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In this article we investigates the practical fixed-time consensus tracking problem for multiple Euler–Lagrange systems, which is subject to, concurrently, stochastic packet losses in directed communication networks (DCNs) and input/output constraints. In practice, the packet losses in the communication network may lead to communication failure and control accuracy degradation. To address this impact, a novel fully distributed observer is established to estimate the leader’s states within fixed time. Subsequently, the state constraint function is introduced so that the designed adaptive control protocol can be used in a uniform structure with unconstrained or asymmetric/symmetric output constraints without changing the adaptive structure. Combining the backstepping technique with fuzzy-logic systems, an adaptive fixed-time local control protocol is constructed to drive each agent to track the leader’s states estimated by the observer. Besides, a novel auxiliary system is introduced to solve the constraint problem of the control input. Furthermore, through rigorous theoretical analysis, it is verified that all signals converge to the compact set close to zero with guaranteed fixed-time convergence rate. At last, a representative simulation is carried out to demonstrate the practicability of the proposed method.

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

The issues of input-output finite-time stability and stabilization of Takagi-Sugeno fuzzy systems with time-varying state delay and exogenous input signal are studied in this work. For the stabilization process, a controller that does not share the same membership functions as the system is considered and the state feedback is quantized by using a logarithmic quantizer. The desired stability criterion for the addressed fuzzy system is first derived without the control term. It is then extended to the case in which the proposed controller is present. The stability criteria are given in the form of matrix inequalities, which are supported by the augmented Lyapunov-Krasovskii functional, generalized free-weighting-matrix approach and Finsler's lemma. Moreover, as a special case, an asymptotic stability criterion is obtained and used in a comparative study. As a result, the time-delay interval is much larger than some of the works published very recently, which is due to the augmented Lyapunov-Krasovskii functional construction. In addition, the effectiveness and practicability of the theoretical findings are validated with simulation examples.

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.

This paper presents a phase rotator (PR)-based delay-locked loop (DLL) for a dynamic random-access memory interface in 28-nm CMOS technology. A direct input-output comparison using a sub-sampling technique reduces the effect of a timing mismatch of a replica delay line for synchronizing an input clock and output strobe clock. A divided clock samples the input clock for phase alignment. A 4-b analog-to-digital converter was used for input phase acquisition. The output phase is aligned with respect to the sampling clock phase using a bang-bang phase detector. Two DLLs for the input-output synchronization are implemented in a cascade structure, and the loop delay of the replica delay line is considerably reduced. In the proposed DLL, a PR delay line using an 8-phase clock generator is adopted for linearity to reduce the phase difference of the phase interpolation from π/2 to π/4. The resolution of the PR-DLL is 7-b of 2π, which consists of a 3-b coarse and a 4-b fine control. The DLL operates from 2.4 to 8 GHz, and the power dissipation at the maximum input frequency is 20.2 mW. The measured clock root mean square jitter was 1.68 ps. According to the simulation results, the phase mismatch between the input and output clocks was reduced by 80.5%.

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00 × and 15.24 × , respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90 × and 3.38 × , respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods.

In this article, we consider a feedback interconnection, which consists of a networked multi-agent system with its distributed controller and two sets of external input signals. Assuming that the agents are identical, and the dynamics of each agent and its local controller are represented by transfer functions, the well-posedness condition, internal stability and input-output stability of the feedback interconnection are investigated. A controller is designed by solving a Diophantine equation, and a gain for the network is selected using root-locus of a unity feedback system to achieve input-output stability. The developed results are demonstrated with a numerical example.

In this paper, the optimal output synchronization of heterogeneous multi-agent systems is studied. To overcome the shortcoming of previous methods that require system model, a data-based optimal control policy is proposed for the output synchronization problem . Besides, the intrinsic equivalence relationship between model-based optimal solution and the proposed data-based optimal solution is proved. To solve the proposed control policy, a reinforcement learning algorithm based on measured input-output data is presented, which is different from existing model-free algorithms based on internal state. According to the algorithm, the optimal output synchronization problem of heterogeneous multi-agent systems can be solved when the full-state vector is unavailable. Finally, the effectiveness of proposed algorithm is verified by simulation examples.

This chapter describes how file input/output operations can be performed using MATLAB. The difference between text and binary files is made clear and built-in MATLAB functions for reading data from and writing data to these files are introduced. Simple input/output operations for use with text files are described, such as load, save, dlmread and dlmwrite. More sophisticated and flexible functions such as fscanf, fprintf and textscan are also described. Functions for performing lower level file operations such as fgetl and feof are also covered. The concept of opening and closing of files is introduced. Built-in functions for working with binary files are described, such as fread and fwrite.

