Scalable Production of Wearable Solid-State Li-Ion Capacitors from N-Doped Hierarchical Carbon

Smart and wearable electronics have aroused substantial demand for flexible portable power sources, but it remains a large challenge to realize scalable production of wearable batteries/supercapacitors with high electrochemical performance and remarkable flexibility simultaneously. Here, a scalable approach is developed to prepare wearable solid-state lithium-ion capacitors (LICs) with superior performance enabled by synergetic engineering from materials to device architecture. Nitrogen-doped hierarchical carbon (HC) composed of 1D carbon nanofibers welded with 2D carbon nanosheets is synthesized via a unique self-propagating high-temperature synthesis (SHS) technique, which exhibits superior electrochemical performance. Subsequently, inspired by origami, here, wave-shaped LIC punch-cells based on the above materials are designed by employing a compatible and scalable post-imprint technology. Finite elemental analysis (FEA) confirms that the bending stress of the punch-cell can be offset effectively, benefiting from the wave architecture. The wearable solid-state LIC punch-cell exhibits large energy density, long cyclic stability, and superior flexibility. This study demonstrates great promise for scalable fabrication of wearable energy-storage systems.     

Three-stage least squares-based iterative estimation algorithms for bilinear state-space systems based on the bilinear state estimator

Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.     

Hierarchically porous carbon derived from the activation of waste chestnut shells by potassium bicarbonate (KHCO3) for high-performance supercapacitor electrode

3D hierarchical porous carbon consisting of micropores, mesopores and macropores was successfully prepared through the activation of chestnut shell via KHCO₃. The influence of KHCO₃/chestnut shell ratio on the textural properties was carefully investigated and the structure, micrographs, and surface chemical status of the materials were expressed via X-Ray diffraction (XRD), transmission electron micrographs (TEM), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), respectively. With the optimized amount of KHCO₃, high specific pore surface area (2298 m² g^-1) and high total pore volume (1.51 cm³ g^-1) were achieved, endowing an advantageous electrochemical characteristics for the electrode as ultracapacitors in three-electrode system. At 2 A g^-1, a high specific electric capacity of 387 F g^-1 was reached. The remarkable electrochemical performances are mainly due to hierarchical porous structure with the high specific surface area (SSA) and the eminent total pore volume. It means that this hierarchical porous carbon prepared by activated by using KHCO₃ would have more promising foreground in the field of energy storage.     

Image-Based Lifelogging: User Emotion Perspective

Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users’ emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users’ emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summaize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience.     

基于间隔理论的过采样集成算法

针对传统集成算法不适用于不平衡数据分类的问题，提出基于间隔理论的AdaBoost算法（MOSBoost）。首先通过预训练得到原始样本的间隔；然后依据间隔排序对少类样本进行启发式复制，从而形成新的平衡样本集；最后将平衡样本集输入AdaBoost算法进行训练以得到最终集成分类器。在UCI数据集上进行测试实验，利用F-measure和G-mean两个准则对MOSBoost、AdaBoost、随机过采样AdaBoost（ROSBoost）和随机降采样AdaBoost（RDSBoost）四种算法进行评价。实验结果表明，MOSBoost算法分类性能优于其他三种算法，其中，相对于AdaBoost算法，MOSBoost算法在F-measure和G-mean准则下分别提升了8.4%和6.2%。     

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.     

A Bayesian network incorporating observation error to predict phosphorus and chlorophyll a in Saginaw Bay

Empirical relationships between lake chlorophyll a and total phosphorus concentrations are widely used to develop predictive models. These models are often estimated using sample averages as implicit surrogates for unknown lake-wide means, a practice than can result in biased parameter estimation and inaccurate predictive uncertainty. We develop a Bayesian network model based on empirical chlorophyll-phosphorus relationships for Saginaw Bay, an embayment on Lake Huron. The model treats the means as unknown parameters, and includes structure to accommodate the observation error associated with estimating those means. Compared with results from an analogous simple model using sample averages, the observation error model has a lower predictive uncertainty and predicts lower chlorophyll and phosphorus concentrations under contemporary lake conditions. These models will be useful to guide pending decision-making pursuant to the 2012 Great Lakes Water Quality Agreement.     

