Search

In order to improve the monitoring effect of electronic communication power supply, this paper applies the intelligent communication signal processing method to the monitoring system, and deeply analyzes the basic principle of OTA. Moreover, from the perspective of improving linearity, a signal attenuation OTA is designed in this paper, and its linearity has been greatly improved. In addition, this paper designs a cross‐coupled POTA that can not only ensure high linearity but also achieve tunable translinearity. The tunable POTA circuit has simple structure, wide linear input range, tunable translinearity and easy implementation, and can meet its performance requirements in FPAA arrays. Through data analysis, it can be seen that the intelligent‐oriented electronic communication power monitoring system proposed in this paper has a good data monitoring effect. This study provides an effective approach for the development of electronic communication power monitoring technology, improves the signal processing efficiency of electronic communication power monitoring, and also provides some reference for the improvement of signal strength in electronic communication technology. [ABSTRACT FROM AUTHOR]

The increasing demand for energy presents challenges in efficient energy management. This study aims to develop and implement an Internet of Things (IoT)-based energy monitoring system called SENSE Plug, integrated with a Fiber To The Home (FTTH) network at the Universitas Pendidikan Indonesia campus laboratory. The system is designed to monitor real-time electricity consumption, detect voltage anomalies, and manage the electrical usage of connected electronic devices. The research uses an experimental approach by designing and testing the system in the laboratory. Data was collected using the PZEM-004T sensor connected to the NodeMCU ESP8266 microcontroller, with the measurements displayed on an OLED screen and sent to the IoT Cloud for remote monitoring. The system was tested on various electronic devices to measure voltage, current, active power, and power factor. The results indicate that the SENSE Plug system achieved high accuracy, with an average error of 0.44% for voltage and 0.41% for current. The system can also detect voltage anomalies and automatically disconnect the power supply to prevent damage to devices. In conclusion, SENSE Plug contributes to improving energy efficiency and offers a solution applicable to smart labs and other building environments. [ABSTRACT FROM AUTHOR]

Recently, the increasing concern for climate control has led to the widespread application of grid-connected inverter (GIC)-based renewable-energy systems. In addition, the increased usage of non-linear loads and electrification of the transport sector cause ineffective grid-frequency management and the introduction of harmonics. These grid conditions affect power quality and result in uncertainty and inaccuracy in monitoring and measurement. Incorrect measurement leads to overbilling/underbilling, ineffective demand and supply forecasts for the power system, and inefficient performance analysis. To address the outlined problem, a novel, three-phase frequency component extraction and power measurement method based on Digital Lock-in Amplifier (DLIA) and Digital Lock-in Amplifier–Frequency-Locked Loop (DLIA–FLL) is proposed to provide accurate measurements under the conditions of harmonics and frequency offset. A combined filter, with a lowpass filter and notch filter, is employed to improve computation speed for DLIA. A comparative study is performed to verify the effectiveness of the proposed power measurement approach, by comparing the proposed method to the windowed interpolated fast Fourier transform (WIFFT). The ZERA COM 3003 (a commercial high-accuracy power measurement instrument) is used as the reference instrument in the experiment. [ABSTRACT FROM AUTHOR]

Abstract The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

Grinding has been extensively applied to meet the urgent need for tight tolerance and high productivity in manufacturing industries. However, grinding parameter settings and process control still depend on skilled workers' engineering experience. The process stability in complicated non-uniform wear can't be guaranteed. Moreover, it is impossible to obtain energy-saved grinding strategies. Intelligent monitoring methods are well-recognized to help conquer present trial–error processing deficiencies. However, discrete manufacturing companies have to face increasing difficulties to identify the monitored big data and make credible decisions directly. A decision-making expert system driven by monitored power data (EconG©) is thus developed. EconG© provides a 4-level database structure to efficiently manage multi-source heterogeneous data. Signal conditioning, peaks-valleys feature exaction, and compression approaches are proposed for reducing the storage volume of real-time monitored data. The data size has been reduced to 6.5% of the source. A mathematical comparison model based on the power feature is embedded to diagnose burns, which has been validated by the 16th and 55th surface grinding results. Mapping relation model from inputs, signals to outputs has been built by the power feature-extended artificial neural network algorithm. Prediction accuracy is improved by introducing adaptive control and dynamic changes in material removal. EconG© breaks a single analysis based on grinding parameters. Energy-saved grinding strategies could be intelligently acquired through the presented Pareto optimization method. In the future, a broader and deeper implementation of EconG© will guild manufacturers to respond quickly to explosive demands on intellectualization, sustainability, and flexibility in the arrived 4th industrial revolution. [ABSTRACT FROM AUTHOR]

