Water quality analysis in Lake Hachiroko,Japan, using ALOS AVNIR‐2 data

In recent years, remote sensing has been used to assessing water pollution distribution. In this study, water quality is analyzed using data collected by the Advanced Visible and Near Infrared Radiometer type‐2 (AVNIR‐2) of the Advanced Land Observing Satellite (ALOS) at various points in time. We carried out fuzzy regression analysis of the AVNIR‐2 data and direct measurements of local water quality. The relationship between the water quality data and the AVNIR‐2 data was analyzed by solving both the min and max problems. By comparing the maps of estimated water quality with actual distributions of water quality in the study area, we found that the method used in this study allows effective derivation of water quality conditions from AVNIR‐2 data, which provides 10‐m spatial resolution. Furthermore, by comparing the maps created using AVNIR‐2 data collected at different times, we obtained results suggesting temporal changes in water quality. We also compared the results obtained using data collected by the optical sensor of the Landsat thematic mapper (TM) with 30‐m resolution and those obtained using data collected by the active sensor of JERS‐1 synthetic aperture radar (SAR), and examined the differences in classification results resulting from differences in resolution and sensors. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Analysis of seasonal change for water conditions in Lake Hosenko,Japan, using ALOS AVNIR‐2 data

This letter reports the development of a technique to understand the water conditions appearing in the Advanced Land Observing Satellite Advanced Visible and Near Infrared Radiometer type‐2 (ALOS AVNIR‐2) data using fuzzy c‐means. Also, waters of Lake Hosenko, into which the Tamagawa hot spring flows, were analyzed because the lake contains amorphous iron hydroxide. On the basis of the experimental results for the months of September and May data obtained by the proposed method, it is found that seasonal differences in the water surface conditions of the lake are caused by the changes in the amount of water as well as the water temperature. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

A fuzzy C‐regression model algorithm using a new PSO algorithm

In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature.     

Analysis of water quality in Miharu dam reservoir,Japan, using UAV data

This letter reports on the water quality conditions of the Miharu Dam Reservoir. The water quality conditions appear in near‐infrared (NIR) data collected by an unmanned aerial vehicle (UAV). Because blue‐green algae occur in summer, the water quality of the small lake will be significantly worse at that time. On the basis of the experimental results for data from the months of July and August obtained using a fuzzy regression model, it is found that the UAV data is useful in assessing the water quality conditions in Lake Sakurako, and that the NIR data is effective in estimating the water surface conditions of the lake, especially caused by the occurrence of blue‐green algae. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于模糊C均值聚类和改进相关向量机的变压器故障诊断

为了提高电力变压器故障诊断准确率和单一相关向量机核函数存在的固有二分类属性及对预测分类鲁棒性弱的问题,提出基于模糊C均值聚类和改进相关向量机的变压器故障诊断模型。首先对样本进行模糊C均值聚类,然后再采用相关向量机的完全二叉树结构进行划分。相关向量机核函数采用组合高斯核函数和多项式核函数构造的混合核函数,并利用双子群果蝇算法对混合核函数参数进行优化。实验表明,相比单核函数、粒子群算法优化混合核函数参数,所提方法准确率高、稳定性好,同时分类速度快,满足实时在线故障诊断。     

A combination of genetic algorithm‐based fuzzy C‐means with a convex hull‐based regression for real‐time fuzzy switching regression analysis: application to industrial intelligent data analysis

Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well‐known techniques such as regression analysis becomes essential and highly advantageous. In this paper, we extend the concept of regression models so that they can handle hybrid data coming from various sources which quite often exhibit diverse levels of data quality. The major objective of this study is to develop a sound vehicle of a hybrid data analysis, which helps in reducing the computing time, especially in cases of real‐time data processing. We propose an efficient real‐time fuzzy switching regression analysis based on a genetic algorithm‐based fuzzy C‐means associated with a convex hull‐based fuzzy regression approach. The method enables us to deal with situations when one has to deal with heterogeneous data which were derived from various database sources (distributed databases). In the proposed design, we emphasize a pivotal role of the convex hull approach, which is essential to alleviate the limitations of linear programming when being used in modeling of real‐time systems. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

住宅小区管道直饮水站工艺流程和自动控制设计

介绍管道直饮水净水站工艺流程和其生产全自动化监控设计,以及应用因特网技术进行远距离多站控制设计方案。     

The water quality monitoring of rural drinking water and analysis of improvement by nanofiltration membrane in Poyang Lake area

ABSTRACT

Water quality monitoring is the main basis for water quality assessment and water pollution control. According to Standard Test Method for sanitary standard of drinking water (GB/T5750-2006), we monitor COD, nitrate, ammonia nitrogen, arsenic, TDS and pH of 20 samples in Poyang Lake area rural drinking water. Results show that pass rate of ammonia nitrogen, arsenic and TDS were 100%, pass rates of nitrate was 78.95%, pass rates of pH was 68.42%, and COD indicators pass rate was only 26.32%. Based on the analysis of the main pollutants in drinking water in the area, the nanofiltration membrane technology was applied to the deep treatment of drinking water in the centralized water supply department.     

