A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process

Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft sensor.However,only linear relations are captured between the input and output data in the PLS.It is difficult to obtain the remaining nonlinear information in the residual subspaces,which may deteriorate the prediction performance in complex industrial processes.To fully utilize data information in PLS residual subspaces,a deep residual PLS(DRPLS)framework is proposed for quality prediction in this paper.Inspired by deep learning,DRPLS is designed by stacking a number of PLSs successively,in which the input residuals of the previous PLS are used as the layer connection.To enhance representation,nonlinear function is applied to the input residuals before using them for stacking highlevel PLS.For each PLS,the output parts are just the output residuals from its previous PLS.Finally,the output prediction is obtained by adding the results of each PLS.The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Application of Key Technologies in Green School Buildings: Taking Sino-German Future City Primary School as an Example

Green building is a manifestation of the response to the national“dual carbon”strategy.With the large-scale promotion of green buildings,the country has successively issued multiple evaluation standards for green buildings.Schools are places for preaching,teaching,and solving doubts,and the campus environment plays an important role in improving students’learning efficiency and promoting their physical and mental health.This article is based on the“Green Building Evaluation Standards”GB/T 50378-2019,analyzing and exploring the integration and application of key technologies in green schools,providing reference for green building designers.

Integral Event-Triggered Attack-Resilient Control of Aircraft-on-Ground Synergistic Turning System With Uncertain Tire Cornering Stiffness

This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception attacks. First, a novel AoG synergistic turning model is established with synergistic reverse steering of the front and main wheels to decrease the steering angle of the AoG fuselage, thus reducing the steady-state error when it follows a path with some large curvature. Considering that the tire cornering stiffness of the front and main wheels vary during steering, a dynamical observer is designed to adaptively identify them and estimate the system state at the same time.Then, an integral-based event-triggered mechanism(I-ETM) is synthesized to reduce the transmission frequency at the observerto-controller end, where stochastic deception attacks may occur at any time with a stochastic probability. Moreover, an attackresilient controller is designed to guarantee that the closed-loop system is robust L2-stable under stochastic attacks and external disturbances. A co-design method is provided to get feasible solutions for the observer, controller, and I-ETM simultaneously. An optimization program is further presented to make a tradeoff between the robustness of the control scheme and the saving of communication resources. Finally, the low-and high-probability stochastic deception attacks are considered in the simulations. The results have illustrated that the AoG synergistic turning system with the proposed control method follows a path with some large curvature well under stochastic deception attacks. Furthermore,compared with the static event-triggered mechanisms, the proposed I-ETM has demonstrated its superiority in saving communication resources.

Multi-Objective Adaptive Optimization Model Predictive Control:Decreasing Carbon Emissions from a Zinc Oxide Rotary Kiln

The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.

Finite-time Stabilization of the Double Integrator Subject to Input Saturation and Input Delay

Abstract--The time-optimal control design of the double integrator is extended to the finite-time stabilization design that compensates both input saturation and input delay. With the aid of the Artstein＇s transformation, the problem is reduced to assigning a saturated finite-time stabilizer. Index Terms--Finite-time stabilization, input delay, saturated design.

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,

Modeling, Optimization, and Control of Solution Purification Process in Zinc Hydrometallurgy

The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This paper summarizes the authors' recent work on the modeling, optimization, and control of solution purification process. The online measurable property of the oxidation reduction potential(ORP) and the multiple reactors, multiple running statuses characteristic of the solution purification process are extensively utilized in this research. The absence of reliable online equipment for detecting the impurity ion concentration is circumvented by introducing the oxidationreduction potential into the kinetic model. A steady-state multiple reactors gradient optimization, unsteady-state operationalpattern adjustment strategy, and a process evaluation strategy based on the oxidation-reduction potential are proposed. The effectiveness of the proposed research is demonstrated by its industrial experiment.

Smart manufacturing of nonferrous metallurgical processes:Review and perspectives

The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.

