Demand For Reliability Research Articles

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

The future communication requirements for industrial automation will differ significantly from existing technologies. Traffic in Industry 4.0 imposes real-time requirements, requiring ultra-reliable communication (URC) with high reliability and minimal latency. The demand for ultra-high reliability as high as 99.999999 percent and as low as 1 ms end-to-end latency is the major challenge of the NOMA communication system in the factory of the future. The high expectations on reliability and latency need modifications to the radio system’s baseband signal processing, medium access control (MAC) layer, and application layer to protect against packet losses. Thus, this paper investigates the utilization of Raptor Q codes, which is a type of Application Layer Forward Error Correction (AL-FEC), by developing an end-to-end system level simulator to evaluate and analyze the performance of the transmission signals based on cross-layer approach; from the physical layer (PHY), MAC layer, and application layer parameters in an indoor factory network setting. The factory is assumed to be operated with various factory robots of different speeds, from static to 10 km/h. The 5G technology relies heavily on flexible network operations. High user density, high user mobility, deployment, and coverage are all qualities that allow for this flexibility. Through extensive simulations, the results showed that the Raptor Q codes are not only able to give good results, i.e., packet reception rate PRR = 0.9 of 10 m or 1.8% to 10 m or 3.4% depending on different scenarios, but are also able to meet PRR = 0.9 in the mobility scenario at 10 km/h. Thus, the Raptor Q codes can be seen as a good candidate for obtaining results within a strict range of requirements set by URC communications for the factory of the future replacing RLNC.

Breaking Mirror for the Customers: The Demand-Side Contingencies of the Mirroring Hypothesis

Much of the literature on modularity suggests that increased product modularity is associated with advantageous increases in organizational modularity, otherwise known as the mirroring hypothesis. However, there is growing contradictory evidence. This study proposes demand-side contingent factors that would reduce the extent of mirroring between product and organization. Specifically, it is proposed that firms adopting industry-standard modular architecture would “break the mirror” (i.e., remain relatively more integrated) if the target customers have high performance or reliability demands. Logit regression is employed to test the proposed hypotheses on the cross-sectional data collected from 173 computer systems integration firms (177 strategic business units). Results support the proposed demand-side contingent factors, i.e., an increase in target customers’ performance and reliability demand indeed reduces the extent of mirroring for systems integration firms in this industry.

Study of Building Demand Response Method Based on Indoor Temperature Setpoint Control of VRV Air Conditioning

Demand response has been attracting increasing attention due to the promotion of renewable energy applications and the benefits of carbon emission reduction it brings for the utility grid. The development of the smart grid pays great attention to investigating reliable demand response technologies provided by energy users. Buildings are energy users with high-level load regulation capacity and energy flexibility, which indicates they have the potential to conduct demand response services to achieve power regulation targets for the grid. This paper presents the latest investigation of a building demand response method based on indoor temperature setpoint control of air conditioning systems. The proposed method can be adopted for all air conditioning systems with basic feedback control functions and communication protocol in buildings. A load prediction model which considers impacts of temperatures and building thermal characteristics is developed. An on-site test in an office building is implemented to validate the effects of the proposed method in a temporary demand response case. Results show that about 40% of the air conditioning rated load is reduced while 26.8% of the energy consumption is saved during the demand response event, which earns substantial economic benefits for building users.

Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems

With increasing attention to climate change, the penetration level of renewable energy sources (RES) in the electricity network is increasing. Due to the intermittency of RES, gas-fired power plants could play a significant role in backing up the RES in order to maintain the supply–demand balance. As a result, the interaction between gas and power networks are significantly increasing. On the other hand, due to the increase in peak demand (e.g., electrification of heat), network operators are willing to execute demand response programs (DRPs) to improve congestion management and reduce costs. In this context, modeling and optimal implementation of DRPs in proportion to the demand is one of the main issues for gas and power network operators. In this paper, an emergency demand response program (EDRP) is implemented locally to reduce the congestion of transmission lines and gas pipelines more efficiently. Additionally, the effects of optimal implementation of local emergency demand response program (LEDRP) in gas and power networks using linear and non-linear economic models (power, exponential and logarithmic) for EDRP in terms of cost and line congestion and risk of unserved demand are investigated. The most reliable demand response model is the approach that has the least difference between the estimated demand and the actual demand. Furthermore, the role of the LEDRP in the case of hydrogen injection instead of natural gas in the gas infrastructure is investigated. The optimal incentives for each bus or node are determined based on the power transfer distribution factor, gas transfer distribution factor, available electricity or gas transmission capability, and combination of unit commitment with the LEDRP in the integrated operation of these networks. According to the results, implementing the LEDRP in gas and power networks reduces the total operation cost up to 11% and could facilitate hydrogen injection to the network. The proposed hybrid model is implemented on a 24-bus IEEE electricity network and a 15-bus gas network to quantify the role and value of different LEDRP models.

