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The explosive growth of modern web-scale applications has made cost-effectiveness a primary design goal for their underlying databases. As a backbone of modern databases, LSM-tree based key–value stores (LSM store) face limited storage options. They are either designed for local storage that is relatively small, expensive, and fast or for cloud storage that offers larger capacities at reduced costs but slower. Designing an LSM store by integrating local storage with cloud storage services is a promising way to balance the cost and performance. However, such design faces challenges such as data reorganization, metadata overhead, and reliability issues. In this article, we propose RocksMash , a fast and efficient LSM store that uses local storage to store frequently accessed data and metadata while using cloud to hold the rest of the data to achieve cost-effectiveness. To improve metadata space-efficiency and read performance, RocksMash uses an LSM-aware persistent cache that stores metadata in a space-efficient way and stores popular data blocks by using compaction-aware layouts. Moreover, RocksMash uses an extended write-ahead log for fast parallel data recovery. We implemented RocksMash by embedding these designs into RocksDB. The evaluation results show that RocksMash improves the performance by up to 1.7 \( \times \) compared to the state-of-the-art schemes and delivers high reliability, cost-effectiveness, and fast recovery.

The extra acoustic noise caused by the high–frequency (HF) signal injection imposes restrictions on its extensive applications for sensorless synchronous reluctance motor (SynRM) drives. To attenuate the undesired acoustic noise further, a hybrid pseudorandom signal injection scheme is proposed in this paper. The hybrid pseudorandom signal injection is implemented by injecting square wave signals chosen in a random manner from a set of signals having either different frequency or phase from each other. The optimal assembly of hybrid pseudorandom signals is designed via current power spectral density analysis, aiming at reducing acoustic noise as much as possible. In this way, the proposed scheme achieves a success in a massive acoustic noise reduction by means of completely eliminating the discrete harmonics of the current spectrum and distributing the current spectrum to a wider range. Besides, the low pass filters used to separate the fundamental frequency component from injected HF components of currents could be eliminated, because no discrete harmonics at injected frequencies exist with the proposed hybrid pseudorandom signal injection. The validity and superiority of the proposed sensorless control scheme are verified on a 3-kW position sensorless SynRM drive platform.

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq -axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq -axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis.

INTRODUCTION Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

In order to extend the lifetime and save the system cost, the film capacitor is applied to the dc link of interior permanent magnet synchronous motor (IPMSM) drives. Many active damping control methods have been carried out to improve the drive system stability, which needs the accurate value of the dc-link film capacitor. In this letter, an online dc-link capacitance estimation method is investigated for reduced capacitance IPMSM drives, which do not need any additional signal injection or sensor. The power coupling characteristics are analyzed to obtain the instantaneous power of the dc-link capacitor from the inverter and the grid sides. The bandpass filter is applied to extract the dc-link voltage and capacitor power with twice the frequency of the grid voltage. The dc-link capacitance could be estimated by the fundamental component of the dc-link voltage. The proposed method can be used for different kinds of load types and motor types of the drive system. The experimental results are performed to verify the estimation method, and the estimation error is within 1%.

This article proposes a position and speed detection method based on the cross-decoupling network filtering (CDNF) method combined with phase-locked loop (PLL). This method is used to enhance the detection precision of position and reduce the speed fluctuations at low-speed operation for gearless traction motor drives with an ordinary-resolution encoder. The harmonic distributions of the discontinuous orthogonal position signal in low-speed region are analyzed. On this basis, the position and speed detection method based on CDNF with PLL is presented to obtain the harmonics of the discontinuous position signal adaptively for eliminating the position detection error and improving the accuracy of the speed feedback. The criteria of the filter parameter design are presented based on the maximum phase margin theory to maintain the stability of the system. Experiments were carried out on an 11.7 kW permanent magnet gearless traction motor drive with an ordinary-resolution encoder installed. The experimental results verify the effectiveness of the proposed method at low-speed operation.

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position offset error (RPOE) caused by the cross-saturation effect and other factors in high-frequency signal injection based methods, a current vector angle adaptive adjustment based online compensation method is proposed. This method can accurately detect and compensate the RPOE online only using two current vectors and has low sensitivity to the motor parameters, which improves the robustness of the system. Additionally, the two current vector angles can be adaptively adjusted according to the load conditions to improve the signal-to-noise ratio and reduce the current amplitude fluctuation during the RPOE detection process. Since neither offline measurement nor finite-element analysis is required, this method has superior adaptability and universality for applications. The effectiveness and feasibility are verified by experiments on a 2.2-kW interior PMSM drive platform.

