High-power InP MISFETs

InP MISFETs with SiO2 as the gate insulator and a deep channel recess have been fabricated. At 9 GHz the highest power output with 4 dB gain was 3.5 W/mm gate width with 33% power added efficiency. This power is more than twice that of the best GaAs MESFET.     

REINFORCEMENT LEARNING FOR POMDP USING STATE CLASSIFICATION

Reinforcement learning (RL) has been widely used to solve problems with a little feedback from environment. Q learning can solve Markov decision processes (MDPs) quite well. For partially observable Markov decision processes (POMDPs), a recurrent neural network (RNN) can be used to approximate Q values. However, learning time for these problems is typically very long. We present a new combination of RL and RNN to find a good policy for POMDPs in a shorter learning time. This method contains two phases: firstly, state space is divided into two groups (fully observable state group and hidden state group); secondly, a Q value table is used to store values of fully observable states and an RNN is used to approximate values for hidden states. Results of experiments in two grid world problems show that the proposed method enables an agent to acquire a policy with better learning performance compared to the method using only a RNN.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Improved fracture toughness and fatigue life of carbon fiber reinforced epoxy composite due to incorporation of rubber nanoparticles

In this study, core–shell rubber (CSR) nanoparticles with approximate particle size of 35 nm were used as a modifier for the epoxy polymer. The effects of various CSR contents in the epoxy matrix on mode I interlaminar fracture toughness, tensile strength, and fatigue life of the carbon fabric reinforced epoxy (CF/EP) composites were investigated. The experimental results showed that the mode I interlaminar fracture toughness at crack initiation and propagation significantly improved by 71.21 and 58.47 %, respectively, when 8.0 wt% CSR was dispersed in the epoxy matrix. The fatigue life of the modified CF/EP composites at all of CSR contents dramatically increased 75–100 times longer than that of the unmodified CF/EP composites at high cycle fatigue while tensile strength slightly increased by about 10 %. Field emission scanning electron microcopy (FESEM) observations of the fracture surfaces were conducted to explain failure mechanisms of CSR addition to the CF/EP composites. The evidences of the rubber nanoparticle debonding, plastic void growth, and microshear banding were credited for delaying the onset of matrix crack, and reducing the crack growth rate, as a result, attributed to increase in the mechanical properties of the CF/EP composites.     

Experimental analysis of particle deposition on single collectors

Experimental studies of particle deposition from liquid suspensions of monodispersed particles flowing past single spherical collectors were carried out and compared with theoretical results based on trajectory calculations. The experimental observations indicate the significant effect of particle flocculation and thus suggest the importance of sedimentation as a collection mechanism. The agreement between the theoretical results and the experimental data is good when the surface force interactions are favorable for collection. However, when the electrokinetic repulsion dominates in the neighborhood of the collector, the rate of collection drops rapidly, although not to zero as predicted by theory.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Plasma deposition of GaAs

The deposition of an amorphous film of GaAs with a low frequency and low power glow discharge is described. The structure and composition of the film were determined by IR, secondary ion mass spectrometry, electron paramagnetic resonance and electron diffraction measurements. First results for the dark conductivity and the optical absorption are reported.     

色谱测UNIFAC参数的方法研究

UNTFAC参数通常从汽液平衡数据回归得出.鉴于汽液平衡实验繁琐且不经济,而气液色谱实验简单、迅速、经济、可靠,近年来巳用于测UNIFAC参数,其中又有两种方法:一种用单体系无限稀释活度系数γ~∞,加上单参数Flory-Huggins方程回归;另一种由几个体系的γ~∞联立回归.前者仅适用于分子大小悬殊的体系,后者则相对来说精度较差、工作量较大.本工作采用了单体系γ~∞与无限稀释条件下活度系数-组成曲线斜率结合回归(简称斜率法).斜率测定用Valentin-Guiochon公式.比较了三种方法,工作表明:多γ~∞法一般较差,斜率法结果较为满意.分析了UNIFAC参数对γ~∞及((?)γ)/(?)x)_x=0的灵敏度,发现γ~∞的影响比((?)γ/(?)x)_x=0大.用极大似然法讨论了参数估计与置信区间.     

Short-term effects of selenium supplementation of cows' feed on the content and distribution of selenium, copper and zinc in bovine milk, whey and blood plasma

The effect of selenium supplementation of feed on the Se content in bovine milk, whey and plasma, and on the distribution of Se, Zn and Cu in whey and plasma was investigated. In a cross-over study two groups of cows were given a basal feed with 0.16 ppm selenite (approx. 3 mg Se/d) with or without 25 mg yeast Se/d for 2 weeks. In the supplemented group the Se content increased 10-fold in milk, 10-fold in whey and 2-fold in plasma, and after the cessation of the supplementation, selenium in milk decreased with a calculated half-life of 3.5 d. In another experiment, two groups of cows were given either 100 mg yeast Se/d for 1 week or only the basal feed. The increase in Se content in both whole and defatted milk was 40-50-fold, and in whey it was approx. 20-fold. Size-exclusion chromatography of whey using inductively coupled plasma mass spectrometry for detection showed that supplementation increased the proportion of Se in the beta-lactoglobulin-alpha-lactalbumin fraction. Distribution of Cu and Zn was essentially unaffected. In plasma, supplementation increased the Se content in all major Se fractions like selenoprotein P, albumin and low-molecular-weight compounds, but the distribution profiles of Zn and Cu underwent no major changes. The study showed for the first time the rapid kinetics of the Se increase and decrease in milk after the initiation and cessation of supplementation, respectively, and the preferential appearance of Se in the beta-lactoglobulin-alpha-lactalbumin fraction of whey. Milk highly enriched in selenium will be a useful tool for different research purposes.     

Scanline-based stereo matching by genetic algorithms

The computer stereo-vision system (CSVS) is a non-emissive, non-scanning, economic and flexible vision system. It adopts a pair of cameras at different positions and acquires simultaneously two images of an object to derive information on depth. The problem of correspondence between images is one of most important issues in the study of CSYS. The key concern of this problem is to determine which feature in one image corresponds to a given feature in the other image. Owing to the high complexity of the correspondence problem, the matching process usually suffers from slow computation time and imprecise results. The present paper proposes a scanline-~ased stereo-matching model. The objective function of the proposed model is a function with the means and variance of intensity, gradient magnitude and direction, and related constraints including uniqueness, geometry and epipolar constraints. The formulated energy function is then solved using genetic algorithms. Binary representation is used to match the relationship between the candidate features of both images. Two operators, the roulette selection operator and elite principle, are adopted for offspring selection. A modified position-based crossover operator and its repair mechanism are developed to exchange partially the chromosomes. In addition, an order-based mutation is used to prevent early convergence. Experiments show that the designed selection, crossover and mutation operators can effectively derive the matching relations line by line for real-world testing images. The proposed method was also compared with another method and was superior in computation speed and required less demand of the memory.