Search

The air humidity is one essential measurable characteristic widely used in engineering and science. It is highly associated with the air temperature and closely involved with our daily life and work in indoor or outdoor environments. As high humidity can cause damage to building materials and resident health, the humidity issue in buildings seems to have been one widespread concern recently. There is a growing recognition that humidity control is crucial for various occasions. Changes of daily 24-h air humidity, which may be influenced by many factors, can also be expressed as one time series. Contributions of this study are: Firstly, for engineering application purposes, one concise and well-formed called the LS ("least-squares")-extended DCT ("discrete cosine transform") model is proposed for hourly air humidity forecasting from the perspective of time-series analyzing or data-driven modeling. Actually, conventional Fourier-based models cannot be straightly applied for forecasting. To reckon the LS-optimal DCT coefficients for forecast modeling on the basis of limited humidity time-series observations, the LS approach is integrated in implementation of the proposed LS-extended DCT forecast model. Then, the proposed method is applied to tasks of forecasting hourly air humidity changes at eight observing stations. Computer-based modeling and simulation results of hourly air humidity prediction at the observing stations as well as comprehensive comparisons indicate potentiality of the proposed method that it provides one concise and well-formed approach for hourly humidity modeling and prediction. To conclude, the proposed method can be used for reference to other related engineering applications. [ABSTRACT FROM AUTHOR]

In pattern recognition, patterns are described in terms of features. The features form feature vectors in the feature space. In the light of the phenomenon of gravitation in star clusters, we define patterns in the feature space to self-organize into clustering networks called "vector gravitation clustering networks" in this study. In the proposed clustering method, one called "vector gravitational force" is employed for the similarity measure in the feature space. Then by means of the "vector gravitational force", patterns self-organize clustering networks called "vector gravitation clustering networks" in the feature space. The proposed clustering method is applied to experiments. The experimental results show workability of the proposed clustering method. It is revealed that patterns tend to have more called "vector gravitational force" between ones of the same categories than between ones of the different categories in the feature space. Finally, further performance analysis employing the ANOVA ("analysis of variance") and the Newman-Keul procedure indicates potentiality of the proposed clustering method. As being inspired by the phenomenon of gravitation in star clusters and by using the "vector gravitational force" for similarity measure, "interpretability" is one obvious advantage of the proposed clustering method, and it may be viewed as one natural clustering method. [ABSTRACT FROM AUTHOR]

Water-level is one of the critical parameters for a river. It has a close relation to human living & production and socio-economic sustainability development. WLF (water-level fluctuation) evaluation and forecasting for a river is then becoming increasingly important. For water resource planning and management, traditionally, mathematical models are separately developed and designed for sectorial applications. As predictions utilizing more different forecast variables require additional efforts and costs to acquire and predict the variables, advantages of time-series-based or data-driven modeling lie on its conciseness and good performance even higher accuracy. The Fourier-based analysis technology is a classical tool widely used for time-series analysis. However, the Fourier-related approach in its conventional form is not directly applicable to prediction. Addressing hourly WLF prediction from the viewpoint of time-series analysis, a called DCT-LS-extended ('discrete cosine transform (DCT)-based least-squares-extended') forecast model is presented in this study. The DCT coefficients for the proposed DCT-based forecast modeling are determined in the least-squares sense on the basis of previous hourly WLF observations. Experiments at hydrological monitoring stations in the XiangJiang River of China yield stultifying results. Potentiality of the proposed method is demonstrated by further analysis. The proposed DCT-LS-extended model forecasts hourly WLFs best fitting with less than 12-term DCT coefficients. The proposed method may benefit other applications. [ABSTRACT FROM AUTHOR]

The definition of the correlation image for digital image is presented to show that the correlation images of different images exhibit distinct texture-like appearances. Then inspired by the universality of the compressive sensing theory which indicates that random measurements can be employed for signals sparse in any basis, the compressive representation for digital image is further defined by employing random matrix as sensing matrix to measure the correlation image. It is found that l vectors of the compressive representation for one image exhibit a highly coherent similarity while display distinct characteristics between different images as well. Thus it shows that one image can be well compressively represented by using one of the l vectors as the feature vector of compressive representation in the sensing (measurement) domain. As data dimension of the feature vector is much less than that of both the original image and the correlation image, it facilitates pattern analysis for its less computational complexity. Application results of face recognition indicate potentiality of the proposed method. Further performance analysis based on tests of the leave-one-out cross-validation, twofold cross-validation as well as analysis of variance shows that the proposed method outperforms the classical PCA on the face recognition tasks. [ABSTRACT FROM AUTHOR]

