Minimum Response Time Research Articles

Highly sensitive and selective LPG sensor working below lowest explosion limit (LEL) at room temperature using as-fabricated indium doped SnO2 thin film

In the present work, an extremely responsive and cost-effective Liquified Petroleum Gas (LPG) sensor operating at room temperature has been fabricated with indium-doped SnO2 nanospheres. In/SnO2 was prepared via cost effective sol-gel route and thin films were fabricated using spin-coating technique. Further, the sample was characterized to study the elemental information, crystal structure, phase formation, morphology, optical absorption, etc through EDS, XRD, TEM, SEM, Raman, FTIR and UV–visible spectroscopy. The sensing characteristics of In/SnO2 thin film have been studied and it was found quite useful to detect the LPG below LEL (0.5–2.0 vol%). The maximum value of % sensor response was found 365.11 at 2.0 vol% whereas the minimum response and recovery times were found as 10 s and 14 s for 0.5 vol% of LPG.

An experimental study on the effectiveness of fire warnings on evacuation from a metro train: The response phase

Voice alarms play an important role in the evacuation of metro systems, while few studies focus on voice alarms inside train carriages. In this study, the effect of a fire warning on the initial phase of evacuation in a carriage is investigated. Both a questionnaire investigation and evacuation experiments with different fire voice alarms in a metro train model were conducted to quantitatively analyze the behaviors of evacuees during the response phase of evacuation. According to the analysis of the questionnaire, the response time calculated from the danger signal (smoke) is suitable for assessing the evacuees' response during the response phase. As indicated by the Pearson correlation coefficient between the minimum and mean response times (0.96), the first person's response has a significant influence on the behaviors of other evacuees. Moreover, comparisons among different categories of experiments demonstrate that the danger signal (smoke), fire alarm bell, and 3–4 units of a voice alarm with a normal and comfortable tone can deliver effective information to evacuees during the initial phase of evacuation. In addition, the effect of a voice alarm with an urgent and fast tone still needs further investigation. The results can help design fire voice alarms and improve the efficiency of evacuation in metro systems.

Burst-Aware Predictive Autoscaling for Containerized Microservices

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq1-2995937.gif"/></alternatives></inline-formula>1.09 in total processed requests, a reduction of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq2-2995937.gif"/></alternatives></inline-formula>5.17 for SLO violations, and an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq3-2995937.gif"/></alternatives></inline-formula>0.767 cost as compared to the baseline method.

Small-signal analysis of a fast central control for large scale PV power plants

Large scale Photovoltaic (PV) power plants are expected to comply with future grid code requirements that will demand a faster dynamic response. Such scenario will impact the operation of the Power Plant Controller (PPC), resulting in a reduction of response time that might bring potential interactions with the PV inverters and the AC network. This paper analyses the dynamic response and interactions within a large scale PV power plant. A small-signal model of the PV power plant is implemented including the PV inverter control, the AC collection grid and the PPC. Also, this model includes the dynamics associated to the delays of PPC measurements, communications, inverters and the Maximum Power Point Tracking. A case study based on a 4-inverter PV power plant is considered in the paper. Then, the stability and oscillation modes are analysed for different delay times and PPC parameters. A PPC design is approached implementing an H2-based parametric design on the corresponding PI controllers and the minimum response time is assessed. Moreover, in order to deal with large systems and facilitate the PPC design, a simplified model is proposed based on the relevant dynamics from the complete small-signal model.

Performance Evaluation of Distributed Database Strategies Using Docker as a Service for Industrial IoT Data: Application to Industry 4.0

Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored in the cloud. This paper presents the authors’ proposition for cloudcentric sensory measurements and measurements acquisition. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS); MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. Several experimental cases have been performed to measure database queries’ response time, achieved throughput, and corresponding failures. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput.

