Input Entity Research Articles

Decision-Making and Coordination of Remanufacturing Closed-Loop Supply Chain with PIR under the Different Government Subsidy Strategies

Government subsidies and process innovation for remanufacturing (PIR) have become effective measures to improve the recycling and remanufacturing efficiency of waste products and promote sustainable economic development. Under different government subsidy strategies, the PIR decision and coordination of the closed-loop supply chain (CLSC) of authorized competition remanufacturing are studied. This paper constructs five centralized and decentralized decision models of CLSC and analyzes the influence of government subsidy strategy and PIR input entity differences on the competition and cooperation relationship between manufacturers and remanufacturers and the performance of CLSC. A fixed license fee contract is designed to realize CLSC coordination and to improve the performance and operational efficiency of the CLSC. It is found that patent licensing fees can always play a role in sharing government subsidies between the manufacturer and remanufacturer, and making changes in government subsidy strategies only affects formulation of patent licensing fees. Manufacturer PIR input mode is more conducive to improving the market demand for new products, while remanufacturer PIR input mode is more conducive to improving the PIR input level, remanufactured product market demand, CLSC members and overall performance. Moreover, government subsidies can produce better efficiency and effects under remanufacturer PIR input mode. A fixed license fee contract can coordinate a CLSC effectively. The increase in government subsidies and PIR input effect can effectively expand the negotiation space between the manufacturer and remanufacturer, which is conducive to achievement of coordination contracts. Finally, the main conclusions are verified by numerical analysis.

Application of tensor factorisation for CAE model preparation from CAD assembly models

Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.

Building an Operational Solution Assistant System for Foreign SMEs in ROK

Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and technology. In recent years, the Korean government has continuously formulated tax policies, policies to support the business economy and import policies to support foreign businesses in Korea. The Pangyo Valley Creative Economy Valley is being groomed as a global startup hub in Asia. Small and medium enterprises (SMEs) in foreign countries are increasingly interested and eager to seek investment opportunities in the Korean market. Nonetheless, for these companies, language barriers and cultural and institutional differences make it more difficult and time-consuming to learn about the Korean market (such as investment trends, laws, visa policies, taxes and business establishment issues in Korea, etc.). In this study, we explored the process of searching information and seeking investment opportunities and built a business consulting and support application in the first stages of starting a business in ROK to increase effectiveness and save time, which is also an innovative business practice in Use-case ROK. We designed our Virtual Assistant system that can crawl and analyze data on foreign investments in ROK from open data resource websites (data.co.kr) and used analytic and aggregation techniques to explore trends in investments of foreign enterprises. We also researched the process of searching information and seeking investment opportunities for SMEs when investing in ROK, government support policies, laws and taxes as well as a number of other related issues. We built datasets and used Natural Language Processing (NLP) together with Natural Language Understanding (NLU) algorithms to build chatbot applications. Friendly framework for new developers to add and build up the dataset of AI Assistant is built by providing input intent data function, input Entity data function, input utterance data function as well as training and test function. In addition, we built a web-app connected to the server to visualize all the results of research so that SMEs owners can easily use and look for information on investments. Based on the research results, we can make recommendations to SMEs in keeping with the changing investment trends in ROK.

Unsupervised Learning Approach for Clustering Leaf Images

With appropriate representation methods, the clustering techniques are found to be efficient with neural networks. The present work aims to propose various feature representation techniques for efficient clustering. The methods used for feature representation in this paper are, a method using random closed set, a method using edge information of input entity, a method that uses Huff transformation and a method that uses boundary moments. A comparative study of these representation methods for clustering the input objects using artificial neural networks, specifically Self-Organizing Map (SOM) is focused.

System Analysis of Public Health Surveillance in School-Age Children

School-age children are more susceptible to disease transmission. Health efforts to control health problems are public health surveillance. School institutions have the potential to be the place for conducting surveillance for school-age children, considering that the school institution is a strategic place because its existence will facilitate the outreach of health programs to school-age children. The purpose of this study was to analyze the public health surveillance system for the development of public health surveillance in school-age children. This research was operational research with cross-sectional design. Research variables include data types, data sources, data collection methods, data analysis, health information and information dissemination. Development of a surveillance information system model using the System Development Life Cycle approach. The results of data identification were carried out on the types of data and information that could be monitored and collected by School Health Program teachers and students in schools. The data includes symptoms, immunization data, Adverse Events Following Immunization, student absenteeism and healthy behavior and risk factors for non-communicable diseases. The input entity as a data source is the school and the output entity as the recipient of information is the district/city health office and the education office. Information generated from this surveillance is to complete health program data from health services.