The need for small-scale renewable energy generation is predicted to increase. Distributed energy production, in general, can be more profitable due to the cost of distribution and use of energy storage, especially from the microgrid. However, the utility and consumers face difficulties maintaining demand imbalance, frequent load-shedding, and a drop in power quality. To address this issue, a portable power quality meter and protection based on the Internet of Things (IoT) for low-voltage distribution are proposed. The proposed prototype has several advantages. First, the procedure for implementing a portable IoT-based power meter with power quality and protection features for residential networks. Compared to other devices, this type has the advantages of power quality measurement, such as power factor, frequency, and harmonics. Second, an approach is proposed device with a parallel function in maintaining network security and quality. This increases the advantage of monitoring loads connected to the grid. Third, perform IoT power monitoring devices that provide notifications in real-time. Fourth, an experiment using a power meter on a microgrid connected to renewable energy combines a LiFePO4 battery and methanol to get the maximum benefit from green energy. The test results found that the IoT model can work reliably, where access to monitoring can be done via the website. The smart meter consists of a voltage transformer, current transformer, and microcontroller unit with an embedded communication module. The existence of more affordable monitoring and protection tools can increase the user’s opportunity to gain profitability.

With the rapid development of social economy, the demand for electric power engineering is gradually increasing. The power supply system is constantly developing in the direction of large space and automation, and various high and new technologies are also constantly improving. The power monitoring data access control system is used to monitor and control the power production and supply process and improve the power supply efficiency. The further development of the region also has a higher demand for power and energy supply. For the problem that the natural environment of transmission and distribution lines in various power grids is uncertain, which makes the line operation unsafe. This paper proposed a power monitoring data access control system based on BP (back propagation, abbreviated as BP) neural network. This paper described the related concepts of BP neural network and power monitoring system, and described the functions and construction methods of power monitoring data access control system. On this basis, relevant experiments were carried out to verify the performance of the proposed system. The experimental results showed that the fault detection accuracy of the traditional algorithm was about 93 %, while the fault detection accuracy of the algorithm in this paper was more than 98 %. The highest accuracy rate was 99.88 %, and the accuracy rate of fault detection was greatly improved. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

By referring to semiconductor measurement technology, we have established an evaluation system (CMCI‐G100, or G100 for short), which can provide a novel evaluation model for electro‐optical characteristics of display module. G100 can achieve ns‐ level synchronous acquisition of electric and optical parameters, which is attributed to the optimization of optical, mechanical and electrical design. On this basis, the software algorithm uniformly models the high‐precision synchronous electro‐optical signal parameters, and evaluates the display quality from a new dimension of electro‐optical collaboration. Besides, G100 support high‐precision electrical and optical parameter measurement, which will bring great help to laboratory work. Owing to the electro‐optical synchronization, G100 can greatly improve the efficiency of Gamma correction. [ABSTRACT FROM AUTHOR]

In response to the accuracy limitations and response delays in current power and fault detection of electromechanical equipment, long short-term memory (LSTM) network algorithm was applied for intelligent optimization and management. Firstly, voltage and power data were collected through a potential transformer (PT), and wavelet transform (WT) was applied to remove noise. Fast Fourier transform (FFT) was utilized to extract key features. Secondly, a multi-layer long short-term memory network was designed, and back propagation algorithms and time series power data were used for LSTM network training to analyze abnormal fluctuations and trends in power data in real-time, and adjust threshold settings. Then, combining the model output and historical fault data, a fault mode knowledge base was established. Potential faults were determined through pattern matching, and signals were sent out through remote indicators. Finally, the algorithm model was evaluated. The research results showed that the weighted response time of voltage drop faults was shortened by 3.3%, and the information entropy values of nine experiments were distributed from 5.11 to 5.28. The diagnostic accuracy for frequency drift was improved by 11.1%. The comprehensive algorithm model used can effectively improve the accuracy of power monitoring and response speed. This study optimizes power monitoring and fault diagnosis by applying the LSTM network algorithm, addressing the limitations of existing methods in terms of real-time performance and accuracy. It effectively enhances the fault prediction capability and response speed of power systems, offering significant application value in the fields of smart grids and equipment management. [ABSTRACT FROM AUTHOR]

