Similarity matching method of power distribution system operating data based on neural information retrieval

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

An Ontology-Based Information Retrieval Model for Vegetables E-Commerce

With the rapid increment of the information on the web, traditional information retrieval based on the keywords is far from user＇s satisfaction in recall and precision. In order to improve the recall ratio and the precision radio of IR engine in the vegetables e-commerce, an information retrieval model based on the vegetables e-commerce ontology is presented in this paper, vegetables e-commerce ontology was constructed by gathering and the analyzing vegetables e-commerce domain information on the web. The vegetables e-commerce ontology is composed of some kinds of vegetable classes and hierarchy relationship of vegetables classes. In the process of information retrieval, domain ontology helps to index information and information inference. An ontology-based information retrieval model is implemented, and which has more functions than the keyword-based web information retrieval engines. The experiment results show that the recall ratio and the precision ratio of ontology-based information retrieval model are higher than that of the information retrieval engine based on keyword at a certain extent.

Bayesian-based information extraction and aggregation approach for multilevel systems with multi-source data

The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation (BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism (DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification. ? 2017 Beijing Institute of Aerospace Information.

Research and Application on Web Information Retrieval Based on Improved FP-Growth Algorithm

A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

Optimization in the Migration Problem of Mobile Agents in Distributed Information Retrieval Systems

In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.

Intelligent Agent-Based System for Digital Library Information Retrieval

A new information search model is reported and the design and implementation of a system based on intelligent agent is presented. The system is an assistant information retrieval system which helps users to search what they need. The system consists of four main components: interface agent, information retrieval agent, broker agent and learning agent. They collaborate to implement system functions. The agents apply learning mechanisms based on an improved ID3 algorithm.

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

A new method for information retrieval in two-dimensional grating-based X-ray phase contrast imaging

Grating-based X-ray phase contrast imaging has been demonstrated to he an extremely powerful phase-sensitive imaging technique. By using two-dimensional （2D） gratings, the observable contrast is extended to two refraction directions. Recently, we have developed a novel reverse-projection （RP） method, which is capable of retrieving the object information efficiently with one-dimensional （1D） grating-based phase contrast imaging. In this contribution, we present its extension to the 2D grating-based X-ray phase contrast imaging, named the two-dimensional reverse- projection （2D-RP） method, for information retrieval. The method takes into account the nonlinear contributions of two refraction directions and allows the retrieval of the absorption, the horizontal and the vertical refraction images. The obtained information can be used for the reconstruction of the three-dimensionak phase gradient field, and for an improved phase map retrieval and reconstruction. Numerical experiments are carried out, and the results confirm the validity of the 2D-RP method.

A Survey of Web Information System and Applications

The fourth international conference on Web information systems and applications （WISA 2007） has received 409 submissions and has accepted 37 papers for publication in this issue. The papers cover broad research areas, including Web mining and data warehouse, Deep Web and Web integration, P2P networks, text processing and information retrieval, as well as Web Services and Web infrastructure. After briefly introducing the WISA conference, the survey outlines the current activities and future trends concerning Web information systems and applications based on the papers accepted for publication.

An Integrated Information Retrieval Support System for Campus Network

This paper presents a new integrated information retrieval support system （IIRSS） which can help Web search engines retrieve cross-lingual information from hereto geneous resources stored in multi-databases in Intranet. The IIRSS, with a three-layer architecture, can cooperate with other application servers running in Intranet. By using intelligent agents to collect information and to create indexes on the-fly, using an access control strategy to confine a user to browsing those accessible documents for him/her through a single portal, and using a new cross-lingual translation tool to help the search engine retrieve documents, the new system provides controllable information access with different authorizations, personalized services, and real-time information retrieval.

Cross-Language Information Extraction and Auto Evaluation for OOV Term Translations

OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition or context information of OOV terms. Furthermore, non-existing methods focus on cross-language definition retrieval for OOV terms. Never the less, it has always been so difficult to evaluate the correctness of an OOV term translation without domain specific knowledge and correct references. Our English definition ranking method differentiate the types of OOV terms, and applies different methods for translation extraction. Our English definition ranking method also extracts multilingual context information and monolingual definitions of OOV terms. In addition, we propose a novel cross-language definition retrieval system for OOV terms. Never the less, we propose an auto re-evaluation method to evaluate the correctness of OOV translations and definitions. Our methods achieve high performances against existing methods.

