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Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30–200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.

Spike sorting, which classifies spiking events of different neurons from single electrode recordings, is an essential and widely used step in neural data processing and analysis. The recent development of brain-machine interfaces enables online control of external devices and closed-loop neuroprosthetics using single-unit activity, making online spike sorting desired. Most existing spike sorters work in an offline manner, i.e., sorting after data collection. However, offline spike sorters usually suffer from performance degradation in online tasks due to the instability of neural signals. In an online process, neuronal properties can change over time (such as waveform deformations), and new neurons can appear. Therefore, a static spike sorter requires periodic recalibration to maintain its performance. This study proposes a novel online spike sorter based on neuromorphic models (NeuSort), which can adaptively adjust itself to cope with changes in neural signals. NeuSort can robustly track individual neurons' activities against waveform deformations and automatically recognize new coming neurons in real-time. The adaptation ability of NeuSort is achieved by online parameter updates of the neuromorphic model, according to the plasticity learning rule inspired by biological neural systems. Experimental results on both synthetic and neural signal datasets demonstrate that NeuSort can classify spiking events automatically and cope with non-stationary situations in neural signals. NeuSort also provides ultra-low energy cost computation with neuromorphic chips.

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing research in this domain has primarily focused on maximizing the utilization of potentially relevant information in images or incorporating external knowledge from explicit knowledge bases (KBs). However, these methods either neglect the necessity of providing the model with relevant external knowledge, or the retrieved external knowledge suffers from high redundancy. To address these problems, we propose a conceptually simple two-stage framework called Prompt ChatGPT In MNER (PGIM) in this paper. We leverage ChatGPT as an implicit knowledge engine to acquire auxiliary refined knowledge, thereby bolstering the model's performance in MNER tasks. Specifically, we first utilize a Multimodal Similar Example Awareness module to select suitable examples from a small number of manually annotated samples. These examples are then integrated into a formatted prompt template tailored to the MNER task, guiding ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the raw text and inputted into the downstream model for further processing. Extensive experiments show that our PGIM significantly outperforms all existing state-of-the-art methods on two classic MNER datasets.

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage.

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance., Comment: Accepted by CVPR 2023

Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self-calibration of extrinsic and intrinsic parameters in a system containing multiple cameras and a lidar sensor. Existing methods typically require specially designed calibration targets and human operators, or they only attempt to solve for a subset of calibration parameters. We resolve these issues with a fully automatic method that requires no explicit correspondences between camera images and lidar point clouds, allowing for robustness to many outdoor environments. Furthermore, the full system is jointly calibrated with explicit cross-camera constraints to ensure that camera-to-camera and camera-to-lidar extrinsic parameters are consistent., Comment: 7 pages, 6 figures, ICRA 2023 conference

In the Beyond Moore Law era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50x faster than Moore law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges., Comment: 88 pages currently under peer review with Nano

Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current network structures and their training methods. As discrete signals, typical SNNs cannot apply the gradient descent rules directly into parameters adjustment as artificial neural networks (ANNs). Aiming at this limitation, here we propose a novel method of constructing deep SNN models with knowledge distillation (KD) that uses ANN as teacher model and SNN as student model. Through ANN-SNN joint training algorithm, the student SNN model can learn rich feature information from the teacher ANN model through the KD method, yet it avoids training SNN from scratch when communicating with non-differentiable spikes. Our method can not only build a more efficient deep spiking structure feasibly and reasonably, but use few time steps to train whole model compared to direct training or ANN to SNN methods. More importantly, it has a superb ability of noise immunity for various types of artificial noises and natural signals. The proposed novel method provides efficient ways to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios.

This study aims to investigate the effects of adding Lactobacillus buchneri (LB), Lactobacillus brevis (LBR) and Bacillus subtilis (BS) on the fermentation quality, nitrate degradation and bacterial community of sorghum-sudangrass silage. The results showed that the addition of LB significantly increased the pH and acetic acid content (p 2O at 3–7 days of ensiling (p 2O at 1–40 days of ensiling (p Weisslla, over 3 days of ensiling all silages were dominated by Lactobacillus. Acinetobacter, Serratia, Aquabacterium, and unclassified_f_enterobacteriaceae showed significant negative correlations with nitrate degradation during sorghum-sudangrass ensiling (p p

Additional file 1. Details about the data and methods including examples.

Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a Conservative Update Policy algorithm with a theoretical safety guarantee. We derive the CUP based on the new proposed performance bounds and surrogate functions. Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE). GAE significantly reduces variance empirically while maintaining a tolerable level of bias, which is an efficient step for us to design CUP; (ii) The proposed bounds are tighter than existing works, i.e., using the proposed bounds as surrogate functions are better local approximations to the objective and safety constraints. (iii) The proposed CUP provides a non-convex implementation via first-order optimizers, which does not depend on any convex approximation. Finally, extensive experiments show the effectiveness of CUP where the agent satisfies safe constraints. We have opened the source code of CUP at https://github.com/RL-boxes/Safe-RL.

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associating reflectance to light conditions or resolving light conditions in a supervised manner. This paper establishes an implicit relation between light clues and light estimation and solves UPS in an unsupervised manner. The key idea is to represent the reflectance as four neural intrinsics fields, i.e., position, light, specular, and shadow, based on which the neural light field is implicitly associated with light clues of specular reflectance and cast shadow. The unsupervised, joint optimization of neural intrinsics fields can be free from training data bias as well as accumulating error, and fully exploits all observed pixel values for UPS. Our method achieves a superior performance advantage over state-of-the-art UPS methods on public and self-collected datasets, under regular and challenging setups. The code will be released soon.

Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components and decode each component separately. Specifically, we decode shape and semantic representations from the lower and higher visual cortex respectively, and merge the shape and semantic information to images by a generative adversarial network (Shape-Semantic GAN). This 'divide and conquer' strategy captures visual information more accurately. Experiments demonstrate that Shape-Semantic GAN improves the reconstruction similarity and image quality, and achieves the state-of-the-art image reconstruction performance.

The underground sewage system is a crucial part of the infrastructure of any modern municipality across the globe. The underground sewage system is a crucial part of the infrastructure of any modern municipality across the globe. However, with the passage of time, sewer pipes systematically become prone to distortion depending upon the degree of usage and location factors such as the passage of heavy traffic nearby or deep-rooted trees. So, the regular inspection of these pipes requires a lot of technical attention as failure to do so can cause a huge financial and environmental disaster. In this regard, CCTV inspection is considered an effective way of monitoring buried pipes worldwide. As these drainage pipes are extensively spread across several miles, their inspection becomes a time-consuming, tiring, and expensive task. In this study, we propose a crack localization framework by taking the advantages of deep learning algorithms. The framework focuses on the proper organization of input data by using deformable bounding boxes to label the images and prepare them in a suitable format for training the model. An effective Convolutional Neural Network (CNN) model is proposed which addresses the problems of detecting arbitrary-shaped objects, is implemented. The proposed model provides the best tradeoff between speed and accuracy as it uses a segmentation head consisting of a convolutional block with two pivotal attention modules in a sequential manner that can enhance the quality of extracted features with very low computational cost, to support the lightweight ResNet backbone detection algorithm. The model trained with 3150 unique crack image samples, with 700 extra images for validation and testing. To determine the performance of the model, final ground-truth results are compared with testing input bounding box values and other state-of-the-art quantitative metrics e.g. Precision × Recall curve and Average Precision (AP) are explained in which model showed AP value of 80.06%. The model showed the output images in which cracks are localized with excellent accuracy.

