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| 1 | Comparison of Energy Harvesting Systems for Wireless Sensor Networks显示文摘Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security, and process monitoring problems. However, one barrier to their fuller adoption is the need to supply electrical power over extended periods of time without the need for dedicated wiring. Energy harvesting provides a potential solution to this problem in many applications. This paper reviews the characteristics and energy requirements of typical sensor network nodes, assesses a range of potential ambient energy sources, and outlines the characteristics of a wide range of energy conversion devices. It then proposes a method to compare these diverse sources and conversion mechanisms in terms of their normalised power density. | James M.Gilbert Farooq Balouchi | 2008 | International Journal of Automation and computing2008，5，4： | 25 |
| 2 | 机械振动无线传感器网络节点高精度数据采集方法显示文摘为满足通用机械振动监测中高采样率、采集精度的需求,提出一种基于IEPE加速度传感器的无线传感器网络节点高精度数据采集方法。采用双核心处理器构架,降低网络维护与采集控制的耦合性,独立高精度时钟控制采集时序,提高数据采集频率精度。设计低噪声DC-DC电源转换方案,在充分考虑转换效率的前提下抑制电源噪声为30μVRMS;设计能自抑制电源噪声的恒流源激励,减小IEPE加速度传感器的信号噪声;设计通带平稳的三阶低通滤波器,有效避免频率混叠,提高数据采集幅值精度。实验结果表明:节点采集信号噪声仅40.7μVRMS,滤波通带范围内平均幅值误差仅0.37%;与有线采集系统NI9234数据采集对比分析,频率误差低于最小频率分辨率,幅值误差最大仅2.99%,验证了该无线传感器网络节点高精度数据采集方法的有效性。 | 曾贵伟 汤宝平 邓蕾 肖鑫 | 2016 | 振动与冲击2016，35，16： | 19 |
| 3 | A survey of web resources and tools for the study of TCM network pharmacology显示文摘Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM? Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis. Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM. | Jing Zhao Jian Yang Saisai Tian Weidong Zhang | 2019 | Frontiers of Electrical and Electronic Engineering in China2019，7，1： | 17 |
| 4 | Robust optimization for improving resilience of integrated energy systems with electricity and natural gas infrastructures显示文摘The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a threestage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Nonconvex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program(MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis. | Hao CONG Yang HE Xu WANG Chuanwen JIANG | 2018 | Journal of Modern Power Systems and Clean Energy2018，6，5： | 17 |
| 5 | Exponential stability and periodic solutions of delayed cellular neural networks显示文摘A set of criteria are presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov function-als, introducing many parameters qij* , rij* , qij, rij∈ R and wi>0 (i, j = 1, 2, …, n) and combining them with the elementary inequality 2ab≤a2 + b2 technique. These criteria have important significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, the results in literature are extended and improved. Two examples are given to illustrate the theory. | 曹进德 | 2000 | Science China(Technological Sciences)2000，43，3： | 17 |
| 6 | Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks显示文摘This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create 'fake' images that the discriminator cannot distinguish from 'real' images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend. | Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey | 2019 | Petroleum Science2019，16，3： | 16 |
| 7 | On Improved DV-Hop Localization Algorithm for Accurate Node Localization in Wireless Sensor Networks显示文摘Node Localization is a fundamental issue for many critical applications in Wireless sensor networks(WSNs). Traditional DV-Hop localization algorithm and corresponding improved ones still cannot provide sufficient localization accuracy in such WSNs. To ensure accurate localization, this paper proposes an improved Distancevector-Hop(DV-Hop) localization algorithm. Under such an algorithm, we determine a corrected average hopdistance of beacon nodes by employing the differences between actual and estimated distance among beacon nodes in WSNs. We propose a probability information based selective strategy for the selection of beacon nodes.Based on these selected beacon nodes, we adopt a two dimensional hyperbolic function to predict the locations of unknown nodes. Simulation results are provided to illustrate the localization accuracy of our algorithm compared with traditional DV-Hop algorithm and its two improved algorithms in WSNs. | SHEN Shikai YANG Bin QIAN Kaiguo SHE Yumei WANG Wu | 2019 | Chinese Journal of Electronics2019，28，3： | 15 |
| 8 | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks显示文摘X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,respectively.We propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of misclassification.Finally,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less. | Felipe Oviedo Zekun Ren Shijing Sun Charles Settens Zhe Liu Noor Titan Putri Hartono Savitha Ramasamy Brian L.DeCost Siyu I.P.Tian Giuseppe Romano Aaron Gilad Kusne Tonio Buonassisi | 2019 | npj Computational Materials2019，，1： | 15 |
| 9 | Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods显示文摘Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs. | Jiajun Wang Tufan Kumbasar | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，1： | 14 |
| 10 | Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks显示文摘A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case,where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create 'pluralistic ignorance', where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure,disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals. | Brian D.O.Anderson Mengbin Ye | 2019 | International Journal of Automation and computing2019，16，2： | 13 |
| 11 | A harmonious unifying hybrid preferential model and its universal properties for complex dynamical networks显示文摘To describe the real world which is a harmonious unification world with both de- terminism and randomness, we propose a harmonious unifying hybrid preferential model (HUHPM) of a certain class of complex dynamical networks. HUHPM is gov- erned only by the total hybrid ratio d/r according to the practical need. As some typical examples, the concepts and methods of the HUHPM are applied to the un-weighted BA model proposed by Barabási et al., the weighted BBV model pro- posed by Barat et al. and the weighted TDE model proposed by Wang et al. to get the so-called HUHPM-BA network, HUHPM-BBV network and HUHPM-TDE network. These HUHPM networks are investigated both analytically and numerically. It is found that the HUHPM reveals several universal properties, which more approach to the real-world networks for both un-weighted and weighted networks and have potential for applications. | FANG JinQing BI Qiao LI Yong LU XinBiao LIU Qiang | 2007 | Science China(Physics,Mechanics & Astronomy)2007，50，3： | 13 |
