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In control engineering, numerical stability and real time are the two most important requirements for the matrix inversion. This article presents an efficient and robust method for the field-programmable gate array (FPGA) calculation of the matrix inversion. We initially consider the scenario that the matrix to be processed is a nonsingular Hermitian matrix. The proposed computation procedures are composed of the matrix decomposition, triangular matrix inversion, and matrices multiplication. The first procedure is completed by LDL factorization based on the outer form of Cholesky's method, whereas the recursive algorithm for block submatrices is adopted to achieve the triangular matrix inversion. The new method has the high level in the parallel pipelining mechanism and steals the characteristics of both the upper triangular matrix and its inversion to reduce the computation load and improve the numerical stability. Furthermore, the non-Hermitian matrix inversion can be easily solved if another procedure is added in the new method. Finally, we compare our method with the exiting FPGA-based techniques on one Xilinx Virtex-7 XC7VX690T FPGA. Meanwhile, it has solved one array antenna control problem of the adaptive digital beam forming for one phased array radar successfully.

In the context of white complex Gaussian noise, this study presents one constant false-alarm rate mono-pulse detector for both the narrowband and wideband pulse-Doppler (PD) radars. To this end, coherent-like integration is proposed to improve the signal-to-noise ratios of one radar echo. The PD radar returned signals are first performed by stretch processing. Stretched signals are analysed by short-time Fourier transform (STFT) in the time-frequency representation. Coherent-like integration is then utilised to operate the two-dimensional (2D) STFT matrix and the new 2D matrices after coherent-like integration are decomposed to obtain singular values. Finally, the largest singular value, which is assumed to be corresponding to an isolated scatterer for narrowband PD radars (dominate scatterer for wideband PD radars) is selected as the test static to be compared with the detection threshold. Since the new detector is not highly sensitive to the target motion, it can solve the detection problem of the high-speed manoeuvring target. Furthermore, the experiments based on raw data are conducted to illustrate the performance of the proposed detector.

In this paper, we use a stochastic dynamic programming model to evaluate the impacts of uncertainties on the abatement planning process. By involving the endogenous emission path, we differentiate two types of uncertainties during the planning process, which come from the volatility of the abatement cost and the ambiguity of emission rate. Results suggest that the considered uncertainties influence the decision-making process in several aspects by shaping the abatement path as well as emission path. (1) The impacts vary with the expected value and the level of variance of the uncertainty effects. Uncertainties caused by abatement costs from 0.02 to 0.06 and emission factors from 0.01 to 0.03 increase the total abatement costs around 7% and 5% respectively. (2) Both of these uncertainties can generate precautionary abatement in short-term. Especially during the early stages, the abatement task will be increased by 1% around in each period due to the uncertainties. However such an action will be diminishing as the duration elapses. (3) Both of these uncertainties influence the long-term abatement performances, however, with different forms and mechanisms. With small volatility, the emission rate changes the priority sequence of abatement actions more substantially in the short-term than the emission rate does. (4) The combined uncertainties can behave in a compound way to improve the uncertainty performance in the model. The difference between the emission peaks of conservative and extreme cases is significant with the gap being about 5 million metric ton. These results have potentially important policy implications and can provide a rationale for abatement actions.

Various signals of finite length are often analysed by the Wigner-Ville distribution (WVD). Thus, this article deals with the implementation of their discrete WVDs on field-programmable gate arrays (FPGAs). For the discrete WVD, its calculation procedures are composed of the autocorrelation function and Fourier transform. Since the autocorrelation procedure is the most time-consuming step in the entire computation process, the authors analyse the calculation procedure of autocorrelation function in detail. Here the proposed efficient method just calculates partial non-zero elements of the autocorrelation matrices in order to reduce the computation complexity. The FFT (built-in FPGAs) is then utilised to acquire the discrete WVD. In addition, the idea of the fully-pipelined and parallelism is borrowed to reduce the running time but at the cost of recourses. Finally, the authors provide implementations of both authors proposed method and two other algorithms on different FPGAs. The experimental results show that the Intel Arria-10 FPGA families are the better choice in the floating point arithmetic and that authors method has the best performance.

