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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 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.

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 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.

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.

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.

This article addresses the issue of input–output finite-time stability (IO-FTS) for nonlinear systems by employing sliding-mode control (SMC) methodology. Many practical systems subject to complex factors, such as the single-link robot arm model (SLRAM), can be characterized as nonlinear systems. Our attention is focused on designing a fuzzy-model-based finite-time SMC law to attenuate the influences of uncertainty, nonlinear term, and external disturbance during the finite-time region. First, a novel integral sliding-mode surface is proposed based on the Takagi–Sugeno fuzzy rule. Then, by using the key point of Lyapunov function theory, an appropriate fuzzy SMC law is designed to make sure that the signal variables can arrive at a domain within the assigned fixed-time level. Moreover, some new IO-FTS criteria are constructed for the resulting sliding dynamics over the whole finite-time level, including reaching phase and sliding motion phase. Via the SLRAM, we demonstrate the effectiveness of the proposed SMC approach.

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input–output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input–output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches.

The emergence of Industry 4.0 has resulted in a rapid increase in the demand for bandwidth due to the proliferation of the industrial Internet of Things (IIoT). Increased utilization of the network also gives rise to undesirable consequences such as high latency and increased likelihood of data loss through packet drops. Encoding inputs using event-based sampling is a potential solution for decreasing network traffic generated when continuous input variables are sampled. The problem addressed by this article is the fact that currently there is no effective method for comparing different encoders. The resulting contribution is the use of mutual information to compare the input and encoded output in terms of accuracy for the currently most efficient encoders that use either memory based event triggering (MBET) or deadband error modulation (DEM). This allows a practitioner to select a suitable encoder for a given input specification.

Multi-regional input–output (MRIO) data are a powerful tool to analyze complex interdependencies in the international trade and supply network. Their field of application is however limited by the ...

This paper will focus on optimizing parameters of sliding mode controllers (SMC) for hybrid stepper motor models simulated in Matlab/Simulink. The main objective is to achieve a smooth transient and robust, steady-state to track reference rotor position when the stepper motor is subjected to load disturbances. Two different structures of SMC controllers will be studied, which are based on the flat system concept that is applicable to the stepper motor model. The hassle to determine controller parameters will be optimized using the Simulink Response Optimizer application. The performance of the controllers will be evaluated by considering load torque and variation in the model parameters. Although the results showed that an open-loop controller could move the rotor to the desired position, however, the transient response had undesired oscillations before the output settled at the steady state. The response was improved by optimizing SMC controllers’ parameters to meet the desire step response requirement. Despite both SMC methods have successfully tracked the reference, there are some challenges to deal with each method in regard to the state measurements, the number of optimized controllers’ parameters, and the scattering of control inputs.

In this paper, we develop a metric of reshoring at the macroeconomic level, at the level of industries or countries. Our dynamic measure computes the change in the domestic–foreign input ratio and therewith captures the idea of moving production back to the home country. Additional conditions ensure that the measure is confined to actual reshoring and does not pick up, for example, mere changes in the scale and composition of production. We show that reshoring is only weakly approximated by reduced offshoring. We compute the reshoring intensity from the World Input Output Database and illustrate the use of the data with several applications. Specifically, we compute trends in reshoring for examples of countries and industry sectors, we investigate reshoring from China, and examine the impact of an increasing degree of automation for reshoring in emerging and developing economies.

The main problems of constructing multiregional input-output (IO) models used for the development of spatial and sectoral long-term national economy forecasts are caused by the absence of proper statistics. Therefore, it is necessary to make up the input-output tables (IOTs) based on a limited set of direct and indirect indicators that measure spatial patterns of production, consumption, capital formation, etc., sufficiently accurate for filling up multiregional IO models. The paper discusses Russian IOT spatial mapping problems and proposes an approach to partial automation of the procedures necessary for regional IOT construction (by federal districts). The regionalization was carried out using a static IO model with the bounding sum control. As a result, a set of consistent regional tables for 2015 was developed, with the eight IOT sum equalling Russia’s IOT. Based on the estimated regional IOTs, a static multiregional input-output model (OMIOM) across federal districts for 2015 was made up. The model allows us to proceed to the follow-up phase, i. e., developing a semi-dynamic model for long-term national economic projection computations

