Search results

As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

There has been an increased interest in the use of semantic description and matching techniques, to support service discovery and to overcome the limitations in the traditional syntactic approaches. However, the existing semantic matching approaches lack certain desirable properties that must be present in an effective solution to support service discovery. The purpose of this paper is to present a solution to facilitate the effective semantic matching of resource requests and advertisements in pervasive environments.

The paper presents a semantic description and matching approach to facilitate resource discovery in pervasive environments; the approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request.

The solution has been evaluated for its effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. The solution was also evaluated for its efficiency/scalability and from the experimental results obtained, it can be observed that for most practical situations, matching time can be considered acceptable for reasonable numbers of advertisements and request sizes.

The proposed approach improves existing semantic matching solutions in several key aspects. Specifically; it presents an effective approximate matching and ranking criterion and incorporates priority consideration in the matching process. As shown in the evaluation experiments, these features significantly improve the effectiveness of semantic matching.

A research project is reported in which techniques for the automatic classification of book material were investigated. Attention was focussed on three fundamental issues, namely: the computer‐based surrogation of monographic material, the clustering of book surrogates on the basis of content association, and the evaluation of the resultant classifications. A test collection of 250 books, which was assembled on behalf of the project, is described together with its surrogation by means of the complete back‐of‐the‐book index, table of contents, title and Dewey classification code(s) of each volume. Some properties of hierarchic and non‐hierarchic automatic classifications of the test collection are discussed, followed by their evaluation with reference to a small set of queries and relevance judgements. Finally, a less formal evaluation of the classifications in terms of the logical appeal of the cluster membership is reported. The work has shown that, on a small experimental scale and in the context of the test data used, automatic classifications of book material represented by index list can be produced which are superior, on the basis of a generalized measure of effectiveness, to a conventional library classification of the same material.

The existing authentication procedures (pin, pattern, password) are not very secure. Therefore, the Gait pattern authentication scheme is introduced to verify the own user. The current research proposes a running Gaussian grey wolf boosting (RGGWB) model to recognize the owner.

The biometrics system plays an important role in smartphones in securing confidential data stored in them. Moreover, the authentication schemes such as passwords and patterns are widely used in smartphones.

To validate this research model, the unauthenticated user's Gait was trained and tested simultaneously with owner gaits. Furthermore, if the gait matches, the smartphone unlocks automatically; otherwise, it rejects it.

Finally, the effectiveness of the proposed model is proved by attaining better accuracy and less error rate.

The identification (listing) and classification (grading) of urban heritage buildings for conservation is a challenging task for urban planners and conservation architects. Most of the world's cities depend on the expert-based evaluation method (EBEM) for listing and grading heritage buildings. The Panaji city in India provided a unique opportunity to assess the performance of the EBEM as two independent agencies carried out the heritage listing and grading process. Considering the case of Panaji, this research aims to measure the performance of EBEM used for listing and grading heritage buildings and identify the issues associated with the existing methodology.

This research presents a comparative analysis of the building listed and graded by the two agencies. The buildings that both agencies graded were identified and analysed using a confusion matrix. The grading classification was tested for accuracy, precision, sensitivity and F-score.

The result shows a low accuracy and F-score, which reflects the level of buildings misclassified. The misclassification is the product of the lack of standardisation of methodology and the subjectivity level involved in the EBEM.

Heritage listing and grading is a time-consuming process, and no city has the time and resource to conduct studies to check the accuracy. The cities in India and across the world, which follow a similar EBEM process, should consider this study's finding and revisit their methodology and develop a more reliable methodology for listing and grading heritage buildings.

Given the centrality of data and information to the evaluation and operation of policies to reduce carbon emissions, the purpose of this paper is to investigate potential sources of data within in the UK on the commercial building stock in terms of its physical characteristics, financial performance, energy consumption and environmental performance. The research aimed to increase understanding of the potential sources of data on property attributes, financial performance and energy use or environmental performance, with a particular emphasis on evaluating their strengths and weaknesses in terms of scope, quality, availability and practicability.

This is an explorative, qualitative study that uses semi‐structured interviews conducted with 11 different data holding organisations.

Whilst public sector organisations have the potential to provide the data required for large samples, there are major barriers to obtaining and linking the different databases. Data on the three key data elements (prices, attributes and environmental performance) tend to be split between different governmental agencies. There are likely to be substantial problems (and costs) of linking the databases due, in particular, to definitional differences. There is a conflict between government's public good objective of increasing knowledge of the environmental performance of buildings and its objective of maximising revenues from the commercial exploitation of data. Since the private sector organisations that hold data are commercially motivated, the samples that they have gathered are largely client driven and, consequently, tend to be partial, particular, proprietary, private and product‐related.

Since pricing studies are central to evaluating the effectiveness of eco‐labelling in property markets, this paper improves one's understanding of the role of information and data barriers to improving the allocative efficiency of commercial property markets.

Searching the tender notices that publish every day in open tendering websites is a common way for finding business opportunity in public procurement. The heterogeneity of tender notices from various tendering marketplaces is a challenge for exploiting semantic technologies in the tender search.

Most of the semantic matching approaches require the data to be structured and integrated according to a data model. But the integration process can be expensive and time-consuming especially for multi-source data integration.

In this paper, a product search mechanism that had been developed in an e-procurement platform for matching product e-catalogues is applied to the tender search problem. The search performance has been compared using two procurement vocabularies on searching tender notices from two major tender resources.

The test results show that the matching mechanism is able to find tender notices from heterogeneous resources and different classification systems without transforming the tenders to a uniform data model.

The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query.

The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records.

The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies.

The metadata harvest required manual intervention and the evaluation was resource intensive. Future research will look at evaluation methodologies that take account of issues of consistency and ecological validity.

The method does not require training data and is easily scalable. The pipeline can be customized for individual use cases, for example, recall or precision enhancing.

The approach can provide centralized access to information from multiple domains currently provided by individual digital libraries.

The approach addresses metadata normalization in the context of web resources. The automatic classification approach accounts for matches within hierarchies, aggregating lower level matches to broader parents and thus approximates the practices of a human cataloger.

Nowadays, applications dealing with information extracted from images are commonplace. The widespread use of multimedia information (images, video, audio etc.) makes necessary applications capable of storing, and therefore retrieving, it. Information extracted from images is usually complex and high dimensional. The extraction of non‐textual low‐level indexing features from images is now a research field, and this process principally suffers because of the computational cost of the high dimensionality of those features. A new way to classify and match low‐level features extracted from images, for retrieval purposes, is presented in this paper. M‐tree and R‐tree structures are used, as well as an incremental version of the k‐means classification alogrithm. This set of alogrithms is used to solve the problem of low performance when retrieving previously catalogued images.

The purpose of this paper is to propose and evaluate a new matching sample comparison method, the industry size peer matching method.

Based on archival financial data from Compustat and econometric methods, the paper first validates that such a method will result in firms being divided into more homogenous groups, making peer‐performance comparison more meaningful. Then it compares this new peer matching method with previous methods through two resource‐based related studies in the IT valuation context.

The results show that the industry size matching method is a better method because: it is theoretically grounded, addressing industry, size, and random shock effects and, at the same time, avoids the selection bias caused by using a single firm as benchmark; and empirically such a technique results in more homogeneous groups and can explain more firm‐level returns than the industry‐only classification.

Matched sample comparison group analysis is widely used in both academy and industry. The paper's theoretically grounds and empirically validated matching sample comparison method provides researchers and practitioners with a tool for their future research, performance evaluation, earning management detection, or compensation contract design, when selecting the right peers is called for.