Search results for: text clustering.

Authors: C. Pislaru, A. Shebani

Abstract:

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: System identification, Nonlinear system, Neural networks, RBF neural network.

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Authors: Yinggan Tang, Xinping Guan

Abstract:

Color image segmentation can be considered as a cluster procedure in feature space. k-means and its adaptive version, i.e. competitive learning approach are powerful tools for data clustering. But k-means and competitive learning suffer from several drawbacks such as dead-unit problem and need to pre-specify number of cluster. In this paper, we will explore to use competitive and cooperative learning approach to perform color image segmentation. In competitive and cooperative learning approach, seed points not only compete each other, but also the winner will dynamically select several nearest competitors to form a cooperative team to adapt to the input together, finally it can automatically select the correct number of cluster and avoid the dead-units problem. Experimental results show that CCL can obtain better segmentation result.

Keywords: Color image segmentation, competitive learning, cluster, k-means algorithm, competitive and cooperative learning.

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Authors: María-Dolores Cubiles-de-la-Vega, Rafael Pino-Mejías, Esther-Lydia Silva-Ramírez

Abstract:

A dissimilarity measure between the empiric characteristic functions of the subsamples associated to the different classes in a multivariate data set is proposed. This measure can be efficiently computed, and it depends on all the cases of each class. It may be used to find groups of similar classes, which could be joined for further analysis, or it could be employed to perform an agglomerative hierarchical cluster analysis of the set of classes. The final tree can serve to build a family of binary classification models, offering an alternative approach to the multi-class SVM problem. We have tested this dendrogram based SVM approach with the oneagainst- one SVM approach over four publicly available data sets, three of them being microarray data. Both performances have been found equivalent, but the first solution requires a smaller number of binary SVM models.

Keywords: Cluster Analysis, Empiric Characteristic Function, Multi-class SVM, R.

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Authors: M. Ben Slimene, M.-S. Ouali

Abstract:

Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: Artificial intelligence, clustering, culvert, regression model, slow degradation.

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Authors: A. R. Vazifedoost, M. Rahgozar, F. Oroumchian

Abstract:

Knowledge discovery from text and ontology learning are relatively new fields. However their usage is extended in many fields like Information Retrieval (IR) and its related domains. Human Plausible Reasoning based (HPR) IR systems for example need a knowledge base as their underlying system which is currently made by hand. In this paper we propose an architecture based on ontology learning methods to automatically generate the needed HPR knowledge base.

Keywords: Ontology Learning, Human Plausible Reasoning, knowledge extraction, knowledge representation.

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Authors: Ali Nouri

Abstract:

Neuroeducation is one of the most exciting research fields which is continually evolving. However, there is a need to develop its theoretical bases in connection to practice. The present paper is a starting attempt in this regard to provide a space from which to think about neuroeducational theory and invoke more investigation in this area. Accordingly, a comprehensive theory of neuroeducation could be defined as grouping or clustering of concepts and propositions that describe and explain the nature of human learning to provide valid interpretations and implications useful for educational practice in relation to philosophical aspects or values. Whereas it should be originated from the philosophical foundations of the field and explain its normative significance, it needs to be testable in terms of rigorous evidence to fundamentally advance contemporary educational policy and practice. There is thus pragmatically a need to include a course on neuroeducational theory into the curriculum of the field. In addition, there is a need to articulate and disseminate considerable discussion over the subject within professional journals and academic societies.

Keywords: Neuroeducation studies, neuroeducational theory, theory building, neuroeducation research.

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Authors: Shamim Ahmed Koichi Nishigaki

Abstract:

Genome profiling (GP), a genotype based technology, which exploits random PCR and temperature gradient gel electrophoresis, has been successful in identification/classification of organisms. In this technology, spiddos (Species identification dots) and PaSS (Pattern similarity score) were employed for measuring the closeness (or distance) between genomes. Based on the closeness (PaSS), we can buildup phylogenetic trees of the organisms. We noticed that the topology of the tree is rather robust against the experimental fluctuation conveyed by spiddos. This fact was confirmed quantitatively in this study by computer-simulation, providing the limit of the reliability of this highly powerful methodology. As a result, we could demonstrate the effectiveness of the GP approach for identification/classification of organisms.

Keywords: Fluctuation, Genome profiling (GP), Pattern similarity score (PaSS), Robustness, Spiddos-shift.

