Search

Pulsewidth modulation (PWM) strategies, including continuous PWM (CPWM) and discontinuous PWM (DPWM), have been efficiently applied in the three-phase voltage source converters (VSCs). In the three-phase VSCs, dead-time distortion is inevitable. The characteristics of the dead-time distortion of three-phase VSCs with CPWM strategies have been studied extensively, whereas those of DPWM strategies have not been studied in detail. In this article, a comparative analysis of the dead-time distortion of CPWM strategies and DPWM strategies is conducted. Furthermore, the improvement of the PWM strategies to mitigate dead-time distortion is presented. The improved PWM strategies inject the compensation signals to the modulation signals to reconstruct the dead-time voltage error, which can significantly reduce the dead-time distortion of various PWM strategies. Experimental tests are conducted and the experimental results are provided to verify the analysis results of the dead-time distortion of DPWM strategies and the improved PWM strategies. [ABSTRACT FROM AUTHOR]

In this letter, we consider a non-orthogonal multiple access (NOMA) network with a half-duplex (HD) decode-and-forward (DF) relay to improve physical layer security (PLS) of two users. Two legitimate users of U1 and U2 are far from the multi-antenna base station (BS). Thus, a relay with multi-antenna is used to enhance communication and obtain diversity gain. In the first stage, the BS transmits the superimposed signal to the relay via NOMA. Meanwhile, U1 generates the jamming signal to the eavesdropper (Eve). In the second stage, the relay forwards the signal to two users while the BS transmits the jamming signal to disrupt the eavesdropping. It is assumed that the Eve has strong eavesdropping ability. Thus, we need to introduce jamming to guarantee security. Furthermore, we use U1 instead of a full-duplex (FD) relay to send the jamming signal in the first stage to avoid self-interference. To measure the secrecy performance, a closed-form expression of secrecy outage probability (SOP) is derived for the two users. Simulation results are provided to verify the correctness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Recently, intelligent data processing and interpretation based on deep learning (DL) have received considerable attention. Training data are vital for DL-based approaches. In geosciences, researchers have been facing a significant obstacle, i.e., the absence of authoritative and representative open data for training and testing artificial neural networks (ANNs). Although open-source works in geosciences are increasing, the quantity is currently limited. With the aid of the Society of Exploration Geophysicists (SEG) 2020 Machine Learning (ML) Blind-Test Challenge Data, we open source a package for DL-based seismic facies classification (SFC). We regard SFC as translating the seismic data into the facies by training a flexible end-to-end encoding–decoding-style ANN “BridgeNet” revised from U-Net. Inspired by the residual network (ResNet), we further enhance the BridgeNet by inserting the identity shortcut connections, which can theoretically ease the notorious problem of vanishing/exploding gradients. The evaluated framework, however, is not restricted to SFC. We hope that it can provide some insights that help researchers to construct and train ANNs that yield reliable and robust results in their own tasks. We carry out benchmarking experiments to investigate some crucial factors impacting the ANN’s performance to elucidate how we obtained our current optimal results on the SEG 2020 ML Challenge Data. The accuracy and continuity of predicted facies along the training and testing sections indicate that the results are consistent with geologic sedimentation, verifying the generalization capability of the enhanced flexible end-to-end encoding–decoding-style BridgeNet. For SFC, we recommend a memory-saving sparse categorical cross-entropy (CC) loss function to improve the efficiency. The codes are available at https://doi.org/10.5281/zenodo.5787673. [ABSTRACT FROM AUTHOR]