ADAPTIVE AND ACTIVE COMPUTING PARADIGM FOR PERSONALIZED INFORMATION SERVICE IN DISTRIBUTED HETERONGEOUS ENVIRONMENT

To solve the problem that traditional pull based information service can’t meet the demand of long term users getting domain information timely and properly, an adaptive and active computing paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user centered, push based higsh quality information service timely in a proper way, the motivation of which is generalized as R 4 Service: the right information at the right time in the right way to the right person, upon which formalized algorithms framework of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push based, event driven, interest related, adaptive and active information service mode, which is useful and promising for long term user to gain fresh information instead of polling from kinds of information sources.

A High-Speed Information Retrieval System

We cleveloped a high-speed information retrieval system. The system hased on the IXP 2800 is one of the dedicute device. The velocity of the information retrieval is 6.8 Gb/s. The protocol support Telnet, FTP, SMTP, POP3 etc. various networks protocols. The information retrieval supports the key word and the natural language process. This paper explains the hardware system, software system and the index of the performance. Key words network processor - IXP2800 - information retrieval - IXA CLC number TP 309 Foundation item: Supported by the National Natural Science Foundation of China (69873016 & 69972017) and the National High Technology Development Program of China (863-301-06-1)Biography: SHI Shu-dong (1963-), male, Ph. D. candidate, research direction: network & information security.

Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant information.The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval outcomes.The prevailing procedures for IR consume considerable time,and they could not recover information proficiently.In this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated downsides.The proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user query.In the initial phase,a crop ontology is developed and evaluated by gathering crop information.In the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the database.In the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the database.The performance of the proposed system is measured and compared with that of existing techniques.Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.

Application of Quicksort Algorithm in Information Retrieval

With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents in the inventory has reached nearly one million.So how do you quickly and effectively retrieve the information you want in such a huge database?This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval,improve retrieval efficiency,and adapt to the rapid expansion of document data.The Quicksort Algorithm gives different weights to each position of the document,and multiplies the weight of each position with the number of matches of that position,and then adds all the multiplied sums to set a feature value for Quicksort,which can achieve the full accuracy of Information Retrieval.Therefore,the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval,and to use the position weighting algorithm to improve the matching quality of Information Retrieval,so as to achieve the overall effect of improving the efficiency of Information Retrieval.

Subject-Based Information Retrieval System in Digital Libraries

Objective: Information visualization is the study of interactive depictions of abstract and data to strengthen the human cognition. Designing an appropriate information visualization system may be very useful technique for scholars, who intent to get scientific information from digital libraries. The objective of current study was to map and visualize the key-information of dissertations in academic libraries. To achieve the aim, an information retrieval system was designed to present the interactive graphic view of dissertations’ subjects in academic. Methods: An information retrieval system was designed by information visualization toolkit that presents the related subjects of dissertations in academic libraries. In addition, the satisfaction-levels of library-users were analyzed by administrating a standard questionnaire (QUIS Questionnaire). Results: The study indicated that the designed IR system helped to provide a user-friendly environment through displaying subjective relations of dissertations, overwhelming variety of colors in displaying information. Fast and easy access to the cover-to-cover information of dissertations and user-interaction facilities are the advantages of designed IR. Analysis of data furthermore indicated that the users’ satisfaction from the system was from medium to high grade. Conclusion: Designing the IR-system revealed an excessive influence on users’ satisfaction;therefore, proposing such systems for employing in academic libraries is very suitable and its implementation is necessary.

Role of Ontology in Information Retrieval

Based on the comparison between ontology and thesaurus, and the analysis of an ontology-based Information Retrieval （IR） model, the potential advantages that ontology may contribute to IR are analyzed. Then a general architecture of ontology-based Information Retrieval System （IRS） and the approach of constructing it are presented. Based on the researches, the role of ontology in IR is summarized from four aspects and a typical system called Textpresso is analyzed. Finally, a conclusion is drawn that utilizing ontology is the trend of IR and can really improve the IRS.

Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.