We study the convergence of $\mathtt{Expected~Sarsa}(��)$ with linear function approximation. We show that applying the off-line estimate (multi-step bootstrapping) to $\mathtt{Expected~Sarsa}(��)$ is unstable for off-policy learning. Furthermore, based on convex-concave saddle-point framework, we propose a convergent $\mathtt{Gradient~Expected~Sarsa}(��)$ ($\mathtt{GES}(��)$) algorithm. The theoretical analysis shows that our $\mathtt{GES}(��)$ converges to the optimal solution at a linear convergence rate, which is comparable to extensive existing state-of-the-art gradient temporal difference learning algorithms. Furthermore, we develop a Lyapunov function technique to investigate how the step-size influences finite-time performance of $\mathtt{GES}(��)$, such technique of Lyapunov function can be potentially generalized to other GTD algorithms. Finally, we conduct experiments to verify the effectiveness of our $\mathtt{GES}(��)$., This submission has been accepted by AAAI2021

Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup. However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems. The traces are cut immediately when a non-greedy action is taken, which may lose the advantage of eligibility traces and slow down the learning process. Alternatively, some non-probabilistic measurement methods such as General Q($��$) and Naive Q($��$) never cut traces, but face convergence problems in practice. To address the above issues, this paper introduces a new method named TBQ($��$), which effectively unifies the tree-backup algorithm and Naive Q($��$). By introducing a new parameter $��$ to illustrate the \emph{degree} of utilizing traces, TBQ($��$) creates an effective integration of TB($��$) and Naive Q($��$) and continuous role shift between them. The contraction property of TB($��$) is theoretically analyzed for both policy evaluation and control settings. We also derive the online version of TBQ($��$) and give the convergence proof. We empirically show that, for $��\in(0,1]$ in $��$-greedy policies, there exists some degree of utilizing traces for $��\in[0,1]$, which can improve the efficiency in trace utilization for off-policy reinforcement learning, to both accelerate the learning process and improve the performance., 8 pages

Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q$(��,��)$ is the first approach unifies them with eligibility trace through the sampling degree $��$. However, it is limited to the tabular case, for large-scale learning, the Q$(��,��)$ is too expensive to require a huge volume of tables to accurately storage value functions. To address above problem, we propose a GQ$(��,��)$ that extends tabular Q$(��,��)$ with linear function approximation. We prove the convergence of GQ$(��,��)$. Empirical results on some standard domains show that GQ$(��,��)$ with a combination of full-sampling with pure-expectation reach a better performance than full-sampling and pure-expectation methods.

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationary property due to abrupt noises and neuroplastic changes in brain activities during motor control. Current state-of-the-art neural signal decoders such as Kalman filter assume fixed relationship between neural activities and motor movements, thus will fail if this assumption is not satisfied. We propose a dynamic ensemble modeling (DyEnsemble) approach that is capable of adapting to changes in neural signals by employing a proper combination of decoding functions. The DyEnsemble method firstly learns a set of diverse candidate models. Then, it dynamically selects and combines these models online according to Bayesian updating mechanism. Our method can mitigate the effect of noises and cope with different task behaviors by automatic model switching, thus gives more accurate predictions. Experiments with neural data demonstrate that the DyEnsemble method outperforms Kalman filters remarkably, and its advantage is more obvious with noisy signals.

In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces challenge in continuous control tasks. Conventional methods always compute the derivatives of the optimal goal with a costly computation resources, and are inefficient, unstable and lack of robust-ness when dealing with such tasks. Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning. The combination of both methods so as to get the best of the both has raised attention. However, most of the existing combination works adopt complex neural networks (NNs) as the policy for control. The double-edged sword of deep NNs can yield better performance, but also makes it difficult for parameter tuning and computation. To this end, in this paper we presents a novel method called FiDi-RL, which incorporates deep RL with Finite-Difference (FiDi) policy search.FiDi-RL combines Deep Deterministic Policy Gradients (DDPG)with Augment Random Search (ARS) and aims at improving the data efficiency of ARS. The empirical results show that FiDi-RL can improves the performance and stability of ARS, and provide competitive results against some existing deep reinforcement learning methods, Comment: I found some theoretical errors

Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.

Additional file 2: Table S1. The pH in piggery wastewater during the process of culturing C. pyrenoidosa. Data are presented as the means Âą standard deviation of the mean. CA means sparging air, CC means sparging simulated flue gas, PA means culturing C. pyrenoidosa with sparging air, and PC means culturing C. pyrenoidosa with sparging simulated flue gas.

Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes $\mathtt{VRMPO}$ algorithm: a sample efficient policy gradient method with stochastic mirror descent. In $\mathtt{VRMPO}$, a novel variance-reduced policy gradient estimator is presented to improve sample efficiency. We prove that the proposed $\mathtt{VRMPO}$ needs only $\mathcal{O}(\epsilon^{-3})$ sample trajectories to achieve an $\epsilon$-approximate first-order stationary point, which matches the best sample complexity for policy optimization. The extensive experimental results demonstrate that $\mathtt{VRMPO}$ outperforms the state-of-the-art policy gradient methods in various settings.

The data presented in this article are related to the research article entitled “Bicarbonate-rich wastewater as a carbon fertilizer for culture of Dictyosphaerium sp. of a giant pyrenoid” (Cheng et al., 2018) [1] . This article provides data about the expression levels of the genes involved in the synthesis of structural molecules in the carbon-deficient algal cell and the carbon-treated algal cell, which can be helpful for analyzing the observed disruption of the structural integrity in the carbon-deficient microalgae at molecular level.

Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess potential of achieving energy-efficiency while keeping comparable performance to deep neural networks (DNNs). However, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet). ResNet is considered as a kind of the state-of-the-art convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons, named Spiking ResNet (S-ResNet). We propose a shortcut conversion model to appropriately scale continuous-valued activations to match firing rates in SNN, and a compensation mechanism to reduce the error caused by discretisation. Experimental results demonstrate that, compared with the state-of-the-art SNN approaches, the proposed Spiking ResNet achieves the best performance on CIFAR-10, CIFAR-100, and ImageNet 2012. Our work is the first time to build a SNN deeper than 40, with comparable performance to ANNs on a large-scale dataset.

Spectral graph theory has been widely applied in unsupervised and semi-supervised learning. In this paper, we find for the first time, to our knowledge, that it also plays a concrete role in supervised classification. It turns out that two classifiers are inherently related to the theory: linear regression for classification (LRC) and normalized radial basis function network (nRBFN), corresponding to linear and nonlinear kernel respectively. The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk. With the theory, ideal working conditions for LRC and nRBFN are presented, which ensure not only zero fitting error but also low overfitting risk. For quantitative analysis, two concepts, the fitting error and the spectral risk (indicating overfitting), have been defined. Their bounds for nRBFN and LRC are derived. A special result shows that the spectral risk of nRBFN is lower bounded by the number of classes and upper bounded by the size of radial basis. When the conditions are not met exactly, the classifiers will pursue the minimum fitting error, running into the risk of overfitting. It turns out that $\ell_2$-norm regularization can be applied to control overfitting. Its effect is explored under the spectral context. It is found that the two terms in the $\ell_2$-regularized objective are one-one correspondent to the fitting error and the spectral risk, revealing a tradeoff between the two quantities. Concerning practical performance, we devise a basis selection strategy to address the main problem hindering the applications of (n)RBFN. With the strategy, nRBFN is easy to implement yet flexible. Experiments on 14 benchmark data sets show the performance of nRBFN is comparable to that of SVM, whereas the parameter tuning of nRBFN is much easier, leading to reduction of model selection time.

Hybrid dielectric barrier discharge-catalytic steam reforming is proposed here for use in chemically recuperated gas turbines, so as to be compatible with the low-temperature exhaust heat from turbines. Four different types of reactor were designed for comparison, and extensive experiments were performed to evaluate the exhaust heat recovery in terms of effective carbon recovery rate, methane conversion, fuel heating value increase rate, and total enthalpy increase rate. The effect of methane space velocity and reactor wall temperature on methane conversion have been analyzed. The results showed that, for any reactor, there was an optimum methane space velocity for the most heat recovery. With increasing wall temperatures, the parallel synergistic reforming technology induced more methane conversion than the other reactors. Under the same input power, parallel synergistic reactors could recover more exhaust heat than the sum of catalyst-only and plasma-only reactors, with a relative total enthalpy increase as much as three times the sum of the latter two. Moreover, the parallel synergistic reforming technology resulted in a higher relative total enthalpy increase for reformed gas at low temperatures. These results demonstrate that parallel synergistic reforming technology is a promising technology to significantly recover exhaust heat under different working conditions.