Machine learning based video segmentation of moving scene by motion index using IO detector and shot segmentation

Video segmentation has a major role in several applications like autonomous vehicles, medical image analysis, video surveillance, and augmented reality. Portrait segmentation, a subset of semantic image segmentation, is the key step of preprocessing step in numerous applications and a few among them are entertainment applications, security systems, video conferences, and so on. For the given video, every object exhibiting independent motion in at least a single frame is segmented. This is formulated as a learning problem and designed as an Ensemble Soft Voting Algorithm based on Supervised Machine Learning (ESVA-SML). The motion stream requires an ensemble learning method trained on synthetic videos for segmenting independent objects at motion in the optical flow field. The spatial–temporal features are extracted from each sub-bands which efficiently resembles the human visual system characteristics concerning the feature extract using Gray Level Size Zone Matrix (GLSZM). This method linear combines the previous vectors within a stipulated time interval thereby describing each spatial–temporal feature vector. Based on the prediction errors, shot boundaries of every shot are efficiently identified from which at least a single keyframe is extracted for further analysis. The proposed method is extensively evaluated using SegTrack-v2, DAVIS, and Fusion databases by comparing eight standard methods in various parameters. By analyzing all these three databases, SegTrack-v2 is best in terms of accuracy, precision, and recall. While both DAVIS and Fusion databases produce less MAE. Finally, Fusion produces less RMSE and RAE with minimal response time.

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

&lt;span lang="EN-US"&gt;Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.&lt;/span&gt;

Photorefractive Response Enhancement in Poly(triarylamine)-Based Polymer Composites by a Second Electron Trap Chromophore.

Photorefractive (PR) performances are affected by the components of the photoconductor, sensitizer, nonlinear optical dye, and plasticizer. A photoconductor with high hole mobility promises a faster response time, whereas it induces higher photoconductivity, which leads to easy dielectric breakdown. Adding a second electron trap is effective in controlling photoconductivity. In this study, the role of a second electron trap 1,3,5-tri[(3-pyridyl)-phen-3-yl]benzene (TmPyPB) was investigated in a PR composite consisting of a photoconductor of poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] with a high hole mobility, a nonlinear optical chromophore of piperidinodicyanostyrene, a plasticizer of (2,4,6-trimethylphenyl)diphenylamine, and a sensitizer of [6,6]-phenyl C61 butyric acid-methyl ester. The minimum time response with the maximum optical diffraction efficiency and sensitivity was measured at a 1 wt % content of TmPyPB. These results were consistent with the number of charge carriers trapped per unit volume and per unit time Nc (cm–3 s–1), which is defined as the ratio between the initial trap density Ti (cm–3) and response time τ (s), at a 1 wt % content of TmPyPB. A faster response time of 149 μs, optical diffraction of 24.1% (external diffraction of 4.8%), and a sensitivity of 2746 cm2 J–1 were measured at 50 V μm–1 for the sample with 1 wt % TmPyPB. High loading of 5 wt % TmPyPB led to a large decrease in photoconductivity and effectively suppressed the dielectric breakdown under a stronger electric field, whereas a slower response time with lower diffraction efficiency was observed for optical diffraction.

Synergistic Enhancement Properties of a Flexible Integrated PAN/PVDF Piezoelectric Sensor for Human Posture Recognition.

The flexible pressure sensor has attracted much attention due to its wearable and conformal advantage. All the same, enhancing its electrical and structural properties is still a huge challenge. Herein, a flexible integrated pressure sensor (FIPS) composed of a solid silicone rubber matrix, composited with piezoelectric powers of polyacrylonitrile/Polyvinylidene fluoride (PAN/PVDF) and conductive silver-coated glass microspheres is first proposed. Specifically, the mass ratio of the PAN/PVDF and the rubber is up to 4:5 after mechanical mixing. The output voltage of the sensor with composite PAN/PVDF reaches 49 V, which is 2.57 and 3.06 times that with the single components, PAN and PVDF, respectively. In the range from 0 to 800 kPa, its linearity of voltage and current are all close to 0.986. Meanwhile, the sensor retains high voltage and current sensitivities of 42 mV/kPa and 0.174 nA/kPa, respectively. Furthermore, the minimum response time is 43 ms at a frequency range of 1–2.5 Hz in different postures, and the stability is verified over 10,000 cycles. In practical measurements, the designed FIPS showed excellent recognition abilities for various gaits and different bending degrees of fingers. This work provides a novel strategy to improve the flexible pressure sensor, and demonstrates an attractive potential in terms of human health and motion monitoring.