Selection Strategy of Jaw Tracking in VMAT Planning for Lung SBRT.

PurposeThis study aimed to investigate the dosimetric effect and delivery reliability of jaw tracking (JT) with increasing planning target volume (PTV) for lung stereotactic body radiation therapy (SBRT) plans. A threshold of PTV was proposed as a selection criterion between JT and fixed-jaw (FJ) techniques.MethodsA total of 28 patients with early-stage non-small-cell lung cancer were retrospectively included. The PTVs ranged from 4.88 cc to 68.74 cc, prescribed with 48 Gy in four fractions. Three-partial-arc volumetric modulated arc therapy (VMAT) plans with FJ and with JT were created for each patient with the same optimization objectives. These two sets of plans were compared using metrics, including conformity index (CI), V50%, R50%, D2cm, dose–volume parameters of organs at risk, and monitor units (MUs). The ratio of small subfields (<3 cm in either dimension), %SS, was acquired as a surrogate for the small-field uncertainty. Statistical analyses were performed to evaluate the correlation between the differences in these parameters and the PTV.ResultsThe V50%, R50%, D2cm, and V20Gy, D1,500cc, and D1,000cc of the lung showed a statistically significant improvement in JT plans as opposed to FJ plans, while the number of MU in JT plans was higher by an average of 1.9%. Between FJ and JT plans, the PTV was strongly correlated with the differences in V50%, moderately correlated with those in V20Gy of the lung, and weakly correlated with those in D2cm and D1,500cc of the lung. By using JT, %SS was found to be negatively correlated with the PTV, and the PTV should be at least approximately 12.5 cc for an expected %SS <50%, which was 15 cc for a %SS <20% and 20 cc for a %SS <5%.ConclusionsConsidering the dosimetric differences and small-field uncertainties, JT could be selected using a PTV threshold, such as 12.5, 15, or 20 cc, on the basis of the demand of delivery reliability for lung SBRT.

Multivariate monthly water demand prediction using ensemble and gradient boosting machine learning techniques

Water management planning requires reliable and accurate water demand forecasting. Water demand prediction is affected by variables, such as climate, socio-economic, and demographic data. This paper investigates urban monthly average water demand prediction, using classical, ensemble, and gradient boosting-based machine learning models, using the available monthly water demand, climatic, economic, and demographic data. Three train-test data split schemes on water demand timeseries were considered to determine the effect of data size on water demand prediction. Sensitivity analysis was employed to reduce input feature dimensionality while maintaining model accuracy. A univariate timeseries (water demand only) produced R2 scores up to 0.91, which increased to 0.94 with the addition of calendar and climatic features. Increasing the training data size from 70% to 90% improved the RMSE and MAE scores by ensemble and gradient boosting methods, with the random forest and the AdaBoost models showing improvements of up to 69%. The sensitivity analysis revealed a successful input reduction scheme from a potential 17 input attributes to seven inputs. Gradient boosting models showed robust and faster execution time, especially with the increase in training data, which is attractive for medium-term urban water demand forecasting.

Three-dimensional interface and property of SnPb solder joint under extreme thermal shocking

To meet the demand of cryogenic reliability on the electronic packaging for deep space satellite, the three-dimensional interfacial morphology and property of eutectic SnPb solder joint under an extreme thermal shocking of 77–423 K were investigated. After thermal shocking, Cu-Sn IMC layers coarsened in the cross-sectional observation and micro-cracks formed in Cu6Sn5 layer. The rough Cu6Sn5 layer in the top view gradually smoothed, with broken Cu6Sn5 particles and exposed Cu3Sn. In the bottom-view observation, the Cu6Sn5 layer of as-soldered joint was overlapped by Cu3Sn layer. But no defects were generated in Cu3Sn layer. The shear force of solder joint was greatly reduced due to broken Cu6Sn5 with the changing fracture characteristics from the solder-controlled mode to the solder/IMC mixed mode.

Shaking Table Test of a Transfer-Purge Chamber in Nuclear Island Structure.

