Hierarchical Coding/Retrieval System of Mechanically Oriented Products

2009 ◽  
Author(s):  
A. O. M. Adeoye ◽  
T. Sze´csi
Keyword(s):  
Retrieval System ◽  
Group Technology ◽  
Similarity Index ◽  
Hierarchical Classification ◽  
Priority Vector ◽  
Hierarchical Coding ◽  
Retrieval Systems ◽  
Candidate Image ◽  
Wide Range ◽  
Type Classification

Much attention has been given to parts hierarchical classification, coding and retrieval systems. Much work has been carried out in the retrieval of design images that are similar to the candidate image from the database. However, little has been said or done when it comes to using Group Technology (GT) techniques for the classification, coding and identification of products that are similar by searching through the database using a wide variety of search parameters, especially of mechanically oriented products in a manufacturing environment. This paper presents a method of using the GT to classifying, coding and retrieving of identical or similar products from a database for the purposes of Customer-Led Design, based on the principle of Group Technology classification and coding. The proposed method is based on a hybrid (Chain-Hierarchical type) classification system using a wide range of product parameters. The search and sorting procedure uses the weight factor and similarity index (SI) methods for the identification of similar products. This method generates a list of product codes for the similar products, and ranks the Product Attributes according to their priority vector. The method is successfully implemented in a new Customer-led Design (CLD) environment.

WAVELET-BASED MULTIRESOLUTION HISTOGRAM FOR FAST IMAGE RETRIEVAL

2004 ◽  
Vol 02 (01) ◽  
pp. 59-73 ◽  
Author(s):  
PAWAN JAIN ◽  
S. N. MERCHANT
Keyword(s):  
Data Structure ◽  
Image Retrieval ◽  
Retrieval System ◽  
Performance Enhancement ◽  
Image Database ◽  
Retrieval Systems ◽  
Candidate Image ◽  
Image Retrieval System ◽  
Resolution Level ◽  
Structure Algorithm

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.4 In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

Relocation and Storage Assignment Strategies Evaluation in a Multiple-deep Tier-captive Automated Vehicles Storage and Retrieval System with Undetermined Retrieval Sequence

2021 ◽  
Author(s):  
Jakob Marolt ◽  
Nenad Kosanić ◽  
Tone Lerher
Keyword(s):  
Retrieval System ◽  
Discrete Event ◽  
Nearest Neighbour ◽  
Throughput Performance ◽  
Storage And Retrieval ◽  
Event Simulation ◽  
Retrieval Systems ◽  
Storage Assignment ◽  
And Storage

Abstract This paper studies multiple-deep automated vehicle storage and retrieval systems (AVS/RS) known for their high throughput performance and flexibility. Compared to a single-deep system, multiple-deep AVS/RS has a better space area utilisation. However, a relocation cycle occurs, reducing the throughput performance whenever another stock-keeping unit (SKU) blocks a retrieving SKU. The SKU retrieval sequence is undetermined, meaning that the arrangement is unknown, and all SKUs have an equal probability of retrieval. In addition to the shuttle carrier, a satellite vehicle is attached to the shuttle carrier and is used to access storage locations in multiple depths. A discrete event simulation of multiple-deep AVS/RS with a tier captive shuttle carrier was developed. We focused on the dual command cycle time assessment of nine different storage and relocation assignment strategies combinations in the simulation model. The results of a simulation study for (i) Random, (ii) Depth-first and (iii) Nearest neighbour storage and relocation assignment strategies combinations are examined and benchmarked for five different AVS/RS case study configurations with the same number of storage locations. The results display that the fivefold and sixfold deep AVS/RS outperform systems with fewer depths by utilising Depth-first storage and Nearest neighbour relocation assignment strategies.

scholarly journals A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 946 ◽  
Author(s):  
Wenzhao Feng ◽  
Chunhe Hu ◽  
Yuan Wang ◽  
Junguo Zhang ◽  
Hao Yan
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Transmission Efficiency ◽  
Signal Interference ◽  
Progressive Transmission ◽  
Wireless Multimedia ◽  
Transmission Method ◽  
Hierarchical Coding ◽  
Object Region

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

Fabric retrieval system for apparel e-commerce considering Kansei information

2019 ◽  
Vol 32 (1) ◽  
pp. 148-159 ◽  
Author(s):  
Masayuki Takatera ◽  
Ran Yoshida ◽  
Julie Peiffer ◽  
Moe Yamazaki ◽  
Kenya Yashima ◽  
...  
Keyword(s):  
Retrieval System ◽  
Search Method ◽  
Content Type ◽  
Wide Range ◽  
Category Search ◽  
Word Search ◽  
Fashion Designers ◽  
Expert Designer ◽  
Free Word ◽  
Selection Of

