Search

We address the problem of motion planning for four-way intersection crossings with right-of-ways. Road safety typically assigns liability to the follower in rear-end collisions and to the approaching vehicle required to yield in side crashes. As an alternative to previous models based on heuristic state machines, we propose a planning framework which changes the prediction model of other cars (e.g. their prototypical accelerations and decelerations) depending on the given longitudinal or lateral priority rules. Combined with a state-of-the-art trajectory optimization approach ROPT (Risk Optimization Method) this allows to find ego velocity profiles minimizing risks from curves and all involved vehicles while maximizing utility (needed time to arrive at a goal) and comfort (change and duration of acceleration) under the presence of regulatory conditions. Analytical and statistical evaluations show that our method is able to follow right-of-ways for a wide range of other vehicle behaviors and path geometries. Even when the other cars drive in a non-priority-compliant way, ROPT achieves good risk-comfort tradeoffs.

Through an "isoreticular expansion" strategy, a large number of highly porous zirconium-based metal-organic frameworks (Zr-MOFs) have been achieved using extended organic linkers in the past few years. However, interpenetrated Zr-MOFs with ftw topology have scarcely been reported, mainly owing to the used bulky tetratopic linkers that effectively prevent the network interpenetration. Here, we report a new family of zirconium and lanthanide (Ln) MOFs with ftw topology, constructed by hexanuclear Zr or Ln (Ln = Eu, Tb, Gd, Dy, Tm, Yb, Nd, and Er) clusters and a spirobifluorene-center tetracarboxylate linker. Our studies reveal that the isostructural Zr and Ln MOFs are all doubly interpenetrated with ultrahigh thermal and chemical stability. The observed unusual interpenetration can be attributed to the specific geometry of the spirobifluorene-center tetratopic linker. Gas adsorption studies show that the interpenetrated Zr-MOF is still highly porous and exhibits high performance for CO2 storage, which can be attributed to the strong CO2 binding environment contributed by the reduced pore size. In addition, the presented MOFs display strong characteristic luminescence in the UV-vis-NIR region. Moreover, the incorporation of the spiro-center linker into the framework can efficiently produce two-photon-excited photoluminescence with a large action cross-section value, which also benefited from the high packing density of the nonlinear optical chromophore linker in the interpenetrated structure.

Willow (Salix matsudana) bark is the bark of willow trees, it is rich in resources. Extracting these resources becomes difficult due to the relative lack of systematic and in-depth analysis of the total chemical composition. Analysis was performed using TDS-GC/MS, TG, FTIR and PY-GC/MS techniques. Results and discussion: the extracts form volatile organic compounds are mainly included in the analysis of TDS-GC/MS, such as, organic acids, exters, alcohols, etc. Extracts are rich in VOCs:1,2-Benzenedicarboxylic acid, bis(2-methylpropyl) ester, its function is mainly as plasticizer. FTIR analysis are further confirmed, the ingredients of total extracts include ethers, The treatment of total heat loss is divided into two distinct phases: the first phase of 50 °C to 100 °C, the second phase of 150 °C to 250 °C, the sequence of quality loss is the second and the first stage, important turning point temperature is 245 °C, with significant changes in quality, for example, high polymer pyrolysis into small volatile molecules. In addition, various pyrolysis VOCs have been analyzed by PY-GC/MS, And have good development space, such as, Carbamic acid, monoammonium salt (4.80%), Glycolaldehyde dimer (4.04%), Catechol (0.44%). etc. Both of them have good development prospects, including many new chemical components produced by the pyrolysis of total extracts and residues, which is a new method to provide high quality applications. Keywords: Willow bark, Volatile organic component (VOC), Extraction of organic solvents, Component characteristics

Elliptical Kiwifruit (Actinidia chinensis Planch) is a popular fruit among consumers. It has abundant nutrition. And it also has medicinal value and economic value. In our research, we detected and analyzed the chemical components by Fourier transform infrared spectroscopy (FT-IR) and gas chromatography mass spectrometry (GC–MS) technologies. In FT-IR analysis, we known that there it main had O-H stretching vibration, C-H stretching vibration, CC stretching vibration, benzene ring stretching vibration, C-H C-O stretching vibration and anomeric carbon vibrational frequency absorption peak attribution. The types of compound that may be in this band is cellulose, carboxylic acid, alcohol, phenol, amine and ester. In GC–MS analysis, we known that the main chemical compounds were 5-hydroxymethylfurfural, 1,2,3,5-cyclohexanetetrol, quinic acid, d-alanine, N-propargyloxycarbonyl-, isohexyl ester, 4H-Pyran-4-one, cyclotetrasiloxane, octamethyl- and furfural. And these organic components can be used at food processing, medical treatment and light industry fields. Keywords: Elliptical Kiwifruit, FT-IR, GC–MS, Chemical composition, Application

