信息工程最新文献

{"title":"A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning","authors":"Yichen Zhou,&nbsp;Wenhe Han,&nbsp;Heng Zhou","doi":"10.1007/s43684-024-00078-6","DOIUrl":"10.1007/s43684-024-00078-6","url":null,"abstract":"<div><p>Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00078-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformative advances in veterinary laboratory practices: Evaluating the impact of preliminary training in Khyber Pakhtunkhwa and Balochistan provinces of Pakistan","authors":"Javed Khan ,&nbsp;Asghar Ali ,&nbsp;Shaukat Khan ,&nbsp;Murad Khan ,&nbsp;Saima Mohsin ,&nbsp;Cecelia Madsen","doi":"10.1016/j.jobb.2024.10.001","DOIUrl":"10.1016/j.jobb.2024.10.001","url":null,"abstract":"<div><div>Veterinary laboratories face distinct challenges in Pakistan, including inadequate infrastructure, resources, and training opportunities, especially in the Khyber Pakhtunkhwa and Balochistan regions. This study aimed to evaluate the impact of training sessions for veterinary laboratory staff to improve methods and protocols related to sample collection, storage, and transport, while ensuring strict compliance with biosafety and biosecurity guidelines. The study employed a mixed methods approach, incorporating qualitative and quantitative research techniques. Hands-on training, essential laboratory equipment, and a comprehensive training kit, including personal protective equipment (PPE), were provided to 13 laboratories within the Livestock and Dairy Development Departments of Khyber Pakhtunkhwa and Balochistan. A random sample of 152 individuals from a cohort of 314 trained personnel was selected to assess procedural changes post-training, supplemented by Training Needs Assessments (TNAs) and follow-up visits. Data collection involved a combination of open- and closed-ended questionnaires, individual interviews, and focus group discussions by trained enumerators to maintain a standardized approach. Significant improvements were observed in laboratory practices and procedures, staff competency in sample collection, necropsy techniques, labeling, storage, a chain of custody, packaging, and transport, as well as biosafety and biosecurity practices, such as effective use of PPEs, good laboratory practices, standard operating procedures, handling of sharps, and waste management. However, areas needing refinement, particularly waste management protocols, were identified. The integrated approach combining TNAs, training initiatives, and resource distribution, including laboratory equipment and PPEs, was pivotal in achieving these outcomes. This comprehensive strategy provides a basis for improving biosafety and biosecurity measures within laboratories, thereby contributing to the global effort to mitigate unauthorized access to high-risk pathogens.</div></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":"6 4","pages":"Pages 258-264"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation","authors":"Haibo Xiao,&nbsp;Baoyun Gao,&nbsp;Shoukang Yu,&nbsp;Bin Liu,&nbsp;Sheng Cao,&nbsp;Shitong Peng","doi":"10.1007/s43684-024-00079-5","DOIUrl":"10.1007/s43684-024-00079-5","url":null,"abstract":"<div><p>Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO₂ equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00079-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons for biosecurity education from the International Nuclear Security Education Network","authors":"Iris Magne ,&nbsp;Olivia Ibbotson ,&nbsp;Lijun Shang ,&nbsp;Malcolm Dando","doi":"10.1016/j.jobb.2024.09.002","DOIUrl":"10.1016/j.jobb.2024.09.002","url":null,"abstract":"<div><div>With the rapid advances in technology and life science, biological security is now at a defining moment. The mandate of the 2022 Biological and Toxin Weapons Convention 9th Review Conference emphasised the urgent need for new tools to strengthen the Convention. In this paper, we review the development and efforts of the International Nuclear Security Education Network (INSEN) to provide examples of best practice for implementation of the newly founded International Biological Security Education Network (IBSEN). Learning from the lessons of the INSEN, the sustainability of the network through continuous engagement of its members is essential for the further development of global biosecurity education.</div></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":"6 4","pages":"Pages 252-257"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-training transformer with dual-branch context content module for table detection in document images","authors":"Yongzhi Li ,&nbsp;Pengle Zhang ,&nbsp;Meng Sun ,&nbsp;Jin Huang ,&nbsp;Ruhan He","doi":"10.1016/j.vrih.2024.06.003","DOIUrl":"10.1016/j.vrih.2024.06.003","url":null,"abstract":"<div><h3>Background</h3><div>Document images such as statistical reports and scientific journals are widely used in information technology. Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction. However, because of the diversity in the shapes and sizes of tables, existing table detection methods adapted from general object detection algorithms, have not yet achieved satisfactory results. Incorrect detection results might lead to the loss of critical information.</div></div><div><h3>Methods</h3><div>Therefore, we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections. To better deal with table areas of different shapes and sizes, we added a dual-branch context content attention module (DCCAM) to high-dimensional features to extract context content information, thereby enhancing the network's ability to learn shape features. For feature fusion at different scales, we replaced the original 3×3 convolution with a multilayer residual module, which contains enhanced gradient flow information to improve the feature representation and extraction capability.</div></div><div><h3>Results</h3><div>We evaluated our method on public document datasets and compared it with previous methods, which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score. <span><span>https://github.com/YongZ-Lee/TD-DCCAM</span><svg><path></path></svg></span></div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 408-420"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-salient object detection with iterative purification and predictive optimization","authors":"Yang Wen,&nbsp;Yuhuan Wang,&nbsp;Hao Wang,&nbsp;Wuzhen Shi,&nbsp;Wenming Cao","doi":"10.1016/j.vrih.2024.06.002","DOIUrl":"10.1016/j.vrih.2024.06.002","url":null,"abstract":"<div><h3>Background</h3><div>Co-salient object detection (Co-SOD) aims to identify and segment commonly salient objects in a set of related images. However, most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation. These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.