We analytically characterize the aggregate productivity loss from allocative distortions in a setting that accounts for the sectoral linkages of production. We show that the effects of distortions and the role of sectoral linkages depend crucially on how substitutable inputs are. We find that the productivity loss is smaller if input substitutability is low. Moreover, with low input substitutability, sectoral linkages do not systematically amplify the effects of distortions. In addition, the impact of the sectors that supply intermediate inputs becomes smaller. We quantify these effects in the context of the distortions caused by market power, using industry-level data for 35 countries. With our benchmark calibration, which accounts for low input substitutability, the median aggregate productivity loss from industry-level markups is 1.3%. To assume instead unit elasticities of substitution (i.e., to use a Cobb-Douglas production function) would lead to overestimating the productivity loss by a factor of 1.8. Sectoral linkages do amplify the cost of markups, but the amplification factor is considerably weaker than with unit elasticities.

This paper investigates the effects of Chongqing’s rural and urban residents and total resident population on economic development based on the residents’ consumption structure and analyses of economic development theories concerned by using the input-output table of Chongqing during 2002–2017 and SDA (Structure Decomposition Analysis) model. The study found that, compared with the previous years, the direct consumption of the primary industry’s unit output to the industrial products has decreased significantly in 2017, while the direct consumption to the tertiary industry has increased significantly; The direct consumption per unit output of the second industry is basically equal to that of the products of the industry, while the direct consumption of the products of the third industry has increased; The direct consumption per unit output value of the tertiary industry is basically equal to that of the primary industry. In the long run, the changes in consumption structure of rural and urban residents and total resident population and the increase in proportion of tertiary industry accelerate the transformation and upgrading of industrial structure. However, the effect of consumption structure on GDP (Gross Domestic Product) varies from year to year. On the whole, the changes of residents’ consumption have a positive effect on GDP (Gross Domestic Product).

This paper explores a scheme of tuning a basic input-output (I/O) gain (BIOG) depending on usage conditions for improving the work performance of human-operated machines. I/O gain tuning is effective for improving operability and workability, but continual and/or huge changes makes operators confuse the machine dynamics, and then degrade the operability. Thus, the proposed tuning system tunes the BIOG at long terms according to comprehensive characteristics of the work content and operator, collected from a control lever input histogram. The target value is specified by a Gaussian distribution, meaning that all ranges of the spring-type control lever are equally used. This approach was chosen because leveling the control lever histogram independently of the operator and work content provides a consistent operational experience, leading to comfortable operations. In this study, BIOG curves are specified as a polygonal line involving a break and saturation point. These points are adjusted by reciprocal conversion of differential areas between the Gaussian distribution and obtained histogram curves. Experimental results obtained with a robot arm indicated that the developed BIOG tuning system could improve time efficiency by decreasing the differential area and increase the subjective usability as compared to a conventional fixed BIOG system. Moreover, the relationship between the histogram and BIOG curve could reveal features of both the work content and the operators skill level.

Based on the capital input/output theory, this paper establishes the input and output value model of Sharing bicycles. In the big data era, it analyzes the acquired data using mathematical statistics, as well as using the Logit model to analyze the current Sharing bicycles model to evaluate profit conditions. The research results have shown that the Sharing bicycle has greatly enhanced people’s green travel style since its launch, and it has great development potentials as well. The investors can clearly define different profit models based on the growing, developing, and stabilizing periods of the Sharing bikes business. The survey data shows that the Sharing bicycle adapts to the concept of green travel, low carbon, and environmental protection concept, which also has met the urgent demand for short-distance riding. This research provides effective ways and references for the future value-adding in the Sharing travel field.

Neural networks (NNs) are effective models for data-driven system identification. However, these data-based models do not give the probability properties of the system. Is it possible to use NNs to learn both the dynamics and input–output probability distributions to improve the modeling? In this article, we propose a special neural model, which combines the restricted Boltzmann machine (RBM) and the feedforward NNs. The stochastic RBM learns the input–output probability distributions. The feedforward NN learns the dynamics of nonlinear systems. For the input–output probability distributions, we give the calculation methods of their conditional and joint probabilities. The approximation abilities of this neural model are analyzed. We use two nonlinear systems to compare our guaranteed probability distribution method with the other black-box identification methods. The results show that this novel model is much better, when there are big noises and the dynamics of the system are complex.

This paper presents an input–output linearization (IOL) control strategy to achieve PFC (power factor correction) and output voltage constant in a boost converter. The method transforms the nonlinear system to a linear one in new coordinates by the appropriate nonlinear coordinate transformation and state feedback, ultimately allowing the controller to be designed according to the classical linear control theory. An example implementation is simulated using PSIM software. The simulation results show that the phases of input voltage and input current are consistent, and the output voltage remains constant under load step changes. A set of experimental devices are set up to verify the control method, and the results are consistent with the simulation. Thus, the method is proved to be effective in achieving PFC and provides robust constant voltage output in the boost converter.

This article investigates an output antisynchronization problem of multiagent systems by using an input–output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor–critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

Input-output pairing effectively minimizes the loop interaction in MIMO processes and avoids integral instability. In this article, a modified Relative Normalized Gain Array is proposed for the Inp...