Nonintrusive identification of the energy consumption of individual loads from an aggregate power stream typically relies on relatively well-defined transient signatures. However, some loads have non-constant power demand that varies with loading conditions. These loads, such as computer-controlled machine tools, remain stubbornly resistant to conventional nonintrusive electrical monitoring methods. The power behavior of these loads can be modelled with stochastic processes. This paper presents statistical feature extraction techniques for identification of this fluctuating power behavior. An energy estimation procedure is presented and evaluated for two case studies: load operation on a shipboard microgrid and laboratory machine shop equipment.

In order to be able to achieve state maintenance, keep historical data of the operation of each system, improve the precision of computer room management, improve work efficiency, reduce the work intensity of computer room managers, and reduce manual errors and potential accidents, this paper combines the Internet of Things technology to construct an image encryption transmission method that can be used for power monitoring. Moreover, this paper constructs a power system intelligent monitoring system based on the Internet of Things, and encrypts relevant data. In addition, this paper proposes a surveillance video encryption algorithm based on foreground and background separation. This video encryption algorithm is different from ordinary video encryption methods. It fully taps the characteristics of the surveillance video taken by a still camera. According to the experimental research results, it can be seen that the power monitoring image encryption transmission system based on the Internet of Things constructed in this paper has good performance in power monitoring image processing, image encryption, image transmission, etc. The evaluation results of the three functional parameters are all above 80%, which have great advantages compared with traditional algorithms. Therefore, the method proposed in this paper has good practical effects. [ABSTRACT FROM AUTHOR]

In this paper, we take a deep dive into writing a plugin to export power, energy, and other system environmental data from a Cray® XC™ system. With the release of the System Management Workstation 8.0 software, Cray has enabled customers to create site-specific plugins to export all of the data that can flow into the Cray Power Management Database into site-specific infrastructure. In this paper, we give practical information on what data are available using the plugin, and how to write, test, and deploy a plugin. We also share and explain example plugin code, detail design considerations when architecting a plugin, and look at some practical use cases supported by exporting telemetry data off a Cray® XC™ system. This paper is targeted at plugin developers, system administrators, data scientists, and site planners. The plugin feature was developed by Cray in response to discussions with and requirements from select members of the CUG XTreme SIG. This paper demonstrates lessons learned developing prototype plugins that export data off Cray® XC™ systems using Kafka, Redis Pub/Sub, and RabbitMQ. This plugin capability is in-use internally at Cray, used in production at NERSC, and is under consideration for deployment on systems at LANL and Sandia.

At present, power companies face many threats to the network security, and the power monitoring system has insufficient information security capabilities. Network security protection monitoring is particularly important. In this context, the construction of a defense-in-depth system based on cyber security offensive and defensive exercises is launched, deployment and protection from both technical and management aspects are carried out and system protection is combined with corporate management and technical support，to solve solving security problems at different levels, strengthen the overall enterprise network security protection capabilities，and lay a good foundation for future safe production and management.

Precise fine-grained office occupancy detection can be exploited for energy savings in buildings. Based on such information one can optimally regulate lighting and climatization based on the actual presence and absence of users. Conventional approaches are based on movement detection, which are cheap and easy to deploy, but are imprecise and offer coarse information. We propose a power monitoring system as a source of occupancy information. The approach is based on sub-metering at the level of room circuit breakers. The proposed method tackles the problem of indoor office occupancy detection based on statistical approaches, thus contributing to building context awareness which, in turn, is a crucial stepping stone for energy-efficient buildings. The key advantage of the proposed approach is to be low intrusive, especially when compared with image- or tag-based solutions, while still being sufficiently precise in its classification. Such classification is based on nearest neighbors and neural networks machine learning approaches, both in sequential and non-sequential implementations. To test the viability, precision, and saving potential of the proposed approach we deploy in an actual office over several months. We find that the room-level sub-metering can acquire precise, fine-grained occupancy context for up to three people, with averaged kappa measures of 93-95% using either the nearest neighbors or neural networks based approaches.

