Performance Of Systems Research Articles

Random access memory page caching: a strategy for enhancing shared virtual memory multicomputer systems performance

This study examines a modified approach to optimizing the performance of support vector machine (SVM)-type multicomputer systems through a distinct type of caching method that allocates space in the random access memory (RAM) of a computing node for caching pages. The article extensively describes research on enhancing the performance of the SVM system through memory page caching in RAM at the hardware level by implementing the SVM system based on field-programmable gate arrays (FPGA). A systematic comparative evaluation highlights a discernible enhancement in system performance relative to systems not equipped with the revised caching algorithm. These findings could prove instrumental for subsequent studies focused on optimizing the performance of SVM systems, providing empirical data to inform future investigations and potential applications in multicomputer system performance enhancement.

Gait Recognition Under Different Clothing Conditions Via Deterministic Learning

In real-world scenarios, the clothing difference constitutes one of the most common covariate factors that can affect the performance of gait recognition systems. This paper proposes a gait recognition method which is invariant to maximum number of clothing conditions. First, four kinds of timevarying silhouette features are selected to capture the spatiotemporal characteristics of gait motion. Second, frame-to-frame gait dynamics underlying different individuals ʼ gait features is effectively modeled by radial basis function ( RBF ) neural networks through deterministic learning. Gait patterns are represented as the gait dynamics underlying time-varying gait features. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.

Performance of grid-connected photovoltaic systems in Northern and Southern Hemispheres under equatorial climate

This work studied the actual and simulated technical performance between two grid-connected photovoltaic (GCPV) systems representing opposite latitudes. The system with a capacity of 5.4 kWp installed in Kelantan, Malaysia represents the northern equator, and the 183.6 kWp system installed in Cikarang, Indonesia, denotes the southern equator. The performance was simulated using PVsyst software, which included the energy output (E_outt), reference yield (Y_r), final yield 〖(Y〗_f), performance ratio (PR), and capacity factor (CF). The mean bias error (MBE) between the actual and simulated technical performance were as follows; for system A, the yearly MBE for the E_out, Y_r, Y_f, PR, and CF were -0.4%, 17.1%, -1.4%, -15.8%, and 1.4%, respectively, and for system B, the E_out, Y_r, 〖 Y〗_f, PR, and CF values were 9.80%, 18.3%, 10.0%, -7.2%, and 10.0% respectively. The results have proven that PVsyst has successfully simulated the yearly E_out, 〖 Y〗_f and CF for both systems including PR, for system B, with MBE less than 10%. However, it is noteworthy to highlight that PVsyst significantly overestimated the Y_r of both systems up to 18.3% and conversely underestimated the PR for system A by 15.8%, which highly likely caused by the Meteonorm imported weather data.

Classifying possible hate speech from text with deep learning and ensemble on embedding method

Hate speech can be defined as the use of language to express hatred towards another party. Twitter is one of the most widely used social media platforms in the community. In addition to submitting user-generated content, other users can provide feedback through comments. There are several users who intentionally or unintentionally provide negative comments. Even though there are regulations regarding the prohibition of hate speech, there are still those who make negative comments. Using the deep learning method with the long short-term memory (LSTM) model, a classifier of possible hate speech from messages on Twitter is carried out. With the ensemble method, term frequency times inverse document frequency (TF-IDF) and global vector (GloVe) get 86% accuracy, better than the stand-alone word to vector (Word2Vec) method, which only gets 80%. From these results, it can be concluded that the ensemble method can improve accuracy compared to only using the stand-alone method. Ensemble methods can also improve the performance of deep learning systems and produce better results than using only one method.

Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

Enhancing thermal management efficiency in robotics engineering: Exploring mechanisms, techniques, and modeling approaches

Thermal management is pivotal in robotics engineering, ensuring optimal performance and reliability of robotic systems. This comprehensive review explores various heat dissipation mechanisms, cooling techniques, and thermal modeling approaches employed in robotics engineering. Key topics include conduction-based, convection-based, and radiation-based heat transfer mechanisms, alongside liquid cooling, air cooling, and phase-change cooling techniques. Analytical thermal models, computational fluid dynamics (CFD) simulations, and finite element analysis (FEA) modeling are discussed for their roles in predicting thermal behaviors and optimizing heat dissipation strategies. By synthesizing these advancements, this review provides valuable insights into enhancing thermal management efficiency and ensuring the longevity of robotic systems.

