Prediction of tri-modal microstructure under complex thermomechanical processing history in isothermal local loading forming of titanium alloy

To control the tri-modal microstructure and performance,a prediction model of tri-modal microstructure in the isothermal local loading forming of titanium alloy was developed.The staged isothermal local loading experiment on TA15alloy indicates that there exist four important microstructure evolution phenomena in the development of tri-modal microstructure,i.e.,the generation of lamellarα,content variation of equiaxedα,spatial orientation change of lamellarαand globularization of lamellarα.Considering the laws of these microstructure phenomena,the microstructure model was established to correlate the parameters of tri-modal microstructure and processing conditions.Then,the developed microstructure model was integrated with finite element(FE)model to predict the tri-modal microstructure in the isothermal local loading forming.Its reliability and accuracy were verified by the microstructure observation at different locations of sample.Good agreements between the predicted and experimental results suggest that the developed microstructure model and its combination with FE model are effective in the prediction of tri-modal microstructure in the isothermal local loading forming of TA15alloy.

Recent progress on equation-oriented optimization of complex chemical processes

Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.

Generic reconstruction technology based on RST for multivariate time series of complex process industries

In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.

Predicting Model for Complex Production Process Based on Dynamic Neural Network

Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.

Phase Transition in Recovery Process of Complex Networks

The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks： external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally, a largest degree node protection strategy is proposed for improving the robustness of networks.

Inner-Sphere Reorganization Eneregy For Metal Ion-Benzene Complex in Electron-Transfer Process

Based on the capture force field potential model and the adiabatic invariant proposed by Bates, adopting improved average dipole orientation (IADO) theory, the force constants between transition metal ions and benzene (bz) and also a series of inner-sphere reoganization energy (REin) were calculated. The reasons for the differences between theoretical predictions and experimental results were discussed.

COPOLYMERIZATION PROCESS OF TRIPHENYLMETHYL METHACRYLATE AND MMA WITH CHIRAL ANIONIC COMPLEX INITIATOR

The copolymerization process of triphenylmethyl methacrylate (TrMA) and methylmethacrylate (MMA) using chiral anionic complex initiator (-) SP-FlLi (Scheme 1) has beenstudied in toluene and THF, respectively. The copolymer obtained in toluene possessed muchhigher specific rotation than that in THF. These copolymers have shown a tendency to a random and a like alternating structure, respectively.

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

Study on Mutagenic Effect of Space Flight in Two-line Sterile Rice

Seeds of two-line sterile rice cultivars Zhu 1S and Lu 18S were carried into space by ＂Shijian 8＂ breeding recoverable satellite, then planted in ground. Mutagenic effects from space induction were compared with those from y-irradiation and complex processing of space induction and y-irradiation. The results showed that agronomic effect was stimulated in Mo progenies of the two-line sterile rice varieties treated by space flight, and their radiosensitivities to the irradiation of space flight performed non-sensitive. The order of mutation frequency was determined to be SP ＋ γ 〉 γ 〉 SP in M2 generation. And a series of mutated elites（individuals） were screened. Physiological indices of mutants screened like the activity of protective enzymes were measured to explore the physiological and biochemical basis of biological effect in space environment to two-line sterile rice. The results of this study show that space mutation breeding is an effective novel mean for breeding.

Evolution of windblown sand flux and dune field——Trans-scale modeling and simulation

Windblown sand flux and dune field evolving toward the oasis have been a common ecological and environmental threat confronted by many countries.Meanwhile,it is also a kind of complex dynamical process involving multiple temporal and spatial scales which is still out of accurate description through current field observations.Available models and reliable quantitative simulations are of significant value to predict the spreading rate of desertification and provide an optimal design for sand prevention.This paper presents a 'triple-jump' method to realize quantitative simulations to the formation and evolution of an aeolian dune field from an arbitrary initial configuration.Simulated results achieve a satisfactory agreement with observations qualitatively and quantitatively,which also reveal the characteristics and dynamical behaviors of dunes and dune field.Such a paradigm is of a good level of generality,which provides an exploratory probe into the subject of multi-scale physics.

低压断路器群组控制器设计

介绍了一种基于DSP和CPLD的低压断路器智能控制器的设计。该控制器以模块化的形式,能够控制多个断路器实现故障保护。重点叙述了控制器的硬件系统构成,包括信号调理电路、脱扣控制电路和GPRS通信模块电路。设计出的智能控制器具有结构灵活、可靠性高、通信能力强、智能化程度高等特点。

COMPLEX WIENER-ITO CHAOS DECOMPOSITION REVISITED

In this article, some properties of complex Wiener-It? multiple integrals and complex Ornstein-Uhlenbeck operators and semigroups are obtained. Those include Stroock’s formula, Hu-Meyer formula, Clark-Ocone formula, and the hypercontractivity of complex Ornstein-Uhlenbeck semigroups. As an application, several expansions of the fourth moments of complex Wiener-It? multiple integrals are given.

Semantic Complex Event Detection System of Express Delivery Business with Data Support From Multidimensional Space

The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.

Rare Earth Dragons

The invention covers the process for preparing an oxide complex that can absorb and release oxygen. The covered procedures include:preparation of CeO2, ZrO2 and HfO2 as raw material; preparation of the complex oxide with the raw material by employing heating and deoxidation method;and the process for re-heating and oxidizing the deoxidized complex oxide.

Optimization Techniques for RFID Complex Event Processing

One research crucial to wider adoption of Radio Frequency Identification （RFID） technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata （NFA） implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include： （1） taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; （2） with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; （3） utilizing the slot-based or B＋-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.

Aggressive Complex Event Processing with Confidence over Out-of-Order Streams

In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.

Digital Twin Framework and Its Application to Power Grid Online Analysis

Digital twin(DT)framework is introduced in the context of application for power grid online analysis.In the development process of a new power grid real-time online analysis system,an online analysis digital twin(OADT)has been implemented to realize the new online analysis architecture.The OADT approach is presented and its prominent features are discussed.The presentation,discussion,and performance testing are based on a large-scale grid network model(40K+buses),exported directly from the EMS system of an actual power grid.A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.

A New Solution Architecture for Online Power System Analysis

The current DSA system used in the dispatching control centers in China is a near real-time analysis system with response speed in the order of minutes.Based on a review of the state-of-the-art in online analysis and discussion of distributed data processing and computation architecture patterns,a new online analysis architecture is proposed.The primary goal of the new architecture is to increase the online analysis response speed to the order of seconds.A reference implementation of the proposed online analysis architecture to validate the feasibility of implementing the architecture and some performance testing results are presented.

PLATFORM SELECTION FOR COMPLEX SYSTEMS:BUILDING AUTOMATION SYSTEMS

Automation systems for buildings interconnect components and technologies from the information technology industry and the telecommunications industry.In these industries,existing platforms and new platforms（that are designed to make building automation systems work） compete for market acceptance and consequently several platform battles among suppliers for building automation networking are being waged.It is unclear what the outcome of these battles will be and also which factors are important in achieving platform dominance.Taking the fuzziness of decision makers＇ judgments into account,a fuzzy multi-criteria decision-making methodology called the Fuzzy Analytic Hierarchy Process is applied to investigate the importance of such factors in platform battles for building automation networking.We present the relative importance of the factors for three types of platforms（subsystem platforms,system platforms,and evolved subsystem platforms）.The results provide a first indication that the set of important factors differs per type of platform.For example,when focusing on other stakeholders,for subsystem platforms,the previous installed base is of importance;for system platforms,the diversity of the network of stakeholders is essential;and for evolved subsystem platforms,the judiciary is an important factor.