The transfer-purge chamber is an operation room for nuclear fuel transport and purging in a nuclear power plant, which has a demand for structural reliability and radiation protection. The transfer-purge chamber has features such as large curvature, heavy concrete, long overhang, and irregular cross-sections, and it is constructed of double steel plates reinforced concrete (SC) structure. This study performed shaking table tests for a 1:4.5 scale model of the transfer-purge chamber. Three sets of ground motions were input in the scale model in the horizontal and vertical directions to study its structural reliability and seismic performance. Acceleration response and strain response of the structure were analyzed to evaluate the dynamic characteristics of the transfer-purge chamber under the ground motion. The results show that the transfer-purge chamber has great stiffness and short periods. The periods slightly increase with the rise of intensity of seismic ground motions. Under the excitation of ground motions, the dynamic response of the transfer-purge chamber is slight. No obvious deformation or damage occurred on the transfer-purge chamber, and cracking in concrete or buckling on steel plate did not appear. The transfer-purge chamber has excellent seismic performance, and it is sufficiently safe and reliable from a structural perspective.

Integrated Remote Laser Source for 6G Advanced Antenna Systems

Future generations of mobile communications promise near unlimited access to information and data sharing with ubiquitous wireless connectivity for any kind of device and application that may benefit from being connected. This poses new design challenges and requires a renewal of the hardware platforms to meet the increased demand of capacity and reliability of the network. Next generation Radio Access Network (RAN) will evolve towards an increase of the radio bandwidth, with carrier frequency extended to the millimeter waves (mmW) range. Other technologies that will be extensively used to increase the RAN capacity will be beamforming and spatial multiplexing with advanced antenna systems (AAS) comprising hundreds of antenna elements. Optical technologies are the natural candidates to support the traffic increase, by providing high-capacity interconnection within the radio unit and towards the base band unit (BBU). However, the higher level of integration of AAS will cause an increase of the temperature to values >100C, due to power dissipation of RF power amplifiers (PA), digital front-end (DFE) and baseband processing Application Specific Circuits (ASIC). This makes the AAS an inhospitable environment for optical transceivers that include lasers, typically operating at temperatures <85C. A promising approach to solve this issue is to place the laser outside the transceiver as a remote laser source (RLS) in a less harsh environment or in a thermally controlled environment e.g. in the same cabinet of the BBU. In this article we demonstrate a fully integrated RLS enabling a reduction of power consumption and packaging cost.

Experimental study on the aerodynamic influence of purge flow in a turbine cascade with different mainstream incidence angles

Demand for high reliability and long life of modern turbine requires that turbine components should be cooled adequately. The cooling flow purged into the rotor-stator disk cavity will inevitably interact with the mainstream. The current paper mainly focuses on the aerodynamic influence of cooling flow on the secondary flows in the mainstream. Both particle image velocimetry and blade wall pressure measurement were utilized to study the flow field within the turbine blade passage under different mainstream incidence angles and purge flow rates. The purge flow was found to promote the development of the passage vortex by inducing vortices which can enhance the vorticity of the passage vortex. In addition, the mainstream incidence angle also has an impact on the development of the passage vortex through affecting the blade loading and the horseshoe vortex. Furthermore, the transient results demonstrate that the time-averaged vortex is the superposition of large number of transient vortices, and the purge flow causes more transient vortices with large size and high strength.

МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ПІДВИЩЕННЯ ЕНЕРГОЕФЕКТИВНОСТІ УНІВЕРСИТЕТУ В СИСТЕМІ ЕНЕРГОХАБА ЗНАНЬ

