Applied Soft Computing最新文献_第10页

A MIL-based framework via contrastive instance learning and multimodal learning for long-term ECG classification 基于 MIL 的框架，通过对比实例学习和多模态学习实现长期心电图分类

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112372

Haozhan Han , Cheng Lian , Bingrong Xu , Zhigang Zeng , Adi Alhudhaif , Kemal Polat

{"title":"A MIL-based framework via contrastive instance learning and multimodal learning for long-term ECG classification","authors":"Haozhan Han , Cheng Lian , Bingrong Xu , Zhigang Zeng , Adi Alhudhaif , Kemal Polat","doi":"10.1016/j.asoc.2024.112372","DOIUrl":"10.1016/j.asoc.2024.112372","url":null,"abstract":"<div><div>Recently, deep learning-based models are widely employed for electrocardiogram (ECG) classification. However, classifying long-term ECGs, which contain vast amounts of data, is challenging. Due to the limitation of memory with respect to the original data size, preprocessing techniques such as resizing or cropping are often applied, leading to information loss. Therefore, introducing multi-instance learning (MIL) to address long-term ECG classification problems is crucial. However, a major drawback of employing MIL is the destruction of sample integrity, which consequently hinders the interaction among instances. To tackle this challenge, we proposed a multimodal MIL neural network named CIMIL, which consists of three key components: an instance interactor, a feature fusion method based on attention mechanisms, and a multimodal contrastive instance loss. First, we designed an instance interactor to improve the interaction and keep continuity among instances. Second, we proposed a novel feature fusion method based on attention mechanisms to effectively aggregate multimodal instance features for final classification, which selects key instances within each class, not only enhances the performance of our model but also reduces the number of parameters. Third, a multimodal contrastive instance loss is proposed to enhance the model’s ability to distinguish positive and negative multimodal instances. Finally, we evaluated CIMIL on both intrapatient and interpatient patterns of two commonly used ECG datasets. The experimental results show that the proposed CIMIL outperforms existing state-of-the-art methods on long-term ECG tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112372"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A few-shot learning methodology for improving safety in industrial scenarios through universal self-supervised visual features and dense optical flow 通过通用自监督视觉特征和密集光流提高工业场景安全性的少量学习方法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112375

Juan Jesús Losada del Olmo , Ángel Luis Perales Gómez , Pedro E. López-de-Teruel , Alberto Ruiz

{"title":"A few-shot learning methodology for improving safety in industrial scenarios through universal self-supervised visual features and dense optical flow","authors":"Juan Jesús Losada del Olmo , Ángel Luis Perales Gómez , Pedro E. López-de-Teruel , Alberto Ruiz","doi":"10.1016/j.asoc.2024.112375","DOIUrl":"10.1016/j.asoc.2024.112375","url":null,"abstract":"<div><div>Industrial safety aims to prevent and mitigate workplace accidents and property damage. One common approach to identifying and analyzing potentially risky situations involves the use of static cameras to capture images or videos of facilities and production processes. However, current state-of-the-art deep learning-based solutions require extensive labeled datasets and substantial computational power to detect these dangerous situations. To address these limitations, this paper presents <span>DINOFSAFE</span>, a methodology that combines dense optical flow and the DINOv2 model, a vision transformer that learns universal visual features without supervision. Our methodology demonstrates dual efficacy by both minimizing the manual labeling efforts necessary for model training and ensuring computational efficiency. Optical flow estimates the apparent motion of objects in the input video streams, while the DINOv2 model generates high-dimensional universal representations capturing their visual properties. Using these representations, we train simple linear classifiers to identify moving objects and categorize them. This information aids in identifying and preventing hazardous conditions in industrial settings, such as pedestrians crossing paths with forklifts, forklifts approaching dangerous areas, loads falling from forklifts, and similar situations. We tested our solution on real videos sourced from industrial environments, resulting in promising outcomes. Furthermore, we compiled a comprehensive dataset consisting of approximately 6500 images, which we have made publicly available for research and development purposes.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112375"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learnable feature alignment with attention-based data augmentation for handling data issue in ancient documents 利用可学习的特征对齐和基于注意力的数据增强技术处理古文献中的数据问题

