient method for ambient particle removal in the view of its long-term performance.

A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal frequency sensors. In order to reduce the interference of modal frequency sensors for transmission towers, a Kriging surrogate model is proposed in this study. The finite element model of typical transmission towers is created and validated to provide the effective original database for the Kriging surrogate model. The prediction accuracy and convergences of the Kriging surrogate model are measured and confirmed. Besides the merits in computational cost and high-efficiency, the Kriging surrogate model is proven to have a satisfied and robust interference reduction capacity. Therefore, the Kriging surrogate model is feasible and competitive for interference filtration in the settlement surveillance sensors of steel transmission towers.

Propylene - propane separation using Zeolitic-Imidazolate Framework (ZIF-8) membranes: Process techno-commercial evaluation

Several recent studies investigated the use of the novel Zeolitic-Imidazolate Framework (ZIF-8) membranes for olefin-paraffin separation. In this manuscript, a techno-commercial model is developed to examine the use of these membranes for separating propylene from propane. Single-stage and two-stage membrane processes were assessed for their performance compared to distillation. The assessment was conducted considering 70 wt% propylene feed, typically produced from the upstream depropanizer. The single-stage process was found technically capable and commercially competent to produce the chemical grade propylene (93 wt%), but not the polymer grade (99.5 wt%). Alternatively, the two-stage process was capable of producing both propylene grades at promising recovery and cost figures. The published propylene/propane selectivity of 35 appears adequate in meeting the separation demands, subject to the adoption of proper unit design. Future research should grant more attention towards aspects such as ZIF-8 membranes’ manufacturability, cost, and performance in real environments.

A compact air cooling system for testing silicon detectors based on a vortex chiller

The testing of irradiated silicon detectors requires maintaining low ( ≤ -10 °C) temperatures, to simulate a realistic operating environment and prevent annealing effects from distorting the results of the measurement. Keeping a device cool and dry is challenging, particularly if the apparatus must be portable. This paper presents a solution for a providing a stable, cool and dry environment for testing an irradiated silicon detector, that is easy to transport and can be installed in charged particle beam areas and irradiation facilities.

Exothermal mechanisms in the charged LiNi1/3Mn1/3Co1/3O2 electrode layers for sulfide-based all-solid-state lithium batteries

Sulfide-based all-solid-state lithium batteries are a possible next-generation power source due to their incombustibility and large energy density. A composite comprising LiNi1/3Mn1/3Co1/3O2 (NMC) and 75Li2S·25P2S5 (LPS) glass electrolytes are used as a positive electrode material, which exhibits excellent charge–discharge cycle characteristics. In this study, to understand the origin of exothermal reactions during heating, we investigated structural changes in initially charged NMC–LPS composites mainly by in-situ synchrotron X-ray diffraction measurements and ex-situ transmission electron microscopy observations. We found that significant structural changes occurred in the NMC–LPS composites after heating above 300 °C. NMC decomposed into transition metal sulfides, such as MnS and CoNi2S4, while crystallization occurred in LPS predominantly precipitating a Li3PO4 crystalline phase. The formation of Li3PO4 is attributable to oxidation reactions between Li3PS4 and oxygen, which was generated during NMC decomposition. Thus, the exothermic peaks that were characteristic of the charged NMC–LPS composites are fundamentally related to heat generation that accompanies the formation of transition metal sulfides and Li3PO4 crystalline phases. As a result, when exposed to real cell operating environments, LPS and NMC yield a chemical reaction via heat treatment that leads to the exothermal reactions.

A WASN-Based Suburban Dataset for Anomalous Noise Event Detection on Dynamic Road-Traffic Noise Mapping.

Traffic noise is presently considered one of the main pollutants in urban and suburban areas. Several recent technological advances have allowed a step forward in the dynamic computation of road-traffic noise levels by means of a Wireless Acoustic Sensor Network (WASN) through the collection of measurements in real-operation environments. In the framework of the LIFE DYNAMAP project, two WASNs have been deployed in two pilot areas: one in the city of Milan, as an urban environment, and another around the city of Rome in a suburban location. For a correct evaluation of the noise level generated by road infrastructures, all Anomalous Noise Events (ANE) unrelated to regular road-traffic noise (e.g., sirens, horns, speech, etc.) should be removed before updating corresponding noise maps. This work presents the production and analysis of a real-operation environmental audio database collected through the 19-node WASN of a suburban area. A total of 156 h and 20 min of labeled audio data has been obtained differentiating among road-traffic noise and ANEs (classified in 16 subcategories). After delimiting their boundaries manually, the acoustic salience of the ANE samples is automatically computed as a contextual Signal-to-Noise Ratio (SNR) together with its impact on the A-weighted equivalent level (). The analysis of the real-operation WASN-based environmental database is evaluated with these metrics, and we conclude that the 19 locations of the network present substantial differences in the occurrences of the subcategories of ANE, with a clear predominance of the noise of sirens, trains, and thunder.