Fourier transform infrared (FT-IR) spectroscopy and gas chromatography-mass spectrometer (GC-MS) were used to analyze organic solvent extracts of Populus tomentosa Carriere, the results show that the Chinese Buckeye Seed is mainly healthy and rich in Chinese Buckeye Seed extracts, The main representative of the active ingredient are Furfural, Isosorbide Dinitrate, Catechol, 1,2-Benzenediol, 4-methyl-, 3-Amino-s-triazole, 2,3-Quinoxalinedione, 1,4-dihydro-, Docosanoic acid, Medetomidine. Chinese Buckeye Seed extract contains a large amount of biologically active ingredients, including acids, phenols, alcohols, ketones and ethers, not only in the fields of bioenergy, biomedicine, cosmetics, skin care and fragrance, but also potential application prospects, Chinese Buckeye Seed?s chemical composition research provides a scientific basis for the development and utilization of this plant.

Chinese fir is an important economic tree species in China. In addition to the application of alive trees and wood, the abundant chemical compositions of Chinese fir as a non-wood resource also have very important position. This study used Chinese fir wood powder as material, analyzing the chemical composition of Chinese fir volatiles by Fourier transform infrared spectroscopy (FT-IR), thermogravimetry (TG), pyrolisis-gas chromatography-mass spectrometry (Py-GC-MS), thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Through the analysis of the detected we can know that volatiles main were phenolic (cedrol, alpha-bisabolol and beta-eudesmol), ester (dimethyl phthalate, diisobutyl phthalate and dibutyl phthalate), ketone (hydroxyacetone and benzofuran) and aldehyde (hexanal and nonanal) and so on. This compounds and their derivatives can use at wide medical, biology, cosmetics and textile industry.

Traditionally, bandgap materials are a prerequisite to photocatalysis since they can harness a reasonable range of the solar spectrum. However, the high impedance across the bandgap and the low concentration of intrinsic charge carriers have limited their energy conversion. By contrast, metallic nanoparticles possess a sea of free electrons that can effectively promote the transition to the excited state for reactions. Here, an atomic layer of a bimetallic concoction of silver–gold shells is precisely fabricated onto an Au core via a sonochemical dispersion approach to form a core–shell of Au–Ag that exploits the wide availability of excited states of Ag while maintaining an efficient localized surface plasmon resonance (LSPR) of Au. Catalytic results demonstrate that this mix of Ag and Au can convert solar energy to hydrogen at high efficiency with an increase of 112.5% at an optimized potential of −0.5 V when compared to light-off conditions under the electrochemical LSPR. This outperforms the commercial Pt catalysts by 62.1% with a hydrogen production rate of 1870 µmol g−1 h−1 at room temperature. This study opens a new route for tuning the range of light capture of hydrogen evolution reaction catalysts using fabricated core–shell material through the combination of LSPR with electrochemical means.

Ionic floating-gate memories Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low-power alternative, but most devices cannot update the synaptic weights uniformly or scale to large array sizes. Fuller et al. developed an integrated device, ionic floating-gate memory, that has the gate terminal of a redox transistor electrically connected to a diffusive memristor. This low-power device enabled linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 10 9 write-read cycles. Science , this issue p. 570

A novel high voltage gain non-isolated boost converter based on the normal quadratic boost converter topology is introduced. In the referenced circuit, a powerful charging capacitor can pow...

An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal.

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

In this paper, we investigate a secure unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, where multiple ground users and a UAV collaborate to complete computing tasks in the presence of multiple eavesdroppers on the ground with exact locations. The UAV acts as a computing server and users can offload part of computing tasks to the air in order to relieve calculation pressure. The total energy consumption of the UAV is minimized by jointly optimizing the task allocation, the transmit power of each user equipment, and the trajectory of UAV, subject to the secrecy offloading rate constrains, the transmit power and UAV’s trajectory constrains. To solve the difficult nonconvex optimization problem, an alternating algorithm based on the successive convex approximation is proposed to optimize the parameters iteratively. The simulation results demonstrate that the developed algorithm is effective and can decrease the energy consumption compared with other benchmark methods.