Purpose The purpose of this paper is to create a fabric retrieval system for designers that is based on a database that includes designers’ criteria and Kansei (sense and feeling) information, designed for the selection of a fabric from a wide range in e-commerce. Design/methodology/approach The database included sensory expressions for each type of fabric taken from fashion journals and values of smoothness, softness, luster and thinness (referred to as Kansei values) for each fabric. The Kansei values were determined by a Japanese expert designer using standard fabric samples of a fabric type. The system uses two search methods to find the desired type of fabric: a category search method and a free word search method. After finding appropriate types of fabric, the user further narrows down the fabrics of the selected type to more suitable fabrics using the Kansei values. The validity of the Kansei values and the effectiveness of the system were verified by 11 professional designers from Japan and Sweden. Findings The Japanese and Swedish designers were satisfied with the fabrics retrieved for specific items and found that the system was effective. The Kansei values were similar among fashion designers and shown to be effective for fabric retrieval. Originality/value The system will allow designers to find appropriate types of fabric and to narrow their search for fabrics among selected types to find candidate fabrics easily and quickly with their Kansei values and experience without technical knowledge of fabrics.

An Information Retrieval System Based on Portable Device

2013 ◽  
Vol 712-715 ◽  
pp. 2706-2711
Author(s):  
Xiao Qing Yu ◽  
Wen Gen Wang ◽  
Jian Hua Shi ◽  
Yun Hui Wang
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Relevant Information ◽  
Information Retrieval System ◽  
Text Indexing ◽  
Traditional System ◽  
Related Information ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Audio Information

Information retrieval is the activity to organize information in a certain way, and according to the users demand to find out the related information from a collection of resources. Retrieval process and technology can be based on metadata or full-text indexing. Most of the relevant information retrieval systems are devised on the computer. However, with the highly development of the embedded technology, some popular application have been developed on the platform. In this paper, we will introduce an information retrieval system on the iOS platform which is more convenient, practical, and effective compared with the traditional system. And we will introduce an application based on this system design. The experiments shown that this system was exactly effective utilized to retrieval audio information.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 9 (2S3) ◽  
pp. 590-592
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1358-1369
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System

2019 ◽  
Vol 11 (14) ◽  
pp. 3817 ◽  
Author(s):  
Emanuele Guerrazzi ◽  
Valeria Mininno ◽  
Davide Aloini ◽  
Riccardo Dulmin ◽  
Claudio Scarpelli ◽  
...  
Keyword(s):  
Energy Recovery ◽  
Retrieval System ◽  
Autonomous Vehicle ◽  
Operating Characteristics ◽  
Storage And Retrieval ◽  
Energy Evaluation ◽  
System A ◽  
Retrieval Systems ◽  
Recovery System ◽  
Automated Warehouses

With the rise of a consciousness in warehousing sustainability, an increasing number of autonomous vehicle storage and retrieval systems (AVS/RS) is diffusing among automated warehouses. Moreover, manufacturers are offering the option of equipping machines with energy recovery systems. This study analyzed a deep-lane AVS/RS provided with an energy recovery system in order to make an energy evaluation for such a system. A simulator able to emulate the operation of the warehouse has been developed, including a travel-time and an energy model to consider the real operating characteristics of lifts, shuttles and satellites. Referring to a single command cycle with a basic storing and picking algorithm for multiple-depth channels, energy balance and recovery measurements have been presented and compared to those of a traditional crane-based system. Results show significant savings in energy consumption with the use of a deep-lane AVS/RS.

Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home

2016 ◽  
Vol 25 (03) ◽  
pp. 1650017 ◽  
Author(s):  
Hyeokju Ahn ◽  
Harksoo Kim
Keyword(s):  
Information Retrieval ◽  
Smart Home ◽  
Retrieval System ◽  
Generation Model ◽  
Query Term ◽  
Post Processing ◽  
Retrieval Systems ◽  
Sentence Retrieval ◽  
Information Retrieval Systems ◽  
Performance Results

With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g., voice-activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users’ spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs), the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post-processing model of an ASR engine. The post-processing model consists of a re-ranking and a query term generation model. The re-ranking model rearranges top-n outputs of the ASR engines using the ranking support vector machine (Ranking SVM). The query term generation model extracts meaningful content words from the re-ranked queries based on term frequencies and query rankings. In the experiments, the re-ranking model improved the top-1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain.

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