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Authors: Edward Guillén, Stephanne Rodríguez, Jhordany Rodríguez

Abstract:

Throughput enhancement can be achieved with two main approaches. The first one is by the increase of transmission rate and the second one is reducing the control traffic. This paper focuses on how the throughput can be enhanced by increasing Maximum Transmission Unit -MTU. Transmission of larger packets can cause a throughput improvement by reducing IP overhead. Analysis results are obtained by a mathematical model and simulation tools with a main focus on wireless channels.

Keywords: 802.11, Maximum Transfer Unit, throughput enhancement, wireless networks.

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Authors: T. S. Chou, K. K. Yen, J. Luo

Abstract:

The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users- activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors.

Keywords: Intrusion detection, feature selection, k-nearest neighbors, fuzzy clustering, Dempster-Shafer theory

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Authors: Ondruska Marek

Abstract:

The article presents the whole model of IS/IT architecture exception governance. As first, the assumptions of presented model are set. As next, there is defined a generic governance model that serves as a basis for the architecture exception governance. The architecture exception definition and its attributes follow. The model respects well known approaches to the area that are described in the text, but it adopts higher granularity in description and expands the process view with all the next necessary governance components as roles, principles and policies, tools to enable the implementation of the model into organizations. The architecture exception process is decomposed into a set of processes related to the architecture exception lifecycle consisting of set of phases and architecture exception states. Finally, there is information about my future research related to this area.

Keywords: Architecture, dispensation, exception, governance, model

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Authors: Suphat Rittirat, Sutha Klaocheed, Kanchit Thammasiri

Abstract:

The effects of thidiazuron (TDZ) and benzyladenine (BA) on protocorm-like bodies (PLBs) induction from leaf explants was investigated. It was found that TDZ was superior to BA. The highest percentage and number of PLBs per leaf explant at 30 and 5.3, respectively were obtained on ½ MS medium supplemented with 9µM TDZ. The regenerated plantlets were potted and acclimatized in the greenhouse. These plants grew well and developed into normal plants after 3 month of transplantation. The 100% survival of plantlets was achieved when planted on pots containing sphagnum moss. 

Keywords: Orchid, PLBs, sphagnum moss, thidiazuron.

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Authors: S. Sowmyayani

Abstract:

The advancement of computer-aided design helps the medical force and security force. Some applications include biometric recognition, elderly fall detection, face recognition, cancer recognition, tumor recognition, etc. This paper deals with different machine learning algorithms that are more generically used for any health care system. The most focused problems are classification and regression. With the rise of big data, machine learning has become particularly important for solving problems. Machine learning uses two types of techniques: supervised learning and unsupervised learning. The former trains a model on known input and output data and predicts future outputs. Classification and regression are supervised learning techniques. Unsupervised learning finds hidden patterns in input data. Clustering is one such unsupervised learning technique. The above-mentioned models are discussed briefly in this paper.

Keywords: Supervised learning, unsupervised learning, regression, neural network.

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Authors: Yee Ling Boo, Damminda Alahakoon

Abstract:

Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.

Keywords: Multimodal, Granularity, Hierarchical Clustering, Growing Self Organising Maps, Data Mining

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Authors: Abder-Rahman Ali, Adélaïde Albouy-Kissi, Manuel Grand-Brochier, Viviane Ladan-Marcus, Christine Hoeffl, Claude Marcus, Antoine Vacavant, Jean-Yves Boire

Abstract:

In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due to the image modality (presence of speckle noise, low contrast, etc.), and to calculate the optimum inter-cluster threshold. Fine boundaries are detected by a local recursive merging of ambiguous pixels. The method has been tested on a representative database. Compared to both Otsu and type-I Fuzzy C-Means techniques, the proposed method significantly reduces the segmentation errors.

Keywords: Defuzzification, fuzzy clustering, image segmentation, type-II fuzzy sets.

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Authors: Cheng-Lung Huang, Han-Yu Chien, Michael Conyette

Abstract:

Social bookmarking is an environment in which the user gradually changes interests over time so that the tag data associated with the current temporal period is usually more important than tag data temporally far from the current period. This implies that in the social tagging system, the newly tagged items by the user are more relevant than older items. This study proposes a novel recommender system that considers the users- recent tag preferences. The proposed system includes the following stages: grouping similar users into clusters using an E-M clustering algorithm, finding similar resources based on the user-s bookmarks, and recommending the top-N items to the target user. The study examines the system-s information retrieval performance using a dataset from del.icio.us, which is a famous social bookmarking web site. Experimental results show that the proposed system is better and more effective than traditional approaches.