TiO2-based powder materials have been widely studied as efficient photocatalysts for water splitting due to their low cost, photo-responsivity, earthly abundance, chemical and thermal stability, etc. In particular, the recent breakthrough of nitrogen-doped TiO2, which enhances the presence of structural defects and dopant impurities at elevated temperatures, exhibits an impressive visible-light absorption for photocatalytic activity. Although their electronic and optical properties have been extensively studied, the structure-activity relationship and photocatalytic mechanism remain ambiguous. Herein, we report an in-depth structural study of rutile, anatase and mixed phases (commercial P25) with and without nitrogen-doping by variable-temperature synchrotron X-ray powder diffraction. We report that an unusual anisotropic thermal expansion of the anatase phase can reveal the intimate relationship between sub-surface oxygen vacancies, nitrogen-doping level and photocatalytic activity. For highly doped anatase, a new cubic titanium oxynitride phase is also identified which provides important information on the fundamental shift in absorption wavelength, leading to excellent photocatalysis using visible light., Nitrogen-doped TiO2 exhibits improved photocatalytic water-splitting activity partially due to enhanced oxygen vacancy formation. Here, authors demonstrate the temperature-dependent lattice distortion of oxygen vacancies, and identify the presence of a titanium oxynitride phase in high activity catalysts.

Infrared focal plane arrays imaging system tend to be affected by the interior thermal radiation that drifts slowly over the time, resulting in the continuous spatial low-frequency nonuniformity noise in the image. In this paper, a scene-based nonuniformity correction method for interior thermal radiation is proposed. The gradient domain least mean square (LMS) algorithm is used to estimate the gradient caused by the interior radiation noise according to the fixed spatial low-frequency information in multiframe images. After that, the calibration parameters are obtained by fitting the derivatives of the polynomial-based correction model with the calculated gradient image. The performance of the proposed method is thoroughly studied with infrared image sequences with simulated nonuniformity and real nonuniformity. The results show that the proposed algorithm can remove interior radiation noise more completely without ghosting artifacts than the state-of-art algorithms and is more robust to different scenarios.

Li X FePO 4 (0 X FePO 4 exhibits a nonequilibrium solid solution behavior at elevated cycling rates. This article reviews recent research on nonequilibrium solid solution in Li X FePO 4 ; these insights have been largely enabled by operando characterization techniques. Such studies have not only unambiguously confirmed the existence of this solid solution, but also show how surface reaction and diffusion kinetics ultimately affect phase separation and other spatially nonuniform lithiation and delithiation behavior.

Phase transformations driven by compositional change require mass flux across a phase boundary. In some anisotropic solids, however, the phase boundary moves along a non-conductive crystallographic direction. One such material is LiXFePO4, an electrode for lithium-ion batteries. With poor bulk ionic transport along the direction of phase separation, it is unclear how lithium migrates during phase transformations. Here, we show that lithium migrates along the solid/liquid interface without leaving the particle, whereby charge carriers do not cross the double layer. X-ray diffraction and microscopy experiments as well as ab initio molecular dynamics simulations show that organic solvent and water molecules promote this surface ion diffusion, effectively rendering LiXFePO4 a three-dimensional lithium-ion conductor. Phase-field simulations capture the effects of surface diffusion on phase transformation. Lowering surface diffusivity is crucial towards supressing phase separation. This work establishes fluid-enhanced surface diffusion as a key dial for tuning phase transformation in anisotropic solids.

Here, the abilities of carbon nanocages (C60, C72 and C84) as anode electrodes in lithium-ion battery (LIB) and potassium-ion battery (KIB) were examined by theoretical method. The effects of adsorbed Gallium oxide (GaO) and Tin dioxide (SnO2) on performances of C60, C72 and C84 as anode electrodes in LIB and KIB were investigated. The cell voltage (Vcell), Gibbs free energy (Gad), band gap energy (EHLG) and charge transferred (q) of studied complexes were calculated. Results indicated that Gibbs free energies of adsorption of GaO on C60, C72 and C84 nanocages were higher than SnO2. The band gap energy of GaO-C84 was lower than GaO-C60 and GaO-C72. Results showed that adsorbed SnO2 and GaO can increase the |q|, |Gad| and Vcell values of C60, C72 and C84 nanocages, significantly. Finally, the C84-SnO2 as novel anode electrodes in LIB and KIB with the highest Vcell was proposed to use in electrical machines.

Insertion is a widely utilized process for reversibly changing the stoichiometry of a solid through a chemical or electrochemical stimulus. Insertion is instrumental to many energy technologies, including batteries, fuel cells, and hydrogen storage, and has been the subject of extensive investigations. More recently, solid-state switching devices utilizing insertion have drawn significant interest; such devices dynamically switch a material's chemical stoichiometry, changing it from one state to another. This review illustrates the fundamental properties and mechanisms of insertion, including reaction, diffusion, and phase transformation, and discusses recent developments in characterization in these fields. We also review new classes of recently demonstrated insertion devices, which reversibly switch mechanical and electronic properties, and show how the fundamental mechanisms of insertion can be used to design improved switching devices.