Improved Results on H∞, Performance for Semi-Markovian Jump LPV Systems Under Actuator Saturation and Faults

Improved Results on H∞, Performance for Semi-Markovian Jump LPV Systems Under Actuator Saturation and Faults

Performance Analysis and Optimization of Compressed Air Energy Storage Integrated with Latent Thermal Energy Storage

Recovering compression waste heat using latent thermal energy storage (LTES) is a promising method to enhance the round-trip efficiency of compressed air energy storage (CAES) systems. In this study, a systematic thermodynamic model coupled with a concentric diffusion heat transfer model of the cylindrical packed-bed LTES is established for a CAES system, and the numerical simulation model is validated by experimental data in the reference. Based on the numerical model, the charging–discharging performance of LTES and CAES systems is evaluated under different layouts of phase change materials (PCMs) in LTES, and the optimal layout of PCM is specified as a three-stage layout, since the exergy efficiency of LTES and round-trip efficiency are improved by 8.2% and 6.9% compared with a one-stage layout. Then, the proportion of three PCMs is optimized using response surface methods. The optimization results indicate that the exergy efficiency of LTES and round-trip efficiency of the CAES system are expected to be 80.9% and 73.3% under the PCM proportion of 0.48:0.3:0.22 for three stages, which are 7.0% and 13.1% higher than the original three-stage PCMs with equal proportions.

Synergistic role of MoS2 in gelation-induced fabrication of graphene oxide films.

Supporting materials for electrocatalysts must exhibit relative chemical inertness to facilitate unimpeded movement of gas, and demonstrate electrical conductivity to promote efficient electron transfer to the catalyst. Conventional catalyst electrodes, such as glassy carbon, carbon cloths, or Ni foam, are commonly employed. However, the challenge lies in the limited stability observed during testing due to the relatively weak adhesion between the catalyst and the electrode. Addressing this limitation is crucial for advancing the stability and performance of catalyst-electrode systems in various applications. Here, we suggest a novel fabrication method for a freestanding conducting film, accomplished through gelation, incorporating 1T-MoS2 and graphene oxide. 1T-MoS2 nanosheets play a crucial role in promoting the reduction of graphene oxide (GO) on the Zn foil. This contribution leads to accelerated film formation and enhanced electrical conductivity in the film. The synergistic effect also enhances the film's stability as catalyst supports. This study provides insights into the effective utilization of MoS2 and graphene oxide in the creating of advanced catalyst support systems with potential applications in diverse catalytic reaction.

A force-restricted inerter damper for enhancing the resilience of seismically isolated structures

An inerter is a mechanical device that generates a resisting force proportional to the relative acceleration between its two terminals. Several inerter-based dampers have been developed and studied to protect critical structures from the damaging effects of strong earthquake events. However, under high-frequency excitations, conventional inerter-based dampers may generate high resisting forces, which can result in excessive stresses in the supporting members and degrade the performance of the isolation systems. To overcome this issue, a novel force-restricted inerter damper (FRID) is proposed in this study for use in base-isolated structures. A simplified mechanical model of the proposed FRID was developed, which consists of a parallel layout of a viscous damping element and a nonlinear inerter element with a friction force restriction mechanism. The nonlinear hysteretic behavior of the FRID is discussed with an emphasis on the frequency dependence. Time- and frequency-domain methods were developed to evaluate the nonlinear dynamic response of the FRID-controlled structure. The performance of the FRID was investigated and compared with that of commonly used fluid viscous dampers (FVDs) and viscous mass dampers, when incorporated into base-isolated structures under short- and long-period ground motions. Parametric studies were conducted to investigate the influence of the FRID’s characteristic parameters on the seismic performance of controlled structures. The analysis demonstrated that the viscous mass damper and FRID provided superior control than the FVD on the displacement of a base-isolated structure, and the FRID is more effective than the viscous mass damper in reducing the control force and structural acceleration. This confirms that the proposed FRID is a promising, high-performance, and cost-effective device for enhancing the resilience of seismically isolated structures.