Physical and/or economic constraints cause acquired seismic data to be incomplete; however, complete data are required for many subsequent seismic processing procedures. Data reconstruction is a crucial and long-standing topic in the exploration seismology field. We extended our previous works on deep learning (DL)-based irregularly and regularly missing 2-D data reconstruction to 3-D data. A key motivation is that the 3-D convolutional neural network (CNN) can take full advantage of the 3-D nature of the data, and the additional dimension allows more information to contribute to the data reconstruction. DL also avoids many assumptions (e.g., linearity, sparsity, and low-rank) limiting conventional nonintelligent reconstruction methods. We built an artificial neural network (ANN) based on an end-to-end U-Net encoder–decoder-style 3-D CNN. The ANN was trained on large quantities of various synthetic and field 3-D seismic data using a mean-squared-error (MSE) loss function and an Adam optimizer. We demonstrated that the developed 3-D CNN reconstruction method appears to outperform the 2-D CNN for 3-D restoration. We benchmarked the ANN’s generalization capacity for recovery of irregularly and regularly sampled 3-D data on several typical seismic data sets, particularly those with high missing percentages or large gaps. An ANN trained with irregularly sampled data can be partly applied to regularly sampled cases. We investigated how a key parameter, i.e., the learning rate, can be experimentally determined. In the context of the presented examples, our methodology provided a substantial improvement over an open-source state-of-the-art rank-reduction-based approach in terms of data fidelity and efficiency. [ABSTRACT FROM AUTHOR]

Log-structured-merge (LSM) trees are a highly write-optimized data structure for lightweight, high-performance key-value (KV) stores. Furthermore, solid-state drives (SSDs) are a crucial component for I/O acceleration. Conventional LSM-over-SSD designs involve multiple software layers, including the LSM tree, host file system, and flash translation layer (FTL), which introduce cascading write amplifications. To manage the write amplifications from different layers, we propose KVSSDs, which are a close integration of LSM trees and the FTL. KVSSDs exploit the FTL mapping mechanism to implement copy-free compaction of LSM trees, and they enables direct data allocation in flash memory for efficient garbage collection. Our design also uses a fine-grained, dynamic striping policy to fully exploit the rich internal parallelism of multichip SSDs. The experimental results indicated that our LSM-SSD integrated design reduced the write amplification by 86% and improved the throughput by 383% compared with a conventional LSM-over-SSD design. [ABSTRACT FROM AUTHOR]

The proliferation of the greedy heterogeneous wireless network demands the ability to handle the sharply increasing resource requirements of power and spectrum. Thus, the study of novel and effective methods to allocate power and spectrum resources in order to reduce energy consumption for heterogeneous devices has become an urgent issue. In this context the article proposes a DJRA in which data fusion, combined with NOMA, SIC and beamforming techniques, are utilized to allocate power and spectrum resources to heterogeneous devices with a reduced energy consumption, where different radio access technologies are integrated into a cellular network for communication between devices. The data correlations between the devices are computed to group the devices so as to reduce data transmission. In addition, NOMA, SIC and beamforming are used in combination to reduce network interference between BSs and the heterogeneous devices. The main contribution of this article is the design of a large-scale heterogeneous wireless network paradigm to improve the percentage of resource utilization also reducing the energy consumption. [ABSTRACT FROM AUTHOR]

Haptic feedback is crucial to immersive experience in virtual and augmented reality applications. The existing promising maglev haptic devices have advantages of no mechanical friction and low inertia. However, their performance is limited by the navigation approach, which mainly results from the challenge that it is difficult to obtain high precision, high frequency, and good stability with lightweight design at the same time. In this study, we reformulate visual-inertial navigation as a regression problem, and adopt deep learning to perform fusion navigation for maglev haptic interaction. A cascade-LSTM-based q-increment learning method is first proposed to progressively learn the increments of target variables. Two cascade LSTM networks are then constructed to estimate the increments of position and orientation, which are pipelined to accomplish visual-inertial fusion navigation. Additionally, we set up a maglev haptic platform as the system testbed. Experimental results show that our cascade-LSTMbased visual-inertial fusion navigation approach can reach 200 Hz while maintaining high-precision navigation (the mean absolute error of the position and orientation is less than 1 mm and 0.02°, respectively) for a maglev haptic interactive deformation application. [ABSTRACT FROM AUTHOR]