In modern computing platforms, power monitors (PwrMons) are employed to deliver online power estimates to support different runtime power-performance optimization methodologies. However, the possibility of setting up a successful side-channel attack by analyzing the power estimates imposes the use of a suitable and systematic approach in the design of such PwrMons. This article proposes a design methodology to automatically identify and implement side-channel-resistant PwrMons at the hardware level, for generic computing platforms. The methodology works by designing a PwrMon for which the switching activity of the signals used to compute the power estimates is not a function of both the secret key and the plaintext/ciphertext values processed by the computing platform. According to the most recent standardized methodologies to assess the side-channel security, our experimental validation leverages both correlation power analysis and $t$ -test analysis considering a general purpose System on Chip executing different cryptographic primitives and an application-specific accelerator implementing the AES-128 algorithm. Our results confirm the impossibility of retrieving the secret key from the power estimates provided by our side-channel-resistant PwrMon. Considering several temporal resolutions, we highlight an accuracy error of the power estimates limited to less than 2.7%, as well as an average area and power overheads for the protected PwrMons lower than 6% and 5%, respectively. To this end, the proposed methodology is able to deliver a side-channel-resistant PwrMon within state-of-the-art accuracy error and overheads. [ABSTRACT FROM AUTHOR]

With the recent spread of the 4th Industrial Revolution, the intellectualization of industry is progressing rapidly. In particular, companies in various field are interested in converting existing factories into smart factory, and the number of cases where the smart factory template is applied is increasing. In this paper, we design and implement an IoT-based power monitoring and data collection system that enables monitoring of power consumption as well as the detection of abnormal power consumption in a smart factory. The system consists of power measurement devices, data analysis servers, and knowledge-based web and smartphone applications. The power measurement device uses IoT sensors to measure power consumption and sends collected data to the server. The server analyzes the data collected from the device using R and exploits the analysis results to provide predictions about the failure of equipment and facilities in the smart factory. From this point of view, we can expect improvement in not only cost-efficiency but also product quality. [ABSTRACT FROM AUTHOR]

With the significant increase in energy demands in the last decade, the issues of unnecessary energy usage have increased rapidly. Therefore, there is an immediate need to provide a cheap and easily accessible monitoring tool for the energy consumed by an appliance used in homes and industries. Instead of monitoring the total power consumption of the houses and/or industries, it is useful to monitor the power consumption of the individual appliance, which in turn, helps in saving the overall energy usage and thereby makes it cost-effective. This paper presents a cost-efficient design and implementation of a monitoring system that can precisely measure the current and voltage of each appliance. The design provides tracking of device activity in a real-time environment for the industries and helps in adopting to the green initiative. The design comprises of Arduino based micro-controller and Raspberry Pi, that performs precise measurements of current and voltage of the device, followed by measuring the power consumed by the device. This paper presents two different system designs, one for the single-phase measurements and the other for the DC measurements. The single-phase measurement device comprises of 10-bit ADC whereas, the 24 V DC measurement device comprises of a 12-bit ADC, which provides higher measurement accuracy compared to other systems available in the market. The implemented design uses the EmonCMS web application to accumulate and envision the monitored data. It provides a flexible and user-friendly solution to monitor the measured data easily on any android or iOS devices. [ABSTRACT FROM AUTHOR]

Anomaly detection in home power monitoring can be categorized into two main types: detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the daily activities of residents. Focusing on the application and practicality of anomaly detection, we propose sample efficient home power anomaly detection (SEPAD) with improved monitoring performance in terms of electricity usage as well as changes in the daily living activities of residents via provision of detailed feedback. SEPAD consists of two classifiers: an appliance pattern matching classifier (APMC) and an energy consumption habit classifier (ECHC). The APMC uses a single-source separation framework based on a semi-supervised support vector machine (semi-SVM) model. This semi-supervised learning method requires only a small amount of labeled data to achieve high accuracy in near real time and is a sample efficient detection method. The hidden Markov model (HMM)-based ECHC improves the rationality of SEPAD by providing anomaly detection functionality with respect to the daily activities of householders, especially the elderly and residents in developing areas. When SEPAD detects the appearance of an unknown pattern or known patterns contrary to the household’s electricity usage habits, it triggers an alarm. SEPAD was applied to monitor power consumption data from Mkalama, a rural area in Tanzania with 52 households containing nearly 150 occupants connected to a solar powered off-grid network. The results of the practical test demonstrate the high accuracy and practicality of the proposed method.