Is E-FAST possible and useful on the battlefield? A feasibility study during medical courses in hostile environment (MEDICHOS): preliminary results

IntroductionThe extent of the French forces’ territory in the Sahel band generates long medical evacuations. In case of many victims, to respect the golden hour rule, first-line sorting is essential....

On the dynamics of micro-tubes conveying fluid on various foundations

In this paper, using modified couple stress theory, dynamic stability of a cantilevered micro-tube embedded in several types of elastic media is studied. The governing equation for lateral vibrations of the micro-tube conveying fluid is derived using the extended Hamilton’s principle. The numerical results are obtained by employing the extended Galerkin’s method. For validation purposes, the obtained results for simple cases are compared and findings indicate a very good agreement with those available in the literature. The stability maps of different configurations with different flow velocities are studied and the influences of various parameters such as material length scale, external diameter and different elastic properties on the stability of the system are considered. Results indicate that elastic environments may enlarge the stability regions significantly at larger values of mass ratio parameter while decrease it for smaller values of mass ratio parameter. Moreover, using elastic media mathematically defined by series functions provides the capability to simulate almost any real time operational environment the micro-tube embedded in and results in an optimal stability state of the micro-structure carrying fluid flow.

High-Fidelity Autonomous Surface Vehicle Simulator for the Maritime RobotX Challenge

This paper presents a high-fidelity autonomous surface vehicle (ASV) and an above-water environment simulator developed for, and used by, the Queensland University of Technology (QUT) team at the 2016 Maritime RobotX Challenge. This simulator was developed to address a gap in an existing robotic marine vehicle simulation software by producing high-fidelity sensor data from a realistic operating environment and simulating ASV motion dynamics as a direct interface for hardware-in-the-loop simulation. The key functions required for the simulator to be an effective model of the QUT ASV included a high-fidelity camera, LiDAR (Velodyne HDL-32E), Inertial Measurement Unit (IMU), and GPS sensor simulation, as well as buoyancy and physics simulation for vehicle motion and control performance in a spatiotemporally varying modeled sea state. Another key feature of the simulator was the provision to directly interface the Robotic Operating System (ROS) to publish and subscribe to sensor topics (in real time) and allow hardware-in-the-loop simulation of the ASV systems for algorithm development. The fidelity and performance of the simulator was benchmarked against the existing best available alternative simulator, V-Rep, with results shown to demonstrate that the autonomous marine surface vessel simulator achieved improved computational performance and sensor fidelity when simulating above surface marine environments.

Mitigating scalability issues of perovskite photovoltaic technology through a p-i-n meso-superstructured solar cell architecture

The relevance of perovskite photovoltaics is fully supported by its impressive development in recent years. Besides high efficiency, adoption of this technology requires obtaining long operational stability and cost-effective fabrication methods of large area devices. With the current perovskite solar cell (PSC) configurations (planar or meso n-i-p), it is difficult to deposit uniform pin-hole free films over large area at low temperature (<100 °C). In order to solve the above issues, we adopt a fabrication strategy for the scaling up of perovskite solar cells. Particularly, we study and fabricate a meso-superstructured p-type/intrinsic/n-type (p-i-n) cell (p-i-n-meso) configuration in which the insulating mesoporous layer of Al2O3 plays a key role in the fabrication of perovskite solar modules and therefore we also draw attention to this structure that has been out of the map for the fabrication of large area perovskite devices. The potential of this architecture is demonstrated through fabrication of devices ranging from small area (9 mm2) up to eight-cell CH3NH3PbI3 perovskite modules over substrates as large as 100 cm2. A record photovoltaic conversion efficiency (PCE) of 9.3% and geometrical fill factor (GFF) of 84% was reached for a module with 17 cm2 of active area. Additionally, we carried out an in-situ monitoring of the photovoltaic parameters of one perovskite module for 2000 h under outdoor conditions, giving evidence of the high performance and stability of encapsulated perovskite photovoltaic technology in a real operational environment. This study paves the way to the upscaling of stable, efficient and full-solution-processed PSCs.

Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement

Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without global constraints or limited local representation of global appearance. To solve these problems, we propose a new multi-layer progressive face alignment method that combines global matches for a whole face with local refinement for a given region, where the errors caused by local optima are restricted by globally-matched appearance, and the local misalignments in the global method are avoided by supplementing the representation of local details. Our method consists of the following processes: Firstly, an input image is encoded as a multi-mode Local Binary Pattern (LBP) image to regress the face shape parameters. Secondly, the local multi-mode histogram of oriented gradient (HOG) features is applied to update each landmark position. Thirdly, the above two alignment shapes are weighted as the final result. The contributions of this paper are as follows: (1) Shape initialization by applying an affine transformation to the mean shape. (2) Face representation by integrating multi-mode information in a whole face or a face region. (3) Face alignment by combining handcrafted features with convolutional neural networks (CNN). Extensive experiments on public datasets show that our method demonstrates improved performance in real environments in comparison to some state-of-the-art methods which apply single scale features or single CNN networks. Applying our method to the challenging HELEN dataset, the samples with fewer than 8 mean errors reach 81.1%.

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

The acoustic classification technology of moving vehicles is a significant application in wireless sensor networks (WSNs). However, the wild environment makes it more intractable for the acoustic target classification owing to the complicated interference noise. The widely used mel-frequency cepstral coefficients (MFCCs) are somewhile contaminated by the acoustic noise, resulting in degrading the classification performance in real environments. To increase the classification accuracy of the moving vehicles, this paper presents an acoustic feature extraction method, which integrates the nonlinear function with the MFCC method, termed as NMFCC. Through nonlinear transformation, the proposed method could amplify the acoustic features in a nonlinear way, and obtain more robust acoustic features, thereby achieving a superior classification performance. Comparing the NMFCC with the baseline feature extraction method, experimental results not only demonstrate the viability of the proposed algorithm but also show a satisfactory performance of the moving vehicle classification for diverse practical scenarios in the wild.

Real-Time Motion Planning Approach for Automated Driving in Urban Environments

Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bezier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.

Assistive Robot Simulator for Multi-Objective Evolutionary Algorithm Application

This paper presents a new assistive robot simulator for multi-objective optimization application. The main function of the simulator is to simulate the trajectory of the robot arm when it moves from initial to a goal position in optimized manner. A multi-objective evolutionary algorithm (MOEA) is utilized to generate the robot arm motion optimizing three different objective function; optimum time, distance, and high stability. The generated neuron will be selected from the Pareto optimal based on the required objectives function. The robot will intelligently choose the best neuron for a specific task. For example, to move a glass of water required higher stability compare to move an empty mineral water bottle. The simulator will be connected to the real robot to test the performance in real environment. The kinematics, mechatronics and the real robot specification are utilized in the simulator. The performance of the simulator is presented in this paper.

Cyber Teaming and Role Specialization in a Cyber Security Defense Competition.

A critical requirement for developing a cyber capable workforce is to understand how to challenge, assess, and rapidly develop human cyber skill-sets in realistic cyber operational environments. Fortunately, cyber team competitions make use of simulated operational environments with scoring criteria of task performance that objectively define overall team effectiveness, thus providing the means and context for observation and analysis of cyber teaming. Such competitions allow researchers to address the key determinants that make a cyber defense team more or less effective in responding to and mitigating cyber attacks. For this purpose, we analyzed data collected at the 12th annual Mid-Atlantic Collegiate Cyber Defense Competition (MACCDC, http://www.maccdc.org), where eight teams were evaluated along four independent scoring dimensions: maintaining services, incident response, scenario injects, and thwarting adversarial activities. Data collected from the 13-point OAT (Observational Assessment of Teamwork) instrument by embedded observers and a cyber teamwork survey completed by all participants were used to assess teamwork and leadership behaviors and team composition and work processes, respectively. The scores from the competition were used as an outcome measure in our analysis to extract key features of team process, structure, leadership, and skill-sets in relation to effective cyber defense. We used Bayesian regression to relate scored performance during the competition to team skill composition, team experience level, and an observational construct of team collaboration. Our results indicate that effective collaboration, experience, and functional role-specialization within the teams are important factors that determine the success of these teams in the competition and are important observational predictors of the timely detection and effective mitigation of ongoing cyber attacks. These results support theories of team maturation and the development of functional team cognition applied to mastering cybersecurity.

