Product platform two-stage quality optimization design based on multiobjective genetic algorithm

AbstractProduct platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs

An Adaptive Maintenance Model Oriented to Process Environment of the Manufacturing Systems

We explored an adaptive maintenance model of the process environment to diagnose progressive faults in manufacturing systems. Progressive faults are usually caused by deterioration of the operating environment or aging and show stochastic properties. Many researchers have reported how to detect faults on the machine body in manufacturing systems. However, little research has been conducted on the process environment which causes progressive faults. To tackle this problem, we explored an adaptive maintenance model to detect progressive faults and repair the process environment on the E-repair location. When a difference of the environmental factor state is detected, it will combine the transcription factor and the state enzyme to locate fault source. Then the comprehensive maintenance program is derived to repair the operating environment while eliminating progressive faults. For the purpose of validation, this model was implemented on the process environment of the air separation plant. And the simulation experiments validated the feasibility and effectiveness of this method

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.Comment: 9 pages, 6 figures, AAAI 2024 accepte

Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics. To address this issue, we introduce Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human preferences on images from a wide range of sources. HPD v2 comprises 798,090 human preference choices on 433,760 pairs of images, making it the largest dataset of its kind. The text prompts and images are deliberately collected to eliminate potential bias, which is a common issue in previous datasets. By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict human preferences on generated images. Our experiments demonstrate that HPS v2 generalizes better than previous metrics across various image distributions and is responsive to algorithmic improvements of text-to-image generative models, making it a preferable evaluation metric for these models. We also investigate the design of the evaluation prompts for text-to-image generative models, to make the evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for text-to-image generative models using HPS v2, which includes a set of recent text-to-image models from the academic, community and industry. The code and dataset is available at https://github.com/tgxs002/HPSv2 .Comment: Revisio

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure

Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis

The problem of large amounts of carbon emissions causes wide concern across the world, and it has become a serious threat to the sustainable development of the manufacturing industry. The intensive research into technologies and methodologies for green product design has significant theoretical meaning and practical value in reducing the emissions of the manufacturing industry. Therefore, a low carbon-oriented product reliability optimal design model is proposed in this paper: (1) The related expert evaluation information was prepared in interval numbers; (2) An improved product failure analysis considering the uncertain carbon emissions of the subsystem was performed to obtain the subsystem weight taking the carbon emissions into consideration. The interval grey correlation analysis was conducted to obtain the subsystem weight taking the uncertain correlations inside the product into consideration. Using the above two kinds of subsystem weights and different caution indicators of the decision maker, a series of product reliability design schemes is available; (3) The interval-valued intuitionistic fuzzy sets (IVIFSs) were employed to select the optimal reliability and optimal design scheme based on three attributes, namely, low carbon, correlation and functions, and economic cost. The case study of a vertical CNC lathe proves the superiority and rationality of the proposed method

Optimisation of cutting parameters for improving energy efficiency in machining process

Reducing the machining energy consumption (MEC) of machine tools for turning operations is significant to promote sustainable manufacturing. It has been approved that selecting optimal cutting (turning) parameters is an effective approach to reduce the cutting energy consumption (CEC) within the MEC. However, the potentiality for this approach to reduce the non-cutting energy consumption (NCEC) has not received sufficient attentions. Especially, the energy consumed for spindle rotation change (SRCE) was neglected. Thus, this article aims at developing an integrated MEC model with NCEC and SRCE considered. Then, Simulated Annealing (SA) is employed to find the optimal spindle rotation speed (SRS) and feed rate which result in the minimum MEC. A case study is conducted, where five parts with different cutting lengths are processed on a lathe. The experiment results show that SA can obtain the global optimum in a short computation time when the step sizes for SRS and feed rate are 0.1 and 0.001, respectively. The optimal solution achieves a 19.28% MEC reduction. Finally, the relation between the part length and the optimal SRS is analysed, and the consequence of MEC minimisation on machining time is discussed.acceptedVersio

Resource Constrained Time-cost Trade-off Problem and its Genetic Algorithm Solution

As the fact that renewable resources were used in majority projects in modern enterprises, the classic time-cost trade-off problem was extended and a new resource-constrained time-cost trade-off problem based on random chance constrained programming was proposed, where the renewable resources were emphasized. The project cost was calculated based on renewable resources. Each activity could be implemented with compressed manner in which the renewable resources were devoted to shorten the activity duration. According to the characteristics of the model, an genetic algorithm based on random simulation technique was presented to solve this model. Finally, the practical example verified the validity of the model and effectiveness of the proposed algorithm

Resource Constrained Time-cost Trade-off Problem and its Genetic Algorithm Solution

As the fact that renewable resources were used in majority projects in modern enterprises, the classic time-cost trade-off problem was extended and a new resource-constrained time-cost trade-off problem based on random chance constrained programming was proposed, where the renewable resources were emphasized. The project cost was calculated based on renewable resources. Each activity could be implemented with compressed manner in which the renewable resources were devoted to shorten the activity duration. According to the characteristics of the model, an genetic algorithm based on random simulation technique was presented to solve this model. Finally, the practical example verified the validity of the model and effectiveness of the proposed algorithm

MicroRNA-4287 alleviates inflammatory response via targeting RIPK1 in osteoarthritis

Studies have confirmed the regulatory effects of microRNAs (miRNAs) in osteoarthritis (OA) progression. MiR-4287 has been identified by a previous study as a downregulated miRNA in chondrocytes treated with IL-1β and TNF-α. However, the function of the underlying mechanism of miR-4287 in OA is elusive. IL-1β-treated chondrocytes were used as OA cell models. RNA expression was accessed using RT-qPCR. Cell Counting Kit-8 (CCK-8) assay was used to determine the chondrocytes' viability and proliferation. The protein levels of inflammation factors (IL-8, IL-6, and TNF-α), matrix metalloproteinases (MMP 1, MMP3, MMP13), and chondrogenic genes (COL2A1, SOX9, and Aggrecan) were detected using western blot analysis. Luciferase reporter assays were performed for interaction exploration. HE staining and Safranin O/Fast Green staining was used to access the pathological changes in OA mouse tissues and cartilage degeneration in OA mouse. MiR-4287 was downregulated in chondrocytes treated with IL-1β and OA mouse models. MiR-4287 overexpression promoted the viability, and proliferation and attenuated the inflammation response and destruction of cartilage in IL-1β-stimulated chondrocytes. Receptor-interacting protein kinase 1 (RIPK1) was a target gene of miR-4287 in chondrocytes. MiR-4287 negatively regulated RIPK1 expression. RIPK1 overexpression was revealed to reverse the miR-4287-mediated effects on proliferation and inflammatory response in IL-1β-stimulated chondrocytes. Moreover, miR-4287 was demonstrated to inhibit the pathological changes, cartilage degeneration and inflammation response in OA mice models. In conclusion, miR-4287 is a critical molecule in OA development, which attenuates inflammatory response in vivo and in vitro by targeting RIPK1