With the increased interest in smart cities and smart infrastructures, the need for energy conservation is increasing. Especially with the current electrical energy production mainly relying on nonrenewable resources, conservation of electrical energy is one of the challenging aspects across the globe. However, one can only perform energy conservation optimally by identifying consumption patterns at a granular level, which requires accurate and ubiquitous monitoring infrastructure. Because the electrical energy wastage can occur at any granularity (from a small house-hold appliance to grid-level wastage), the development of a low-cost, easy-to-install, and accurate power-monitoring infrastructure is need of the hour. Hence, in this article, we propose the developed designs for IoT-enabled power monitoring. First is the noninvasive power monitor with voltage connection. The second design introduces a novel split architecture with centralized voltage measurement, which removes the need for local voltage measurements. We have proposed the third and final version of the IoT-enabled power monitor to fulfill the need for three-phase power monitoring. Unlike first and second designs, this design can be used with noninvasive and invasive current sensors. The proposed architecture also supports essential features, such as secure data transfer. Developed devices transmit real-time data to the cloud server, which makes the data ubiquitously available anywhere and anytime. For analyzing the performance of the proposed architecture, the developed devices are deployed in real industrial scenarios. As an example use case, the electrical anomaly detection framework using the data collected is also explained, and the corresponding results are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recent increase in electricity tariff and the introduction of feed-in tariff from renewable resources have increased the interest of energy consumers, such as those in commercial and residential buildings, in reducing their energy usage. This paper proposes a smart power socket and central control system that utilizes the Zigbee communication protocol to control energy usage. The system is designed such that smart sockets wirelessly provide the necessary data to a central controller. Then, the system analyzes the data to generate control commands to turn the devices attached to the smart socket ON or OFF. Experimental results show that the proposed smart socket can correctly read the power consumption of wirelessly connected devices from up to 18 m away without loss of data. The central controller can effectively control multiple sockets on the basis of a scheduled user program code. A 24-min implementation of the proposed energy management algorithm shows a reduction of 0.811 kWmin (0.0134 kWh) in energy usage after the use of the smart sockets as load controllers. Thus, the proposed smart socket system can be fully utilized in a home energy management system with a proper scheduling algorithm.

In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment.

The pervasive use of virtualization techniques in today's datacenters poses challenges in power monitoring since it is not possible to directly measure the power consumption of a virtual entity such as a virtual machine (VM) and a container. In this paper, we present cWatts++, a lightweight virtual power meter that enables accurate power usage measurement in virtualized computing environments such as VMs and containers of Cloud data centers. At the core of cWatts++ is its application-agnostic power model. To this end, we devise two power models (eventModel and raplModel) that are driven by CPU event counters and the Running Average Power Limit (RAPL) feature of modern Intel CPUs, respectively. While eventModel is more generic and, thus, applicable to a wide range of workloads, raplModel is particularly good for CPU-bound workloads. We have evaluated cWatts++ with its two power models in a real system using the PARSEC benchmark suite and our in-house benchmarks. Our evaluation study demonstrates that these power models have an average error of 4.55 and 1.25 percent, respectively, compared with actual power usage measurements of a real power meter, Cabac Power-Mate. [ABSTRACT FROM AUTHOR]

The electron cyclotron resonance heating (ECRH) is a very important plasma heating method in experimental advanced superconducting tokamak (EAST). There are four gyrotrons in the EAST ECRH system that can generate 4-MW/100-s/0.14-THz wave. The ECRH system is a complex terahertz system that involves ultralow temperature, ultrahigh vacuum, strong magnetic field, high-power electromagnetic wave, and weak diagnostic signal. There are many electromagnetic compatibility problems in the ECRH system. This is the first report about the electromagnetic compatibility for the ECRH system. In the operation of the ECRH system, many electromagnetic compatibility problems have been found and resolved. We can suppress the interference and improve the accuracy of power monitoring and the effectiveness of radio frequency protection by using the magnetic rings. The influence of the magnetic field on the current transformers was analyzed. Some electromagnetic compatibility issues in the data acquisition system were discussed. After solving the electromagnetic compatibility problems, the EAST ECRH system can stably output high-power terahertz wave. [ABSTRACT FROM AUTHOR]