This paper develops a symmetric atmospheric plasma source integrated with electrospray ionization (ESI) tip as a dual-ion source for simultaneously detecting nonpolar and polar compounds using the ambient mass spectrometry. A cross-shaped tube with symmetric discharge electrodes is used to generate dielectric barrier discharge to produce ionized gas charging nonpolar molecules, while a coaxial capillary is placed at the center of the cross-shaped tube for generating electrospray discharge. Since there is no floating voltage at the outlet of the cross-shaped tube, the potential interference for the ESI can be excluded. Results show that dual-ion source can stably produce high-intensity ions of 109 ion $\cdot $ cm−3. Moreover, the relationship between the measured ion intensity and the gas flow rate, gas temperature, and applied voltage for ESI are systematically investigated. Various samples including volatile organic compounds, pesticides, Chinese herbal medicines, and vitamins are used for evaluating the sensing performance of the dual-ion source in plasma mode, electrospray mode, and dual-ion source mode, respectively. Results show that the developed dual source is efficient to simultaneously detect both the polar and nonpolar compounds. [ABSTRACT FROM AUTHOR]

With the rapid development of earth observation technology, the number of available remote sensing data has soared dramatically. It becomes a significant problem that how to use remote sensing images and how to improve the accuracy of change detection effectively. In this paper, a novel approach for change detection using weight-clustering sparse autoencoders (WCSAE) combined object-oriented classification with difference images (DIs) is proposed. First, bi-phase images are segmented as patches through the density-based spatial clustering of applications with noise algorithm. Afterward, the average and variance of superpixels are stacked as the input of WCSAE. To reduce the redundant information of extracted features, similar weights in the hidden layer of WCSAE are clustered layer-wise under termination conditions by using the hierarchical agglomerative clustering algorithm. For the improvement of the classification accuracy, L1/2 regularization is introduced in the objective function to extract more sparse features and avoid over-fitting. Next, the post-classification change detection map is obtained by means of comparing with classification results of two phase images. Then, by using the change vector analysis technology, the difference map is yielded and also classified under WCSAE to acquire the DI classification map. Finally, the joint probability judgment is implemented on the joint scopes to determine changed and unchanged areas. The effectiveness and superiority of the proposed method are verified in accordance with the experimental results on standard datasets and actual remote sensing images. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission. [ABSTRACT FROM AUTHOR]

A reconfigurable hybrid beamforming (HBF) system is presented to dynamically exploit the channel diversities and antenna array directivity in millimeter wave (mmWave) channels. To develop an effective method for HBF architecture adaptation in wideband mmWave channels, the performance of an architecture is evaluated based on its average frequency-domain channel capacity. According to our analysis and simulations, the spatial covariance of the frequency-domain channels of using HBF can still be approximately modeled in a Kronecker form. Furthermore, beamforming directivity and channel spatial diversity have different advantages on capacity improvement in different transceiving conditions. Based on the results of channel modeling, system analysis, and simulations, a reconfigurable HBF architecture is thus proposed to enable flexible adjustments of the active subarrays, the antennas and polarizations of each subarray, and the active analog front-end and radio frequency chains linked to the active subarrays. A training procedure is meanwhile developed for the HBF system to learn proper architectures according to the system constraints, the channel statistics, the baseband precoder structure, and the channel capacity between the transmitter and the receiver. Compared with a fixed HBF system, such a reconfigurable system provides 3–15-dB power gains in channel capacity under various simulated channel conditions. [ABSTRACT FROM PUBLISHER]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods. [ABSTRACT FROM PUBLISHER]