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

The purpose of the study was to determine the problem of control of a dynamic system of higher dimension. Relying on Leontev input-output balance, we formalized the dynamic system and synthesized its control. Within the research, we developed a mathematical model that combines different working objects that consume and release various resources. The value of the penalty for all nodes and objects is introduced into the matrix representation of the problem, taking into account various options for their interaction, i.e., the observation problem. A matrix representation of the planning task at each working object is formed. For the formed system, a control loop is created; the influencing parameters of the external environment are indicated. We calculated the system operational mode, taking into account the interaction of the nodes of objects with each other when the parameters of the external environment influence them. Findings of research show that in achieving a complex result, the system is inefficient without optimal planning and accounting for the matrix of penalties for the interaction of nodes and objects of the dynamic system with each other. In a specific example, for a dynamic system with a dimension of 4.8 million parameters, we estimated the control taking into account the penalty matrix, which made it possible to increase the inflow of additional resources from the outside by 2.4 times from 130 billion conv. units up to 310 conv. units in 5 years. Taking into account the maximum optimization of control in the nodes, an increase of 3.66 times in the inflow of additional resources was ensured --- from 200.46 to 726.62 billion rubles

The main problems of constructing models used to develop of spatial and sectoral long-term economic forecasts are due to the lack of direct statistical data in full, i.e. regional tables of distribution of goods and services. Their calculation has to be carried out on the basis of a limited set of direct and indirect indicators that make it possible to determine the spatial structures of production, consumption, accumulation of fixed capital, etc. with an accuracy sufficient for further use in the information content of interregional intersectoral models. The report examines the problems of spatial allocation of the all-Russian table “input-output” and proposes the author's approach to the partial automation of the procedures necessary to construct a set of regional tables (by federal district). Regionalization was carried out using a static interregional intersectoral model with control of bordering outcomes. The result is a set of consistent regional tables as of 2015 - the sum of eight input-output tables exactly matches the all-Russian table. The constructed static multiregional input-output model (OMIOM) across federal districts is necessary for the next stage of work - the development of a semi-dynamic model used to calculate options for long-term national economic forecasts.

Recent input–output (IO) literature offers original proposals on using linear programming (LP) to make ‘tolerable limits’ approach suitable for measuring the importance of IO coefficients to an eco...

In black-box testing, test sequences may be constructed from systems modelled as deterministic finite-state machines (DFSMs) or, more generally, observable non-deterministic finite state machines (ONFSMs). Test sequences usually contain state identification sequences, with unique input output sequences ( UIOs ) often being used with DFSMs. This paper extends the notion of UIOs to ONFSMs. One challenge is that, as a result of non-determinism, the application of an input sequence can lead to exponentially many expected output sequences. To address this scalability problem, we introduce ${\mathcal K}$ K - UIOs : UIOs that lead to at most ${\mathcal K}$ K output sequences from states of $M$ M . We show that checking ${\mathcal K}$ K - UIO existence is PSPACE-Complete if the problem is suitably bounded; otherwise it is in EXPSPACE and PSPACE-Hard. We provide a massively parallel algorithm for constructing ${\mathcal K}$ K - UIOs and the results of experiments on randomly generated and real FSM specifications. The proposed algorithm was able to construct UIOs in cases where the existing UIO generation algorithm could not and was able to construct UIOs from FSMs with 38K states and 400K transitions.

Mathematical modeling is the tool to reveal and fully understand the input output relationship in most of the dynamic systems including fuel cells. The dynamic behavior of fuel cell system is deter...

How to identify the key nodes effectively in urban traffic networks to achieve the equitable resource allocation face to the complex traffic network? This issue needs to be solved in current traffic management. This study considered the urban traffic network topology and network traffic status, put forward an improved model based on the economics of the input-output method by introducing a virtual node to the selected network set up with the flow of urban traffic network, sensor nodes by Leontief inverse matrix calculation coefficient to determine node importance, according to the node importance to deliberate attack traffic network to analyze its robustness, to test the accuracy and practicability of the method. The results show that this improved method adopted to measure the importance of traffic nodes from the global scope has the advantages of fast calculation and simple process and provides a more reliable basis for rational allocation of transport resources.

