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.

Shortcut Procedure for Inverted Batch Distillation Column (I) Multicomponent Ideal System

Inverted batch distillation column(stripper) is opposed to a conventional batch distillation col-umn(rectifier). It has a storage vessel at the top and products leave the column at the bottom. The batch stripper is favourable to separate mixtures with a small amount of light components by removing the heavy components as bottom products. In this paper, we are presenting a shortcut procedure based on our earlier work for design and simulation of the inverted batch distillation column, which is equivalent to the Fenske-Underwood-Gilliland procedure for continuous distillation. Given a separation task, we propose to compute the minimum number of stages(Nbmin) and the minimum reboil ratio(Rbmin) required in a batch stripper,which are the stages and reboil ratio required in a hypothetical inverted batch distillation column operating in total reboil ratio or having an infinite number of stages, respectively. Then, it is shown that the performance of inverted batch columns with a finite number of stages and reboil ratios could be correlated in Gilliland coordinates with the minimum stages Nbmin and the minimum reboil ratio Rbmin.

Nitrous Oxide Production in a Sequence Batch Reactor Wastewater Treatment System Using Synthetic Wastewater

The rate of nitrous oxide emission from a laboratory sequence batch reactor （SBR） wastewater treatment system using synthetic wastewater was measured under controlled conditions. The SBR was operated in the mode of 4 h for aeration, 3.5 h for stirring without aeration, 0.5 h for settling and drainage, and 4 h of idle. The sludge was acclimated by running the system to achieve a stable running state as chemical oxygen demand, NO2^-, NO3^-, NH4^＋, pH, and N2O. indicated by rhythmic changes of total N, dissolved oxygen, Under the present experimental conditions measured nitrous oxide emitted from the off-gas in the aerobic and anaerobic phases, respectively, accounted for 8.6%-16.1% and 0-0.05% of N removed, indicating that the aerobic phase was the main source of N2O emission from the system. N2O dissolved in discharged water was considerable in term of concentration. Thus, measures to be developed for the purpose of reducing N2O emission from the system should be effective in the aeration phase.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System

Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.

The Dynamic Flexibility of Batch Exothermic Reaction System： Take into Account the Effect of the Initial Operational Temperature

It is shown in this article that by changing the initial operation condition of the batch processes, the dynamic performance of the system can be varied largely, especially for the initial operational temperature of the exothermic reaction. The initial operation condition is often ignored in the designing batch processes for flexibility against disturbances or parameter variations. When the initial condition is not rigid as in the case of a batch reactor, where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the ＂best＂ initial condition to use. Problems for dynamic flexibility analysis of exothermic reaction including initial temperature and process operation can be formulated as dynamic optimization problems. Formulations are derived when the initial conditions are considered or not. When the initial conditions are considered, the initial condition can be transferred into control variables in the first optimal step. The solution of the dynamic optimization is on the basis of Rugge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear process problems, enables the determination of the optimal level. The dynamic performance is improved by the proposed method in the two exothermic reaction examples.

Treatment of Practical Dye Wastewater Using a Double Dielectric Barrier Discharge System Combined with a Sequencing Batch Reactor System

Synthetic dyes are substances that are relatively stable and difficult to degrade in wastewater treatment plants using normal physical,chemical or / and biological treatment. The present work explored the synergistic effect of non-thermal plasma( NTP) and biological wastewater treatment technologies on practical dye wastewater degradation by establishing a double dielectric barrier discharge( DDBD) system combined with a sequencing batch reactor( SBR) system. The biodegradation and degradation efficiency of the DDBD-SBR system was investigated. The investigation results indicated that the DDBD technology was effective in treating the practical dye wastewater as a pre-treatment process. After a 10-min treatment,although the total organic carbon( TOC) removal efficiency was not so significant, the decolouration and the biodegradation were improved greatly. The microbial toxicity test revealed that the sample after degradation became less toxic than the original dye,which demonstrated the treatment had a significant effect on the reduction of toxicity. In addition,the SBR technology remedied the defects of DDBD treatment and improved TOC removal efficiency noticeably. The hybrid DDBD-SBR system made full use of the advantages of the individual technologies and exhibited an efficient capability for practical dye wastewater treatment.

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.