The efficacy of channel modeling and training for multiple-input multiple-output (MIMO) time-varying flat faded Rayleigh channels is studied herein from the information-theoretical perspective. To characterize the channel dynamics in wide-sense stationary uncorrelated scattering wireless environments, proper autoregressive (AR) channel models for different fading speeds are discussed from the viewpoints of the mean squared error (MSE) and Bayesian Cramér-Rao lower bound (BCRB) of channel estimation. Furthermore, the training efficacy is examined with the achievable capacity when the MSE of channel estimation attains the BCRB. Our numerical simulations show that neither is the first-order AR model enough, nor is a large-order AR model needed for modeling time-varying fading channels. The analysis on BCRB also shows that the influence of the multiple access interference among transmit antennas is not negligible for channel estimation in time-varying fading channels even if using orthogonal sequences for training. As channel tracking can utilize the current and all past training symbols, the optimal training lengths for each data packet may be less than the number of transmit antennas. These results help re-examine the efficacy of model complexity and training overhead, and characterize the achievable rate for MIMO systems that use more practical methods in estimating Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSSs), such as the Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS), are the primary sensors for localization of most unmanned helicopters. However, GNSS signals cannot be tracked reliably due to geographic restrictions or deliberate jamming, and this has been an unsolved problem for a long time to localize the position of helicopters robustly and accurately when the GNSS signals are lost. A new algorithm is presented for this unsolved problem based on the unscented Kalman filter (UKF) using measurement data of inertial sensors. The proposed algorithm localizes the position of an unmanned helicopter using three new techniques. First, it models noises of acceleration measurement of inertial sensors by the white noise bias in addition to the commonly used random walking process. Then, this algorithm prioritizes the propagations of states in the UKF. Third, it leverages the time-varying GNSS dilution of precision in line with adjustments of the measurement noise covariances. The combination of these techniques makes it possible to localize the position of unmanned helicopters that are equipped with two-stroke engines, which generate large vibrations that result in noisy acceleration data. To facilitate automated tuning of the filter parameters, we further develop a population-based tuning method. The proposed algorithm with the auto-tuning method enables the positioning of unmanned helicopters and prompt reaction in the case of a GNSS outage without requiring tedious manual calibrations. The performance of this algorithm is experimentally validated on a fully instrumented model-sized helicopter. [ABSTRACT FROM AUTHOR]

Motivated by the goals of automatically extracting vessel segments and constructing retinal vascular trees with anatomical realism, this paper presents and analyses an algorithm that combines vessel segmentation and grouping of the extracted vessel segments. The proposed method aims to restore the topology of the vascular trees with anatomical realism for clinical studies and diagnosis of retinal vascular diseases, which manifest abnormalities in either venous and/or arterial vascular systems. Vessel segments are grouped using extended Kalman filter which takes into account continuities in curvature, width, and intensity changes at the bifurcation or crossover point. At a junction, the proposed method applies the minimum-cost matching algorithm to resolve the conflict in grouping due to error in tracing. The system was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images. The dataset contained a mixture of normal and pathological images. In addition, six pathological fluorescein angiogram sequences were also included in this study. The results were compared against the groundtruth images provided by a physician, achieving average success rates of 88.79% and 90.09%, respectively. [ABSTRACT FROM PUBLISHER]

Landscape degradation, soil depletion, scarcity of water and fuel resources are common threats to ecosystem services, agricultural production and human livelihoods in the developing world. Observatory or monitoring networks which focus on the dynamics of coupled human-natural systems are challenged by scarce data, data heterogeneity across multiple domains and spatial scales, and complex models required to produce meaningful sustainability indicators. An additional challenge is to visualize these complex data and indicators in a unified, easily understandable framework. This paper presents an environmental sustainability dashboard that integrates GIS data with household and plot surveys, field data and remote sensing imagery to compute a variety of metrics of ecosystem stress. The dashboard, a web-based decision support tool, is a key cyberinfrastructure component designed to satisfy the objectives of a Tanzanian agricultural and ecosystem services monitoring pilot. Based on this experience we discuss our framework and how it can be generalized for building decision support tools, and their associated cyberinfrastructure, for multi-scale, interdisciplinary monitoring networks. [ABSTRACT FROM PUBLISHER]