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

The rapid thermal cracking technology of biomass can convert biomass into bio-oil and is beneficial for industrial applications. Agricultural and forestry wastes are important parts of China's energy, and their high-grade utilization is useful to solve the problem of energy shortages and environmental pollution. To the best of our knowledge, the impact of nanocatalysts on converting biowastes for bio-oil has not been studied. Consequently, we examined the production of bio-oil by pyrolysis of Aesculus chinensis Bunge Seed (ACBS) using nanocatalysts (Fe2O3 and NiO catalysts) for the first time. The pyrolysis products of ACBS include 1-hydroxy-2-propanone (3.97%), acetic acid (5.42%), and furfural (0.66%). These chemical components can be recovered for use as chemical feedstock in the form of bio-oil, thus indicating the potential of ACBS as a feedstock to be converted by pyrolysis to produce value-added bio-oil. The Fe2O3 and NiO catalysts enhanced the pyrolysis process, which accelerated the precipitation of gaseous products. The pyrolysis rates of the samples gradually increased at DTGmax, effectively promoting the catalytic cracking of ACBS, which is beneficial to the development and utilization of ACBS to produce high valorization products. Combining ACBS and nanocatalysts can change the development direction of high valorization agricultural and forestry wastes in the future.

Lead dioxide has absorbed much attention for its good resistance to corrosion, high electronic conductivity, good stability, long lifetime, low cost as electrode material. However, there still exist problems about the selection of electrolyte, which may bring trouble in energy conservation and environmental protection. The present study was to provide more environmentally electrolyte of methanesulfonic acid (MSA) and evaluate MSA effect on electrosynthesis of lead dioxide by using rotating disk electrode (RDE) and scanning electron microscope (SEM) as well as X-ray diffraction (XRD) so as to make sure suitable concentration of MSA and to gain more environmental and energy benefit. The results show that methanesulfonic acid has great influence on the electrodeposition of lead dioxide and adequately characterise the PbO2 synthesized on Pt electrode surface. By controlling the composition of the electrodeposition bath at MSA concentration 0.1 M and Pb(II) 0.2 M can we get better reversible process, longer electrode lifetime and more satisfactory environmental protection.

In this work, the organic light-emitting diodes (OLEDs) based on Alq3 are fabricated. In order to make clear the transport mechanism of carriers in organic light-emitting devices at low temperature, detailed electroluminescence transient response and the current-voltage–luminescence (I–V–L) characteristics under different temperatures in those OLEDs are investigated. It founds that the acceleration of brightness increases with increasing temperature is maximum when the temperature is 200 K and it is mainly affected by the electron transport layer (Alq3). The MoO3 injection layer and the electroluminescent layer have great influence on the delay time when the temperature is 200 K. Once the temperature is greater than 250 K, the delay time is mainly affected by the MoO3 injection layer. On the contrary, the fall time is mainly affected by the electroluminescent material. The Vf is the average growth rate of fall time when the temperature increases 1 K which represents the accumulation rate of carriers. The difference between Vf caused by the MoO3 injection layer is 0.52 us/K and caused by the electroluminescent material Ir(ppy)3 is 0.73 us/K.

In this study, commercial Li(Ni1/3Co1/3Mn1/3)O2/graphite (NCM/C) lithium-ion batteries were cycled at −10 °C under different current rates ranging from 0.2 C to 1C. Electrochemical measurements and post-mortem analysis were performed to identify the root causes of the degradation in the electrochemical performance of the cells. The results reveal that apart from the increase of lithium plating on the anode, there is a considerable and abnormal capacity loss on the NCM cathode with the increase in current rate. The different degradation mechanisms including the loss of lithium inventory (LLI) and the specific capacity loss of NCM material (LAM) during cycling at −10 °C were analyzed quantitatively. It is shown that the evolution trend of LLI with the increase in current rate (8.6%, 35.0%, 55.8% for 0.2 C, 0.5 C and 1 C respectively) corresponds closely to that of the capacity loss of the full-cells (8.6%, 45.5%, 63.6% for 0.2 C, 0.5 C and 1 C, respectively), which is different to the trend of LAM (7.2%, 8.8%, 22.3% for 0.2 C, 0.5 C and 1 C, respectively). Further analysis by XRD and HR-TEM clearly indicates that the crystallinity of the hexagonal layered structure of NCM was greatly impaired after low-temperature cycling at −10 °C, and spinel phase can be observed among the layered structure.