Knowledge entity learning and representation for ontology matching based on deep neural networks

We study the task of ontology matching that is used mainly for solving the semantic heterogeneity problems, which concentrates on finding semantically related entities between different ontologies. Many previous works exploit the character-level or token-level information of the descriptions of an entity in ontology directly when applying the string-based matcher or token based matcher to find the corresponding entities. They ignored the higher level correlations between different descriptions of an entity. To address this problem, we propose a representation learning method based on deep neural networks which aim at learning the high level abstract representations of the input entity. Particularly, the representations of the entities are learned in an unsupervised way firstly, and then fine-tuned in a supervised manner with the training data. The experiment results show that our approaches can learn useful representations for entities from its descriptive information to better measure the similarity between entities.

Discovering User Pattern Analysis from Web Log Data using Weblog Expert

Objective: This article tries to discover the hidden knowledge and identifying user behavior on the web by using the web data sources. With the help of this knowledge, the overall performance of future accesses, the typical browsing behavior of a user and subsequently to predict desired pages, a user wants to access in future. Methods/Statistical Analysis: The user pattern is analyzed by using the modified Web Log Expert tool from the web access log file collected from the organization. This modified tool tries to conduct a web mining in a domain independent manner. This algorithm consists of three parts: 1. Given an input entity, extracting a set of IP addresses and visor lists and then ranking them according to comparability, 2. Extracting the domains in which the given entity takes part and 3. Identifying and summarizing the competitive evidence that details the organization’s strength. Findings: The main aim of the research work is extracting of user frequent access page using web log data, which is based on user session time, IP addresses, browser details, operating system, top user. This complete analysis work has been implemented in the Web Log Expert tool. The experimental results provide an easier way to navigate the website and improve the website design architecture. This work deliberates the detailed results of a website in a specific education domain application. We investigate the statistics of hourly based, daily based, week and monthly based report of the web usage patterns. The goal is to capture, model and analyze the behavioral patterns and profiles of users interacting with a website. The knowledge about users and their behavior on the web helps the organization benefits and leads directly to profit increase.

Development of a Tailored Analysis System for Korean Working Conditions Survey.

BackgroundKorean Working Conditions Surveys (KWCS), referencing European Working Conditions Surveys, have been conducted three times in order to survey working condition and develop work-related policies. However, we found three limitations for managing the collected KWCS data: (1) there was no computerized system for managing data; (2) statistical KWCS data were provided by limited one-way communication; and (3) the concept of a one-time provision of information was pursued. We suggest a web-based public service system that enables ordinary people to make greater use of the KWCS data, which can be managed constantly in the future.MethodsAfter considering data characteristics, we designed a database, which was able to have the result of all pairwise combinations with two extracted data to construct an analysis system. Using the data of the social network for each user, the tailored analysis system was developed. This system was developed with three methods: clustering and classification for building a social network, and an infographic method for improving readability through a friendly user interface.ResultsWe developed a database including one input entity consisting of the sociodemographic characteristics and one output entity consisting of working condition characteristics, such as working pattern and work satisfaction. A web-based public service system to provide tailored contents was completed.ConclusionThis study aimed to present a customized analysis system to use the KWCS data efficiently, provide a large amount of data in a form that can give users a better understanding, and lay the ground for helping researchers and policy makers understand the characteristics.

The contribution of co-reference resolution to supervised relation detection between bacteria and biotopes entities.