Modern Telecom Sector is eventually facing exceptionally tough challenges because of continuous and unexpected increase in power density requirement for the communicating machinery and equipment. To fulfil the power requirements for the equipment, a significant architecture and an optimal technique must be introduced. In this paper, a microcontroller-based optimization use of power-density has been carried out. Meeting above requirements, various equipment and electronic devices are employed. We have designed a microcontroller-based system via PROTEUS Virtual System Modeling to acquire efficient and effective results. The main focus of our work is to supply the power to Telecom equipment in meantime. The power is feeding on batteries and DG (Diesel Generator) set, depending on the condition of the power requirements. The changeover operations are performed by different relays, which are dully programmed via a microcontroller in Keil software. The power capacity of Telecom ((Telecommunication) equipment is ranged from 39-48 Volts DC. The rectification process is done by switch mode rectifiers instead of linear rectifiers. Because the switch-mode rectifier technology has brought fabulous improvements in power density as compared to linear rectifiers. This is done via simulation of the smart switch in PROTEUS software. The outcomes of the proposed system are costeffective in terms of fuel consumption of DG. [ABSTRACT FROM AUTHOR]

Abstract The power consumption is a key metric to design computing platforms. In particular, the variety and complexity of current applications fueled an increasing number of run-time power-aware optimization solutions to dynamically trade the computational power for the power consumption. In this scenario, the online power monitoring methodologies are the core of any power-aware optimization, since the incorrect assessment of the run-time power consumption prevents any effective actuation. This work proposes PowerTap , an all-digital power modeling methodology for designing online power monitoring solutions. In contrast with state-of-the-art solutions, PowerTap adds domain-specific constraints to the data-driven power modeling problem. PowerTap identifies the power model iteratively to balance the accuracy error of the power estimates and the complexity of the final monitoring infrastructure. As a representative use-case, we employed a complex hardware multi-threaded SIMD processor, also considering different operating clock frequencies. The RTL implementation of the identified power model targeting an Xilinx Artix 7 XC7A200T FPGA highlights an accuracy error within 1.79% with an area overhead of 9.95% (LUT) and 3.87% (flip flops) and an average power overhead of 12.17 mW regardless of the operating conditions, i.e., number of software threads and operating frequency. [ABSTRACT FROM AUTHOR]

This paperwork represents a mathematical calculus method for deriving the RMS value of an analog signal which has a DC offset. The analog signal is generated either by a current transformer based sensor or a voltage transformer based one, which is used for an off-grid household power consumption monitoring system. The received analog signal values are converted by the microcontroller's ADC into digital values. Based on a mathematical modelling of the signal, an algorithm is thus developed to be uploaded into a microcontroller based embedded device, which further converts the digital values into comprehensive numerical values about the measured power consumption. [ABSTRACT FROM AUTHOR]

In this paper, a simple and low-cost sensor system based on dual photodetector for reliable laser wavelength and power monitoring, is presented. Different spectral sensitivities of Ge and InGaAs photodiodes were used to obtain ratiometric measurement of wavelength. As a signal splitter, the 2 × 2 fiber-optic coupler is used in order to increase further the spectral sensitivity of the system. This system is investigated with tunable laser source in the 1525–1630 nm range and has showed to successfully operate in the L-range of wavelengths with high linearity of R 2 = 0.99942. Measurement error around ±0.2 nm and repeatability of ±0.16 nm are achieved. By measuring system stability, the system resolution of 0.017 nm is estimated. Temperature dependence of the system is investigated and measured results proved to be consistent with theory of semiconductor behavior in the function of temperature. The sensor system dependence on changing power of tunable laser source is also investigated. In the range of 4–26 mW of the source optical power, the proposed sensor system shows wavelength stability of ±0.1 nm. [ABSTRACT FROM AUTHOR]