Torque smoothness is a strict requirement for synchronous reluctance motor (SynRM) industrial applications. The speed fluctuations induced by the high-frequency and low-frequency torque ripples deteriorate the drive performance. To suppress high-frequency torque ripples chronically omitted by conventional high-frequency signals injection methods at low-speed operation, this article proposes a bi-axes high-frequency signals injection (Bi-HFSI) scheme. A novel signal injection method is adopted, and the torque power spectrum can be broadened with discrete peaks effectively suppressed. In addition, aiming at decreasing low-frequency torque ripples, the Fourier iterative learning control algorithm that is effective in suppressing periodic disturbance is applied. It is combined with the Bi-HFSI scheme which mainly limits torque ripples at low frequencies including 1st, 2nd, and 6th harmonics. Theoretical analysis is presented, and the proposed method is verified by experiments on a 3-kW SynRM drive platform.

Because of the time-sharing sampling logic, the three-phase currents reconstructed by single dc-link shunt present significant errors in the permanent magnet synchronous motor drives. A traceback-prediction current correction method for sampled currents is proposed in this article to eliminate the reconstruction errors. According to the analysis of time-sharing sampling errors, the concept of equivalent current change rate during one pulsewidth modulation (PWM) period is introduced, and the phase current characteristic model based on state observer is established. Both the equivalent and instantaneous current change rates can be obtained by the observer-based calculation method. In order to improve the current reconstruction precision, the ultimate values of three-phase currents can be predicted by adopting the proposed traceback-prediction current correction method, which does not require major computation and is easy to implement with no restrictions on the PWM strategy. The effectiveness of the proposed method is verified by experimental results.

Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the application’s execution environment. Containers are created from images which are shared between users via a registry. The amount of data registries store is massive. For example, Docker Hub, a popular public registry, stores at least half a million public images. In this article, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3 percent of the files in images are unique while others are redundant file copies, which means file-level deduplication has a great potential to save storage space. Furthermore, we carry out a comprehensive analysis of both small I/O request performance and copy-on-write performance for multiple popular container storage drivers. Our findings can motivate and help improve the design of data reduction and caching methods for images, pulling optimizations for registries, and storage drivers.

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq -axes. This method identifies the resistance and the dq -axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions.

Permanent magnet synchronous motor (PMSM) drives equipped with slim film capacitors have many advantages, including longer lifetime, smaller volume, and lower cost. However, improving the dc-link voltage utilization rate by the typical overmodulation strategy will cause the distortion of the stator voltage and current due to the dc-link voltage fluctuation. In this article, the influence of the dual-mode overmodulation strategy used in the small dc-link capacitor PMSM drive, including the stator voltage distortion and uncontrollable time for entering the six-step operation, is analyzed. A novel overmodulation strategy named as the optimized voltage boundary-based overmodulation strategy is proposed to reduce the distortion of the stator voltage and current. By switching between the actual dc-link voltage and the fixed dc-link voltage used for space vector pulse width modulation (SVPWM), the performance can be improved in the overmodulation region if the value of the dc-link voltage for SVPWM is selected appropriately. Experimental results on a PMSM drive platform are provided to verify the effectiveness of the proposed strategy.

Electrolytic capacitor-less permanent magnet synchronous motor (PMSM) drives could improve the reliability and reduce the cost and volume of a drive system, which would be an important improvement over a conventional motor drive system. This has been successfully applied in aerospace, industry, and household applications. In this study, the development of the electrolytic capacitor-less PMSM motor drives is introduced, including the high power factor control, the grid harmonics suppression, and the stability control method. The key technique and related studies are summarized, and the advantages and disadvantages of this solution are illustrated and evaluated. Finally, the key issues and the development trend toward electrolytic capacitor-less PMSM drives were analyzed and summarized.