A novel multiple rule sets data classification algorithm based on ant colony algorithm

Ant colony optimization (ACO) algorithms have been successfully applied in data classification, which aim at discovering a list of classification rules. However, due to the essentially random search in ACO algorithms, the lists of classification rules constructed by ACO-based classification algorithms are not fixed and may be distinctly different even using the same training set. Those differences are generally ignored and some beneficial information cannot be dug from the different data sets, which may lower the predictive accuracy. To overcome this shortcoming, this paper proposes a novel classification rule discovery algorithm based on ACO, named AntMiner_mbc, in which a new model of multiple rule sets is presented to produce multiple lists of rules. Multiple base classifiers are built in AntMiner_mbc, and each base classifier is expected to remedy the weakness of other base classifiers, which can improve the predictive accuracy by exploiting the useful information from various base classifiers. A new heuristic function for ACO is also designed in our algorithm, which considers both of the correlation and coverage for the purpose to avoid deceptive high accuracy. The performance of our algorithm is studied experimentally on 19 publicly available data sets and further compared to several state-of-the-art classification approaches. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.     

Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

This paper proposes a new method of detecting an object containing multiple colors with non-homogeneous distributions in complex backgrounds and subsequently estimating the depth and shape of the object using a stereo camera. To extract features for object detection, this paper proposes fuzzy color histograms (FCHs) based on the self-splitting clustering (SSC) of the hue-saturation (HS) color space. For each scanning window in a pyramid of scaled images, the FCH is obtained by accumulating the fuzzy degrees of all of the pixels belonging to each cluster. The FCH is fed to a fuzzy classifier to detect an object in the left image captured by the stereo camera. To find the matched object region in the right image, the left and right images are first segmented using the SSC-partitioned HS space. The depth of the object is then found by performing stereo matching on the segmented images. To find the shape of the object, a disparity map is built using the estimated object depth to automatically determine the stereo matching window size and disparity search range. Finally, the shape of the object is segmented from the disparity map. The experimental results of the detection of different objects with depth and shape estimations are used to verify the performance of the proposed method. Comparisons with different detection and disparity map construction methods are performed to demonstrate the advantage of the proposed method.     

0D/3D ZnIn2S4/Ag6Si2O7 nanocomposite with direct Z-scheme heterojunction for efficient photocatalytic H2 evolution under visible light

Constructing heterojunction structure is a feasible way to realize an efficient and durable photocatalysts. Herein, a novel Z-scheme zero/three dimensional (0D/3D) ZnIn₂S₄/Ag₆Si₂O₇ (ZIS/ASO) composite was rationally designed, synthesized and analyzed. ZIS/ASO composite possesses a layer structure for increasing light response, a special 0D/3D structure for reducing the photo-induce carriers migration path, and numerous active sites for absorbing H₂O and producing H₂. This composite retains the high oxidation and reduction ability by facilitating separation and migration as well as limiting recombination of photo-induced carriers via the intimate interface between ZIS and ASO. Undoubtedly, the synthesized ZIS/ASO photocatalyst achieved a high photocatalytic H₂ activity, and the optimum sample shows a satisfactory H₂ evolution rate of 590.56 μmol g⁻¹ h⁻¹, distinctly better than that of pure ZIS. More importantly, this composite exhibits high stability and recyclability and is expected to be applied in practical application. Based on the H₂ evolution experimental results and electrochemical tests, the Z-scheme heterostructure construction of the composite was confirmed. This work expects to inspire a unique protocol for synthesizing Z-scheme photocatalysts for water splitting under visible light irradiation.