Due to the use of acoustic channels with limited available bandwidth, Underwater Sensor Networks (USNs) often suffer from significant performance restrictions such as low reliability, low energy-efficiency, and high end-to-end packet delay. The provisioning of reliable, energy-efficient, and low-delay communication in USNs has become a challenging research issue. In this paper, we take noise attenuation in deep water areas into account and propose a novel layered multipath power control (LMPC) scheme in order to reduce the energy consumption as well as enhance reliable and robust communication in USNs. To this end, we first formalize an optimization problem to manage transmission power and control data rate across the whole network. The objective is to minimize energy consumption and simultaneously guarantee the other performance metrics. After proving that this optimization problem is NP-complete, we solve the key problems of LMPC including establishment of the energy-efficient tree and management of energy distribution and further develop a heuristic algorithm to achieve the feasible solution of the optimization problem. Finally, the extensive simulation experiments are conducted to evaluate the network performance under different working conditions. The results reveal that the proposed LMPC scheme outperforms the existing mechanism significantly. [ABSTRACT FROM AUTHOR]

This paper details the authors' efforts to push the baseline of emotion recognition performance on the Geneva Multimodal Emotion Portrayals (GEMEP) Facial Expression Recognition and Analysis database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this paper. The approach toward solving this problem involves face detection, followed by key-point identification, then feature generation, and then, finally, classification. An ensemble of features consisting of hierarchical Gaussianization, scale-invariant feature transform, and some coarse motion features have been used. In the classification stage, we used support vector machines. The classification task has been divided into person-specific and person-independent emotion recognitions using face recognition with either manual labels or automatic algorithms. We achieve 100% performance for the person-specific one, 66% performance for the person-independent one, and 80% performance for overall results, in terms of classification rate, for emotion recognition with manual identification of subjects. [ABSTRACT FROM AUTHOR]

A new 3-D vertical bipolar junction transistor (BJT) resistive-switching memory (ReRAM) cell with complimentary metal–oxide–semiconductor-compatible process has been demonstrated and characterized. A new logic-compatible BJT is vertically formed underneath the resistive stacked film of \TiN/Ti/HfO2\/\TiN as a high-performance current driver and bit-cell selector. Using a shallow and tiny N-type lightly doped drain to be an emitter connects with ReRAM film as the bitline, a very thin and self-aligned P-pocket implant layer to be the wordline, and the N-well is the collector of the cells. As a result, the new 3-D ReRAM cell is very area saving and efficiently operated by the high-gain (\beta > \50) BJT at a low voltage of 2 V for reset and 1.5 V for set. By adapting the highly shrinkable 3-D BJT current driver in ReRAM, the ReRAM is fully decoupled with the gate length and oxide thickness of logic metal–oxide–semiconductor field-effect transistors; furthermore, it can easily be scaled down to \4F^2 under the lithographic limitation of defining ReRAM film with \F^2 area. [ABSTRACT FROM PUBLISHER]

Detecting text and caption from videos is important and in great demand for video retrieval, annotation, indexing, and content analysis. In this paper, we present a corner based approach to detect text and caption from videos. This approach is inspired by the observation that there exist dense and orderly presences of corner points in characters, especially in text and caption. We use several discriminative features to describe the text regions formed by the corner points. The usage of these features is in a flexible manner, thus, can be adapted to different applications. Language independence is an important advantage of the proposed method. Moreover, based upon the text features, we further develop a novel algorithm to detect moving captions in videos. In the algorithm, the motion features, extracted by optical flow, are combined with text features to detect the moving caption patterns. The decision tree is adopted to learn the classification criteria. Experiments conducted on a large volume of real video shots demonstrate the efficiency and robustness of our proposed approaches and the real-world system. Our text and caption detection system was recently highlighted in a worldwide multimedia retrieval competition, Star Challenge, by achieving the superior performance with the top ranking. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more. suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization. [ABSTRACT FROM AUTHOR]