Cloud Computing Load Balancing Techniques: Retrospect and Recommendations

Load balancing is a research area that seeks to improve the quality of services provided to various clients in cloud computing environments. As cloud users increase around the world, cloud service providers are challenged to develop strategies for distributing tasks to machines for processing at cloud data centers. This work collected and undertook a thorough review of various load balancing techniques, uncovering the key limitations of existing strategies. The publications were chosen from peer-reviewed papers on Google Scholar. Cloud computing, cloud load balancing techniques, approaches to cloud load balancing, and big-data cloud computing systems were among the terms used in the search. Out of 201 studies, 39 met the criteria for inclusion. 5 of the research focused on cloud computing, 6 on cloud load balancing, 7 on resource scheduling in cloud, 16 on techniques for balancing cloud load, and 5 on big-data cloud computing environments. The study identified some research gaps and recommended a throughput-maximization based central-distributive load balancing architecture as a solution to maximize throughput, minimize response time and processing cost, and optimize load balancing architecture. Keywords— Centralized, cloud-computing, distributive, load-balancing.

Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm

Cloud computing relies on the collection and distribution of services from internet-based data centers. With the large resource pool available in internet wide range of users are accessing the cloud. Load balance is important feature involving resource allocation to prevent overloading of any system or optimal use of resources. Major load in cloud network are concerned with CPU, memory and network. This cloud computing aspect has not yet earned too much coverage. Although load balancing is an important feature for cloud computing, concurrent computing etc. In these areas, several algorithms were suggested to solve load balance problem. However, it does recommend very few cloud computing algorithms. Given that cloud storage differs considerably from all other environments, particular load balancing algorithm should will built in sort to serve its needs. This work proposes novel load-balancing algorithm based on artificial bee colony algorithm and load balancing min-min scheduling algorithm for balancing load in cloud computing network. Simulation here is carried out in clouds to generate comparative results. Improving on various parameters like power consumption, resource utilization, stability of system are some major areas focused on. This work has used algorithm that has the best efficiency of resources, optimal performance, minimal response time, scalability and durability in integrated resource planning.

Fabrication of selective gas sensors using Fe3O4 nanoparticles decorated with CuO

Metal-oxide nanoparticles are regarded as favorable candidates for different device applications including gas sensors. Decoration of nanoparticles with smaller ones of different types enables taking advantage of the physical and chemical characteristics of both core and decorate nanoparticles. Fe3O4 nanoparticles decorated with CuO are produced in this work by a coprecipitation method and investigated for their application for H2S gas sensor devices. The average size of Fe3O4 nanoparticles is 33.33∓5.55nm while the average grain size of the CuO nanoparticles is 9.72∓1.39nm. Gas sensors are fabricated by depositing dispersed nanoparticles on substrates with pre-printed interdigitated electrodes. Impedance spectroscopy is utilized to investigate the electrical characteristics of fabricated devices, where their activation energy is evaluated to 0.386±0.076eV. The fabricated sensors are found to be selective to H2S and sensitive to low concentrations, as low as 1.0 ppm, with minimum response time of 1.0 min. The produced sensors indicate potential for field applications due to their various features that include simplified and practical fabrication procedure, low power needs, high sensitivity, reasonable response time, and magnetic properties of nanoparticles that facilitate their recycling.

Improved room temperature liquefied petroleum gas sensing performance of Ni0.5Zn0.5Fe2O4@Cl–doped polypyrrole nanoweb

Ni0.5Zn0.5Fe2O4@Cl–doped polypyrrole (NZF@PPY) nanoweb type hybrid structure has been prepared using oxidation polymerization method at room temperature. The resultant material is extremely porous and uniform possessing a web-like structure. The liquefied petroleum gas (LPG) sensing film was prepared using a spin coater and characterized with the help of several techniques in order to investigate different properties of the material like, surface morphology, temperature dependence, chemical bondings, optical bandgap, crystallinity etc. The nanohybrid is examined thoroughly for LPG sensing and the outcomes are compared with the results received using only the core material (Ni0.5Zn0.5Fe2O4). It was found that the highly porous, weblike NZF@PPY nanohybrid works as an excellent, ultrafast LPG sensor, showing a minimum response time of 5 sec only at 0.5 vol% LPG exposure. The nanohybrid is found to be 66.21% more sensitive towards LPG than its core counterpart.

Random forest based power sustainability and cost optimization in smart grid

Abstract Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

Load Balancing and its Algorithms in Cloud Computing Environment: A Survey

In this paper, the performance of cloud computing system is greatly influenced by the problem of load balancing. The complexity class of the load balancing problem with respect to the complexity class belongs to the NP-system complete which involves intensely huge search space with huge number of potential solutions and also to find the optimal solution, it takes longer time. Based on these circumstances, there is no methodology to solve the problem. A load balancer is like a “traffic cop” which directs the traffic to avoid a situation where few servers are overloaded while few are idle. Proper load balancing can ensure maximum throughput in minimum response time. This paper is a critical review of all the existing techniques of load balancing and comparison between them. Keywords – cloud computing, load balancing, load balancing algorithms (static and dynamic), smart load balancing.