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112394

Amin Jalali , Sangbeom Lee , Minho Lee

{"title":"Learnable feature alignment with attention-based data augmentation for handling data issue in ancient documents","authors":"Amin Jalali , Sangbeom Lee , Minho Lee","doi":"10.1016/j.asoc.2024.112394","DOIUrl":"10.1016/j.asoc.2024.112394","url":null,"abstract":"<div><div>Recognizing ancient cursive handwritten characters presents unique challenges due to the diversity of writing styles and significant class imbalances, where some characters have disproportionately more samples than others. This imbalance leads to higher misclassification rates for minority classes compared to majority classes. To address these challenges, we propose a novel framework that integrates learnable channel and spatial attention modules to effectively align features between source and target domains for better representation. Our approach incorporates a learnable sequential feature alignment process that dynamically adjusts to the specific characteristics of the data, enhancing the transfer of knowledge across domains. Furthermore, we introduce an attention-based augmentation module to amplify the influence of tail classes. This module leverages class activation maps to identify and augment discriminative features, ensuring the model focuses on the most semantically rich regions, particularly for minority classes. As a result, it aligns the weight norms of minority classes with those of majority classes, effectively mitigating the limitations posed by imbalanced class distributions. This approach effectively mitigates the constraints posed by imbalanced character distributions in ancient handwritten documents. The proposed method increases the accuracy for the CCR, Hanja, Nancho, and Kuzushiji datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112394"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Bi-decoding networks for rail-surface defect detection by knowledge distillation 通过知识提炼实现轨道表面缺陷检测的有效双解码网络

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112422

Wujie Zhou , Yue Wu , Weiwei Qiu , Caie Xu , Fangfang Qiang

{"title":"Effective Bi-decoding networks for rail-surface defect detection by knowledge distillation","authors":"Wujie Zhou , Yue Wu , Weiwei Qiu , Caie Xu , Fangfang Qiang","doi":"10.1016/j.asoc.2024.112422","DOIUrl":"10.1016/j.asoc.2024.112422","url":null,"abstract":"<div><div>No-service rail-surface defect detection is a crucial method for assessing the quality of railroad tracks. However, the low-contrast and dark-tone characteristics of track-surface textures pose challenges to current defect-monitoring techniques. Real-time and on-site online inspections are important to ensure safe railway operation; however, most complex models for no-service inspections are difficult to deploy on mobile devices. To address these challenges and overcome the detection difficulties associated with complex scenes, we designed a knowledge distillation-based double decoding-layer refinement network (EBDNet-KD). The first decoding process is guided by a bimodal high-level semantic feature map obtained by extending the attention-based graph convolution to incrementally enhance the dual-stream features and obtain an image restoration prior. A divide-and-conquer decoder is then designed to distinguish features using different decoding layers. The prior is then used in the second decoding layer, which enables the bimodal features to interact fully and obtain the final prediction map. We introduce a knowledge distillation strategy that enables a lightweight, compact student network to learn a complex teacher network’s feature extraction process. This facilitates pixel-consistent learning of the knowledge within the bi-decoder layer, as well as bidirectional learning of the focused contextual response knowledge to optimize the model. The EBDNet-KD significantly reduces computational costs while guaranteeing performance with a parameter count of only 28 M. EBDNet-KD demonstrated superior performance over 15 state-of-the-art methods in experiments conducted on NEU RSDDS-AUG, an industrial RGB-depth dataset. We assessed the generalizability of EBDNet-KD by evaluating its performance on three additional public datasets, yielding competitive results. The source code and results can be found at <span><span>https://github.com/Wuyue15/EBDNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112422"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving policy training for autonomous driving through randomized ensembled double Q-learning with Transformer encoder feature evaluation 通过随机组合双 Q-learning 与 Transformer 编码器特征评估改进自动驾驶的策略训练

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112386

Jie Fan, Xudong Zhang, Yuan Zou, Yuanyuan Li, Yingqun Liu, Wenjing Sun

{"title":"Improving policy training for autonomous driving through randomized ensembled double Q-learning with Transformer encoder feature evaluation","authors":"Jie Fan, Xudong Zhang, Yuan Zou, Yuanyuan Li, Yingqun Liu, Wenjing Sun","doi":"10.1016/j.asoc.2024.112386","DOIUrl":"10.1016/j.asoc.2024.112386","url":null,"abstract":"<div><div>In the burgeoning field of autonomous driving, reinforcement learning (RL) has gained prominence for its adaptability and intelligent decision-making. However, conventional RL methods face challenges in efficiently extracting relevant features from high-dimensional inputs and maximizing the use of environment-agent interaction data. To surmount these obstacles, this paper introduces a novel RL-based approach that integrates randomized ensembled double Q-Learning (REDQ) with a Transformer encoder. The Transformer encoder’s attention mechanism is utilized to dynamically evaluate features according to their relevance in different driving scenarios. Simultaneously, the implementation of REDQ, characterized by a high update-to-data (UTD) ratio, enhances the utilization of interaction data during policy training. Especially, the incorporation of ensemble strategy and in-target minimization in REDQ significantly improves training stability, especially under high UTD conditions. Ablation studies indicate that the Transformer encoder exhibits significantly enhanced feature extraction capabilities compared to conventional network architectures, resulting in a 13.6% to 21.4% increase in success rate for the MetaDrive autonomous driving task. Additionally, when compared to standard RL methodologies, the proposed approach demonstrates a faster rate of reward acquisition and achieves a 67.5% to 69% improvement in success rate.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112386"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust control of uncertain asymmetric hysteretic nonlinear systems with adaptive neural network disturbance observer 利用自适应神经网络干扰观测器实现不确定非对称滞后非线性系统的鲁棒控制