BackgroundThe acquisition of knowledge about relations between bacteria and their locations (habitats and geographical locations) in short texts about bacteria, as defined in the BioNLP-ST 2013 Bacteria Biotope task, depends on the detection of co-reference links between mentions of entities of each of these three types. To our knowledge, no participant in this task has investigated this aspect of the situation. The present work specifically addresses issues raised by this situation: (i) how to detect these co-reference links and associated co-reference chains; (ii) how to use them to prepare positive and negative examples to train a supervised system for the detection of relations between entity mentions; (iii) what context around which entity mentions contributes to relation detection when co-reference chains are provided.ResultsWe present experiments and results obtained both with gold entity mentions (task 2 of BioNLP-ST 2013) and with automatically detected entity mentions (end-to-end system, in task 3 of BioNLP-ST 2013). Our supervised mention detection system uses a linear chain Conditional Random Fields classifier, and our relation detection system relies on a Logistic Regression (aka Maximum Entropy) classifier. They use a set of morphological, morphosyntactic and semantic features. To minimize false inferences, co-reference resolution applies a set of heuristic rules designed to optimize precision. They take into account the types of the detected entity mentions, and take advantage of the didactic nature of the texts of the corpus, where a large proportion of bacteria naming is fairly explicit (although natural referring expressions such as "the bacteria" are common). The resulting system achieved a 0.495 F-measure on the official test set when taking as input the gold entity mentions, and a 0.351 F-measure when taking as input entity mentions predicted by our CRF system, both of which are above the best BioNLP-ST 2013 participant system.ConclusionsWe show that co-reference resolution substantially improves over a baseline system which does not use co-reference information: about 3.5 F-measure points on the test corpus for the end-to-end system (5.5 points on the development corpus) and 7 F-measure points on both development and test corpora when gold mentions are used. While this outperforms the best published system on the BioNLP-ST 2013 Bacteria Biotope dataset, we consider that it provides mostly a stronger baseline from which more work can be started. We also emphasize the importance and difficulty of designing a comprehensive gold standard co-reference annotation, which we explain is a key point to further progress on the task.

결함 심각도에 기반한 소프트웨어 품질 예측

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively. ▸

An exploration of ranking models and feedback method for related entity finding

Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models.

Design, calibration and testing of a thin film temperature gauge array for temperature and heat flux measurements in fluid mixing experiments

Material aging (“thermal fatigue”) in nuclear power plant piping systems due to thermal fluctuations has been identified as a high ranking safety issue (NEA/CSNI/R(2007)13 (2008) [19]). In order to provide a reliable assessment of potential material stresses the temperature fluctuations need to be well characterized as an input entity for analytical investigations. This contribution presents a sensor able to characterize thermal fluctuations on the wall—an entity necessary as input to analytical investigation.An array of resistive temperature detectors (RTDs) based on a micro patterned thin film platinum resistor has been built. The sensors are located in a glass substrate (40×53 mm2), in which the platinum resistors and the electrical contact leads are embedded. The operational temperature range is below 0 °C up to 200 °C, the temperature coefficient of resistivity (TCR) is 0.0011 °C−1 with a high correlation coefficient, and the thermal time constant has been verified at about 30 ms. Such sensors can be used to measure and characterize in terms of frequency and amplitude of thermal fluctuations at the inner wall of T-junction pipelines in order to evaluate the heat flux through the wall and thus the heat transfer coefficient.

Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions --- this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention. We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets. The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the top-level entity. We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art.

Competitor Mining with the Web

This paper is concerned with the problem of mining competitors from the Web automatically. Nowadays the fierce competition in the market necessitates every company not only to know which companies are its primary competitors, but also in which fields the company's rivals compete with itself and what its competitors' strength is in a specific competitive domain. The task of competitor mining that we address in the paper includes mining all the information such as competitors, competing fields and competitors' strength. A novel algorithm called CoMiner is proposed, which tries to conduct a Web-scale mining in a domain-independent manner. The CoMiner algorithm consists of three parts: 1) given an input entity, extracting a set of comparative candidates and then ranking them according to comparability; 2) extracting the fields in which the given entity and its competitors play against each other; 3) identifying and summarizing the competitive evidence that details the competitors' strength. As for evaluation, a prototype system implementing the CoMiner algorithm is presented. An evaluation data set consisting of 70 entities is constructed. 728 competitors and 3,640 competitive fields with 6,381 competitive evidences are discovered with the prototype. The experimental results show that the proposed algorithm is highly effective.