A Sustainable Model for Emergency Medical Services in Developing Countries: A Novel Approach Using Partial Outsourcing and Machine Learning.

IntroductionUnlike Western countries, many low- and middle-income countries (LMIC), like India, have a de-centralized emergency medical services (EMS) involving both semi-government and non-government organizations. It is alarming that due to the absence of a common ecosystem, the utilization of resources is inefficient, which leads to shortage of available vehicles and larger response time. Fragmentation of emergency supply chain resources motivates us to propose a new vehicle routing and scheduling model equipped with novel features to ensure minimal response time using existing resources.Materials and MethodsThe data set of medical and fire-related emergencies from January 2018 to May 2018 of Uttarakhand State in India was provided by GVK Emergency Management and Research Institute (GVK EMRI) also known as 108 EMSs was used in the study. The proposed model integrates all the available EMS vehicles including partial outsourcing to non-ambulatory vehicles like police vans, taxis, etc., using a novel two-echelon heuristic approach. In the first stage, an offline learning model is developed to yield the deployment strategy for EMS vehicles. Seven well researched machine learning (ML) algorithms were analyzed for parameter prediction namely random forest (RF), convolutional neural network (CNN), k-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), logistic regression (LR), and linear discriminant analysis (LDA). In the second stage, a real-time routing model is proposed for EMS vehicle routing at the time of emergency, considering partial outsourcing.Results and DiscussionThe results indicate that the RF classifier outperforms the LR, LDA, SVM, CNN, CART and NB classifier in terms of both accuracy as well as F-1 score. The proposed vehicle routing and scheduling model for automated decision-making shows an improvement of 42.1%, 54%, 27.9% and 62% in vehicle assignment time, vehicle travel time from base to scene, travel time from scene to hospital, and total response time, respectively, in urban areas.

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

Deeply Trained Real-Time Body Sensor Networks for Analyzing the Symptoms of Parkinson’s Disease

Parkinson&#x2019;s Disease (PD) is a neural system disorder that disturbs the mental and physical activities of human beings. Analyzing the symptoms and biosignal data of PD is widely focused in medical research fields. The currently available PD diagnosis models are limited to real-time issues, insufficient deep data extraction, and early monitoring problems. On the scope, the proposed Optimal Health Support and PD Analysis System (OHPAS) analyses the symptoms of PD using a deeply trained biosensors network environment. The novel system trains the biosensor network using complex Machine Learning (ML) and Deep Learning (DL) approaches. The environment of OHPAS sets up acoustic sensors (UT-PF), microphones (MC-1500 unit), and multimodal sensor units (MC-10 sensor). MC-10 is the sensor suite that has an accelerometer sensor, gyro sensor, and Electro Cardio Gram (ECG) sensor to observe the biosignals. For establishing the biosensor data analysis framework, OHPAS initiates the fusion of Variable Auto Encoder (VAER) and K-Means clustering techniques. This phase comprises dataset feature reduction, data regularization, and clustering operations to make the dataset effective for the training process. Finally, the Long Short Term Memory network (LSTM) uses the preprocessed dataset for computing the training dataset. The proposed OHPAS contributes novel features such as a real-time patient monitoring environment, effective sensor data reduction, distributed sensor data analysis, day-wise PD symptom prediction, reactive PD alerts, and accurate early detection solutions. Considering effective medical data analysis with minimal response time, the proposed model creates reactive body sensor network units. Under this sensor platform, each sensor patch unit contains proposed DL procedures in its internal memory for initiating data analysis practices. Consequently, the PD symptoms are commendably detected and predicted with minimal response time. The experimental results indicate the proposed PD system outperforms the existing systems by 8% to 10%.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

The solution of the main types of tasks in the design of a distributed corporate information system of the sanatorium

This paper presents the solution of the problem of optimal allocation of collective use resources by the criterion of minimum average system response time to the requests of all users and the solution of the problem of achieving the required fault tolerance index with a speed limit for the distributed corporate information system of the sanatorium “Rossiyanka” of Anapa.