The carbonisation of energy structures is a principal reason for the high carbon levels of carbon dioxide (CO2) emissions in the steel industry. The implementation of an energy substitution policy in the Chinese steel industry has important practical significance for this industry in terms of reducing CO2 emissions. Based on this, this paper divides 20 types of energy-saving and emission-reduction (ESER) technologies into 4 categories: coal-saving technology, electricity-saving technology, comprehensive energy-saving technology, and linkage technology according to the energy-saving effect of different technology on energy varieties. Considering the energy substitution constraints on energy structures within the steel industry, we construct a bottom-up optimisation model based on a scenario analysis to analyse the emission reductions under 3 different scenarios: the baseline scenario (BAU), policy scenario (PS), and strengthened policy scenario (SPS). Results show that the emission reduction of coal-saving technology and comprehensive energy-saving technology in 2030 is 102 million tons CO2 (MtCO2) and 129 MtCO2, respectively, in the PS, and 116 MtCO2 and 130 MtCO2, respectively, in the SPS. Compared with these types of technology, electricity-saving technology is maintained at the level of the BAU. Linkage technology is developed in the latter period of the SPS. The emission reduction of linkage technology in the SPS in 2030 will be 4.1 MtCO2. During the period of 2015–2020, priority should be given to the development of thin slab continuous casting technology in comprehensive energy-saving technology and the development of blast furnace thick phase high efficiency coal injection technology in coal-saving technology. During the period 2020–2030, priority should be given to the development of thick layer sintering technology, hot delivery & hot charging technology of continuous casting slab, online treatment technology in comprehensive energy-saving technology and low temperature rolling technology, converter ‘negative energy steelmaking’ technology, and double preheating technology for hot stove of blast furnace in coal-saving technology.

The wideband radar transmitting the linear frequency modulation signal often processes its echoes by the stretched processing. This paper deals with the range-spread target detection in white compl...

Outdated capacity and substantial potential for energy conservation are the two main features of energy-intensive sectors in developing countries. Such countries also seek to implement market-based options to further control domestic carbon emissions as well as to promote the withdrawal of outdated capacity and upgrade production level. This paper presents a quantitative assessment of the emission trading scheme (ETS) for China's iron and steel industry. The diverse array of normal and outdated capacities was modeled in a two-country, three-good partial equilibrium model. Simulation results show that the abatement potential can be underestimated if the energy-saving effects that result from emission abatement are not considered. In the scenario analysis, we demonstrated that the free allocation of allowances can cause a competitiveness distortion among domestic normal and outdated capacities. Given the government's intention to promote outdated capacity withdrawal and production-level upgrading, an output-based allocation approach is strongly suggested for China's iron and steel sector.

We specify a general model of the dynamic abatement technology adoption process and show how the accumulated experience among them can alter the adoption timing. Learning-by-doing (LBD) effect is considered to describe the reduction of the costly price to the adoption. Moreover, spillovers effect which reflects how accumulated experience can be shared among different technologies, is integrated into the learning process. To ensure the specification convincing, some reasonable assumptions are considered such as one abatement technology must conduct its own experience in order to realize the spillover. Besides, to investigate how the abatement process is influenced by the technology allocation under the spillovers effect, an endogenous emission path is considered to necessitate a meaningful optimization problem. Finally, apart from obtaining some basic properties of the model, we implement an empirical study to verify the effectiveness of the model. Data from the iron industry in China is used to specify the process, which can highlight the spillovers effect among different technologies from a sector angle. In the results, we find that intrinsic mechanism in the model results in the interplay among abatement coefficient, spillover factor and the growing rate of the emission etc. Collaborative relationships between variables are discussed as well.

Environmental pollution and low resource utilization efficiency caused by pyrometallurgy have seriously restricted the sustainable development of secondary lead smelting industry. A large number of studies focus on the research of hydrometallurgy to replace pyrometallurgy aiming at pollution prevention. However, due to economic cost, quality of secondary lead product and secondary pollution, hydrometallurgy hasn’t been widely applied by now. At present, most of the researches of pyrometallurgy focus on its environmental pollution assessment, or merely focus on the recovery efficiency of secondary lead. There are fewer researches on the synergic optimization methodology of pollution prevention and resource recovery for secondary lead smelting process. BP neural network is a type of model widely used in industrial processes optimization. While the gradient method adopted by the BP neural network often falls into a local minimum. If the fitting function is too complicated, there may be multiple fitting local minima, which will cause great obstacles to practical applications. Therefore, according to the characteristics of secondary lead smelting process, this paper uses a BP neural network-based nonlinear optimization combined with an approximate global optimization method to obtain the optimal smelting parameters. The innovative feature of this paper is that after the first stage of the neural network preprocessing has formed the fitting of the smelting process, the smelting parameters optimization process is carried out based on the fitted neural network model. According to the characteristics of the entire two-stage process, as well as the corresponding conditional constraints and the smoothing process of the BP neural network output function, we have formed a two-stage BPNLP network model with formally unified constraint by adopting the approximate global optimal search method. Compared with the average level of pollution prevention and lead recovery efficiency of pyrometallurgy technology in China, the pollution load Sulphur, lead, arsenic and cadmium in smelting flue gas per unit secondary lead product has been reduced by 78.40%, 52.00%, 72.63% and 16.00% respectively, and the recovery efficiency of lead increased by 8.85%. Thus, the two-stage BPNLP model can achieve the goal of synergic optimization of pollution prevention and resource recovery of pyrometallurgy technology of secondary lead smelting industry which can also provide effective methodological guidance for the related research of other industries.