</div></div><div><h3>Methods</h3><div>To address this issue, this study introduces a novel Co-SOD method with iterative purification and predictive optimization (IPPO) comprising a common salient purification module (CSPM), predictive optimizing module (POM), and diminishing mixed enhancement block (DMEB).</div></div><div><h3>Results</h3><div>These components are designed to explore noise-free joint representations, assist the model in enhancing the quality of the final prediction results, and significantly improve the performance of the Co-SOD algorithm. Furthermore, through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM, POM, and DMEB, our experiments confirmed that these components are pivotal in enhancing the performance of the model, substantiating the significant advancements of our method over existing benchmarks. Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 396-407"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Music-stylized hierarchical dance synthesis with user control","authors":"Yanbo Cheng,&nbsp;Yichen Jiang,&nbsp;Yingying Wang","doi":"10.1016/j.vrih.2024.06.004","DOIUrl":"10.1016/j.vrih.2024.06.004","url":null,"abstract":"<div><h3>Background</h3><div>Synthesizing dance motions to match musical inputs is a significant challenge in animation research. Compared to functional human motions, such as locomotion, dance motions are creative and artistic, often influenced by music, and can be independent body language expressions. Dance choreography requires motion content to follow a general dance genre, whereas dance performances under musical influence are infused with diverse impromptu motion styles. Considering the high expressiveness and variations in space and time, providing accessible and effective user control for tuning dance motion styles remains an open problem.</div></div><div><h3>Methods</h3><div>In this study, we present a hierarchical framework that decouples the dance synthesis task into independent modules. We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences. This novel framework allows the individual modules to be trained separately. Because of the decoupling, dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments, and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network. Each module is replaceable at runtime, which adds flexibility to the synthesis of dance sequences.</div></div><div><h3>Results</h3><div>Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 339-357"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mesh representation matters: investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models","authors":"Robert KOSK ,&nbsp;Richard SOUTHERN ,&nbsp;Lihua YOU ,&nbsp;Shaojun BIAN ,&nbsp;Willem KOKKE ,&nbsp;Greg MAGUIRE","doi":"10.1016/j.vrih.2024.08.006","DOIUrl":"10.1016/j.vrih.2024.08.006","url":null,"abstract":"<div><h3>Background</h3><div>Deep 3D morphable models (deep 3DMMs) play an essential role in computer vision. They are used in facial synthesis, compression, reconstruction and animation, avatar creation, virtual try-on, facial recognition systems and medical imaging. These applications require high spatial and perceptual quality of synthesised meshes. Despite their significance, these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.</div></div><div><h3>Methods</h3><div>We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes. This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with <span><math><mrow><msub><mi>L</mi><mn>1</mn></msub></mrow></math></span> and <span><math><mrow><msub><mi>L</mi><mn>2</mn></msub></mrow></math></span> norm metrics and underperforms on perceptual metrics. In contrast, using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error. The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.</div></div><div><h3>Results</h3><div>The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 383-395"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CURDIS: A template for incremental curve discretization algorithms and its application to conics","authors":"Philippe Latour,&nbsp;Marc Van Droogenbroeck","doi":"10.1016/j.vrih.2024.06.005","DOIUrl":"10.1016/j.vrih.2024.06.005","url":null,"abstract":"<div><div>We introduce CURDIS, a template for algorithms to discretize arcs of regular curves by incrementally producing a list of support pixels covering the arc. In this template, algorithms proceed by finding the tangent quadrant at each point of the arc and determining which side the curve exits the pixel according to a tailored criterion. These two elements can be adapted for any type of curve, leading to algorithms dedicated to the shape of specific curves. While the calculation of the tangent quadrant for various curves, such as lines, conics, or cubics, is simple, it is more complex to analyze how pixels are traversed by the curve. In the case of conic arcs, we found a criterion for determining the pixel exit side. This leads us to present a new algorithm, called CURDIS-C, specific to the discretization of conics, for which we provide all the details. Surprisingly, the criterion for conics requires between one and three sign tests and four additions per pixel, making the algorithm efficient for resource-constrained systems and feasible for fixed-point or integer arithmetic implementations. Our algorithm also perfectly handles the pathological cases in which the conic intersects a pixel twice or changes quadrants multiple times within this pixel, achieving this generality at the cost of potentially computing up to two square roots per arc. We illustrate the use of CURDIS for the discretization of different curves, such as ellipses, hyperbolas, and parabolas, even when they degenerate into lines or corners.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 358-382"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing","authors":"Zheyuan Chen,&nbsp;Jian Liu,&nbsp;Jian Li,&nbsp;Mukun Yuan,&nbsp;Guangping Yu","doi":"10.1007/s43684-024-00076-8","DOIUrl":"10.1007/s43684-024-00076-8","url":null,"abstract":"<div><p>Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00076-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}