Comparative Skin Histology of Frogs Reveals High-elevation Adaptation of the Tibetan Nanorana parkeri

Adaptations to extreme environmental conditions are intriguing. Animal skin, which directly interacts with external environment, plays diverse and important roles in adaptive evolution. The thin and bare skin of amphibians is sensitive to external environmental conditions and, thus, it facilitates investigations into adaptations for living in extreme environments. Herein, we compare the structures of skin in four anuran species living at elevations ranging from 100 m to 4500 m to assess phenotypic innovations in the skin of Nanorana parkeri, which lives at extremely high elevations. Analyses reveal similar basic skin structures, but N. parkeri differs from the other species by having more epidermal capillaries and granular glands, which correlate highly with responses to hypoxia and/or ultraviolet(UV) radiation. Further intraspecific comparisons from frogs taken at ~4500 m and ~2900 m reveal that all of the changes are fixed. Changes occurring only in the higher elevation population, such as possessing more skin pigments, may represent local adaptations to coldness and/or UV radiation. These results provide a morphological basis for understanding further the molecular adaptations of these frogs.

SPAG9 promotes prostate cancer growth and metastasis

Sperm-associated antigen 9(SPAG9)expression is increased in prostate tissues of prostate cancer patients.This experimental study aimed to investigate the role of SPAG9 in bone metastasis of prostate cancer.Immunohistochemical analysis showed that SPAG9 staining was positive in 81.67%of 240 cases of prostatic carcinoma but only in 6.67%of 120 cases of benign prostate hyperplasia.Strong PAG9 staining was positively correlated with Gleason score and bone metastasis in 240 prostate cancer patients(p<0.05),but not with the age or serum prostate-specific antigen level(p>0.05).PC-3 cells were transfected with shRNA against SPAG9,and CCK-8 assay in triplicate showed that PC-3 cell viability was inhibited by SPAG9 knockdown.In addition,transwell assay in triplicate showed that PC-3 cell invasion was inhibited by SPAG9 knockdown.Furthermore,total 2×106 PC-3 cells were injected subcutaneously into the right flank of nude mice which were randomly divided into three groups(N=8)and treated by intratumoral injection of SPAG9 shRNA,control shRNA or PBS,respectively.SPAG9 shRNA inhibited the growth,invasion and angiogenesis while promoted apoptosis of xenografted PC-3 cells.SPAG9 knockdown led to the upregulation of E-cadherin and the downregulation of MMP2 and vimentin in xenografted tumors.In conclusion,this is the first study to provide evidence that SPAG9 promotes bone metastasis of prostate cancer,and SPAG9 is a promising target to prevent or treat bone metastasis of prostate cancer.

Prediction model of slurry pH based on mechanism and error compensation for mineral flotation process

A suitable pH value of the slurry is a key to efficient mineral flotation. Considering the control delay problem of pH value caused by offline pH measurement, an integrated prediction model for pH value in bauxite froth flotation is proposed, which considers the effect of ore compositions on pH value. Firstly, a regression model is obtained for alkali(Na_2CO_3) consumed by the reaction between ore and alkali. According to the first-order hydrolysis of the remaining alkali, a mechanism-based prediction model is presented for the pH value. Then, considering the complexity of the flotation mechanism, an error prediction model which uses time series of the error of the mechanism model as inputs is presented based on autoregressive moving average(ARMA) method to compensate the mechanism model. Finally, expert rules are established to correct the error compensation direction, which could reflect the dynamic changes during the process accurately and effectively. Simulation results using industrial data show that the presented model meets the needs of the industrial process, which laid the foundation for predictive control of pH regulator.

结直肠类器官芯片灌流培养技术研究

器官芯片是近年来快速发展的前沿交叉技术,通过其建立的生理模型,相比传统细胞模型更能表现出生物体的特征,在多个研究和产业领域都展现出巨大的优势。在肿瘤学研究中,类器官由于能够高度模拟肿瘤体内形态结构及生物学特性,引起广泛关注。而传统二维培养无法模拟肿瘤独特区域微环境,因而三维培养被认为是理想模型,利用器官芯片构建体外肿瘤细胞三维类器官模型也逐渐成为研究热点。论文针对肿瘤类器官体外三维培养的需求,根据肿瘤类器官实验要求选择芯片种类,并针对芯片进行流速场仿真,确保合适的流速环境。最后利用灌流培养方式进行结直肠癌类器官芯片模型的构建。结果表明,稳定且具有较低剪切力的灌流培养可以维持结直肠癌细胞三维类器官连续7天的稳定生长,实现了肿瘤类器官芯片体外三维培养模型的构建,为肿瘤发生发展机制研究、抗癌药物筛选提供了有效的手段。