A novel mathematical approach has been proposed for input–output range extension of Pseudo-Taylor-Exponential-Approximation (PTEA) for achieving over seven-decade exponential function, while reducing the linearity error by 1 dB and attaining simple coefficients such as to realize this technique in designing the MOSFET based circuit. This new MPTEA technique, has been successfully utilized for its current-mode (I-I) CMOS implementation involving translinear principle of MOSFETs in weak inversion (WI) region. The CMOS synthesis procedure has been detailed and relevant second-order effects consists of noise, bulk-effect etc. have been explored. For post-layout simulations of the synthesized CMOS implementation, the SPECTRE simulation tool from Cadence, with 180 nM CMOS parameters, has been utilized to justify its practical workability. The proposition, in its CMOS layout, occupies an area of 0.0126mm2 only. It consumes only ≈4.92 μW static power by utilizing ± 0.65 V power supply and produces 146.5 dB range of linear-in-dB output with ± 1 dB linearity error.

In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.

This paper is concerned with the problem of input-output finite-time stability (IO-FTS) for a class of switched singular continuous-time systems. First, the concept of IO-FTS is extended to switched singular systems. Then, by using the average dwell time switching approach and constructing multiple Lyapunov functions, a sufficient condition of IO-FTS for the considered system is presented in the form of linear matrix inequalities (LMIs). Furthermore, based on the derived conditions, a suitable state feedback controller is designed to stabilize the switched singular systems. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed technique.

With the development of virtualization technology, it is practical and necessary to integrate virtual machine software into embedded systems. I/O scheduling is important for embedded systems, because embedded systems always face different situations and their requests have more diversity on the requirement of real-time and importance. However, the semantic information associated with the I/O data is completely lost when crossing the virtualized I/O software stack. Here, we present an I/O scheduling framework to connect the semantic gap between the application threads in virtual machines and hardware schedulers in the host machine. Therefore, the details for the I/O request can be passed through the layers of the software stack and each layer can get the specific information about the device environment. Also, various scheduling points have been provided to implement different I/O strategies. Our framework was implemented based on Linux operating system, KVM, QEMU and virtio protocol. A prototype scheduler, Orthrus , was implemented to evaluate the effectiveness of the framework. Comprehensive experiments were conducted and the results show that our framework can guarantee the real-time requirements, and reserve more system resources for critical tasks, with negligible memory consumption and throughput overhead.

The use of virtualization technology in industrial control is increasing. However, virtual instances or virtual machines (VMs) are confronted with the unreasonable resource allocation in the industrial control cyber range, thereby highlighting the increasing importance of resource scheduling. In the Xen open source system, the input–output (IO)-intensive task response is extended because the system does not distinguish between CPU- and IO-intensive tasks. Therefore, this article presents an improved task performance through the hybrid scheduling of CPU and network IO resources. This method uses Cap- and Timeslice-scheduling algorithms for CPU resource scheduling. First, the Cap-scheduling algorithm uses historical data to train a recurrent neural network model for classification and then utilizes the heuristic method to set the upper limit of cap value for VMs. Second, the Timeslice-scheduling algorithm adjusts the timeslice based on Q-learning to shorten the execution time of the overall tasks. This article proposes an IOb-scheduling algorithm for network bandwidth scheduling. The part that does not exceed the average bandwidth is eliminated and distributed to other VMs by monitoring the bandwidth usage of each VM, thereby improving the utilization of bandwidth. Experimental results showed that the proposed CPU/IO scheduling algorithms improved the overall benchmark performance substantially.

The article examines the dynamics and structure of the resource intensity of agricultural production in Russia using the methodology of input–output tables. Russian and international databases for analyzing the resource intensity of industry complexes are generalized and their advantages and disadvantages are revealed. The article presents a grouping of cost items in agricultural production to assess the level and structure of resource intensity for certain types of resources. The main trends in the dynamics and structure of resource intensity in agricultural production are revealed.

This paper introduces a model-based robust adaptive input-output control framework for a family of robotic systems that include under-actuation, nonholonomic, and constrained properties. The propos...

Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, suc...

The series capacitor converter is one kind of high-gain bidirectional DC–DC converter based on interleaved and switched capacitor techniques. For the n-phase series capacitor converter, n times voltage gain when compared with the traditional boost converter under a specific duty cycle, as well as automatic current sharing among phases can be achieved. The specific duty cycle is (n − 1)/n to 1 in the boost mode and 0 to 1/n in the buck mode, which means that it is not suitable for wide input and/or wide output applications. To solve this issue, an 180° interleaved switching strategy is proposed in this paper. The proposed strategy is more suitable for converters to widen the working duty cycle range. The working principle, voltage gain and current distribution/sharing characteristics with the duty cycle ranging from 0 to 1 are analyzed and calculated in greater detail. In addition, as one of the n-phase series capacitor converters, a small signal model of a three-phase series capacitor converter is built, and a dual closed-loop controller is studied and designed. Finally, an experimental prototype with an input voltage that varies from 10 to 30 V and an output voltage of 100 V is built and tested. Results obtained with the prototype verify the correctness of the proposed switching strategy.