This study attempts to address the issues of enhancing energy efficiency using mathematical modeling methods. The research findings assert that energy saving is a new challenging task of the 21st century, since thermal and electric power consumption is essential to human life and building a favourable living environment. It is observed that boosting the competitiveness, financial stability, energy and environmental security of Ukraine’s economy, as well as improving the living standards and the life quality seem hardly possible without realizing the energy saving potential and increasing energy efficiency through modernization, technological advancements and the transition towards rational and environmentally responsible utilization of energy resources. It is argued that by resolving the above objectives, Ukraine might strengthen its positions among developed economies. The following methods were used to carry out mathematical modeling to enhance the university energy efficiency in the frameworks of the energy knowledge hub: neural network technologies, mean absolute and relative error, mean absolute deviation; statistical comparison of the forecast accuracy based on the mean absolute error, as well as time series forecasting. A model to boost the University energy efficiency has been developed within the knowledge energy hub by implementing neural network patterns based on the experimental data from the Kyiv National University of Technologies and Design for the heating period 2020–2021. In particular, to optimize the operating modes of automatic power supply control for University Building 4, mathematical models with a complex algorithm structure have been employed (offering the increased resource intensity of such tasks). It is argued that making a decision on the feasibility of using an energy hub for University buildings and selecting appropriate equipment should be accomplished with due regard to the structure and the capacity of energy consumers, their types, demands for quality and reliability of electric power supply, their compliance with operating and safety standards, as well as taking into account the results of climate, wind monitoring and monitoring of solar activity. The conclusions resume that to assure the energy quality and the system sustainability, it is considered important to resolve a range of issues related to inconsistency in generation and supply of renewable energy from power plants, ensuring reliability and quality of energy supply through the use of energy storage (batteries) in particular, etc.).

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Optimization algorithms in wireless monitoring networks: A survey

Wireless network has emancipated people from the bondage of wired network and enhanced the quality of human life. However, there remain a few areas pertaining to wireless network that require attention, such as network congestion, low communication reliability and security. With the increasing scale and extended applications of Internet of Things (IoT), there is a growing demand for reliability, stability and security within the network. Wireless network monitoring is an effective system put in place which involves distributed sniffers that capture the transmitted data of wireless users, facilitating status analysis, fault diagnosis and resource management of the network system. Due to the limited number of sniffers, optimization of hardware configuration and channel assignment of sniffers is paramount, which can significantly improve the amount of captured data and the monitoring quality of the wireless network. Primarily, the concept, classification and characteristics of wireless network monitoring are introduced. Secondarily, the application of optimization algorithms in wireless network monitoring, particularly the channel selection algorithms during the data collection process and the channels and time-slot scheduling algorithms during the data aggregation process are summarized. Finally, the challenges faced when building a wireless network monitoring are discussed and to conclude prospects aimed towards the development of this research field are put forward.

Влияние нуклеотидного состава ДНК-матриц на протекание неспецифического (ab initio) синтеза ДНК

Detection of specific nucleic acids (NA) is the basis of modern methods for DNA and RNA analysis, which is carried out during amplification reactions followed by detection of amplification results. Despite the huge number of NA amplification techniques has been developed, the demand for high reliability is still relevant. This article is related to the specificity of the DNA synthesis reaction. Previously, nonspecific amplification was shown for various DNA polymerases even in the absence of templates and primers. In this study, the propensity of strand-displacement DNA polymerases to provide efficient ab initio DNA synthesis is shown. This property is most intrinsic for thermostable polymerases, which is probably due to the higher efficiency of formation of DNA heteroduplexes suitable for elongation during thermal cycling. Ab initio DNA synthesis proceeds at the highest rate in the presence of double-stranded DNA, oligo-dT18, or templates consisting of nucleotide repeats.

Reduce Wafer Remnants Through Pre-Assembly Process Optimization

Wafer fabrication technology has been seeing exponential growth in recent years. This development is driven by the demand for better reliability, superior performance, and lower cost of electronic consumer products. Generally, the standard soft-soldering wafer backside metallization multilayer stack is AuAs/Ag/Ni/Ag. The present backside metallization being developed is Ti/NiV/Ag as the cost of Ti is almost neglectable compared to Au. However, this new wafer backside metallization leads to increased remnants remaining on the dicing tape after die pickup. The reduced effective area of the die backside surface may cause adhesion issues in the subsequent die attach process. In realizing this essential phase, in pre-assembly process optimization has been carried out using some assessment on the process flow and data analysis through statistical analyses image and measurement of wafer skeleton remnants was taken showing a reduction from 70μm to 40μm in width. This method shows that skipping the baking process in the pre-assembly process will undoubtedly lessen the adhesion with the back metal with mounting tape. Without compromising the saw quality will indeed give a reduced wafer remnant. Henceforth, skipping baking in the pre-assembly process will significantly reduce wafer remnants and further improve die attach process.