Hardware monitoring information can be used during system runtime to increase system lifetime and reliability. Examples of such monitoring information are power, temperature, and the aging status of processors. They provide the system with relevant information about the current hardware health. Such information is especially crucial in resource-aware computing concepts that introduce self-organizing behavior to deal with large MPSoCs (Multi-Processor Systems-on-Chip): For resource-aware computing, resources are allocated according to the current requirements. To find suitable resource-application pairs and achieve system targets like optimizing the utilization, current hardware status must be considered during resource allocation. To evaluate and optimize resource allocation strategies during the design phase, FPGA prototyping is often required before its implementation in ASIC. The evolution of power, temperature and aging differ between ASIC implementation and FPGA prototype. The FPGA prototype should react on sensor data characterized from the target ASIC design instead of FPGA’s hardware status. This paper describes the design of an emulated ASIC Temperature and Power Monitoring system (eTPMon) for FPGA-based prototyping. The emulation approach for power monitors is based on an instruction-level energy model. For emulating temperature monitors, a thermal RC model is used. eTPMon can supply MPSoC prototypes with the hardware status information (power and temperature of the cores) needed for efficient load distribution, achieving resource-aware computing targets. Based on the eTPMon data, different operating strategies and control targets were evaluated for a 2-tile resource-aware MPSoC system. Values provided by eTPMon are usable for extracting information about the aging of processors, which can be used for increasing the system lifetime. [ABSTRACT FROM AUTHOR]

• Signal fluctuation of confocal measurement was effectively suppressed by 3 times. • Power monitoring was employed to correct the laser fluctuation. • Laser modulation was utilized to avoid the non-laser fluctuation. Signal fluctuation is an important issue in confocal measurements because minor intensity variation would cause a distinct deviation in the measurement result. In this work, it is proposed to use laser modulation and power monitoring to suppress the signal fluctuation in confocal measurements. The power monitoring is utilized to obtain the reference signal for correcting detection intensities, and the laser was operated at a modulated frequency to avoid unexpected vibrations from the ambient environment. The results indicated that the detection stability was well improved at least 3 times in the proposed way. Based on that, the precise measurement was successfully realized to resolve the nanometer-scaled structure of a blazed grating, and the surface topography was also achieved for a SIM card in a range of 40 μm. The proposed way is practical in confocal measurements, and it is hoped to be applied in more applications. [ABSTRACT FROM AUTHOR]

Dynamic Thermal and Power Management methods highly depend on the quality of the monitoring, which needs to provide estimations of the system's state. This can be achieved with a set of performance counters that can be configured to track logical events at different levels. Although this problem has been addressed in the literature, recently developed highly reactive adaptation techniques require faster, more accurate and more robust estimations methods. A systematic approach (PESel) is proposed for the selection of the relevant performance events from the local, shared and system resources. We investigate an implementation of a neural network based estimation technique which provides better results compared to related works. Our approach is robust to external temperature variations and takes into account dynamic scaling of the operating frequency. It achieves 96% accuracy with a temporal resolution of 100 ms, with negligible performance/energy overheads (less than 1%). [ABSTRACT FROM AUTHOR]

Fine-grained power monitoring, which refers to power monitoring at the server level, is critical to the efficient operation and energy saving of datacenters. Fined-grained power monitoring, however, is extremely challenging in legacy datacenters that host server systems not equipped with power monitoring sensors. Installing power monitoring hardware at the server level not only incurs high costs but also complicates the maintenance of high-density server clusters and enclosures. In this paper, we present a zero-cost, purely software-based solution to this challenging problem. We use a novel technique of non-intrusive power disaggregation (NIPD) that establishes power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. The PMFs that we have developed can support both linear and nonlinear power models via the state feature transformation. To reduce the training overhead, we further develop adaptive PMFs update strategies and ensure that the training data and state features are appropriately selected. We implement and evaluate NIPD over a real-world datacenter with $326$ nodes. The results show that our solution can provide high precision power estimation at both rack level and server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at rack level has mean relative error of $2.18$ percent, and ii) at server level has mean relative errors of $9.61$ and $7.53$ percent corresponding to the idle and peak power, respectively. [ABSTRACT FROM AUTHOR]