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112387

Yangming Zhang , Qi Yao , Biao Luo , Ning Chen

{"title":"Robust control of uncertain asymmetric hysteretic nonlinear systems with adaptive neural network disturbance observer","authors":"Yangming Zhang , Qi Yao , Biao Luo , Ning Chen","doi":"10.1016/j.asoc.2024.112387","DOIUrl":"10.1016/j.asoc.2024.112387","url":null,"abstract":"<div><div>In this article, a novel command-filtered adaptive control scheme is proposed for uncertain asymmetric hysteretic nonlinear systems with unknown external disturbances, where the hysteresis nonlinearities are described by an asymmetric Bouc–Wen model. Firstly, the hysteresis model uncertainties are considered, a robust hysteresis state observer is constructed to compensate the asymmetric hysteresis nonlinearity. Secondly, both tracking errors and prediction errors are employed to develop the adaptive neural networks (NNs) for approximating unknown nonlinear functions of the system, where the acquisition of the prediction error avoids the need to differentiate the state measurements. Then, a nonlinear disturbance observer with the filtered control input and system states is constructed to estimate a total disturbance resulting from both the NN approximation errors and the external disturbances. With the help of filtering differentiators, a command-filtered backstepping control law is designed by using the adaptive NN approximators, the disturbance observers, and the hysteretic compensator. The effects of the filtering errors, the disturbance estimation errors, and the hysteresis compensation error on the closed-loop stability are rigorously analyzed. Finally, the proposed control algorithm is applied to a piezoelectric micro-displacement servo system, the real-time experimental results indicate that the relative average error and the relative maximal error of the sinusoidal trajectory tracking are 0.04% and 0.06%, respectively. Compared with the existing adaptive robust control algorithm, a significant improvement on the tracking accuracy is achieved.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112387"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patient centric trustworthy AI in medical analysis and disease prediction: A Comprehensive survey and taxonomy 医疗分析和疾病预测中以患者为中心的可信人工智能：全面调查和分类

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112374

Avaneesh Singh , Krishna Kumar Sharma , Manish Kumar Bajpai , Antonio Sarasa-Cabezuelo

{"title":"Patient centric trustworthy AI in medical analysis and disease prediction: A Comprehensive survey and taxonomy","authors":"Avaneesh Singh , Krishna Kumar Sharma , Manish Kumar Bajpai , Antonio Sarasa-Cabezuelo","doi":"10.1016/j.asoc.2024.112374","DOIUrl":"10.1016/j.asoc.2024.112374","url":null,"abstract":"<div><div>Artificial Intelligence (AI) integration in healthcare is revolutionizing medical analysis and disease prediction, enhancing diagnostic accuracy and patient care. However, with the growing adoption of AI, concerns surrounding trustworthiness, ethics, and transparency persist. This survey paper explores Trustworthy AI in healthcare, with a distinct focus on a patient-centric approach. By analyzing 132 relevant papers, we present a novel taxonomy across ten dimensions, emphasizing the criticality of safety, robustness, and patient trust. We highlight factors influencing trustworthiness and investigate the ethical frameworks guiding responsible AI deployment. A key contribution is the introduction of the Trustworthy AI Scoring System (TAI-SS), a novel framework to assess AI trustworthiness in healthcare, emphasizing ethics, privacy, and reliability. Case studies, such as AI-powered cancer diagnosis, demonstrate TAI-SS’s practical application. Additionally, we discuss transparency through Explainable AI (XAI) techniques and segmentation approaches. Our analysis underscores the importance of healthcare datasets and AI algorithms while recommending seven Trustworthy AI requirements and four ethical principles. This paper serves as a roadmap for AI-driven, patient-centric healthcare, offering insights for researchers, healthcare professionals, and policymakers.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112374"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ant colony optimization attribute reduction algorithm for hybrid data using fuzzy β covering and fuzzy mutual information 使用模糊β覆盖和模糊互信息的混合数据蚁群优化属性缩减算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112373