It is necessary to optimize operable factors in measurement of pulverized coal flow by Laser-induced Breakdown Spectroscopy(LIBS).Therefore,this paper investigated the effects of three main operable factors,irradiance,integration delay time and the rate of flow per unit area on the measurement of pulverized coal flow by LIBS based on orthogonal experiments.Pulverized coal was chosen for the experimental sample and the statistic analysis and variance analysis were performed.Experiment indicates that the irradiance and integration delay time are two remarkable factors for the effective excitation rate and the following is the rate of flow per unit area.The optimum parameters of control from the experiment are the irradiance of 9.4×1011 W/cm2,the integration delay time of 1 500 ns,and the rate of flow is selected reasonably based on economy principles.The results obtained demonstrate that the method can direct the practical measurement of pulverized coal flows in real time.

In order to reduce the adverse impact of the inhomogeneous of pulverized coal components,different optical path systems were set up and used to research the collection performances of plasma of pulverized coal by laser-induced breakdown spectroscopy.The coal samples,Shenmuhun,was chosen for experiment.The detected count and stability of special lines of coal in different optical collection were investigated on the self-built two-phase particle flow experiment bench,which was used to produce stable pulverized coal stream.The analysis results show that in the steadily repeat measurements,the counts of the lines collected by back-side are weaker because of the intermediate perforated mirror,while the stability of the lines collected by side are worse because of the position change of plasma along the laser beam,the spatial inhomogeneity of plasma and the blocking effect of pulverized coal.

This paper is mainly to examine the economic consequences of the financial restatement based on the auditor's perspective. We find that auditors tend to issued more severe financial audit restatement opinions to companies which made financial restatement and the more risks inherent in the nature of the financial restatement, the more severe audit opinions are likely to make. These results indicate that the auditor can identify the risks inherent in the financial restatement, and will react differently depending on the nature of risk.

A wide variety of physical and chemical detecting methods have been proposed for discriminating between an electric arc beads that caused a fire, versus one that was caused by the fire itself. The simplest proposed method claims that examination of the molten marks in a bead under a microscope will suffice to make the distinction. This paper describes the microscopic investigation of a copper molten mark formed by short circuit on a copper wire used in ordinary life in china (AC 220V). Generally, copper molten marks of the bead are examined by using optical (OM) and scanning electron microscopy (SEM). In this paper, OM and AFM were employed to investigate a molten mark formed in laboratory. AFM observation reveals that AFM could be an auxiliary method to investigate the copper molten mark formed in the fire in order to confirm the reasons of the fire.

Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory underlies a series of these elementary methods and can unify them into a complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations (e.g., method of optimal directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional scaling, Isomap, locally linear embedding), and subspace clustering (e.g., sparse subspace clustering, low-rank representation) are incorporated. We show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together. And when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. SSR combines merits of both sides: its sparse codes reduce dimensionality of data meanwhile revealing cluster structure. For its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. Scut, a clustering approach derived from SSR reaches the state-of-the-art performance in the spectral clustering family. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on various data sets demonstrate the properties and strengths of SSR, NSCrt, and Scut.

Plasma is proposed as a prospective tool for chemical heat recovery process without restriction from reaction temperature. The author designed DBD catalytic reactors and carried out extensive experiments to investigate methane conversion and products yield and analyze the effect laws of steam to methane ratio, resident time and reaction temperature on methane steam reforming (MSR). Based on extensive experimental studies of steam reforming, a detailed reaction mechanism for the plasma-assisted MSR was developed and evaluated by comparison of experimentally derived and numerically predicted conversion and products yield. The comparisons showed the kinetic model well predicted methane conversion and products yield in different operating conditions. By employing the kinetic model and path flux analysis module the kinetic effects of low temperature non-equilibrium plasma assisted CH4 steam reforming on the methane conversion was studied without catalyst. The results showed that CH3 recombination was the limiting reaction for CO production; meantime O was the critical species for CO production. By adding Ni catalyst can reduce methyl recombination and promote hydroxyl into oxygen, which is beneficial to heat recovery. The proposed research ensures the effect laws and characters of MSR by plasma, and contribute to improve the objective products concentration and furthermore the energy efficiency.Copyright © 2013 by ASME

