A self-adaptive k-means classifier for business incentive in a fashion design environment

Vincent, Olufunke Rebecca; Makinde, Ayodeji Samuel; Salako, O S; Osho, Oluwafemi

doi:10.1016/j.aci.2017.05.002

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…Considering that the CXray dataset focuses solely on detecting bubbles as defects, it becomes necessary to re-cluster the actual annotated bounding boxes. Therefore, the K-means++ algorithm [37] is used to re-cluster the prior anchor boxes that are suitable for our datasets. Table 1 displays the predefined anchor boxes and clustered anchor boxes.…”

Section: Anchor Designmentioning

confidence: 99%

YOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5

Wang

Lin

et al. 2023

Electronics

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In the manufacturing of chips, the accurate and effective detection of internal bubble defects of chips is essential to maintain product reliability. In general, the inspection is performed manually by viewing X-ray images, which is time-consuming and less reliable. To solve the above problems, an improved bubble defect detection model YOLO-Xray based on the YOLOv5 algorithm for chip X-ray images is proposed. First, the chip X-ray images are preprocessed by image segmentation to construct the chip X-ray defect dataset, namely, CXray. Then, in the input stage, the K-means++ algorithm is used to re-cluster the CXray dataset to generate the anchors suitable for our dataset. In the backbone network, a micro-scale detection head is added to improve the capabilities for small defect detection. In the neck network, the bi-direction feature fusion idea of BiFPN is used to construct a new feature fusion network based on the improved backbone to fuse the semantic features of different layers. In addition, the Quality Focal Loss function is used to replace the cross-entropy loss function to solve the imbalance of positive and negative samples. The experimental results show that the mean average precision (mAP) of the YOLO-Xray algorithm on the CXray dataset reaches 93.5%, which is 5.1% higher than the original YOLOv5. Meanwhile, the YOLO-Xray algorithm achieves state-of-the-art detection accuracy and speed compared with other mainstream object detection models. This shows the proposed YOLO-Xray algorithm can provide technical support for bubble defect detection in chip X-ray images. The CXray dataset is also open and available at CXray.

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Section: Anchor Designmentioning

confidence: 99%

YOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5

Wang

Lin

et al. 2023

Electronics

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“…According to the numerical design structure matrix and genetic algorithm, Yang et al conducted clustering research on a user's emotional needs to facilitate designers to find users' needs [43]. Vincent et al studied the business incentive mechanism in fashion design, and used adaptive K-means to obtain the characteristics of successful products based on the purchase evaluation records [54]. Pajo et al proposed a classification model to identify leading users and identify potential online users of candy products to evaluate this technology, which further reduces the cost of resources and time [44].…”

Section: Application Of Data Miningmentioning

confidence: 99%

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

Information

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In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

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“…In 2018, Vincent classified the data of a group of customers using data mining techniques in order to achieve success in fashion design. With the results that emerged as a result of this, it was understood what kind of design the customers wanted in their new orders and gave ideas to the companies on this subject (Griva et al, 2018;Vincent et al, 2018).…”

Section: Findings From the Literature Reviewmentioning

confidence: 99%

Industry 4.0 Perception Regarding to New Developments and New Trends of Industries

Erdil¹

2021

European Journal of Science and Technology

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The purpose of this research is to assess the core principles of Industry 4.0, its advantages, literature review, and effective implementation techniques. Searching for published publications may demonstrate recent developments of industries with big data of production system and technology. Industrialization is a catch-all term for a wide range of modern automated systems, data exchanges, and manufacturing technologies. This revolution is a set of ideals comprised of the internet of things, online services, and cyber-physical systems. While the Industry 4.0 model represents a revolution in every aspect, it also delivers numerous breakthroughs, transformations, and adjustments. This research examines the evolution, technical advances, and benefits of Industrial 4.0, gives a brief background to this configuration and its use in industry implementations, and emphasizes the key components influencing successfully businesses. Furthermore, it is underlined those organizational innovations are required for the industry 4.0 revolution in terms of product, process, and business advancements. As a result, a significant deal of innovation is necessary in many sectors before the industry 4.0 model can be implemented. In product, process, business, and operational terms, innovation refers to variety that creates additional value. To that end, the ideas of Industry 4.0 and innovation are described briefly, followed by an examination of innovation through the lens of Industrial 4.0, with illustrations and concepts provided. It is abundantly clear through publications that are being searched those sectors and industries are making use of big data in the production environment and information. This research reflects on the developments of framework-structure in the enviroment of industry for the businesscompanies. The research also contains the aspect-application of the industry 4.0 technology depending on manufacturing system and improvement phases of manufacturing-production of the company. The application via Industry 4.0 technology of a white goods manufacturing company was evaluated on behalf of manufacturing performance and effectiveness of this manufacturing enterprise according to inputs and outputs components of manufacturing system for the analysis in terms of operation management and the business expectation.

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A self-adaptive k-means classifier for business incentive in a fashion design environment

Cited by 15 publications

References 34 publications

YOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5

YOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Industry 4.0 Perception Regarding to New Developments and New Trends of Industries

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