Schizophrenia is a severe mental disorder characterized by impaired perception, delusions, thought disorder, abnormal emotion regulation, altered motor function, and impaired drive. The default mode network (DMN), since it was first proposed in 2001, has become a central research theme in neuropsychiatric disorders, including schizophrenia. In this review, first we define the DMN and describe its functional activity, functional and anatomical connectivity, heritability, and inverse correlation with the task positive network. Second, we review empirical studies of the anatomical and functional DMN, and anti-correlation between DMN and the task positive network in schizophrenia. Finally, we review preliminary evidence about the relationship between antipsychotic medications and regulation of the DMN, review the role of DMN as a treatment biomarker for this disease, and consider the DMN effects of individualized therapies for schizophrenia. [ABSTRACT FROM AUTHOR]

Instant Messaging (IM) has been an effective, popular and important mode for immediate communication in the digital age. Then analysis of daily user-online movement in IM is increasingly important for its significant effect on information propagation, commercial operation, resource allocation and maintenance management. In this study, the so-called elliptic orbit model is proposed to describe daily user-online movement in IM by mapping its time-series into the polar coordinates to establish the elliptic orbit model, in which each 24-hour movement is depicted as one elliptic orbit. Experiments in the most popular IM service in China called the Tencent QQ and result analysis indicate potentiality of the proposed method. It is shown that the daily 24-hour user-online movement in IM is well described by the elliptic orbit model, which provides a vivid approach for modeling and predicting daily user-online movement in IM in a concise and intuitive way. [ABSTRACT FROM AUTHOR]

This study addresses identification of the quasi-periodic sequence based on the discrete Fourier transform spectrum-sparsity. A quasi-periodic signal displays similarities to a periodic signal but does not satisfy its strict definition. Then identification of the quasi-periodic signal is practically a requirement for many applications. The spectrum-sparse property in the Fourier domain is presented for describing the discrete-time periodic signals, and then it is extended and proposed for the quasi-periodic sequence identification. The proposed identification method is applied to real applications including periodicity detection of the sunspot activity, rainfall activity, and air temperature movement as well as the pile driver sound detection. The experimental results and analysis indicate potentiality of the proposed method that it yields satisfying results in the real applications. [ABSTRACT FROM AUTHOR]

Analysis of electric load movement is of increasingly importance for the industry on both economic operation and control of power systems. From the perspective of time-series analysis, in this study, with daily periodic extension for hourly electric load movement, an evaluation model employing finite Fourier series is proposed for describing its movement. As the conventional Fourier analysis cannot be directly used for prediction, in conjunction with the least-squares method to obtain the optimum Fourier coefficients in the least-squares sense based on limited hourly load data observations, the proposed Fourier-series model is extended to predict the load future movement. Experiments and result analysis indicate potentiality of the proposed method. It is shown that daily 24-h load movement is well described by the proposed Fourier-series evaluation model with about 12-term harmonics, and the extended Fourier-series forecast model in the least-squares sense predicts its movement most fitting with about 4-term harmonics. [ABSTRACT FROM AUTHOR]

Air pollution has been an internationally growing concern. Modeling and forecasting daily movement of ambient air mean NO concentration is an increasingly important task for its adverse effects on human health. With weekly quasi-periodic extension for daily movement of mean NO concentration, the elliptic orbit model is introduced to depict its movement. Daily movement of mean NO concentration as a time-series is mapped into the polar coordinates to build the elliptic orbit model, in which each 7-day-movement is described as one elliptic orbit. Experiments and result analysis indicate workability and effectiveness of the proposed method. It is shown that with weekly quasi-periodic extension, daily movements of mean NO concentration at the given monitoring stations in China are well described by the elliptic orbit model, which presents a vivid description for analyzing daily movement of mean NO concentration in a concise and intuitive way. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, the issue of air pollution has continuously been a global public health concern. Modeling and forecasting daily movement of ambient air mean PM concentration is an increasingly important task as it is intimately associated with human health that the air pollution has unignorable negative effects in reducing air quality, damaging environment, even causing serious harm to health. It is demonstrated that daily movement of mean PM concentration approximately exhibits weekly cyclical variations as daily particle pollution in the air is largely influenced by human daily activities. Then, based on weekly quasi-periodic extension for daily movement of mean PM concentration, the called elliptic orbit model is proposed to describe its movement. By mapping daily movement of mean PM concentration as one time series into the polar coordinates, each 7-day movement is depicted as one elliptic orbit. Experimental result and analysis indicate workability and effectiveness of the proposed method. Here we show that with the weekly quasi-periodic extension, daily movements of mean PM concentration at the given monitoring stations in Xiangtan of China are well described by the elliptic orbit model, which provides a vivid description for modeling and prediction daily movement of mean PM concentration in a concise and intuitive way. [ABSTRACT FROM AUTHOR]