Synchronous reluctance machines (SynRMs) have drawn increasing attention in recent years owing to their advantages such as low cost, simple structure, ease of manufacture, and high robustness. The main obstacle to the promotion of SynRMs is severe parameter nonlinearity, which deteriorates drive performance. Sensorless control methods for SynRMs are critical technologies that can broaden the industrial applications of SynRMs. Various methods of parameter identification and sensorless control strategies are reviewed and discussed, including self-commissioning, which is analyzed in detail. Furthermore, sensorless control strategies that can improve the industrial application of SynRMs are described. Finally, future research trends concerning SynRMs are analyzed and discussed.

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.

Ultrasonic flowmeters (UFMs) based on the principle of transit time difference are widely used in process industries. The transit time difference is obtained through analyzing received acoustic signals generated by a couple of transducers located upstream and downstream respectively. The quality of received acoustic signal will directly affect the measurement accuracy of UFM. For this reason, a measurement-calculation coupled approach combining measurement tools and numerical simulation for calculating the acoustic signal of UFM is proposed in this paper. The method can predict the acoustic signals of UFM with different flowrates. First, important boundary condition (vibration velocity on transmitter transducer surface) is measured by measurement tool, which can reduce computational complexity and also avoid modeling transmitter transducers. Second, simulating the wave propagation (difficult to access experimentally) within moving medium by using the measured vibration velocity. Third, obtaining the transfer function of receiver by comparing estimated force acting on the receiver transducer surface with measured voltage signal from receiver, which can avoid modeling receiver transducer. Finally, predicting the received acoustic signal at different flowrates by using the transfer function. The rationality of predicted data was validated by comparing experimental and predicted acoustic signal on receiver transducer at different flowrates. A good agreement between simulated and experimental data was obtained, which means that the working processes of the acoustic signal generated by actual transducers inside UFMs can be accurately predicted based on this method. Analysis of the predicted results will provide good guides and advices to the design of UFMs involving actual application.

The instability issue is an important concern in the reduced dc-link capacitance motor drive system, which is caused by the interaction of the LC resonance and the negative impedance of the constant power load (CPL). Harmonics of the grid current also need to be concerned to meet the requirement of the application standards. In this paper, the impedance model of the drive system is established considering the dc-link voltage fluctuation, which is applied to evaluate the drive system stability. The grid current harmonics issue could also be analyzed by the grid input impedance model. A novel grid current feedback based stabilization control method is proposed to stabilize the drive system, which could formulate the Nyquist plot of the drive system. Besides, the grid current harmonics could also be suppressed effectively by adopting the proposed stabilization control method. Experimental results are performed to verify the effectiveness of the proposed method.

High-frequency (HF) signal injection methods have been widely employed in the sensorless control of interior permanent magnet synchronous motor (IPMSM) drives from zero to low speed. However, the control performance will be deteriorated severely when the motor subjects to the disturbance. To cope with this issue, an enhanced linear active disturbance rejection control (LADRC)-based HF pulse voltage signal injection method is proposed in this paper. The cascaded extended state observer is established to guarantee relatively timely and accurate estimation of the total disturbance. The linear control law is generated to compensate for the total disturbance in a feedforward way, which reduces the plant to approximate a canonical first-order integral. The tracking performance and the stability of the enhanced LADRC are analyzed theoretically. Maximum torque per ampere control is adopted to reduce the estimation burden by making full use of the reluctance torque, which helps to further improve the tracking performance of the enhanced LADRC. Finally, the validity of the proposed sensorless control scheme is verified on a 2.2-kW IPMSM drive platform.

Beat phenomenon is an important issue for the practical application of electrolytic capacitorless motor drives. In this study, the characteristics of stator current harmonics caused by the voltage sampling delay are analyzed. Further, the beat phenomenon generated from the interaction between the harmonics and the fundamental currents is investigated, and the envelope feature of the stator current is derived mathematically. For the purpose of suppressing the beat phenomenon, a voltage reconstruction strategy is proposed to reduce the influence of dc-link voltage sampling delay at the six times of grid frequency. By utilizing the reconstructed dc-link voltage for calculation of PWM duty ratio, the beat phenomenon can be attenuated significantly. The proposed method can be integrated into motor control system easily. Experimental results show the effectiveness of the proposed strategy in a prototype of electrolytic capacitorless permanent magnet synchronous motor drive.

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.