Space charge capacitance and the physical mechanism of negative capacitance in organic light-emitting diodes (OLEDs) by transient current response analysis are investigated for the first time. Space charge capacitance is found to be a fixed value as voltage increases for each device. The inflection points in capacitance–voltage curves correspond to carrier injection, transportation and combination processes in organic layers. A negative capacitance effect at low frequency relates to the internal accumulated carrier state in the OLEDs. The nonsynchronicity between the phases of the internal accumulated carriers’ states changing and the small alternating current leads to electric field reversal. Only the electric field reversal at low frequency results in a negative capacitance effect.

Pb–PbO2, Pb–Co3O4 and Pb–Co3O4–PbO2 composite inert anodes were prepared by vacuum hot pressing technique from the mixtures of lead powders, Co3O4 and PbO2 particles in a fixed mould. The influences of heating temperatures and particles doping on the properties of the composite inert anodes were researched. The surface microstructures, phase structures and electrochemical properties were measured by X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometer (EDS), anodic polarization curves (AP), cyclic voltammetry curves (CV), Tafel polarization curves and electrochemical impedance spectroscopy (EIS). The results showed that the uniform distributions of Co3O4 and PbO2 as the second phase particles in the composite inert anodes have been obtained by the vacuum hot pressing technique. The lead powders were melted and formed a uniform and compact matrix, in which the morphologies and properties of Co3O4 and PbO2 particles were kept. The electrochemical property and corrosion resistance of the composite inert anodes in a zinc electrowinning simulation solution were improved with the rise of heating temperatures during the vacuum hot pressing, and those were also enhanced by the doping of Co3O4 and PbO2 particles. This method solved the problems of the lower dispersion uniformity of the composite inert anodes prepared by composite electrodeposition or traditional casting technique.

Photocatalytic water splitting is attracting enormous interest for the storage of solar energy but no practical method has yet been identified. In the past decades, various systems have been developed but most of them suffer from low activities, a narrow range of absorption and poor quantum efficiencies (Q.E.) due to fast recombination of charge carriers. Here we report a dramatic suppression of electron-hole pair recombination on the surface of N-doped TiO2 based nanocatalysts under enhanced concentrations of H+ and OH−, and local electric field polarization of a MgO (111) support during photolysis of water at elevated temperatures. Thus, a broad optical absorption is seen, producing O2 and H2 in a 1:2 molar ratio with a H2 evolution rate of over 11,000 μmol g−1 h−1 without any sacrificial reagents at 270 °C. An exceptional range of Q.E. from 81.8% at 437 nm to 3.2% at 1000 nm is also reported. Chemical fuels, produced from light, afford an alternative to fossil fuel, but conversion materials suffer from low photon-to-fuel efficiencies. Here, authors incorporate gold/N-doped TiO2 on MgO surfaces and show enhanced photocatalytic water splitting performances at elevated temperatures.

Two novel designs of DC-DC boost converters, one using cascaded structure and one with low duty cycle are presented in this paper. Each of them consists two boosting cells with only one switch to control the on-off state of the circuit. The main advantage of these two designs is providing high step-up output ratio. This is significantly useful for renewable energy system with low output voltage, such as solar energy systems, to boost its output voltage to higher ones for grid-connected equipment. One design works under low duty cycle (less than 36%). The operational modes are designed in continuous-current mode (CCM) and this can simplify the analysis and understanding of the converter circuit. In this paper, the theoretical analysis and simulation results will verify the potential value of two designs and the designed parameters will be listed for further research.

Based on the concept of the conventional quadratic boost converter, an improved topology is presented in this paper. In this design, a coupled-inductor will be utilized to increase the voltage gain of the circuit. The most significant advantage of this converter design is that the circuit has a high step-up ratio with a very low voltage stress on the switch. Moreover, this circuit utilizes minimum additional component to improve the converter performances. The inductor selection of this converter design is to ensure working in continuous-current mode (CCM) for various load conditions. Detailed circuit operation principle and theoretical voltage conversion ratio is derived. Finally, converter prototype was built to verify that using different coupling ratio can achieve high step up voltage gain capability.

Scene-based non-uniformity correction (SBNUC) algorithms are an important part of infrared image processing; however, SBNUC algorithms usually cause two defects: (1) ghosting artifacts and (2) over-correction. In this paper, we use the absolute difference based on guided image filter (AD-GF) method to validate the performance of SBNUC algorithms. We obtain a self-separation source using the improved guided image filter to process the input image, and use the self-separation source to obtain the space-high-frequency parts of the input image and the corrected image. Finally, we use the absolute difference between the two space-high-frequency parts as the evaluation result. Based on experimental results, the AD-GF method has better robustness and can validate the performance of SBNUC algorithms even if ghosting artifacts or over-correction occur. Also the AD-GF method can measure how SBNUC algorithms perform in the time domain, it’s an effective evaluation method for SBNUC algorithm.