El objetivo principal de esta investigación es la generación de la Matriz Insumo Producto Regional y la identificación de los sectores determinantes para el Estado de Durango. Lo anterior se lleva a cabo mediante la metodología indirecta de Flegg y Weber (1997) para determinar las relaciones intersectoriales regionales, a partir de un ajuste sobre los coeficientes de ubicación cruzada de la Matriz de Insumo Producto Nacional (MIPN) 2013. Este análisis muestra como sectores clave:1) Generación, transmisión y distribución de suministro de energía eléctrica, agua y gas; 2) Servicios financieros y de seguros, 3) Información en los medios de comunicación, entre otros. En estudios regionales similares, el parámetro de ajuste es determinado a priori, por lo que una recomendación es la de estimar el valor adecuado para cada región. La principal limitación es el uso de MIPN, que son publicadas cada 5 años, lo que implica que la herramienta realiza un análisis estático, sin embargo, esto puede solventarse mediante pronósticos basados en metodologías para la actualización de los coeficientes técnicos. Hasta donde tenemos conocimiento, somos los primeros en realizar un análisis sobre los sectores productivos del estado de Durango y de implementar un proceso de optimización sobre el parámetro de ajuste. Los resultados de la clasificación concuerdan con la realizada a partir del Censo Económico y los Valores Agregados Brutos Estatales de los sectores que se encuentran en INEGI.

Since the beginning of the 2008 economic crisis, economic growth and development have been in the forefront of economic research. In a global context, the highest levels of poverty as well as malnutrition problems are found in sub-Saharan African countries. Social Accounting Matrices (SAMs) are useful tools to describe the economic situation of these countries, the interactions among economic agents and to support policymakers in implementing their policies. The paper shows the macro- and micro-economic indicators of Kenya, which can be directly extracted from the described SAM 2017 for Kenya The analysis of the SAM multipliers shows that agri-food multipliers are in general above the average reflecting the strength of backward and forward linkages of Kenya’s economy.

This article investigates the problem of input–output finite-time estimation for a class of discrete-time complex networks with switching topology. The dynamic event-triggered communication manner is proposed to conduct the data transmission of the switching signal and measurement outputs subject to random sensor nonlinearities. The asynchronism of the switching and triggering actions also leads to the received switching index by the designed estimator being inconsistent with that of the estimated switched complex networks. Then, a novel piecewise Lyapunov functional, which is proved to be less conservative than the general one, is then constructed for handling the input–output finite-time estimation under the unmatched circumstance. Sufficient conditions are formulated to ensure that the underlying estimation error systems are input–output finite-time stochastically stable with respect to the norm bounded inputs within a finite time. Finally, the superiority and applicability of the developed approach are verified by a simulation example.

In the absence of data on the destination industry of international trade flows most multi-regional input-output (MRIO) tables are based on the import proportionality assumption. Under this assumption imported commodities are proportionally distributed over the target sectors (individual industries and final demand categories) of an importing region. Here, we quantify the uncertainty arising from the import proportionality assumption on the four major environmental footprints of the different regions and industries represented in the MRIO database EXIOBASE. We randomise the global import flows by applying an algorithm that randomly assigns imported commodities block-wise to the target sectors of an importing region, while maintaining the trade balance. We find the variability of the national footprints in general below a coefficient of variation (CV) of 4%, except for the material, water and land footprints of highly trade-dependent and small economies. At the industry level the variability is higher with 25% of the footprints having a CV above 10% (carbon footprint), and above 30% (land, material and water footprint), respectively, with maximum CVs up to 394%. We provide a list of the variability of the national and industry environmental footprints in the Additional files so that MRIO scholars can check if an industry/region that is important in their study ranks high, so that either the database can be improved through adding more details on bilateral trade, or the uncertainty can be calculated and reported. Supplementary Information The online version contains supplementary material available at 10.1186/s40008-021-00250-8.

Financial flows are one of the three majors in a Supply Chain (SC). Ignoring financial flows, regardless of the quality of freight transport and information, could lead the organization to a state of bankruptcy, which is a situation directly resulting from a lack of control over financial inputs/outputs. This study proposes a multi-product mathematical model, which makes it possible to choose among suppliers, manufacturing sites, distribution centres, retailers, and transportation vehicles. The purpose of the model is to integrate physical and material dimensions to maximize net corporate profits through inbound and outbound financial flows; it involves payment mechanisms between the financial and physical flows through maximizing the cash flows of manufacturing sites and suppliers, as two conflicting objectives that must consider the reciprocal effects of their decisions. These objectives are calculated by subtracting costs from the revenue; this process, of course, will ultimately result in an optimization of the organization’s financial flow. To solve the proposed mathematical model, the study relies on two algorithms, namely Particle Swarm Optimization (PSO) and Imperialist Competition Algorithm (ICA). The sample under investigation is solved separately using the three algorithms, and results are then compared. The observations of the study reveal the better performance of PSO.