In sensorless vector control of Permanent Magnet Synchronous Motor (PMSM) drives, it is important to obtain accurate rotor position. High-frequency (HF) voltage injection method is usually used to estimate the rotor position of the motor at low speed. However, the use of the digital filter reduces the estimation accuracy of the rotor position. In this paper, an improved signal processing method based on the HF square-wave voltage injection method is used to obtain the rotor position error signal. A third-order extended state observer (ESO) is used to estimate the rotor position and speed. Compared with the traditional sensorless control strategy, the proposed method reduces the use of the digital filter, simplifies the signal processing, eliminates the influence of the inverter nonlinearity on the accuracy of rotor position estimation and improves the dynamic performance of the system. Experimental results are provided to verify the effectiveness of the proposed method.

Overmodulation strategies can be used to increase the DC-link voltage utilization rate, which enhances the output torque ability. In practice, the motor can operate beyond linear modulation region, even enter the six-step operation by implementing effective overmodulation strategies.

Permanent magenent synchronous machine (PMSM) has been widely developed in the industry and household applications for its high-power density, high efficiency, high power factor, and excellent control performance.

Large volume electrolytic capacitors are usually utilized at the DC-link of PMSM drives to buffer and store energy, which can stabilize the DC-link voltage and supply power for the inverter. Meanwhile, the power factor correction (PFC) circuit is necessary to improve the power factor and reduce the total harmonic distortion (THD) of the grid input current.

With the rapid development of precision medicine industry, DNA sequencing becomes increasingly important as a research and diagnosis tool. For clinical applications, medical professionals require a platform which is fast, easy to use, and presents clear information relevant to definitive diagnosis. We have developed a single molecule desktop sequencing platform, GenoCare™ 1600. Fast library preparation (without amplification) and simple instrument operation make it friendlier for clinical use. Here we presented sequencing data of E. coli sample from GenoCare™ 1600 with consensus accuracy reaches 99.99%. We also demonstrated sequencing of microbial mixtures and COVID-19 samples from throat swabs. Our data show accurate quantitation of microbial, sensitive identification of SARS-CoV-2 virus and detection of variants confirmed by Sanger sequencing.

In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.

High-frequency signal injection methods have been widely used in sensorless control of permanent-magnet synchronous motor (PMSM) drives. However, the acoustic noise and torque ripples caused by the injected high-frequency signal limit the application of the methods. To avoid these drawbacks, this paper proposes a position estimation method that combines derivative calculations of current and zero-voltage-vector (ZVV) injection, which is especially effective for zero- and low-speed operation of sensorless PMSM drives. This investigation mainly focuses on the extraction of rotor position from ZVV period and the improvements of the accuracy of estimated position. The PMSM equation is developed in the stationary frame when ZVV is applied. The proposed $d$ -axis current allocation scheme ensures the application of the proposed method from no load to rated load. A modified space vector pulse width modulation (SVPWM) control strategy and a ZVV extension scheme are employed to improve the accuracy of estimated position, which also makes the method more realizable. Further, a novel current sampling method that selects two suitable phase currents for derivative calculation according to the voltage sector location is proposed, which can help reduce current derivative ripples and decrease position estimation errors. Finally, the effectiveness of the proposed position estimation strategy is verified on a 1.0-kW interior PMSM (IPMSM) drive platform.

In order to mitigate harmonics of the grid input current, passive filters are applied widely in the power electric systems. The LC resonance and the negative input impedance are important concerns in the reduced dc-link capacitance motor drive system. In this paper, an active damping control method is proposed to suppress the LC resonance by the direct damping current approach, which can formulate the pole distribution of the drive system. Different control laws have been analyzed, and the stability analysis is evaluated by the Routh–Hurwitz criterion. Meanwhile, the active damping control method is realized by the voltage command, which can overcome the limited bandwidth of the current controller. Since the generation of the direct damping current only depends on the dc-link voltage, the proposed active damping control method is robust. Compared with the virtual impedance methods, parameters can be accurately optimized by Bode diagrams after adopting the proposed active damping method. Simulation and experimental results are provided to verify the effectiveness of the proposed method.

Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the execution environment. Containers are created from images which are shared between users via a Docker registry. The amount of data Docker registries store is massive; for example, Docker Hub, a popular public registry, stores at least half a million public images. In this paper, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication in Docker Hub. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3% of the files in images are unique, which means file-level deduplication has a great potential to save storage space for the registry. Our findings can motivate and help improve the design of data reduction, caching, and pulling optimizations for registries.

The comparison of control performances by using different waveform generators for electrolytic capacitor-less motor drives is proposed in this paper, which includes the sinusoidal-square waveform, sinusoidal waveform, trapezoid waveform and sinusoidal waveform with third harmonic injection. The key indexes including power factor, total harmonics distortion, peak phase current and efficiency are used to conduct the evaluation. Meanwhile, the minimum controlled DC link voltage, namely, the dead zone of the waveform generators is treated as a variable to further examine the performance of the electrolytic capacitor-less motor drives. The optimal operation point can be consequently obtained for the certain concerned scenario.

The traditional proportional-integral (PI) controller suffers from either large overshoot or slow dynamic response. For performance improvement, an internal model based state feedback control strategy is proposed in this paper, adopting the linear quadratic (LQ) optimal control for permanent magnetic synchronous motor (PMSM) drives. The parameters of the cost function are selected as functions of desired bandwidth. The proposed control method is verified on the sensorless PMSM drive, and the expected performance is confirmed by the experimental results of a single rotor compressor drive system.

The amplitude death (AD) phenomenon is an important dynamical behavior. Seeking the generation condition that induces AD has been an active and popular research field over the last two decades. In this paper, we report the emergence of AD in the identical coupled system with different network topologies through the pinning control. Using the negative self-feedback as the controllers to pin the network nodes, the critical condition that AD appears can be obtained theoretically. Moreover, the numerical scenarios, such as a single oscillator, two coupled oscillators, and coupled oscillators in regular and complex networks, have also been carried out to validate the effectiveness of the strategy, which coincides with the theoretical predictions. We show that, although the high degree nodes should be pinned preferentially to reach better efficiency for complex networks, the location (or significance) of nodes in network is more noteworthy than the degree of nodes for some regular networks. Our findings therefore provide a possibility to induce AD in coupled systems with different network topologies whose internal parameters and coupling schemes cannot be modified.

Due to their tight isolation, low overhead, and efficient packaging of the execution environment, Docker containers have become a prominent solution for deploying modern applications. Containers are created from images which are stored in a Docker registry. An image consists of a list of layers which can be shared among images. Docker registries store a large amount of images and with the increasing popularity of Docker, they continue to grow. For example, Docker Hub-a popular public registry-stores more than half a million public images. In this paper, we analyze over 167TB of uncompressed Docker images and evaluate the potential of file-level deduplication in the registry. Our analysis reveals that only 3% of the files in images are unique and Docker's existing layer sharing mechanism is not sufficient to eliminate this profound redundancy. We then present the design of Slimmer-a Docker registry with file deduplication support-and conduct a simulation-based analysis of its performance implications.

The speed ripple is an important concern in the air conditioning compressor drive system, and it is more obvious in reduced DC-link capacitance motor drive for the large DC-link voltage fluctuation. In this paper, a compensation method is proposed to reduce the speed fluctuation caused by the load torque fluctuation and the DC-link voltage fluctuation, which is realized by the sliding mode observer and PI-type iterative learning controller. The sliding mode observer for the load torque is designed to obtain the load torque. The iterative learning controller is applied to calculate the expected current for the q-axis current tracking compensation. Both fluctuations are periodic, which could be reflected in the q-axis current. Hence the iterative learning controller can effectively compensate the two types of the speed ripple. Compared to the traditional iterative controller, the proposed controller is improved by projecting time domain to angle domain, which is fit for different speed with same parameters. Experimental results are provided to verify the effectiveness of the proposed method.

Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%.

