A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.

Improving the accuracy of mechanistic models for dynamic batch distillation enabled by neural network:An industrial plant case

Neural networks are often viewed as pure‘black box’models,lacking interpretability and extrapolation capabilities of pure mechanistic models.This work proposes a new approach that,with the help of neural networks,improves the conformity of the first-principal model to the actual plant.The final result is still a first-principal model rather than a hybrid model,which maintains the advantage of the high interpretability of first-principal model.This work better simulates industrial batch distillation which separates four components:water,ethylene glycol,diethylene glycol,and triethylene glycol.GRU(gated recurrent neural network)and LSTM(long short-term memory)were used to obtain empirical parameters of mechanistic model that are difficult to measure directly.These were used to improve the empirical processes in mechanistic model,thus correcting unreasonable model assumptions and achieving better predictability for batch distillation.The proposed method was verified using a case study from one industrial plant case,and the results show its advancement in improving model predictions and the potential to extend to other similar systems.

A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

Exact Tail Asymptotics for a Queueing System with a Retrial Orbit and Batch Service

This paper discusses a queueing system with a retrial orbit and batch service, in which the quantity of customers’ rooms in the queue is finite and the space of retrial orbit is infinite. When the server starts serving, it serves all customers in the queue in a single batch, which is the so-called batch service. If a new customer or a retrial customer finds all the customers’ rooms are occupied, he will decide whether or not to join the retrial orbit. By using the censoring technique and the matrix analysis method, we first obtain the decay function of the stationary distribution for the quantity of customers in the retrial orbit and the quantity of customers in the queue. Then based on the form of decay rate function and the Karamata Tauberian theorem, we finally get the exact tail asymptotics of the stationary distribution.

基于TFIDF+LDA和Mini Batch K⁃means算法的在线课程推荐方法研究

在线教育资源急剧增长让学习者难以抉择,研究在线课程分类推荐,能帮助学习者快速获取所需资源。首先将潜在狄利克雷分配算法融入词频-逆向文件频率算法对数据进行预处理,生成词向量矩阵;之后采用Mini Batch K-means算法训练聚类模型,并采用T分布随机邻域嵌入降维算法对训练结果进行可视化分析。实验采用从Pluralsight在线课程API获取8016条数据进行实验,实验结果表明融入潜在狄利克雷分配算法的词频-逆向文件频率算法效果更好。

基于在线梯度下降的Mini Batch K-Prototypes算法

K-Prototypes算法每次迭代都需要花费大量时间来计算所有样本与每个簇中心的相异度以将其划分到各个聚类簇中,这导致K-Prototypes算法在处理大型数据集时运行时间急剧增加。根据在线梯度下降算法对K-Prototypes算法的迭代过程进行优化,减少算法每次迭代所需的计算量从而降低算法的时间复杂度,提升算法运行效率。实验结果表明,提出的基于在线梯度下降的K-Prototypes聚类算法,可以在不影响算法收敛性和有效性的前提下降低算法的时间复杂度,提升算法运行效率。

基于PCS7的BATCH控制在生物制药单抗生产中的应用

随着生物制药的发展,单抗药物的生产越来越朝着系统化、集成化、数字化的方向发展。针对单抗药物复杂的工艺生产过程和配方多变的生产方式。本文采用西门子PCS7全集成DCS系统,利用符合ISA-S88标准的批量控制软件和配方管理,研究单抗控制方案和第三方设备集成方案,设计出适用于单抗生产的批次控制DCS系统,满足其生产工艺、参数多变的柔性化需求和数据完整性要求。

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

基于MOMA的可重入混合流水车间调度问题研究

针对可重入制造系统多具有多品种、大规模、混流生产等特点,构建带批处理机的可重入混合流水车间调度问题(reentrant hybrid flow shop scheduling problem with batch processors,BPRHFSP)模型,提出一种改进的多目标蜉蝣算法(multi-objective mayfly algorithm,MOMA)进行求解。提出了单件加工阶段和批处理阶段的解码规则;设计了基于Logistic混沌映射的反向学习初始化策略、改进的蜉蝣交配和变异策略,提高了算法初始解的质量和局部搜索能力;根据编码规则设计了基于变邻域下降搜索的蜉蝣运动策略,优化了种群方向。通过对不同规模大量测试算例的仿真实验,验证了MOMA相比传统算法求解BP-RHFSP更具有效性和优越性。所提出的模型能够反映生产的基础特征,达到减少最大完工时间、机器负载和碳排放的目的。

Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property loss.The frequency and degree of rockburst damage increases with the excavation depth.Moreover,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering.Therefore,the prediction of rockburst intensity grade is one problem that needs to be solved urgently.By comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient method.This overcomes the low accuracy problem of a single evaluation index prediction method.Following this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed.The batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training time.Moreover,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction accuracy.The experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable models.Finally,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.

Assessing the Impact of Fugitive Dust Emissions from Cement Silos at Cluster of Concrete Batching Facilities Using Air Dispersion Modeling

This research assessed the environmental impact of cement silos emission on the existing concrete batching facilities in M35-Mussafah, Abu Dhabi, United Arab Emirates. These assessments were conducted using an air quality dispersion model (AERMOD) to predict the ambient concentration of Portland Cement particulate matter less than 10 microns (PM10) emitted to the atmosphere during loading and unloading activities from 176 silos located in 25 concrete batching facilities. AERMOD was applied to simulate and describe the dispersion of PM10 released from the cement silos into the air. Simulations were carried out for PM10 emissions on controlled and uncontrolled cement silos scenarios. Results showed an incremental negative impact on air quality and public health from uncontrolled silos emissions and estimated that the uncontrolled PM10 emission sources contribute to air pollution by 528958.32 kg/Year. The modeling comparison between the controlled and uncontrolled silos shows that the highest annual average concentration from controlled cement silos is 0.065 μg/m3, and the highest daily emission value is 0.6 μg/m3;both values are negligible and will not lead to significant air quality impact in the entire study domain. However, the uncontrolled cement silos’ highest annual average concentration value is 328.08 μg/m3. The highest daily emission average value was 1250.09 μg/m3;this might cause a significant air pollution quality impact and health effects on the public and workers. The short-term and long-term average PM10 pollutant concentrations at these receptors predicted by the air dispersion model are discussed for both scenarios and compared with local and international air quality standards and guidelines.

基于SMOTE-IKPCA-SeNet深度迁移学习的小批量生产质量预测研究

随着智能制造技术的发展和客户个性化需求的增加,多品种小批量生产方式逐渐成为制造业的主流。面向大批量生产、以统计过程控制为核心的质量管理方式并不适用于小批量生产。针对复杂生产过程存在参数多、非线性和交互作用的问题,提出利用深度迁移学习的方式将历史生产数据作为源域迁移至小样本目标产品数据进行质量预测。首先,通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)和改进的核主成分分析(improved kernel principal component analysis,IKPCA)算法筛选源域和目标域的可迁移特征,这不仅兼顾了特征重要性和可迁移性,还减少了“负迁移”,提高了模型泛化能力;然后,采用结合通道注意力机制的卷积神经网络SeNet构建基于深度迁移学习的质量预测模型。仿真结果表明,随着目标域样本的增加,所提方法的预测准确性明显优于广泛采用的支持向量机建模方法。同时,所提可迁移特征筛选方法显著提高了深度迁移学习的质量预测效果,为复杂的小批量生产过程质量保证提供了新方法。

Aspen Batch Process Developer在原料药工程设计中的应用研究

基于Aspen Batch Process Developer软件对原料药工程设计进行应用研究。在某原料药项目中,通过软件建立工艺流程模拟模型,分析批次操作时间、年生产批次、年生产规模、物料衡算、设备选型和利用率、公用工程消耗等,在满足生产需求的情况下,达到优化生产排班、设备选型和公用工程量的目的。