Watching batteries fail Rechargeable batteries lose capacity in part because of physical changes in the electrodes caused by electrochemical cycling. Lim et al. track the reaction dynamics of an electrode material, LiFePO 4 , by measuring the relative concentrations of Fe(II) and Fe(III) in it by means of high-resolution x-ray absorption spectrometry (see the Perspective by Schougaard). The exchange current density is then mapped for Li + insertion and removal. At fast cycling rates, solid solutions form as Li + is removed and inserted. However, at slow cycling rates, nanoscale phase separation occurs within battery particles, which eventually shortens battery life. Science , this issue p. 566 ; see also p. 543

Ex situ transmission X-ray microscopy reveals micrometer-scale state-of-charge heterogeneity in solid-solution Li1- x Ni1/3 Co1/3 Mn1/3 O2 secondary particles even after extensive relaxation. The heterogeneity generates overcharged domains at the cutoff voltage, which may accelerate capacity fading and increase impedance with extended cycling. It is proposed that optimized secondary structures can minimize the state-of-charge heterogeneity by mitigating the buildup of nonuniform internal stresses associated with volume changes during charge.

In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.

The slight abuse of lithium ion power batteries is inevitable during the practical charge/discharge process. Herein, we investigated the cycle decay behavior of Li(Ni1/3Co1/3Mn1/3)O2/graphite (NCM/C) high-power battery during slight overcharge (110% SOC) and over-discharge (2 V for lower cut-off voltage). The results show that the cycle life of NCM/C battery is about 1500 cycles at 45 °C, whereas the slight overcharge would markedly accelerate the capacity degradation and shorten the cycle life (only 500 cycles). In contrast, the slight over-discharge causes less damage (about 1300 cycles). A post-mortem study further reveals that the severe aging during overcharge can be mainly ascribed to the capacity loss of the layered oxide cathode material (NCM), followed by an inhomogeneous degradation in the anode. The ex situ XRD results show that the origin of the differing abuse tolerance during overcharge and over-discharge is due to the different crystal structure stability of NCM, which is more unstable in the delithiation state than that in the lithiation state, particularly at an excess delithiation condition corresponding to the overcharge state. Through HR-TEM, it is observed that the NCM suffers from irreversible phase transformation (8–10 nm rock salt phase at surface) even with a slight overcharge. This work provides effective guidance for how to design a voltage control strategy in a battery management system and avoid the capacity decay of NCM/C high-power battery during overcharge and over-discharge application.

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

In this paper, a heuristic algorithm fusing with niche identification (NIT) and Artificial Bee Colony (ABC) technique is developed to solve multimodal optimization problems, and is then applied for structural damage detection. In order to improve the detection accuracy of the proposed algorithm, the Depth First Search (DFS) is adopted, and a new particle update scheme is proposed to maintain the diversity of particle populations. The effectiveness and robustness of the algorithm for multimodal optimization are demonstrated by the well-known benchmark functions. Case studies on structural damage detection are carried out using ANSYS-powered data. Simulation results show that, even for the contaminated data or extreme damage scenarios (e.g., the adjacent damages), the DFS-based nNIT with ABC technique can lead to a satisfactory result.

0.8 to 1 μm Si and Be doped InSb were grown by molecular beam epitaxy. X-ray diffraction and atomic force microscope indicate that growth temperature below 340 °C severely results in dislocations and undulating surface. Silicon shows amphoteric doping nature in InSb at higher growth or Si cell temperatures, for the calculation result reveals it needed more energy to take the place of Sb. ECV measurement demonstrates that InSb/In0.9Al0.1Sb superlattice buffer layer can effectively inhibit Be dopant from diffusing to the substrate in the 420 °C InSb, with which both the doping level and crystalline quality are significantly improved. 790–870 °C Be doped InSb transfer from p- to n-type during the Hall measurement temperature increasing from 77 K to 330 K. The transition temperature of each sample is linearly correlated to the Be doping temperature, which is theoretically interpreted by the excitation mechanism and carrier transportation principles inside InSb.

Recent years have witnessed successful applications of non-thermal plasmas (NTPs) as efficient and flexible tools for direct conversion of liquid alcohols into higher-value energy carriers at mild or even near-room-temperature conditions. Better understanding of the fundamental physical properties of the liquid alcohol discharges is needed for development and optimization of NTP-based applications. Here we investigate the discharge characteristics of a plasma-based liquid ethanol conversion under different process parameters and reveal the general trends of the discharge behavior. Two discharge modes, namely the self-pulsed and direct current (DC) modes, are sustained during the in-liquid discharge. These two modes can be easily interchanged and flexibly controlled by adjusting the processing parameters. The behavior of the bubbles generated during the discharge plays an important role in the transition between the discharge modes. When the discharge is sustained with the diffusion and floating of the bubbles, the DC mode is observed. Otherwise, the discharge develops into the pulse mode. The production rates and composition of the gaseous products are also shown to depend on the discharge modes.