NTAUTO is an auto-configuration software, which is developed by department of technology of Beijing GuoDianZhiShen Control Technology Co., Ltd.,. The configuration software has been applied in the configuration of 1000MW ultra-supercritical units. The automated configuration technology is the advanced technology of Beijing GuoDianZhiShen Control Technology Co., Ltd through years of accumulated technology in thermal power control field. It makes full use of computercontrol and CRT to provide a configuration solution of I/O database, SAMA control sheet, and the man-machine interface. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2378

Porous media are widely used in the production and living, and also in science and technology. With the development of energy, chemical industry, metallurgy, atomic energy and also with the progress of the modern industrial and agricultural production technology, a large number of heat and mass transfer problems in porous media gradually appear. Further promoting the development of the discipline about the formation and development of porous media becomes one of hot research points in the modern science and technology. It is expected that the accurate experimental picture and data can be obtained through the experiment, and the fluid flow picture and experimental data are analyzed in depth by using the corresponding software, so that the reliable data are obtained and the theory is supported intuitively, making the research of porous media more perfect. The experiment combined with particle image velocimetry technology and refractive index matching technique is conducted to test the transformation process of liquid flow in a random ball porous medium filled bed, and to extract the data. The extracted data are processed by using Tecplot software, and the transformation process of liquid flow mechanism is obtained. Experimental solid phase is a 25 mm-diameter crystal glass ball stacked bed, and liquid phase is the matching liquid prepared with the mixture of the 65% benzyl alcohol and 35% anhydrous ethanol. The refractive indexes of liquid phase and solid phase are both 1.477, which can successfully eliminate the laser light bending caused by the nismaching of refractive indexes. The flow field diagram in the pebble bed with Reynolds number Re in a range 4.7 Re 1000 is obtained experimentally. The comparisons of variations of flow field and flow lines among the different Reynolds numbers reveal that with the increase of Reynolds number, flow lines become more and more disorder: When the Reynolds number Rep exceeds 220, stable swirl flow inside the bed changes suddenly, and manifests a random feature in location and configuration, which forebodes its entrance into stable turbulence phase.

Robot postures are transformed continuously until an impact occurs. In order to solve the continuous state problem, a Q-Learning controller based on Back-Propagation (BP) Neural Networks is designed. Instead of Q table, a Multi-input Multi-output BP Neural Network is employed to compute Q value for continuous state. Eligibility trace is used to solve time reliability problem in Q-Learning, and we integrate the eligibility trace algorithm to the gradient descent method for continuous state. To avoid dimension explosion, an inverted pendulum pose-energy model is built to reduce the dimension of the input state space. For the sake of balance between “explore” and “exploit” of Q-Learning, we use a new e-greedy method with a variable stochastic probability, which decreases with the increasing of the step number. Simulation results indicate that the proposed method is effective.

In this paper, we present an improved synthesis algorithm for both 2D and solid textures. Based on the wavelet-based multi-resolution pyramids and the improved optimization of texture synthesis, a coarse texture was synthesized as a skeleton. By adding detail information to the coarse texture, the higher resolution results can be generated. This skeletal growth-like procedure makes the result more controllable. In addition, to make full use of the spatial relationships among texture pixels, wavelet coefficients rather than traditional RGB channels are used to search the nearest neighbor pixels. Experimental study shows that the method plays well for both 2D and solid texture synthesis.

Nowadays, most 3D mesh model protections are based on embedding watermarking system. These methods have to change the original mesh data to achieve the watermarking. Considering the sensitivity and complexity of 3D mesh data, this paper proposes a disturbing free 'zero-watermarking' algorithm, which is based on octree partition. For each octant, we employ parameterization and Singular Value Decomposition (SVD) to analyze the 3D geometry signal and construct the zero-watermark with corresponding octree-codec. The integrity of protected 3D mesh can be checked through computing the similarity of watermark between original and processed mesh. Moreover, the results are represented in a visual way, which means the tendency of deviation and changed region will be colored according to our merits of similarity. The Experiment results verify that the algorithm is robust to various attacks including affine transformation, vetex reordering, noise addition, cropping, simplifying, and even mixed attack.