SS 201 had been reported as a good substitute for SS304 without any significant compromise in performance. However, modeling EBW process using an efficient tool like adaptive neuro-fuzzy inference system (ANFIS) and use of multi-objective optimization to optimize its performance are not reported yet. Thus, the present study employed ANFIS models tuned by genetic algorithm, particle swarm optimization, gray wolf optimizer, and bonobo optimizer (BO) to predict weld attributes during EBW of SS201 as a function of input process parameters. Among the developed models, ANFIS tuned by BO was seen to yield the best prediction accuracy. In multi-objective optimization (MOO), the two conflicting goals were to minimize secondary dendritic arm spacing and maximize Vicker’s hardness number simultaneously. In MOO, some interesting facts were observed, such as the fixed input parameter of power (P) as 3200-W and squeezed experimental range for the welding speed (S).

With the rise and fall of the trade shares of different countries in the world, does the trade network structure change at the same time? Do the dynamics of countries' positions differ in the evolution of trade network structure? Based on the latest world input-output database (WIOD), this paper illustrates the accounting models of international trade, and describes the dynamics of the global trade network structure and the countries' positions. Research shows that China and the emerging countries developed faster than the developed countries during 2000-2014, and play important roles, not only in the trade shares, but also in global trade networks. The innovation of this study is that we present a systemic and explicit portrait of the global pattern of trade linkages between countries based on a set of social network analysis methods, and we find that the dynamic of linkage network is more violative than that of linkage flow.

In this paper, we introduce a new I/O characteristic discovery methodology for performance optimizations on object-based storage systems. Different from traditional methods that select limited access attributes or heavily reply on domain knowledge about applications’ I/O behaviors, our method enables capturing data-access features as many as possible to eliminate human bias. It utilizes a machine-learning based strategy (principal component analysis, PCA) to derive the most important set of features automatically, and groups data objects with a clustering algorithm (DBSCAN) to reveal I/O characteristics discovered. We have evaluated the proposed I/O characteristic discovery solution based on Sheepdog storage system and further implemented a data prefetching mechanism as a sample use case of this approach. Evaluation results confirm that the proposed solution can successfully identify access patterns and achieve efficient data prefetching by improving the buffer cache hit ratio up to 48.24%. The overall performance was improved by up to 42%.

To meet the exascale I/O requirements for the High-Performance Computing (HPC), a new I/O subsystem, Burst Buffer, based on solid state drives (SSD), has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation that requires costly garbage collection (GC) and increases the number of bytes written to the SSD. To address this data fragmentation challenge, a new multi-stream feature has been developed for SSDs. In this work, we develop an I/O Separation scheme called BIOS to leverage this multi-stream feature to group the I/O streams based on the user IDs. We propose a stream-aware scheduling policy based on burst buffer pools in the workload manager, and integrate the BIOS with the workload manager to optimize the I/O separation scheme in burst buffer. We evaluate the proposed framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. Experimental results show that the BIOS could improve the performance by 1.44x on average and reduce the Write Amplification Factor (WAF) by up to 1.20x. These demonstrate the potential benefits of the I/O separation scheme for solid state storage systems.

Cloud computing is being widely adopted in the industry due to improve resource utilization and provide more computation power. In cloud computing systems, many numbers of users execute various types of applications that produce a large amount of data. To store large data, distributed file systems are used in many cloud computing systems. Recently, to improve the performance of distributed file systems, emerging flash-based solid-state drives (SSDs) are widely adopted since they provide high performance compared with existing hard disk drives (HDDs). However, due to the characteristics of SSDs, the performance of distributed file systems is greatly impacted by the access patterns from the applications. For example, the performance of random writes in SSDs is lower than that of sequential writes. In this article, we propose an address reshaping technique for SSDs in distributed file systems to improve the performance of random writes by transforming the access patterns. Our scheme reshapes random write requests into sequential write requests in the distributed file systems. This enables the distributed file systems to issue the sequential write requests to SSDs. The experimental results show that the proposed scheme improves performance by up to 46.26% compared with the existing scheme.