An Efficient LoRa-Enabled Smart Fault Detection and Monitoring Platform for the Power Distribution System Using Self-Powered IoT Devices

Transient stability and supply disturbances are common yet unwelcome phenomena in power distribution systems, particularly in sub-Saharan Africa. The growing demand for greater reliability and dependability in power delivery has aroused the interest of researchers and renewed the pursuit of advanced technological solutions for fault detection and location determination at medium and low-voltage levels. The length of the distribution network typically ranges from hundreds to thousands of kilometers. In this regard, the management of distribution networks, including the identification of faulty segments, is a significant recurrent challenge facing power-system operators. With the ever-expanding distribution network and regulatory demands for service reliability, the challenge for network operators is daunting. However, the deployment of IoT technologies in the energy distribution infrastructure would significantly accelerate the detection and location of faults, thus transforming the electricity delivery service into one that is responsive, communicative, attractive, and robust. This study proposes, designs, and implements a reasonably priced LoRaWAN-based IoT platform for monitoring distribution networks. The study was conducted in Nakuru County, Kenya on an actual and active distribution network owned and managed by Kenya Power Company. Experimental results showed that a trigger is generated at the network-monitoring center in about 100 msec of the occurrence of a fault on the distribution network, thus enabling quick, prompt, and immediate commencement of reparative action. Furthermore, practical evaluation has shown that this platform is well adapted for the context of developing countries where budgetary constraints and cost prohibitions hinder the upgrade of the legacy grid into fully-fledged smart entities.

Research of Integrated Non-stop Operation Based on Emergency Power Supply Vehicle

As the speed of social and economic development accelerates, the demand for reliability of power supply rises gradually in various industries. As an important method of power supply, the emergency power supply vehicle can effectively ensure the smooth implementation of comprehensive non-stop operation. In view of the existing multi-scene integrated non-stop operation methods, the integrated non-stop operation based on emergency power supply vehicle is systematized and modularized, and the integrated non-stop operation management platform based on emergency power supply vehicles is established to further improve the technical system of non-stop operation in the power industry.

'Selling the monster' or the relevance of cultural-cognitive conditions in creating demand for innovation: the case of COVID-19 vaccines and beyond

Starting from the current, problematic inertia in citizens' demand for coronavirus vaccinations, the article argues that public demand for innovations crucially depends on consumers' ability to cultural-cognitively process the degree of novelty that the innovation brings about. This is not only of major importance for entrepreneurs who aim to commercialise innovations or otherwise create markets and demand, but as importantly, it concerns innovation and industrial policy who have a responsibility to establish and maintain reliable demand structures. Lastly, it strongly concerns contemporary 'mission-oriented' or transformative innovation policy, since 'grand challenges' can be addressed, and transitions performed only if the technological dynamics involved are accepted on a wide scale. The proposition is that, given the cultural-cognitive origins and the emotive form of certain impediments to demand, rational incentives are largely ineffective remedies for them. Instead, in the short run, social structures are necessary by which to comfort and 'solace' potential consumers.

Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana

The major challenge in managing blood products lies in the uncertainty of blood demand and supply, with a trade-off between shortage and wastage, especially in most developing countries. Thus, reliable demand predictions can be imperative in planning voluntary blood donation campaigns and improving blood availability within Ghana hospitals. However, most historical datasets on blood demand in Ghana are predominantly contaminated with missing values and outliers due to improper database management systems. Consequently, time-series prediction can be challenging since data cleaning can affect models’ predictive power. Also, machine learning (ML) models’ predictive power for backcasting past years’ lost data is understudied compared to their forecasting abilities. This study thus aims to compare K-Nearest Neighbour regression (KNN), Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) models via a rolling-origin strategy, for forecasting and backcasting a blood demand data with missing values and outliers from a government hospital in Ghana. KNN performed well in forecasting blood demand (12.55% error); whereas, ELM achieved the highest backcasting power (19.36% error). Future studies can also employ ML algorithms as a good alternative for backcasting past values of time-series data that are time-reversible.

A Survey of AIOps Methods for Failure Management

Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O&amp;M teams that perform daily monitoring and repair operations, in contrast with the increasing demand for reliability and scalability of modern applications. For this reason, the study of automated and intelligent monitoring systems has recently sparked much interest across applied IT industry and academia. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to Machine Learning, AI, and Big Data. However, AIOps as a research topic is still largely unstructured and unexplored, due to missing conventions in categorizing contributions for their data requirements, target goals, and components. In this work, we focus on AIOps for Failure Management (FM), characterizing and describing 5 different categories and 14 subcategories of contributions, based on their time intervention window and the target problem being solved. We review 100 FM solutions, focusing on applicability requirements and the quantitative results achieved, to facilitate an effective application of AIOps solutions. Finally, we discuss current development problems in the areas covered by AIOps and delineate possible future trends for AI-based failure management.