Global climate change is one of the most serious environmental problems that has attracted an unprecedented global attention. The scientific community has recognized that human activity is mostly responsible for global climate warming. Therefore, states participating in the United Nations Framework Convention on Climate Change (UNFCCC) have initiated a series of negotiations on how to develop their own emission reduction targets. However, the realization of these targets is at the expense of economic development to a great extent. Therefore, an abatement plan is necessary to decide how and when to implement emission reduction targets. A marginal abatement cost curve (MACC), often used as an important quantitative tool to estimate abatement burden or evaluate abatement performance, should be considered carefully. In this paper, we enrich the traditional MACC by introducing the concept of an “immediate unit marginal abatement cost” to describe its intrinsic incremental abatement difficulty. This cost is proved to be agreed with the actual situation in many ways, and is therefore very suitable for practical use. Two-factor learning curve are considered in the model, which can go some way in improving the performance of the revised MACC. Using a parameterization method, the original problem can be converted into a tractable nonlinear programming problem. We use an algorithm to search for the approximate global optimal solution. The effectiveness and the efficiency of the algorithm are discussed respectively. In the results, we find that the abatement path distinguishes largely under different abatement targets. Some plausible reasons and policy advices are given.

A hybrid model has been proposed to study abatement technology adoption.Output, R&D investment, and adoption time are optimized at the same time.A CVP method has been adopted to solve the model numerically.Revenue relationships can significantly influence the timing of adoption.Firm may skip some technologies to adopt a higher advanced one directly. Advanced abatement technology adoption has been attached a great importance upon the economic profit especially in the traditional energy intensive industries. In making the adoption decision, a firm has to evaluate the economic profit between the adoption of the advanced technologies and the traditional ones. Moreover, due to the learning-by-searching (LBS) effect, it is preferable to invest upon the installed abatement technology to further increasing the economic profit. Thus, to describe a comprehensive decision strategy, we proposed a hybrid model including output, R&D investment as well as the adoption time to be optimized at the same time. Regarding these as the decision variables, our formulation gives rise to an optimal switching problem for a hybrid system, however, which can hardly be solved analytically. To implement the numerical simulation, we make a transformation in the parameter filed in order to use the control vector parameterization method to solve the original problem. Thus, the proposed optimal control problem can be converted into a non-linear programming problem (NLP), which can be solved conveniently and effectively using the current technique. Based on these facts, we shows that some key economic parameters that inherently define the revenue relationships outside the firm can significantly influence the time of adoption. Furthermore, considering the problem in the multi-technology adoption case, the process can perform in such an unusual way that the firm can leap over some transition technologies to adopt a higher advanced one directly, from which corresponding results are discussed as well.

China&rsquo, s steel industry is an energy-intensive sector. Synergistic reduction of emissions of CO2 and air pollutants (SO2, NOx, and PM2.5) in the steel industry has an important practical significance for climate change and air pollution control. According to the CO2 emission reduction intensity targets (CERO) and air pollutant emission targets (PERO) for 2020 and 2030, 28 types of energy-saving and emission reduction technologies (20 types of carbon reduction technology and eight types of air pollution end-of-pipe technology) were selected for examination, and a two-stage dynamic optimization model with collaborative implementation of PERO and CERO was built to assess the near future (2015&ndash, 2020) and long-term (2020&ndash, 2030) implementation plans for synergistic emissions reduction of CO2 and air pollutants. The results show that in the near future, the implementation of PERO will have a greater synergistic effect on CO2 emission reduction. CO2 emission reduction under PERO in 2020 will be 97 million tons (Mt) higher than that of CERO, an increase of nearly 26%. However, the effects of implementing CERO are better in the long run. Under CERO, the emission reductions of SO2, NOx, and PM2.5 in 2030 are 2.44 Mt, 1.47 Mt, and 0.86 Mt, respectively, and 7%, 4%, and 5% higher than the implementation of PERO. As far as marginal abatement cost is concerned, in the near future, the marginal abatement costs of CO2 and air pollutant equivalents are 1.06 yuan/kgCO2 and 133 yuan/kg pollution equivalent (pe) under PERO, which are 23% and 11% lower than that of CERO, while in the long run, the marginal abatement costs of CO2 and pollutant equivalents under CERO are 0.025 yuan/kgCO2 and 2.73 yuan/kgpe, about 96% and 95% lower than that of PERO.