Yuan Chen , Xiaopeng Cai , Zhaowen Li

{"title":"An ant colony optimization attribute reduction algorithm for hybrid data using fuzzy β covering and fuzzy mutual information","authors":"Yuan Chen , Xiaopeng Cai , Zhaowen Li","doi":"10.1016/j.asoc.2024.112373","DOIUrl":"10.1016/j.asoc.2024.112373","url":null,"abstract":"<div><div>As an effective tool for handling the uncertainty and fuzziness of data, fuzzy <span><math><mi>β</mi></math></span> covering can fit the given dataset well. Swarm intelligence algorithms are suitable for solving complex combinatorial optimization problems and then have unique advantages in attribute reduction. This paper proposes an ant colony optimization attribute reduction algorithm based on fuzzy <span><math><mi>β</mi></math></span> covering and fuzzy mutual information. Initially, a fuzzy <span><math><mi>β</mi></math></span> covering decision information system for hybrid data is built based on fuzzy <span><math><mi>β</mi></math></span> covering theory. Then, fuzzy mutual information is introduced to measure the uncertainty of this system. Subsequently, an evaluation function is constructed using fuzzy mutual information for designing a forward attribute reduction algorithm based on heuristic search strategy. Moreover, to identify potentially more optimal attribute subsets, an ant colony optimization attribute reduction algorithm based on random search strategy is designed. Finally, two proposed algorithms are experimentally compared with six existing attribute reduction algorithms. The results indicate that these two algorithms surpass the other six algorithms in terms of classification accuracy and reduction rate.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112373"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust approximation of the conditional mean for applications of Machine Learning 机器学习应用中条件均值的稳健近似

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112389

Amy Parkes , Josef Camilleri , Dominic Hudson , Adam Sobey

{"title":"Robust approximation of the conditional mean for applications of Machine Learning","authors":"Amy Parkes , Josef Camilleri , Dominic Hudson , Adam Sobey","doi":"10.1016/j.asoc.2024.112389","DOIUrl":"10.1016/j.asoc.2024.112389","url":null,"abstract":"<div><div>Machine Learning approaches are increasingly used in a range of applications. They are shown to produce low conventional errors but in many real applications fail to model the underlying input–output relationships. This is because the error measures used only predict the conditional mean under some restrictive assumptions, often not met by the data we extract from applications. However, new approaches to Machine Learning, for example using Evolutionary Computation, allow a range of alternative error measures to be used. This paper explores the use of the Fit to Median Error measure in machine learning regression automation, using evolutionary computation in order to improve the approximation of the ground truth. When used alongside conventional error measures it improves the robustness of the learnt input–output relationships to the conditional median. It is compared to traditional regularisers to illustrate that the use of the Fit to Median Error produces regression neural networks which model more consistent input–output relationships. The problem considered is ship power prediction using a fuel-saving air lubrication system, which is highly stochastic in nature. The networks optimised for their Fit to Median Error are shown to approximate the ground truth more consistently, without sacrificing conventional Minkowski-r error values.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112389"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher 客户驱动的智能产品设计质量功能部署方法：自动洗碗机案例

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2024-10-29 DOI: 10.1016/j.asoc.2024.112403

Decui Liang , Chenghao Ou , Zeshui Xu

{"title":"A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher","authors":"Decui Liang , Chenghao Ou , Zeshui Xu","doi":"10.1016/j.asoc.2024.112403","DOIUrl":"10.1016/j.asoc.2024.112403","url":null,"abstract":"<div><div>Quality Function Deployment (QFD) is one of the very effective customer-driven product development tools. With automatic dishwashers entering the Chinese market, manufacturers can use QFD to develop products that meet the unique Chinese customer requirements and explore the Chinese market. To help manufacturers maximize profits by developing automatic dishwashers to China’s market situation and competitive environment, this paper proposes an improved QFD model considering competitive learning and cost analysis based on market information analysis with Product Life Cycle (PLC) theory. On the one hand, the improved QFD model determines the life cycle stage of the product by analyzing the characteristics of the market. The improvement rate of customer requirements (CRs) determined by competitive learning in different stages has different influence on the importance weights of CRs, so that the new products developed based on design requirements (DRs) converted from CRs can better adapt to the current market environment. On the other hand, the cost strategy will also change according to different PLC stages. In cost analysis, different cost criteria weights will be determined in different life cycle stages to deal with product development risks in different market environments and help enterprises achieve better sustainable development. Finally, we take Chinese HE company’s automatic-dishwasher design as an example to show the specific application of the proposed model. The proposed model identifies the important DRs and their relative importance ranking for the product development. Comparison with existing QFD models shows the ranking obtained through the model is better integrated with market information, enabling the development of products that are better suited to the market, and helping companies to achieve sustainable development.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112403"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0