Keywords: Recommender systems, Social bookmarking, Tag

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Authors: Surender Kumar Soni, Dhirendra Pratap Singh

Abstract:

Wireless Sensor Networks can be used to monitor the physical phenomenon in such areas where human approach is nearly impossible. Hence the limited power supply is the major constraint of the WSNs due to the use of non-rechargeable batteries in sensor nodes. A lot of researches are going on to reduce the energy consumption of sensor nodes. Energy map can be used with clustering, data dissemination and routing techniques to reduce the power consumption of WSNs. Energy map can also be used to know which part of the network is going to fail in near future. In this paper, Energy map is constructed using the prediction based approach. Adaptive alpha GM(1,1) model is used as the prediction model. GM(1,1) is being used worldwide in many applications for predicting future values of time series using some past values due to its high computational efficiency and accuracy.

Keywords: Adaptive Alpha GM(1, 1) Model, Energy Map, Prediction Based Data Reduction, Wireless Sensor Networks

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Authors: Eva Pajorová, Ladislav Hluchý

Abstract:

Developed tool is one of system tools for easier access to various scientific areas and real time interactive learning between lecturer and for hearing impaired students. There is no demand for the lecturer to know Sign Language (SL). Instead, the new software tools will perform the translation of the regular speech into SL, after which it will be transferred to the student. On the other side, the questions of the student (in SL) will be translated and transferred to the lecturer in text or speech. One of those tools is presented tool. It-s too for developing the correct Speech Visemes as a root of total communication method for hearing impared students.

Keywords: Impared people, sing language, communication methods.

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Authors: Zelal Çinar

Abstract:

This paper claims that architecture is a contingent discipline, despite the fact that its contingency has long been denied through a retreat to Vitruvian writing. It is evident that contingency is rejected not only by architecture but also by modernity as a whole. Vitruvius attempted to cover the entire field of architecture in a systematic form in order to bring the whole body of this great discipline to a complete order. The legacy of his theory hitherto lasted not only that it is the only major work on the architecture of Classical Antiquity to have survived, but also that its conformity with the project of modernity. In the scope of the paper, it will be argued that contingency should be taken into account rather than avoided as a potential threat. 

Keywords: Architecture, contingency, modernity, Vitruvius.

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Authors: Nadeem Iftikhar, Zouhaib Zafar, Shaukat Ali

Abstract:

Efficient retrieval of multimedia objects has gained enormous focus in recent years. A number of techniques have been suggested for retrieval of textual information; however, relatively little has been suggested for efficient retrieval of multimedia objects. In this paper we have proposed a generic architecture for contextaware retrieval of multimedia objects. The proposed framework combines the well-known approaches of text-based retrieval and context-aware retrieval to formulate architecture for accurate retrieval of multimedia data.

Keywords: Context-aware retrieval, information retrieval, multimedia databases, multimedia data.

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Authors: Hager Kammoun, Jean Charles Lamirel, Mohamed Ben Ahmed

Abstract:

In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term" knowledge about past queries and concepts in a collection of documents. The “long term" knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.

Keywords: Information Retrieval Systems, machine learning, classification, Galois lattices, Self Organizing Map.

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Authors: Е. V. Kutsina, S. A. Tarasova

Abstract:

In this article we present a methodology which enables preschool and primary school unlanguaged children to remember words, phrases and texts with the help of graphic signs - letters, syllables and words. Reading for a child becomes a support for speech development. Teaching is based on the principle "from simple to complex", "a letter - a syllable - a word - a proposal - a text." Availability of multi-level texts allows using this methodology for working with children who have different levels of speech development.

Keywords: Alalia, analytic-synthetic method, development of coherent speech, formation of vocabulary, learning to read, , sentence formation, three-level stories, unlanguaged children.

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Authors: L. Belkhiri, L. Mouni, A. Tiri, T.S. Narany

Abstract:

In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three.

Keywords: Drinking water, groundwater quality, irrigation water, self-organizing maps.

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Authors: Kazunari Meguro, Daisuke Yamamoto, Shinichi Motomura, Toshihiko Sasama, Takao Kawamura, Kazunori Sugahara

Abstract:

We have developed a distributed asynchronous Web based training system. In order to improve the scalability and robustness of this system, all contents and functions are realized on mobile agents. These agents are distributed to computers, and they can use a Peer to Peer network that modified Content-Addressable Network. In the proposed system, only text data can be included in a exercise. To make our proposed system more useful, the mechanism that it not only adapts to multimedia data but also it doesn-t influence the user-s learning even if the size of exercise becomes large is necessary.