OPERATION AND OPTIMIZATION OF BATCH DISTILLATION FOR A BINARY SYSTEM

Batch distillation,basically different from continuous distillation which is a steady stateprocess,appears to be an unsteady state process in its mathematical description.The theoreticalanalysis of its operation comprises a concomitant consideration of the stage-wise separation andthe equations of material balance as well as enthalpy balance.Based upon the batch distillationpractice of NMP-water system,this paper reveals the necessity and advantage of a computerizedtreatment for this purpose.Numerical results not only explain the experimental phenomena andprovide a design scheme,but also lead to the optimization of the operation condition.

Design of Automatic Batch Calibration and Correction System for IMU

Thanks to its light weight,low power consumption,and low price,the inertial measurement units(IMUs)have been widely used in civil and military applications such as autopilot,robotics,and tactical weapons.The calibration is an essential procedure before the IMU is put in use,which is generally used to estimate the error parameters such as the bias,installation error,scale factor of the IMU.Currently,the manual one-by-one calibration is still the mostly used manner,which is low in efficiency,time-consuming,and easy to introduce mis-operation.Aiming at this issue,this paper designs an automatic batch calibration method for a set of IMUs.The designed automatic calibration master controller can control the turntable and the data acquisition system at the same time.Each data acquisition front-end can complete data acquisition of eight IMUs one time.And various scenarios of experimental tests have been carried out to validate the proposed design,such as the multi-position tests,the rate tests and swaying tests.The results illustrate the reliability of each function module and the feasibility automatic batch calibration.Compared with the traditional calibration method,the proposed design can reduce errors caused by the manual calibration and greatly improve the efficiency of IMU calibration.

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.

视频流点播Dynamic Batched Patching算法研究

本文提出了一个新的视频流点播传输策略 ,用以解决现有传输策略中存在的系统资源利用率低 ,QoS较差等问题 .该策略的思想是服务器根据用户请求到达时刻 ,按动态批处理的方式来接纳并服务请求用户 ,每组用户必须同时从一个或两个信道接收视频内容 .文中对本策略的性能进行了理论推导与定量分析 ,并与现有传输策略作了性能比较 ,最后采用仿真实验对前面的理论分析与比较进行了验证 .理论分析及实验结果表明该策略是一个简单高效的传输策略 。

一种有效的Batch RSA改进算法

Batch RSA算法的解密性能与指数计算阶段的大数模幂运算的实现效率有着直接的关系.针对提升Batch RSA算法的解密性能,提出一种Batch RSA算法的改进方案.提升通过将Batch RSA算法指数计算阶段的一些运算量转移到加密方,并且运用多素数技术使得解密时大数模幂运算的模数位数和指数位数减小.理论分析和实验结果表明该方案不仅提升了批处理RSA算法的解密性能,且该方案易于并行实现,可使得基于多核平台的RSA密码算法的性能得到进一步提升.

一种有效的Batch RSA算法的研究

提出了一种改进的Batch RSA算法来提升Batch RSA算法的解密性能。该改进算法结合了负载转移技术和Multi-Power RSA技术,在Batch RSA算法的指数计算阶段提升Batch RSA算法的解密性能。实验结果和理论分析表明,该改进算法使得Batch RSA算法的解密性能得到显著提升,且易于并行实现,可使基于多核平台的BatchRSA算法的整体性能得到进一步提升。

改进的Batch RSA算法的设计与实现

Batch RSA算法的解密性能与其指数计算阶段的大数模幂运算的实现效率有着直接的关系。提出了一种Batch RSA算法的改进方案,通过将Batch RSA算法解密时指数计算阶段的一些运算量转移到加密方,运用多素数技术使大数模幂运算的模位数和指数位数减小来加速Batch RSA的解密过程。理论分析和实验数据表明该改进算法使得Batch RSA算法的解密性能得到明显提升。

基于Spring Batch数据迁移处理系统的设计与实现

本省自动气象观测系统根据观测要素的不同分为单雨量系统、两要素系统和六要素系统。三类观测系统分别接收各自站点采集的报文进行解码入库,同时通过FTP将数据报文发送到综合处理系统再次进行解码入库。这样的处理流程不仅增加网络负担,还进行了重复的解码工作,资源利用率低。为此本文采用基于Spring Batch的框架,定时对三个不同观测系统的后台数据库的实时数据向综合处理系统的数据库进行迁移。该流程采用数据分区技术,多线程并行处理,在减少了对网络带宽的占用量的同时,降低了各个系统CPU使用量,提高了工作效率。