融合Inception V1-CBAM-CNN的轴承剩余寿命预测模型

针对现有的滚动轴承剩余寿命(RUL)预测方法精度低、轴承健康指标(HI)构建困难等问题,提出了一种基于卷积神经网络(CNN)并融合Inception V1模块和卷积注意力机制模块(CBAM)的滚动轴承RUL预测模型。首先,在CNN中添加了CBAM机制,并进行了加权处理,在通道和空间维度对重要特征进行了强化,对次要特征进行了抑制,通过添加改进的InceptionV1模块,提高了CNN通道间信息交互水平,全面提取了退化特征;然后,进行了网络优化,采用全局最大池化(GMP)方法对模型进行了简化,采用Dropout和批量归一化(BN)方法,避免了过拟合,提高了精度,且克服了训练时出现的梯度消失问题;最后,对数据进行了处理,将降噪后的信号重组为三维张量,将其作为HI,构建了退化标签,引入了评价指标,采用PHM2012轴承数据集进行了实验验证,在3种工况下将其与深度神经网络(DNN)、CNN方法、结合注意力机制的残差网络方法(ResNet)进行了对比。研究结果表明:该方法在变负载条件下的平均RMSE为0.033,较其他方法的RMSE值分别降低了86%、78%和69%,在预测精度和泛化能力方面具有明显优势。

Comparative Analysis of PM10 Emission Rates from Controlled and Uncontrolled Cement Silos in Concrete Batching Facilities

This research study quantifies the PM10 emission rates (g/s) from cement silos in 25 concrete batching facilities for both controlled and uncontrolled scenarios by applying the USEPA AP-42 guidelines step-by-step approach. The study focuses on evaluating the potential environmental impact of cement dust fugitive emissions from 176 cement silos located in 25 concrete batching facilities in the M35 Mussafah industrial area of Abu Dhabi, UAE. Emission factors are crucial for quantifying the PM10 emission rates (g/s) that support developing source-specific emission estimates for areawide inventories to identify major sources of pollution that provide screening sources for compliance monitoring and air dispersion modeling. This requires data to be collected involves information on production, raw material usage, energy consumption, and process-related details, this was obtained using various methods, including field visits, surveys, and interviews with facility representatives to calculate emission rates accurately. Statistical analysis was conducted on cement consumption and emission rates for controlled and uncontrolled sources of the targeted facilities. The data shows that the average cement consumption among the facilities is approximately 88,160 (MT/yr), with a wide range of variation depending on the facility size and production rate. The emission rates from controlled sources have an average of 4.752E-04 (g/s), while the rates from uncontrolled sources average 0.6716 (g/s). The analysis shows a significant statistical relationship (p < 0.05) and perfect positive correlation (r = 1) between cement consumption and emission rates, indicating that as cement consumption increases, emission rates tend to increase as well. Furthermore, comparing the emission rates from controlled and uncontrolled scenarios. The data showed a significant difference between the two scenarios, highlighting the effectiveness of control measures in reducing PM10 emissions. The study’s findings provide insights into the impact of cement silo emissions on air quality and the importance of implementing control measures in concrete batching facilities. The comparative analysis contributes to understanding emission sources and supports the development of pollution control strategies in the Ready-Mix industry.

China set quota for second batch of rare earth mining,smelting and separation in 2023

China set a quota for the second batch of rare earth mining in 2023 at 120,000 tonnes,with a quota for smelting and separation at 115,000 tonnes,according to a joint statement by the Ministry of Industry and Information Technology(MIIT)and the Ministry of Natural Resources.

China issued the third batch of rare earth quotas for 2023

The Ministry of Industry and Information Technology(MIIT)and the Ministry of Natural Resources jointly released the third batch of quota for rare earth mining at 15,000 tons and quota for rare earth smelting and separation at 13,850 tons on December 15,2023.According to MIIT,the third batch of rare earth quotas were issued to China Rare Earth Group and China Northern Rare Earth Group,respectively.China Rare Earth Group was allocated a quota of 5,850tons REO,including 3,000 tons REO of mineral products and 2,850 tons REO of smelting&separation products.China Northern Rare Earth(Group)received a quota of 23,000 tons REO,containing 12,000tons REO of mineral products and 11,000 tons REO of smelting&separation products.