Application of adaptive neuro‐fuzzy inference for wind power short‐term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro‐fuzzy inference approach is proposed for short‐term wind power forecasting. Results from a real‐world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Finite‐time prescribed performance adaptive fuzzy fault‐tolerant control for nonstrict‐feedback nonlinear systems

This paper focuses on a finite‐time adaptive fuzzy control problem for nonstrict‐feedback nonlinear systems with actuator faults and prescribed performance. Compared with existing results, the finite‐time prescribed performance adaptive fuzzy output feedback control is under study for the first time. By designing performance function, the transient performance of the corresponding controlled variable is maintained in a prescribed area. Combining the finite‐time stability criterion with backstepping technique, a feasible adaptive fault‐tolerant control scheme is proposed to guarantee that the system output converges to a small neighborhood of the origin in finite time, and the closed‐loop signals are bounded. Finally, simulation results are shown to illustrate the effectiveness of the presented control method.     

选矿厂生产用水模糊控制系统设计

选矿厂生产用水主要由浓缩循环水和清水补充两部分组成,循环水供水部分在生产中往往不稳定,需要人工经常调节补水控制阀门补充清水,以满足生产用水的恒压需求.由于补水的随机性和补水量的不确定性使得常规的自动控制难以满足控制要求.基于此,设计了一套模糊控制系统,通过模糊控制理论这种先进的控制方式结合人工的经验,对供水管道压力的变...     

模糊控制技术在变频调速恒压供水系统中的应用研究

将模糊控制技术应用于供水系统 ,设计出了自调整修正因子Fuzzy-PID控制器 ,克服了传统PID控制设计中参数调整困难的问题 ,并通过变频调速实现了恒压控制。该系统取代了高位水塔和直接水泵加压等传统供水模式 ,具有明显的节能效果和稳定的控制性能     

模糊多目标决策理论在电能质量综合评价中的应用

运用模糊多目标决策理论对电能质量评价方法进行了阐述,从电能质量指标入手,根据电能质量各指标的特点,确定了相应指标的目标类型,给出了各自的数学模型,并将各目标的指标值联立构成相对优属度矩阵,通过评判方法,得出目标电能质量的优劣排序。该方法可为电力市场环境下的电能质量的分析提供基础。     

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

鄱阳湖枯水期水位变化及其影响因素量化分析

湖泊湿地在维系生物多样性、陆地表层物流平衡与生态平衡等方面有着不可替代的作用。鄱阳湖是长江中下游重要的水源地和调洪场所,独特的水文条件创造了湖区丰富的生境和生物多样性。自三峡水库运行以来,鄱阳湖水位变化显著,枯水期提前且持续时间延长,10月份水位异常偏低,严重影响了越冬候鸟的栖息地。本文采用模糊划分聚类模型,围绕天然径流变化、三峡水库汛末蓄水、地形变化对鄱阳湖枯水期10月份水位变化的影响展开研究,并量化各因素的影响大小。结果表明,湖区及长江干流径流减少是造成鄱阳湖10月水位降低的主要原因,三峡水库汛末蓄水则进一步加快了鄱阳湖水位的消落,研究成果可为鄱阳湖管理及改善枯水期湖区生态环境提供依据。     

模糊层次灰关联在给水泵状态评价中的应用

针对给水泵运行工况复杂、准确评价难的问题,阐述了模糊层次分析法及灰关联理论,建立了模糊层次灰关联的评价模型。提出了一种基于模糊层次灰关联的给水泵状态评价方法,即采用模糊层次分析法(FAHP)确定各指标的权重系数,对灰色关联系数加权得到灰色关联度,由给水泵被评估参数和等级标准参数的相关度评价结果得出给水泵所处的状态。计算结果表明,该模型用于给水泵状态评价有效可行,能提高给水泵的安全性和经济性,为检修决策提供了依据。     

Observer‐based fuzzy adaptive quantized control for uncertain nonlinear multiagent systems

This paper focuses on consensus quantized control design problem for uncertain nonlinear multiagent systems with unmeasured states. Every follower can be denoted through a system with unmeasurable states, hysteretic quantized input, and unknown nonlinearities. Fuzzy state observer and Fuzzy logic systems are employed to estimate unmeasured states and approximate unknown nonlinear functions, respectively. The hysteretic quantized input can be split into two bounded nonlinear functions to avoid chattering problem. By combining adaptive backstepping and first‐order filter signals, an observer‐based fuzzy adaptive quantized control scheme is designed for each follower. All signals exist in closed‐loop systems are semiglobally uniformly ultimately bounded, and all followers can accomplish a desired consensus results. Finally, a numerical example is employed to elaborate the effectiveness of proposed control strategy.     

青海湖水量平衡变化及其对湖水位的影响研究

利用1961—2015年青海湖的实测水位及气象观测资料建立了青海湖年水量平衡方程,估算了青海湖逐年的湖面降水、湖面蒸发及入湖流量,分析了水量平衡各要素的变化趋势。在此基础上,通过回归分析等方法研究了不同因子对青海湖水位变化的贡献率。结果表明,青海湖水位在2005年之前呈下降趋势,2005年之后水位上升。青海湖多年平均降水量为14.6×108 m3,多年平均入湖径流量为24.4×108 m3,多年平均蒸发量为40.1×108 m3。1961—2015年间湖面降水呈增加趋势,入湖径流在2003年后增加显著,而湖面蒸发在2000年后呈减少趋势。入湖径流和湖面降水的增加及湖面蒸发的减少共同导致了青海湖水位在2005年之后回升,其中入湖径流对水位变化的贡献率最大,为38.2%。