Disk data density improvement will eventually be limited by the super-paramagnetic effect for perpendicular recording. While various approaches to this problem have been proposed, Shingled Magnetic Recording (SMR) holds great promise to mitigate the problem of density scaling cost-effectively by overlapping data tracks. However, the inherent properties of SMR limit Shingled Write Disk (SWD) applicability since writing data to one track destroys the data previously-stored on the overlapping tracks. As a result, various data layout management designs have been proposed. In this paper, we present a hybrid wave-like shingled recording (HWSR) disk system, which can improve both the performance and the capacity of a shingled write disk. We propose a novel segment-based data layout management and a new wave-like shingled recording that overlaps adjacent tracks from two opposite radial directions. This new scheme can not only efficiently reduce the write amplification, but also double the areal density of conventional circular log-based shingled recording. A new replacement policy based on least write amplification is also devised to manage the hybrid system to effectively eliminate the performance degradation. Our measurements on HWSR implemented in Linux kernel 2.6.35.6 show that it provides superb performance. For example, HWSR reduces the average I/O response time by an order of magnitude compared to S-block for Financial1 trace, and provides up to 3.7 speedup over standard hard disks without using shingled magnetic recording technology.

This paper presents a GaN-based totem-pole bridgeless boost Power-Factor-Correction (PFC) converter for air conditioning applications. Based on the small signal modeling, the power level transfer function of totem-pole bridgeless boost PFC converter is derived by the state-space averaging method. The control strategy of the voltage and the current loops is proposed to meet the design requirement, and the Bode diagram is given to verify the performance of the control strategy. Finally, an 800W GaN-based totem-pole bridgeless boost PFC converter prototype is developed with 98% efficiency and 0.99 input power factor. The effectiveness of the control strategy has been verified.

Corynebacterium glutamicum (C. glutamicum), an important industrial workhorse, is capable of efficiently producing a variety of value-added chemicals and fuels beyond amino acids. C. glutamicum has a broad natural substrate spectrum and can simultaneously utilize various carbon sources in blends. The substrate spectrum of C. glutamicum has been further extended by detailed knowledge of carbon core metabolism and well-established genetic tools and engineering strategies. At present, many pathways have been successfully engineered in C. glutamicum for access to alternative renewable sources to produce natural or non-natural products, making C. glutamicum a promising and favorable microbial cell factory. In this review, we mainly focus on synthetic biology and metabolic engineering strategies for developing synthetic strains that grow on renewable sources to produce the target products. At the same time, we also explore the promotion and future challenges of existing synthetic biology platforms for industrial platform microorganism metabolic engineering efforts.

NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers; improving I/O performance; and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.

The current power managements in RAID array are mostly designed to conserve energy by spinning down partial disks of standard RAID architecture. However, spinning down several disks not only decreases disk parallelism, but also creates new problems, for example, partial chunks of the stripe cannot be accessed directly or multiple chunks of the same stripe are stored on the same disk, which affect spatial locality. We refer these problems as stripe degradation, which results in further performance degradation. To avoid such problems, this paper proposes a new RAID storage architecture called ThinRAID, which uses a subset of disks to build a capacity-adaptive RAID array based on the volume of the data set. Also, the other non-essential disks are spun down to save energy. When the workload is projected to become heavier based on our forecast model, data are migrated to disks that have recently transitioned from standby to active. Furthermore, we also propose a novel data reorganization algorithm that can minimize data migration. We have implemented ThinRAID in the Linux kernel and evaluated its performance and energy efficiency by replaying seven representative traces. Experimental results show that ThinRAID can save 15-27 percent on energy on average over conventional RAID, with minimum performance degradation. In comparison to PARAID, ThinRAID achieves up to 62 percent performance improvement.

With the increasing of packaging and power density and the reduction of the packaging size, the optimization placement of components becomes a key role on thermal management of the multiple components modules. In this paper, a novel algorithm based on thermal force-directed placement algorithm (TFPA) is proposed, with improved thermal force model and the consideration of the components' size effect. The simulation results show that, while maintaining optimal components placement, the revised algorithm can overcome the components overlapping and out-of-boundary problems occurred in the original TFPA effectively.

Distributed key value storage systems are widely used by many popular networking corporations. Nevertheless, server power consumption has become a growing concern for key value storage system designers since the power consumption of servers contributes substantially to a data center's power bills. In this paper, we propose GreenCHT, a power-proportional replication scheme for consistent hashing based key value storage systems. GreenCHT consists of a power-aware replication strategy — multi-tier replication strategy and a centralized power control service — predictive power-mode scheduler. The multitier replication provides power-proportionality and ensures data availability, reliability, consistency, as well as fault-tolerance of the whole system. The predictive power-mode scheduler component predicts workloads and exploits load fluctuation to schedule nodes to be powered-up and powered-down. GreenCHT is implemented based on Sheepdog, a distributed key value system that uses consistent hashing as an underlying distributed hash table. By replicating twelve real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining an acceptable performance. We observed that GreenCHT can reduce power consumption by up to 35%–61%.

