Effect of coke orientation on the electrochemical properties of lithium-ion battery anode

Journal of Applied Electrochemistry - In this study, Needle and Regular coke, which has different crystalline and orientation characteristics, were graphitized, and the effect of crystal changes on...     

Preparation and characterization of thermoresponsive poly(N-isopropylacrylamide-co-N-isopropylmethacrylamide) hydrogel materials for smart windows

In this study, a series of thermoresponsive cross-linked copolymer poly [N-isopropylacrylamide(NIPAm)-co-N-isopropylmethacrylamide(NIPMAm)] (P-M series samples: P-M-0, 10, 20, 30, 40, where numbers are co-monomer contents) hydrogels were prepared by free radical polymerization using the main monomer N-isopropylacrylamide (NIPAm), co-monomer N-isopropylmethacrylamide (NIPMAm), cross-linking agent N, N-methylenebisacrylamide, initiator (ammonium persulfate)/catalyst, and solvent water. In addition, a series of samples [P-G series samples: P-G-0, 10, 20, 30, 40, where numbers are co-solvent glycerol content) were prepared using P-M-40 as components and water/co-solvent glycerol as a mixed solvent. The effects of co-monomer NIPMAm and co-solvent glycerol contents on the lower critical solution temperature (LCST)/freezing temperature and light transmittance as function of temperature of the prepared copolymer gels were investigated. The resulting thermoresponsive polymer gels had LCSTs in the range of 17.9 to 38.7°C and freezing points in the range of 6.3 to −38.5°C. These gels are suitable materials for smart windows that are responsive to various environmental conditions.     

Effects of anodizing voltage on the anodized and hydrothermally treated titanium surface

Anodic oxidation is the process of creating a titanium oxide layer with various defects more dense and stable. In this study, a dense, stable and porous oxide layer was formed using anodic spark oxidation on pure titanium surface and hydroxyapatite crystals were formed on its surface via a hydrothermal treatment. A mixture of 0.02M−GP (Glycerolphosphate disodium salt) and 0.2M-CA (Calcium acetate) was used as an electrolyte. By increasing the anodizing voltage to 220, 260, 300, and 360 V, the effects of the anodizing voltage were examined by evaluating the film properties after anodization and a hydrothermal treatment. Breakdown occurred around 230 V. As the voltage increased after breakdown, the pore size increased. After the hydrothermal treatment, the amount of HA crystal precipitation was also increased as the voltage increased. The mean surface roughness (Ra) of the anodizing surface was also increased as the voltage increased. The Ra value was larger in the hydrothermally treated group compared with the group treated with anodization as a result of the HA crystals present on the surface after the hydrothermal treatment. Corrosion resistance of the surface modified by anodization was significantly increased in a saline solution compared to that for the non-treated group; this increased further after the hydrothermal treatment. These increases were most likely due to a thick stable oxide layer formed through anodization. Thus, it is believed that titanium with its surface modified through anodic spark oxidation would be a suitable biomaterial due to its corrosion resistance and biocompatibility.     

Object tracking based on an online learning network with total error rate minimization

This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.     

Feature scalability for a low complexity face recognition with unconstrained spatial resolution

Automatic face recognition (FR) based applications in low computing power constrained systems, such as mobile and smart camera, have become particularly interesting topic in recent years. In this context, we present computationally efficient FR framework underpinning the so-called feature scalability algorithm. The proposed framework aims at implementing robust FR systems under low-computing power restriction and varying face resolution. Key beneficial property of our proposed FR framework based on feature scalability is to require low computational complexity without sacrificing a level of FR performance. To do this, using feature scalability algorithm enables to directly estimate the features (from pre-enrolled gallery images) that are well matched with the feature of an input probe image with different resolution (generally lower resolution) without any complex process. In addition, our method is helpful for relieving storage shortage problem as it does not require a large amount of training and gallery images with different face resolutions. Results show that our proposed feature scalability algorithm can be seamlessly embedded into state-of-the-art feature extraction methods extensively used for FR by achieving impressive recognition performance. Also, according to the results on computational complexity measurement, the proposed method is proven to be useful for substantially saving FR operation time.     

Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data

Uncertainty-based multidisciplinary design optimization (UMDO) has been widely acknowledged as an advanced methodology to address competing objectives and reliable constraints of complex systems by coupling relationship of disciplines involved in the system. UMDO process consists of three parts. Two parts are to define the system with uncertainty and to formulate the design optimization problem. The third part is to quantitatively analyze the uncertainty of the system output considering the uncertainty propagation in the multidiscipline analysis. One of the major issues in the UMDO research is that the uncertainty propagation makes uncertainty analysis difficult in the complex system. The conventional methods are based on the parametric approach could possibly cause the error when the parametric approach has ill-estimated distribution because data is often insufficient or limited. Therefore, it is required to develop a nonparametric approach to directly use data. In this work, the nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data is proposed. To handle limited data, three processes are also adopted. To verify the performance of the proposed method, mathematical and engineering examples are illustrated.     

Quantification model of proper curvature for large‐sized curved TVs

We present a feasible modeling method to estimate the proper curvature for large‐sized curved TVs. We especially focused on two factors – preference and perceived distortion – to be considered to determine the proper curvature. The preference includes a number of advantages that people expect to fulfill the possibilities of a more immersive image and sense of realism from curved display. On the other hand, the perceived distortion means the side effects that people can notice uncomfortable feeling caused by the shape of a curved one. In order to find out how two factors would be different as a number of conditions like curvature, size, and viewing angle change, a series of subjective assessments were conducted. The evaluation results show that both the preference and perceived distortion vary with the conditions considerably. We performed the statistical analysis based on the results and proposed the quantification model of proper curvature, which has higher preference and less perceived distortion, for various‐sized curved TVs.     

An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.     

Move blocking strategy applied to re-entrant manufacturing line scheduling

Model predictive control (MPC)-based approach to fab-wide scheduling has been suggested to solve constraint-aware production optimization and in-process inventory level control simultaneously at each scheduling instance. However, application of this approach to real fab suffers from computational difficulties brought by the need to solve a huge optimization problem on-line as real fab scheduling problems are characterized by long cycle times, multiple product types, hundreds of machines/processing steps and re-entrant product flows. This study explores the use of an offset-blocking strategy combined with a modified recursive least square (RLS) estimation in the fab-wide scheduler, in order to alleviate the difficulty. The strategy is tested on a modified version of published case study called Intel Mini-Fab (IMF) problem. Despite its simplicity, the blocking strategy showed excellent performance in the face of realistic demand changes and plant/model mismatch.