Network Quality Research Articles

Monitoring the power flow of a low voltage distribution network using public lighting infrastructure

The production of renewable energy sources can change rapidly in a short period of time, making monitoring the power quality of Low Voltage Distribution Networks (LVDNs) one of the biggest challenges for Distribution System Operators (DSOs). An LVDN can overload some equipment if it does not monitors voltage profiles, voltage unbalance and reverse apparent power flow. Therefore, we propose a monitoring system that uses the Public Lighting Infrastructure (PLI), which is fed directly by the LVDN, to monitor the LVDN. It is based on remote management relays installed in the PLI, and replaces the installation of a power quality analyzer (PQ). The system has been empirically validated and the results show that the proposed system can identify reverse apparent power flow, profiles, and voltage unbalance with an average absolute error of 0.096% compared to a professional PQ analyzer. The proposed system can be a relevant contribution to the challenge of power quality monitoring, with cost savings; for example, DSOs can use the proposed system to access real-time information in the LVDN through a low-cost data acquisition model, without the need to send a team to install a PQ analyzer in the LVDN and then obtain the data.

Comparing search algorithms on the retrosynthesis problem.

In this article we try different algorithms, namely Nested Monte Carlo Search and Greedy Best First Search, on AstraZeneca's open source retrosynthetic tool : AiZynthFinder. We compare these algorithms to AiZynthFinder's base Monte Carlo Tree Search on a benchmark selected from the PubChem database and by Bayer's chemists. We show that both Nested Monte Carlo Search and Greedy Best First Search outperform AstraZeneca's Monte Carlo Tree Search, with a slight advantage for Nested Monte Carlo Search while experimenting on a playout heuristic. We also show how the search algorithms are bounded by the quality of the policy network, in order to improve our results the next step is to improve the policy network.

Analysis of Decentralization in the Central Government's Acquisition of Provincial Road Management

Infrastructure is a crucial aspect necessary for a state to perform its various functions. One type of infrastructure that plays a vital role in ensuring the smooth mobility of the general public is roads. Unfortunately, in Indonesia, the quality of road networks cannot be described as ideal. The substandard condition of these roads has drawn public concern, particularly following a social media post by a resident of Lampung Province criticizing the quality of roads there. In response to this public attention, the Central Government decided to assume the duties, authority, and responsibility for repairing 15 (fifteen) roads in Lampung Province. This decision has sparked new concerns among the public regarding the centralization of duties, authority, and responsibilities, which could undermine the spirit of decentralization. Based on the findings presented in this article, it can be asserted that the central government's assumption of the duties, authority, and responsibilities of local governments does not violate prevailing laws and regulations and is in accordance with them. Nevertheless, the phenomenon of the central government taking over these responsibilities warrants attention. This case highlights that the Lampung Provincial Government is not entirely capable of managing the tasks mandated by the Central Government. Therefore, from the findings of this case, it is evident that there is still a significant need for the maturation of Regional Governments in fulfilling the duties, authority, and responsibilities entrusted to them.

Analysis of 2G and 4G Network Quality in Solok City

Analysis of 2G and 4G Network Quality in Solok City

Newly identified Phonocardiography frequency bands for psychological stress detection with Deep Wavelet Scattering Network

The timely psychological stress detection can improve the quality of human life by preventing stress-induced behavioral and pathological consequences. This paper presents a novel framework that eliminates the need of Electrocardiography (ECG) signals-based referencing of Phonocardiography (PCG) signals for psychological stress detection. This stand-alone PCG-based methodology uses wavelet scattering approach on the data acquired from twenty-eight healthy adult male and female subjects to detect psychological stress. The acquired PCG signals are asynchronously segmented for the analysis using wavelet scattering transform. After the noise bands removal, the optimized segmentation length (L), scattering network parameters namely-invariance scale (J) and quality factor (Q) are utilized for computation of scattering features. These scattering coefficients generated are fed to K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost) classifier and the ten-fold cross validation-based performance metrics obtained are-accuracy 94.30 %, sensitivity 97.96 %, specificity 88.01 % and area under the curve (AUC) 0.9298 using XGBoost classifier for detecting psychological stress. Most importantly, the framework also identified two frequency bands in PCG signals with high discriminatory power for psychological stress detection as 270–290 Hz and 380–390 Hz. The elimination of multi-modal data acquisition and analysis makes this approach cost-efficient and reduces computational complexity.