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge.However,most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution.In fact,due to the harsh environment of industrial systems,the collected data from real industrial processes are always affected by many factors,such as the changeable operating environment,variation in the raw materials,and production indexes.These factors often cause the distributions of online monitoring data and historical training data to differ,which induces a model mismatch in the process-monitoring task.Thus,it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring.In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments,a robust transfer dictionary learning(RTDL)algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.The proposed method regards historical training data and online testing data as the source domain and the target domain,respectively,in the transfer learning problem.Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework,which can reduce the distribution divergence between the source domain and target domain.In this way,a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment.Such a dictionary can effectively improve the performance of process monitoring and mode classification.Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

高速列车信息控制系统故障注入研究进展

高速列车信息控制系统是确保整车运行安全的关键系统之一,也是高速列车高发故障的主要来源之一.实时故障诊断是提升系统运行可靠性和安全性的有效方案之一,故障注入作为检验其是否满足车载应用要求的关键技术,是实现安全、逼真模拟系统各类故障场景的重要手段.高速列车信息控制系统结构复杂,故障场景呈现出多种复杂特性,从而使实现其有效故障注入方法和技术面临诸多挑战,如故障位置不可访问、故障场景时空变迁特性复杂和系统级故障注入架构缺乏、仿真资源受限等.本文首先综述了故障注入研究现状,对比探讨了面向实时仿真的故障注入的研究意义;然后,分析了面向实时仿真高速列车信息控制系统故障注入存在的问题,并在此基础上给出了一些解决方案;最后,指出了高速列车信息控制系统故障注入未来的研究方向.

Genetic polymorphisms of 44 Y chromosomal genetic markers in the Inner Mongolia Han population and its genetic relationship analysis with other reference populations

Y chromosomal genetic markers in the non-recombining region are commonly used for human evolution research,familial searching,and forensic male differentiation since they strictly follow paternal inheritance.Y chromosomal short tandem repeats(Y-STRs)possess extraordinarily advantages in forensic applications because of their high polymorphisms and special genetic pattern.Here,we assessed the genetic diversities of 41 Y-STRs and three Y chromosomal insertion/deletion(Y-InDels)loci in the Chinese Inner Mongolia Han population;besides,genetic differentiation analyses among the studied Han population and other previously reported populations were conducted based on 27 same Y-STRs.Totally,425 alleles were observed in 324 Inner Mongolia Han individuals for these Y-markers.Gene diversities of these Y-markers distributed from 0.0306 to 0.9634.The haplotype diversity and discriminatory capacity of these Y-markers in the Inner Mongolia Han population were 0.9999 and 0.98457,respectively.Haplotype resolution comparisons of different Y-marker groups in the studied Han population revealed that higher haplotype resolution could be achieved for these 44 Y-markers.Population genetic analyses of the Inner Mongolia Han population and other reference populations demonstrated that the studied Han population had relatively closer genetic affinities with Northern Han Chinese populations than Southern Han and other minority groups.To sum up,these 44 Y-markers can be utilized as a valuable tool for male differentiation in the Inner Mongolia Han population.

Gorenstein Flat Complexes with Respect to a Semidualizing Module

In this paper, we introduce and study GC-flat complexes over a commutative Noetherian ring, where C is a semidualizing module. We prove that Ge-flat complexes are actually the complexes of Go-flat modules. This complements a result of Yang and Liang. As an application, we get that every complex has a GF-C（C）-cover, where GFC（C） is the class of Ge-flat complexes. We also give a characterization of complexes of modules in HC（FC） that are defined by Sather-Wagstaff, Sharif and White.