| 12 | Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks显示文摘In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure(PWCCS) based on artificial neural networks(ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs.More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis,defect detection, theoretical modeling, optical interconnects, and so on. | TIAN ZHANG JIA WANG QI LIU JINZAN ZHOU JIAN DAI XU HAN YUE ZHOU KUN XU | 2019 | Photonics Research2019，7，3： | 13 |
| 13 | Feature-Based Aggregation and Deep Reinforcement Learning:A Survey and Some New Implementations显示文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller 'aggregate' Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement. | Dimitri P.Bertsekas | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，1： | 12 |
| 14 | Fault diagnosis for distillation process based on CNN–DAE显示文摘Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%. | Chuankun Li Dongfeng Zhao Shanjun Mu Weihua Zhang Ning Shi Lening Li | 2019 | Chinese Journal of Chemical Engineering2019，27，3： | 12 |
| 15 | STABILITY OF BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DELAYS显示文摘In this paper the globally asymptotic stability of more general two-layer nonlinear feedback associative memory neural networks with time delays is examined. The sufficient conditions of existence, uniqueness and globally asymptotic stability of the equilibrum position are given. Finally, two interesting examples to illustrate the theory are given. | Liao Xiaoxin(Dept. of Auto. Control. Huazhong Univ. of Science & Technology, Wuhan 430074)Liao Yang(Dept. of Computer Science, Nanjing University, Nanjing 210093)Liao Yu (Wuhan Soundy Science & Commerce Company, Wuhan 430070) | 1998 | Journal of Electronics(China)1998，15，4： | 11 |
| 16 | 基于蜂窝虚拟网格的WSN混合多跳分簇路由算法显示文摘为了延长网络生存周期,均衡网络能耗,提出一种蜂窝网格的混合多跳路由算法。该路由机制采用正六边形进行单元格划分,在簇头选择阶段引入节点角度比、距离比和吞吐率优化阈值函数自主选择簇头。数据传输时采用簇内单跳和簇间混合跳的模式,在各簇头节点之间传输数据时对路径进行优化减少能量消耗。仿真实验表明,该协议在保证网络高覆盖率下,与传统LEACH、GAF、CRVB算法比较在平衡能耗,延长网络整体寿命上占有优势。 | 余修武 胡沐芳 刘琴 刘永 | 2018 | 传感技术学报2018，31，10： | 11 |
| 17 | Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects显示文摘Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide accurate diagnosis results automatically,numerous studies have been carried out.Among them,signal-based methods are commonly used,which employ signal processing techniques based on the state signal used for extracting features,and further input the features into the classifier for fault recognition.However,their main deficiencies include the following:(1)The features are manually designed and thus may have a lack of objectivity.(2)For signal processing,feature extraction and pattern recognition are conducted using independent models,which cannot be jointly optimized globally.(3)The machine learning algorithms adopted by these methods have a shallow architecture,which limits their capacity to deeply mine the essential features of a fault.As a breakthrough in artificial intelligence,deep learning holds the potential to overcome such deficiencies.Based on deep learning,deep neural networks(DNNs)can automatically learn the complex nonlinear relations implied in a signal,can be globally optimized,and can obtain the high-level features of multi-dimensional data.In this paper,the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed.The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented,and the main ideas,methods,and principles of several typical DNNs are described and summarized.The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed,and research ideas for applying DNNs for hydraulic fault diagnosis are proposed.Meanwhile,the research advantages and development trend of DNNs(both domestically and overseas)as applied to an intelligent fault diagnosis are reviewed.Furthermore,the fault characteristics of a complex hydraulic system are summarized and discussed,and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed. | Juying Dai Jian Tang Shuzhan Huang Yangyang Wang | 2019 | Chinese Journal of Mechanical Engineering2019，32，5： | 11 |
| 18 | Evaluation of the SMOS and SMAP soil moisture products under different vegetation types against two sparse in situ networks over arid mountainous watersheds, Northwest China显示文摘Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in landatmosphere exchanges. This study presents evaluation of the SMOS(L2) and SMAP(L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network(UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network(EHWSN) over arid high mountainous watersheds, Northwest China.Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show 'dry bias' in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product.Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions. | Lanhui ZHANG Chansheng HE Mingmin ZHANG Yi ZHU | 2019 | Science China Earth Sciences2019，62，4： | 10 |
| 19 | Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks显示文摘During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes. | Tianyu Wang Shaowei Wang Zhi-Hua Zhou | 2019 | China Communications2019，16，1： | 10 |
| 20 | Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders显示文摘Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract LogMel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multilayer perceptron(MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory(LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine(OC-SVM) algorithm. The performance has been evaluated in terms area under curve(AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OCSVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %. | Emanuele Principi Damiano Rossetti Stefano Squartini Francesco Piazza | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，2： | 10 |
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