The contradiction between speediness and stability is a salient problem of control system especially when the system is with character of time-varying parameters and big-inertia. It is hard to be solved perfectly either by classical control or modern control or intelligent control. The integrated control scheme and concept of generalized intelligent control (GIC) is proposed in this paper. The state-space method is adopted to describe the classical control, modern control and intelligent control. Stabilization conditions and mathematical proofs of generalized intelligent control system are given also. Theoretical analysis shows that the control strategy integrated by state feedback based on IFO-KDeltax and PID can make nonlinear controlled system asymptotically stable at large extent. Furthermore, intelligent predictive control such as feed forward, forecast, fuzzy control is merged to the scheme can improve response of control system with time-varying parameter and big inertia. The correctness and the validity of the design method are proved in engineering practice.

This paper proposes a new device in which a single-sweep oscillographic polarograph can be used for the in-situ monitoring of the concentration of metal ions in stirred suspension systems. Usually, polarography is used in static solutions to avoid the effects of vibration. The device works as follows: in the standing time the device is opened to the stirred system and the concentration is homogeneous inside and outside the device. The device is closed before potential scanning of the dropping mercury electrode, so that the solution within it remains static and the polarographic measurement can be carried out. The influence of oxygen is minimized by use of the second-order derivative mode in the polarographic determination. This technique has the advantages of simplicity, speed and reproducibility.

In this paper, the state feedback control based on the incremental function observer combined with the conventional PID control scheme for big capacity power unit is presented. It is designed in the distributed control system (DCS) EDPF-NT which is a new type of DCS developed by Electric Power Research Institute of China. It is useful for the coordinated control system(CCS) and the super-heater and re-heater steam temperature control systems of big capacity power plant in the practice.

It is important to study the diffusion of element chromium in nickel-based alloys during hot isostatic pressing (HIP) treatment. In present paper the effects of pressures on interdiffusion coefficient of chromium in nickel were investigated by using Ni-Cr diffusion couples and diffusion coefficient was increasing with the pressures in the system and had a relation-ship of DNAP. The mechanism of increasing diffusion coefficient has been discussed as well. According to experimental results we be ti eve that there are two types of diffusion mechanism, i.e. volume diffusion and diffusion along dislocation pipe and the later plays an important part in diffusion. Moreover, for further pressure increment this increasing tendency becomes slow down.

This article deals with the interaction of zinc with δ−MnO2, γ−MnOOH and manganite existing in natural water systems. The mechanism of the reaction has been studied in detail. From the fact that the “ratio of ion exchange(%)-pH” graph is an “S shaped” curve, it is possible to deduce that the chemical reaction is of the nature of cation exchange. And since the pH range of ion exchange=4, it is possible to further deduce that the reaction can be explained by the mechanism of monovalence cation exchange.

Proceedings of the 5th Int. Particle Accelerator Conf., IPAC2014, Dresden, Germany 5th International Particle Accelerator Conference, IPAC2014, Dresden, Germany, 15 Jun 2014 - 20 Jun 2014; JACoW, Geneva, Switzerland 3 p. (2014). doi:10.18429/JACoW-IPAC2014-THPME027, As one of the main components of the injector II of China ADS LINAC project, an RFQ working at 162.5MHz is used to accelerate proton beams of 15mA from 30 keV to 2.1 MeV. The four vane RFQ has been designed in collaboration with Lawrence Berkeley National Laboratory and built at the workshop of the Institute of Modern Physics, Chinese Academy of Sciences (IMP, CAS). Low power test of the cavity have been completed, and it shows the field flatness is within ±1% and the unloaded Q is 12600. RF conditioning has been completed, results of preliminary beam test show the output beam energy is 2.16 MeV with energy spread of 3.5% and the transmission efficiency is 97.9%. Continuous wave (CW) beam of 2.3 mA has been accelerated for more than one hour., Published by JACoW, Geneva, Switzerland