Keywords: e-Learning, multimedia, Mobile Agent.

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Authors: G. Van Dijck, M. M. Van Hulle, M. Wevers

Abstract:

A genetic algorithm (GA) based feature subset selection algorithm is proposed in which the correlation structure of the features is exploited. The subset of features is validated according to the classification performance. Features derived from the continuous wavelet transform are potentially strongly correlated. GA-s that do not take the correlation structure of features into account are inefficient. The proposed algorithm forms clusters of correlated features and searches for a good candidate set of clusters. Secondly a search within the clusters is performed. Different simulations of the algorithm on a real-case data set with strong correlations between features show the increased classification performance. Comparison is performed with a standard GA without use of the correlation structure.

Keywords: Classification, genetic algorithm, hierarchicalagglomerative clustering, wavelet transform.

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Authors: Ramin Javadzadeh., Zahra Afsahi, MohammadReza Meybodi

Abstract:

The Artificial immune systems algorithms are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Artificial Immune System Algorithm is introduced for the first time to overcome its problems of artificial immune system. That use of the small size of a local search around the memory antibodies is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the standard artificial immune system algorithms

Keywords: Artificial immune system, Cellular Automata, Cellular learning automata, Cellular learning automata, , Local search, Optimization.

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Authors: Ahmed Amin Mousa, Tamer M. Ismail, M. Abd El Salam

Abstract:

This work presents a robot called Conceptual Robotic Cube, CR-Cube. The robot can be used as an educational tool for children from the age of three. It has a cube shape attached with a camera colours sensor. In addition, it contains four wheels to move smoothly. The researchers prepared a questionnaire to measure the efficiency of the robot. The design and the questionnaire was presented to 11 experts who agreed that the robot is appropriate for learning numbering and colours for preschool children.

Keywords: CR-Cube, robotic cube, conceptual robot, conceptual cube, colour concept, early childhood education.

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Authors: Leo Galambos

Abstract:

The majority of today's IR systems base the IR task on two main processes: indexing and searching. There exists a special group of dynamic IR systems where both processes (indexing and searching) happen simultaneously; such a system discards obsolete information, simultaneously dealing with the insertion of new in¬formation, while still answering user queries. In these dynamic, time critical text document databases, it is often important to modify index structures quickly, as documents arrive. This paper presents a method for dynamization which may be used for this task. Experimental results show that the dynamization process is possible and that it guarantees the response time for the query operation and index actualization.

Keywords: Search engine, inverted file, index management.

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Authors: Bakhtiar Ul Haq, R. Ahmed, A. Shaari, Mazmira Binti Mohamed, Nisar Ali

Abstract:

The capability of exploiting the electronic charge and spin properties simultaneously in a single material has made diluted magnetic semiconductors (DMS) remarkable in the field of spintronics. We report the designing of DMS based on zinc-blend ZnO doped with Cr impurity. The full potential linearized augmented plane wave plus local orbital FP-L(APW+lo) method in density functional theory (DFT) has been adapted to carry out these investigations. For treatment of exchange and correlation energy, generalized gradient approximations have been used. Introducing Cr atoms in the matrix of ZnO has induced strong magnetic moment with ferromagnetic ordering at stable ground state. Cr:ZnO was found to favor the short range magnetic interaction that reflect tendency of Cr clustering. The electronic structure of ZnO is strongly influenced in the presence of Cr impurity atoms where impurity bands appear in the band gap.

Keywords: ZnO, Density functional theory, Diluted magnetic semiconductors, Ferromagnetic materials, FP-L(APW+lo).

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Authors: H.B.Kekre, Archana Athawale, Pallavi N.Halarnkar

Abstract:

Steganography, derived from Greek, literally means “covered writing". It includes a vast array of secret communications methods that conceal the message-s very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. This paper proposes a new improved version of Least Significant Bit (LSB) method. The approach proposed is simple for implementation when compared to Pixel value Differencing (PVD) method and yet achieves a High embedding capacity and imperceptibility. The proposed method can also be applied to 24 bit color images and achieve embedding capacity much higher than PVD.

Keywords: Information Hiding, LSB Matching, PVD Steganography.

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Authors: Mat Syai'in, Adi Soeprijanto

Abstract:

An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Keywords: Optimal Power Flow, Generator Capability Curve, Improved Particle Swarm Optimization, Neural Network

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