The metabolic stability of peptides containing a mixed sequence of α-aminoxy acids and α-amino acids is significantly improved compared to peptides composed of only natural α-amino acids. The introduction of an α-aminoxy acid into peptide chain dramatically improves the stability of the amide bonds immediately before and after it. These peptides containing α-aminoxy acids represent excellent structural scaffold for the design of metabolically stable and biologically active peptides. [ABSTRACT FROM AUTHOR]

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance. [ABSTRACT FROM AUTHOR]

A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients. The estimator is designed based on a vehicle model with three degrees of freedom (3-DOF) and the dual extended Kalman filter (DEKF) technique is employed. Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions (high-friction, low-friction, and joint-friction roads). Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states (e.g., yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy. [ABSTRACT FROM AUTHOR]

To improve the handling performance of a steer-by-wire (SBW) vehicle, a series of control logics are proposed. Firstly, an algorithm for enhancing the maneuvering in steady-state cornering is presented. On this basis, two categories of control strategies are used to dynamically correct and compensate the transient state steering responses and vehicle behaviors. Simulator tests including subjective evaluations and virtual field tests are both conducted to make comprehensive investigations on the series of control logics. The subjective evaluations demonstrate that the SBW vehicle with a specifically selected value of steering sensitivity tends to be more desirable for driving than a conventional one in which a fixed steering ratio exists. The virtual field tests indicate that the control strategies for dynamical correction and compensation could effectively improve the handling performances of an SBW vehicle by reducing the work load of drivers, enhancing the track-holding performance, and improving steering response properties. [ABSTRACT FROM AUTHOR]

Abstract  In this paper, we describe an air temperature movement by mapping its time series to the polar coordinates: the value of the time series is plotted on the radial coordinate and the time on the angular coordinate. In this way, both the yearly and daily air temperature movements from five different locations of China are used to demonstrate that, in an intuitionistic view, air temperature movement is neither random nor chaotic, but has its own orbit. We then propose an elliptic orbit model for the air temperature movement. Our proposed model provides quite good results when it is applied to the evaluation of yearly air temperature movements in eight locations in various parts of the world, and the evaluation of daily air temperature movements in five locations in Hunan Province of China on December 17, 2005. Results show that our approach provides a concise and helpful model for air temperature analysis. [ABSTRACT FROM AUTHOR]

Price, nonprice product information, and purchase intention, together with the intervening variables of perceived price, perceived quality, and perceived value, are empirically examined. The results indicate that perceived price is positively influenced by objective price and negatively influenced by reference price. They support the positive price-perceived quality relationship found in previous studies and further, show that the influence of price on perceived quality is lessened in the presence of substantial direct product information. Finally, the results demonstrate that a trade-off between perceived price and perceived quality leads to perceived value, and perceived value is a primary factor influencing purchase intention. [ABSTRACT FROM AUTHOR]

The authors have investigated the effect of Radix Ginseng on peroxidation injury in myocardium and erythrocytes of streptozocin-induced diabetic rats. In the ginseng administered group (ginseng solution 0.2 g/200 g body weight once daily lasting 15∼16 days), there was a significant decrease in the level of fasting blood glucose and lipid peroxide in myocardium and erythrocytes, in comparison with those of the model group ( P<0.05). The superoxide dismutase activities in myocardium and erythrocytes of the ginseng group were increased ( P<0.05), compared with those of the model group and the vitamin E treated group (vitamin E 10 mg/200 g·body weight once daily, lasting 15∼16 days). The results suggest that the mechanisms of antiperoxidation injury of ginseng might include the following: (1) lowering the level of fasting blood glucose, decreasing monosaccharide auto-oxidation and partially protecting the production of free radicals; (2) elevating the activity of enzymatic free radical scavenger in cells, such as superoxide dismutase; and (3) directly eliminating the superfluous free radicals. [ABSTRACT FROM AUTHOR]

Water-level fluctuation evaluation and forecasting for a river is increasingly important because of its close ties to human living and production. In this study, with annual periodic extension for monthly average water-level fluctuation, Fourier analysis employing finite Fourier series is presented for evaluating its fluctuation. Fourier analysis in the conventional form which is the most common analysis method in the frequency domain, however, cannot be straightly applied for forecasting. Then, the extended version of the Fourier-series model in the least-square sense is proposed for forecasting. The extended forecasting model obtains its optimum Fourier coefficients in the least-squares based on previous monthly water-level observations. Experiments at the different monitoring stations of the Yangtze River in China indicate potentiality of the proposed method. It is shown that the fluctuation of monthly average water-level is well described by about six-term harmonics. And the extended Fourier forecasting model predicts the fluctuation of monthly water level most fitting about three-term harmonics. [ABSTRACT FROM AUTHOR]