Analisis komparatif aplikasi video conference terhadap kualitas jaringan nirkabel kampus berdasarkan variabel QoS

The quality of the WiFi network is very important in supporting the increasing use of video conferencing applications in educational environments. This research aims to analyze the comparative quality of WiFi networks in campus environments in video conference applications based on the Quality of Service (QoS) variable. Research was conducted on three video conferencing applications, namely Zoom, Google Meet, and Jitsi. Network quality is measured from four QoS variables including throughput, delay, packet loss, and jitter. The research results show that the application that runs best on the campus wireless network is Google Meet, with the highest average index among the other applications tested, namely 3.91 or Excellent. It is hoped that this research will provide useful guidance for educational institutions to improve video conferencing services, as well as ensure that students and staff can communicate remotely effectively without significant obstacles due to WiFi network quality problems. With a better understanding of network quality and application needs, institutions can make improvements to network infrastructure and provide recommendations for selecting video conferencing applications that suit existing network conditions.

Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards

Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards

Lyapunov-guided deep reinforcement learning for delay-aware online task offloading in MEC systems

With the arrival of 5G technology and the popularization of the Internet of Things (IoT), mobile edge computing (MEC) has great potential in handling delay-sensitive and compute-intensive (DSCI) applications. Meanwhile, the need for reduced latency and improved energy efficiency in terminal devices is becoming urgent increasingly. However, the users are affected by channel conditions and bursty computational demands in dynamic MEC environments, which can lead to longer task correspondence times. Therefore, finding an efficient task offloading method in stochastic systems is crucial for optimizing system energy consumption. Additionally, the delay due to frequent user–MEC interactions cannot be overlooked. In this article, we initially frame the task offloading issue as a dynamic optimization issue. The goal is to minimize the system’s long-term energy consumption while ensuring the task queue’s stability over the long term. Using the Lyapunov optimization technique, the task processing deadline problem is converted into a stability control problem for the virtual queue. Then, a novel Lyapunov-guided deep reinforcement learning (DRL) for delay-aware offloading algorithm (LyD2OA) is designed. LyD2OA can figure out the task offloading scheme online, and adaptively offload the task with better network quality. Meanwhile, it ensures that deadlines are not violated when offloading tasks in poor communication environments. In addition, we perform a rigorous mathematical analysis of the performance of Ly2DOA and prove the existence of upper bounds on the virtual queue. It is theoretically proven that LyD2OA enables the system to realize the trade-off between energy consumption and delay. Finally, extensive simulation experiments verify that LyD2OA has good performance in minimizing energy consumption and keeping latency low.

연구기관 재직자의 심리적 임파워먼트 연구: 성별의 다양성을 중심으로

by exploring the latent psychological empowerment profiles of research institution employees and examining the differences between groups according to gender. The quality of psychological empowerment vary among employees depending not only on individual factors but also on relational and institutional factors. Since psychological empowerment is directly linked to the personal growth and career development of employees, it is essential to examine the factors that influence it. We conducted a latent profile analysis to examine how psychological empowerment profiles differ. First, we identified five profiles based on the extent of research control and overall psychological empowerment in male samples, while four profiles were identified in female samples. Second, the quality of the social network, perceived career development support, and knowledge sharing significantly influenced the probability of belonging to each profile, particularly when the perceptions of these predictor variables are high, this suggests a higher likelihood of belonging to a group with high psychological empowerment. Finally, the group with high psychological empowerment, regardless of gender, was perceived as having higher organizational commitment, career satisfaction, and technical orientation. Based on these results, we discuss the implications for enhancing the psychological empowerment of employees at research institutions and aligning their individual career development with the organization's objectives.

A Novel Interval Type-2 Fuzzy Logic Controller for Shunt Active Power Filters in Power Quality Enhancement

This paper proposes a novel control strategy for enhancing power quality through the implementation of an Interval Type-2 Fuzzy Logic Controller (T2FLC) for Shunt Active Power Filters (SAPF). The presented method effectively addresses harmonic distortions introduced by non-linear loads, a common issue in modern electrical systems. Extensive simulations conducted in MATLAB/Simulink demonstrate the T2FLC's ability to regulate the DC bus voltage while significantly reducing Total Harmonic Distortion (THD). The method achieves superior performance compared to both traditional passive and active filtering techniques, reducing THD within the limits specified by IEEE 519-2014 standards. The results confirm the robustness and adaptability of the proposed approach across varying load conditions, establishing it as a reliable and scalable solution for improving power quality in complex electrical networks.