In the medium to long term, China must conduct deep emission reduction actions to transform the country’s economy and meet the conditions of the Paris Agreement. As an advanced emission reduction technology, carbon capture and storage (CCS) is undoubtedly an important means of achieving this goal. China must consider how to retrofit existing thermal power plants to install CCS technology. Thus, the expected future roadmap for power plants with CO 2 capture is of significant interest. To achieve this aim, we propose a new CCS project investment model, which helps design the CCS development roadmap for achieving the emission targets for 2050. Reductions in the cost of technologies as a result of learning-by-doing is considered to enrich the model to describe the reality more sensibly. Through some mathematical skills, we transform the original continuous problem into a nonlinear integer programming problem. By solving the model, we are trying to answer questions about when to adopt CCS technology and the cost. The results reveal that early large-scale CCS demonstrations are not necessary. The peak investment period of CCS is around 2035. Operating costs account for 80% of the overall cost of CCS, and thus, policymakers must fully motivate the development and investment of operating technologies, especially capture technologies. The results also show that when the scale of CCS technology is promoted, flexible installation levels can be considered to improve efficiency and reduce costs.

Agricultural intelligent decision system has a positive practical significance for guiding agricultural production, which can provide scientific basis for agriculture. Big data analysis technology can effectively improve the performance of intelligent decision system. The research development of the agricultural intelligent decision system is given. The classification of the agricultural decision system is introduced. The frame designation of the intelligent decision system is studied, and the design process is given.

Online learning resources growth speed is in geometric series with the rapid development of computer and communication technology. Users in distance learning and training process encounter the problems of information loss and resource overload, the main reason is that the distance learning system can't effectively understanding of teaching resources semantic information, different structure of teaching resources can't effectively organized into useful knowledge at present. In addition, the system can't provide personalized learning services for users according to different learning needs and backgrounds. How to quickly and effectively obtain the personalized learning information and learning resources from a large number of learning resources has become an urgent need for users. The CFR algorithm using combined similarity of user attributes and interest topics is proposed. Because of the problem of sparse matrix and cold start and in personalized recommendation, the paper proposes a CFR algorithm with combined similarity of user attributes and interest topics, and through experiment verify the algorithm validity.

In this study, we aim to increase net economic profit by utilizing a systematic approach to optimize the entire network via combining carbon dioxide (CO2) supply of carbon capture and storage (CCS) with CO2 injection of carbon sequestration. We consider a special type of network structure and simultaneously address the optimal location of the potential hubs. The continuous selection problem of the hub location brings practical difficulties to the model, both in terms of solution efficiency and model representation. In the case that the EOR (enhanced oil recovery) conversion level is time-varying, the decision-making variables—such as the hub location, pipeline size, and flow rate design—show different degrees of interplays, which brings great uncertainty to the final decision. We apply our framework to four instances of the CCS-EOR network design combined with the hub location selection problem to demonstrate the effectiveness of the model.

How to minimize the emission of air pollutants while making decisions about mitigating climate change has become a hot research topic. Considering the homologous homology of the two types of emissions, it is completely feasible to achieve synergistic control for different pollutants through a rational allocation of technology investment. To attain this goal, in this paper, I propose a pollutant synergistic control model. Carbon and sulfur emissions are chose as the major control object. Under a weak condition, I obtained analytical expressions for two types of cleaner technology investment paths, technology capital accumulation paths, and emission reduction paths. Further, characteristics of the respective paths, and the conditions for meeting the corresponding theoretical inflection points or peaks, were analyzed, which lays a foundation for applications of the theory. In particular, this study draws the necessary and sufficient conditions for an internal solution to the choice of cleaner technology, which is a powerful and practical evaluation index. The theoretical predictions were verified with numerical simulation. The investment of the two types of cleaner technologies peaked during the 10th period, and the capital accumulation peaked during 15th period after a slight delay. The actual measured investment and subsidy feedback gains were around the 0.04 and 0.02 levels. In addition, different combinations of targets for sulfur and carbon reduction also simulated scenarios in which the model had different internal solutions. The relevant theoretical exploration work in this paper has deep implications for policy decisions in the real world.