Structural acceleration response reconstruction based on BiLSTM network and multi-head attention mechanism

The effectiveness of structural health monitoring often relies on the precise analysis of structural response data, with data completeness directly impacting the performance of monitoring systems. Addressing the common issue of data loss in structural health monitoring systems, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) network based on a multi-head attention mechanism for the reconstruction of missing structural acceleration responses. Firstly, employing a two-layer BiLSTM network to establish an encoder-decoder framework for extracting spatio-temporal correlation features among sensor data. Subsequently, innovatively embedding a multi-head attention mechanism into the network framework enhances the modeling capability of long-distance input elements and establishes dependencies among different sensors, thereby enabling the network to better capture the global features of input information. The proposed method undergoes validation through a numerical example of a simply supported beam, practical monitoring data on the Hardanger Bridge from the Norwegian University of Science and Technology, and an experimental study of steel frame structure from our laboratory. These validations are compared with alternative response reconstruction models. The experimental results make obvious that the proposed method demonstrates superior accuracy in response reconstruction. Additionally, the suitability of this method for modal identification is validated. Through the utilization of reconstructed responses, the method effectively discerns the structural natural frequencies with accuracy.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. A phantom with two iodine inserts (1 and 2mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4cm for first generation and 8cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50 versus 150keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2cm). With increased collimation (4cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8cm collimation width was comparable to a 4cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45 and 0.33mg/mL for the first and second-generation DLCT, respectively). The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems

Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems

Statistical reliability analysis of marine systems with varied levels of redundancy

In designing autonomous vessels for long-duration independent operation, maintaining the performance of machinery systems without human intervention is a key challenge. Designers are faced with a range of potential system architecture choices but have little guidance on which will be optimal. Working only with high-reliability components can increase the probability of completing a voyage successfully, though the availability of such components may be limited. Alternatively, designers can select a redundant architecture to provide options for reconfiguration if a component fails during a voyage, such architectures typically have weight, space, and cost implications. This work presents a parametric exploration of the probability of system failures over time under different architectures. The reliability of individual components is expressed through exponential probability distributions and the weight of each component is approximated. Two systems are presented and the effectiveness of various architectures for both systems is compared. A simple design penalty function is also tracked to capture the different architectures’ weight and implication number of components. From this study optimal architectures for long-term autonomous missions are proposed.

A novel soft‐switching quadratic high voltage gain trans‐inverse DC/DC converter

AbstractThis paper suggests a new non‐isolated high step‐up DC/DC converter with soft‐switching performance and low input current ripple for renewable energy generation systems. In this introduced circuit, a three‐winding coupled‐inductor combined with a quadratic boost circuit and voltage multiplier cell to obtain high voltage gains. The advanced features of the proposed topology are its ultra‐high voltage gain, soft‐switching performance, low voltage stress across the switching components, continuous input current with low ripple, and the existence of a common ground between the input source and output load. Due to the partial trans‐inverse property in the suggested topology, higher voltage gains can be achieved under smaller turns ratios of the coupled in comparison to the other typical topologies, which leads to efficiency improvement. Moreover, in this introduced circuit, the voltage stress on the single power switch is mitigated using a regenerative clamp circuit. The theory and operating principle of the suggested converter, along with the steady‐state analysis and efficiency interpretation of the proposed converter are discussed in detail. Finally, to verify the theoretical analysis, a 200 W (25 V/400 V) sample prototype is established.

Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

AbstractThis article proposes an optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.

Leveraging intent–entity relationships to enhance semantic accuracy in NLU models

AbstractNatural Language Understanding (NLU) components are used in Dialog Systems (DS) to perform intent detection and entity extraction. In this work, we introduce a technique that exploits the inherent relationships between intents and entities to enhance the performance of NLU systems. The proposed method involves the utilization of a carefully crafted set of rules that formally express these relationships. By utilizing these rules, we effectively address inconsistencies within the NLU output, leading to improved accuracy and reliability. We implemented the proposed method using the Rasa framework as an NLU component and used our own conversational dataset AWPS to evaluate the improvement. Then, we validated the results in other three commonly used datasets: ATIS, SNIPS, and NLU-Benchmark. The experimental results show that the proposed method has a positive impact on the semantic accuracy metric, reaching an improvement of 12.6% in AWPS when training with a small amount of data. Furthermore, the practical application of the proposed method can easily be extended to other Task-Oriented Dialog Systems (T-ODS) to boost their performance and enhance user satisfaction.