IGuard: A Real-Time Anti-Theft System for Smartphones

Smartphone theft is a non-negligible problem that causes serious concerns on personal property and privacy. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the ‘take-out’ behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

Feasibility and optimisation of a batch mode capacitive deionization (BM CDI) process for textile cationic dyes (TCD) removal and recovery from industrial wastewaters

Feasibility, optimisation and modelization of textile cationic dyes (TCD) removal and recovery from industrial wastewaters by batch mode capacitive deionization (BMCDI), with activated carbon (AC) electrodes, were investigated.The synergy hypothesis, AC adsorption-electrochemical double layer effects, was verified and measured by the high CDI efficiency (≈99%) which was three times higher than conventional AC adsorption, demonstrating the intensification of the TCD removal by CDI. Unlike the adsorption and biological established treatments, the BMCDI presents real environmental performance and sustainability (recycling of pristine treated water, recovery of TCD and clean regeneration of AC electrodes).This research study was mainly focused on the concentration–time (C–t) and q(X/M)–t curves of batch runs, at different pHs, applied voltages (V) or flow rates (Q). The BMCDI efficiency was measured by the final equilibrium concentration Cfineq and electrode specific adsorption, q(X/M).CDI has rarely been studied as removal and recovery method of TCD. This BMCDI approach is thus quite novel and potentially applicable.

Comparison between Different Types of Sensors Used in the Real Operational Environment Based on Optical Scanning System.

The present paper describes the experimentation in a controlled environment and a real environment using different photosensors, such as infrared light emitting diode (IRLED-as receiver), photodiode, light dependent resistor (LDR), and blue LED for the purpose of selecting those devices, which can be employed in adverse conditions, such as sunlight or artificial sources. The experiments that are described in this paper confirmed that the blue LED and phototransistor could be used as a photosensor of an Optical Scanning System (OSS), because they were less sensitive to sunlight radiation. Moreover, they are appropriate as reference sources that are selected for the experiment (blue LED flashlight and light bulb). The best experimental results that were obtained contained a digital filter that was applied to the output of the photosensor, which reduced the standard deviation for the best case for the phototransistor LED from 100.26 to 0.15. For the best case, using the blue LED, the standard deviation was reduced from 86.08 to 0.11. Using these types of devices the cost of the Optical Scanning System can be reduced and a considerable increase in resolution and accuracy.

Energy Yield Limits for Single-Junction Solar Cells

Summary Energy is a key performance metric that describes generated electricity (kWh) in a realistic operating environment, with significance beyond that of the lab-measured power conversion efficiency. Herein, we present fundamental energy yield limits based on 2015 global satellite data for solar cells with various band-gaps, including established technologies such as Si and CdTe and emerging materials such as perovskites. Based on an adapted detailed-balance model with experimental validation, we derive an empirical relation that allows approximating harvesting efficiencies from standard testing condition efficiencies. This equation provides a straightforward way to calculate energy yield for any new technology and enables a fair comparison of lab-measured devices with different band-gaps. We find that the ideal band-gap for maximizing energy yield is 1.35 eV. We show that harvesting efficiencies correlate with Koppen-Geiger climate zones, which indicates that results obtained in a given climate can be translated to other regions with similar climate.

An expert 2DOF fractional order fuzzy PID controller for nonlinear systems

This work presents a generic two-degree-of-freedom fractional order fuzzy PI-D (2DOF FOFPI-D) controller dedicated to a class of nonlinear systems. The control law for proposed scheme is derived from basic 2DOF fractional order PID controller in discrete domain. Expert intelligence is embedded in overall derived control law by utilizing formula-based fuzzy design methodology. The controller structure comprises of fractional order fuzzy PI (FOFPI) and fractional order derivative filter to handle multiple issues and provides flexibility in design and self-tuning control feature. Further, the proposed scheme is compared with its integer order counterpart and 2DOF PI-D controller for coupled nonlinear 2-link robotic arm in real operating environment. The parameters of designed controllers are optimally tuned using multi-objective non-dominated sorting genetic algorithm-II for attaining low variation in control effort and error index. Intensive simulation studies are performed to analyze trajectory tracking, model uncertainty, disturbance due to cogging, sensor noise and noise as well as disturbance rejection simultaneously. Results demonstrate the superior performance of 2DOF FOFPI-D controller as compared to other designed controllers in the facets of different operating conditions.