Brushless DC motors (BLDCM) are widely used for industry control field. Steady speed control accuracy and speed control bandwidth are the key indexes in industrial application. In this paper, a phase-locked loop control system for BLDCM is proposed to achieve better dynamic performance and steady-state accuracy. A ten-state phase-frequency detector is designed to detect the speed error of the rotor. And an all digital PLL control system for BLDCM is realized with Verilog HDL. Finally, a control and drive circuit based on FPGA is implemented and applied to momentum flywheel to verify the feasibility of the proposed control system. Experimental results show that steady speed accuracy could be achieved better than 0.1%.

Areal density scaling in magnetic hard drives isin jeopardy as magnetic particles become unstable when they are sufficiently small. Shingled recording holds great promise to mitigate the problem of density scaling cost-effectively by overlapping data tracks. However, this innovative technology suffers severely from slow small writes. This prevents shingle recording from being widely adapted in practice. This paper presents a new hybrid storage architecture that combines a shingled-recording magnetic disk and a fast SSD cache to achieve a high-capacity storage system without any compromise to performance. We propose a new wave-like shingled recording that overlaps adjacent tracks from two opposite radial directions. This new schemes doubles the areal density of conventional circular log-based shingled recording. We also design a new replacement strategy to manage the hybrid system to effectively eliminate the performance degradation. We evaluate our design based on a prototype implementation. Experimental results under 12 I/O workloads show that our hybrid system exhibits a sustained performance comparable to a disk with no shingled-recording.

There is no infrastructure in most energy constrained networks, such as WSN. Connected Dominating Set (CDS) has been proposed as virtual backbone. The CDS pays the way for routing, data aggregation and activity scheduling. In order to reduce the backbone size and prolong the lifetime of networks, it is desirable to construct a Minimum CDS (MCDS). Unfortunately, it is a NP-hard problem with a distribute manner. In this paper, a distributive algorithm for MESH CDS is introduced. Theoretical analysis and simulation results are also presented to verify the efficiency of our algorithm.

Based on theory of statistics, main factors affecting effects of loom workshop’s large and small zone ventilation using the CFD simulation in this paper. Firstly, four factors and three levels of orthogonal experimental table is applied to CFD simulation, the order from major to minor of four factors is obtained, which can provide theoretical basis for design and operation. Then single-factor experiment method is applied to CFD simulation, certain factor changing can be obtained with best levels of other factors. Base on above recommend parameters, CFD software is applied to simulate relative humid and PMV on the loom. Lastly, comparison of simulation results and experiment is used to verify feasibility of simulation results.

The CDS (connected dominating set) has been widely used in the topology control of wireless sensor networks. When topology changed due to the node failure or low power, most previous proposals recomputed the structure again as their initialization. This leads to an expensive energy cost which is also unnecessary. In this paper, we propose a localized MESH-CDS algorithm which maintains the structure in a localized manner and ensure its consistency of topology properties. Simulation results show that it has higher energy efficiency than protocols based on tree structure.

Since there is no infrastructure in wireless sensor networks, the CDS (Connected Dominating Set) has been widely used as a virtual backbone. It is desirable that the virtual backbone is fault tolerate because node and link failure are inherent in wireless sensor networks. In this paper, we propose an algorithm which constructs a MESH-CDS in a localized manner in order to resist the failure of one node on the backbone. The size of the MESH-CDS is within a constant factor of optimal CDS for the sake of saving energy. Simulation results show that it can improve the energy efficiency of networks.

Energy efficiency is a key problem of Wireless Sensor Networks. Though we already have many self-organization protocols which can build energy-saving structures, the energy cost of maintenance also must be concerned. We found that organized network structure has great impact on the energy cost in the maintenance stage and the overall energy efficiency. In this paper, we classify the self-organization proposals according to the organized structures and demonstrate how the different structures will affect the energy cost used to maintain the structures when both initialization and maintenance stages are counted. Finally we concluded that a non-loop-free structure is easier to maintain and then has better energy efficiency than global tree structure.