Author(s): Bartelt, NC; Li, Y; Sugar, JD; Fenton, K; Kilcoyne, ALD; Shapiro, DA; Tyliszczak, T; Chueh, WC; El Gabaly, F | Abstract: Incorporation of ions into battery electrodes can lead to phase transformations. When multiparticle phase-transforming electrodes charge or discharge, two processes must occur in each particle: the new phase must nucleate, and then grow until the particle is fully charged or discharged. A fundamental question is which of these two processes is rate limiting. Here we construct a simple stochastic model that shows how the relative rate of nucleation compared with growth determines the particle state-of-charge distributions in the electrode. We find that the number of particles that are partially charged at any time increases as the relative nucleation rate increases. The maximum number of particles that are actively charging occurs just before the time when the first particles are becoming completely charged. By comparing measured state-of-charge distributions with the model, we determine the relative rate of nucleation. We apply this procedure to measurements of the evolution of particles in LiFePO4 cathodes and show we can account for the particle state-of-charge distribution as a function of the electrode state of charge.

With the improvement of people’s living conditions, the applications of various TV display screens are becoming more and more common. Typically, the person viewing the display screen has a fixed angle and does not make full use of the view field, so the viewing quality is lowered. To improve the viewing efficiency, we propose a smart screen rotation platform for the TV Pan Tilt Zoom (PTZ). Through the Raspberry Pi microcomputer, image sensors in front of the screen are used to acquire the images of the viewer’s face and surroundings. Based on the machine vision technique, the human face is recognized, and the face orientation is found. The identification results are delivered to the lower machine–ATMEL328P microcontroller, which controls the PTZ actuator to rotate, so that the screen is able to align the viewer’s face and help to realize the high-quality watching.

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods.

We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density. A merge-in support system has to estimate the chances that a gap between two consecutive vehicles can be taken successfully. In contrast to previous models based on heuristic gap size rules, we present an approach which optimizes the integral risk of the situation using parametrized velocity ramps. It accounts for the risks from curves and all involved vehicles (front and rear on all paths) with a so-called survival analysis. For comparison, we also introduce a specially designed extension of the Intelligent Driver Model (IDM) for entering intersections. We show in a quantitative statistical evaluation that the survival method provides advantages in terms of lower absolute risk (i.e., no crash happens) and better risk-utility tradeoff (i.e., making better use of appearing gaps). Furthermore, our approach generalizes to more complex situations with additional risk sources., Comment: Intelligent Vehicles Symposium

Based on the topology of the quadratic boost converter, this paper presents three new improved topologies of boost converters employing coupled inductors, two converters using one coupled inductor and one converter using two coupled inductors. Generally speaking, the use of coupled inductors is very helpful for increasing the step up ratio of the dc-dc boost converter. Each converter has a high step up ratio and a high power transferring efficiency. In this paper, the operating principles and theoretical analysis will be discussed in details. And also simulation circuits will be developed by using the software LTspice XVII to illustrate the capability and potential value of the proposed circuits.

The dc-dc boost converter is one of the main components in any solar energy system. However, traditional boost converter cannot meet the demand on higher step-up ratio required of the system. Voltage lift technique has made a great success in improving the dc-dc boost converter design. This technique can largely increase the output voltage step-up ratio stage-by-stage. The quadratic dc-dc boost converter has a higher step-up ratio and also a good power transferring efficiency during continuous conduction mode (CCM). In this paper, the basic quadratic boost converter is modified by the voltage lift technique to further increase the step-up ratio. The mathematical analysis and simulation results presented in the paper will illustrate the capacity and potential value of this improvement.

Lithium-rich layered transition metal oxide positive electrodes offer access to anion redox at high potentials, thereby promising high energy densities for lithium-ion batteries. However, anion redox is also associated with several unfavorable electrochemical properties, such as open-circuit voltage hysteresis. Here we reveal that in Li1.17–xNi0.21Co0.08Mn0.54O2, these properties arise from a strong coupling between anion redox and cation migration. We combine various X-ray spectroscopic, microscopic, and structural probes to show that partially reversible transition metal migration decreases the potential of the bulk oxygen redox couple by > 1 V, leading to a reordering in the anionic and cationic redox potentials during cycling. First principles calculations show that this is due to the drastic change in the local oxygen coordination environments associated with the transition metal migration. We propose that this mechanism is involved in stabilizing the oxygen redox couple, which we observe spectroscopically to persist for 500 charge/discharge cycles., Lithium ion battery electrodes employing anion redox exhibit high energy densities but suffer from poor cyclability. Here the authors reveal that the voltage of anion redox is strongly affected by structural changes that occur during battery cycling, explaining its unique electrochemical properties.

The minority carrier diffusion length (LD) is a crucial property that determines the performance of light absorbers in photoelectrochemical (PEC) cells. Many transition-metal oxides are stable photoanodes for solar water splitting but exhibit a small to moderate LD, ranging from a few nanometers (such as α-Fe2O3 and TiO2) to a few tens of nanometers (such as BiVO4). Under operating conditions, the temperature of PEC cells can deviate substantially from ambient, yet the temperature dependence of LD has not been quantified. In this work, we show that measuring the photocurrent as a function of both temperature and absorber dimensions provides a quantitative method for evaluating the temperature-dependent minority carrier transport. By measuring photocurrents of nonstoichiometric rutile TiO2–x nanowires as a function of wire radius (19–75 nm) and temperature (10–70 °C), we extract the minority carrier diffusion length along with its activation energy. The minority carrier diffusion length in TiO2–x increases...

Human activity recognition using wearable sensors plays a significant role in many applications. How to accurately and quickly recognize various activities based on wearable sensors draws more and more attentions. This paper proposes an accelerated sparse representation classification method based on random projection and k-nearest neighbor for human activity recognition. Random projection is first applied to reduce the dimensionality of the activity signal sampled from each wearable sensor. To optimally reconstruct an activity test sample, some nearest neighbor training samples from a few near neighbor classes of the test sample are selected to form a reduced training sample set, based on which the test sample can be sparsely represented. And then the activity class of the test sample is determined by solving an L1 minimization problem. The effectiveness of our method is experimentally validated on an open Wearable Action Recognition Database by recognizing nine common human activities performed by 20 subjects. Our method achieves the highest average recognition rate (92.56%), which beats the traditional sparse representation classification method and the conventional near neighbor algorithm. Meanwhile, the runtime of our method is significantly reduced compared to the traditional sparse representation classification method.

Engineering surfaces with functional polymers is a crucial issue in the field of micro/nanofabrication and cell-material interface studies. For many applications of surface patterning, it does not need cells to attach on the whole surface. Herein, we introduce a novel polymer fabrication protocol of off-stoichiometry thiol-ene (OSTE) polymers to create heterogeneity on the surface by utilizing 3D printing and soft-lithography. By choosing two OSTE polymers with different functional groups, we create a pattern where only parts of the surface can facilitate cell adhesion. We also study the hydrophilic property of OSTE polymers by mixing poly(ethylene glycol) (PEG) directly with pre-polymers and plasma treatments afterwards. Moreover, we investigate the effect of functional groups' excess ratio and hydrophilic property on the cell adhesion ability of OSTE polymers. The results show that the cell adhesion ability of OSTE materials can be tuned within a wide range by the coupling effect of functional groups' excess ratio and hydrophilic property. Meanwhile, by mixing PEG with pre-polymers and undergoing oxygen plasma treatment afterward can significantly improve the hydrophilic property of OSTE polymers.

Body activity recognition using wearable sensor technology has drawn more and more attentions over the past few decades. The complexity and variety of body activities makes it difficult to fast, accurately and automatically recognize body activities. To solve this problem, this paper formulates body activity recognition problem as a classification problem using data collected by wearable sensors. And three different machine learning algorithms, support vector machine, hidden markov model and artificial neural network are presented to recognize different body activities. Various numerical experiments on a real-world wearable sensors dataset are designed to verify the effectiveness of these classification algorithms. Finally the results demonstrate that all the three algorithms achieve satisfactory activity recognition performance.

Fast and accurately recognizing human activities is a major challenge in wireless body sensor networks. In this paper, with the help of wearable sensors, the recognition of human activities is well solved from a compressed sensing perspective. Our proposed method first compresses the original sensing data collected using wearable sensors by applying random projections on each sensor node before the data are transmitted to the central node. On the central node, each activity test sample can be approximately represented as a linear combination of all the activity samples in the training set. Then the L1 minimization method is applied to derive the sparse representation of the test sample. Residuals between the activity test sample and its corresponding sparse representation form with only nonzero entries in each activity class are calculated. The activity test sample is finally classified into the activity class with the smallest residual. Our sparse representation based activity recognition method is evaluated on a real-world dataset and its experimental results are also compared with those of Support Vector Machine and Hidden Markov Model. Outcomes of the numerical experiments show that our proposed method achieves the highest recognition rate among the three methods, which verifies the effectiveness of our method and demonstrates that sparse representation classification method based on random projections is promising in recognizing human activities using wearable sensors.