Identifying Station Importance in Urban Rail Transit Networks Using a Combination of Centrality and Time Reliability Measures: A Case Study in Beijing, China

Time reliability (TR) is a critical factor that affects the efficiency and service quality of the urban rail transit network (URTN). However, previous studies have not incorporated TR into the evaluation of URTN station importance, focusing instead on basic centrality measures. Therefore, this paper proposes a new metric of station-based TR for evaluating and ranking URTN station importance. The new metric in combination with traditional centrality measures was used by the weighted Technique for Order of Preference by Similarity to Ideal Solution (weighted TOPSIS) to identify the combined significance level of individual URTN station importance and rank them accordingly. To investigate the performance of this method, we exploit deliberate attacks on the top-ranked stations through different methods. A case study of Beijing’s URTN during the morning peak hour showed that the proposed method is generally a better indicator for identifying station importance in maintaining network connectivity. The case study also demonstrated the feasibility and validity of the model. This study can provide recommendations for the planning and operation of rail transit systems and can inform the effective design of station protection strategies.

Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality

Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality

A novel stochastic power flow calculation and optimal control method for microgrid based on multivariate stochastic factors fusion – Sensitivity

The stochasticity of power flow of distributed generations (DGs) and load in the microgrid has great influence on power flow distribution and voltage quality of the distribution network. For improving the voltage quality of the distribution network, the questions need to be further studied, which include the description of the stochasticity of the power flow in the microgrid and the impact of the microgrid into the distribution network on the power flow. Therefore, a novel stochastic power flow calculation and optimal control method for the microgrid based on multivariate stochastic factors fusion-sensitivity (MSFF-sensitivity) is proposed in this paper. Firstly, the multivariate stochastic factors fusion (MSFF) function is developed by using the probability density function to extract the stochasticity and correlation of power flow among different stochastic factors in the microgrid, which are effectively unified. Furthermore, the fusion-sensitivity (F-sensitivity) of the power flow in the microgrid integrated into the distribution network is constructed to accurately characterize the influence degree of various stochastic factors in the microgrid on the power flow of the distribution network. Based on this, the output power of the stochastic factor is adjusted to optimally control the power flow of the distribution network. Finally, the algorithm verification suggests that, compared with the conventional power flow methods, the method proposed in this paper is more suitable for the microgrid. The influence of stochastic power flow on the distribution network can be effectively reduced and the voltage quality of the distribution network can be improved by optimizing control of the power flow in the microgrid integrated into the distribution network.

Analysis of 2G and 4G Network Quality on Cross Roads in Lembah Gumanti District

Analysis of 2G and 4G Network Quality on Cross Roads in Lembah Gumanti District

Perancangan Jaringan Pada Client Server Menggunakan Aplikasi Cisco Packet Tracer

Currently, the development of computer networks is progressing rapidly. Along with this, user demands for network quality are increasing, both LAN and WAN. Computer networking is the transmission of data (data communication) from the source computer (sender) to the destination computer (receiver) in the form of bits on the sending medium. The Cisco Packet Tracer application allows you to use simulated network data to provide information about the connectivity status of computers on your network when network connectivity problems occur. The method used is to look for references from various internet sources such as Google and YouTube. Simulate your network system using the Cisco Packet Tracer application. Cisco Packet Tracer is network simulator software introduced by Cisco Systems which functions as a learning, training and research medium for computer network simulations.

Federated learning energy saving through client selection

Contemporary applications leverage machine learning models to optimize performance, often necessitating data transmission to a remote server for training. However, this approach entails significant resource consumption. A privacy concern arises, which Federated Learning addresses through a cyclical process involving in-device training (local model update) and subsequent reporting to the server for aggregation (global model update). In each iteration of this cycle, termed a communication round, a client selection component determines participant devices contributing to global model enhancement. However, existing literature inadequately addresses scenarios where optimized energy consumption is imperative. This paper introduces an Energy Saving Client Selection (ESCS) mechanism, considering decision criteria such as battery level, training time capacity, and network quality. As a pertinent use case, classification scenarios are utilized to compare the performance of ESCS against other state-of-the-art approaches. The findings reveal that ESCS effectively conserves energy while maintaining optimal performance. This research contributes to the ongoing discourse on energy-efficient client selection strategies within the domain of Federated Learning.

Self-Tuning of Signal Detection Level for Energy Detection-Based Carrier Sense in Low-Power Wide-Area Networks.

Carrier sense allows end devices to improve the communication quality through autonomous decentralization by consuming power. In particular, energy detection-based carrier sense can improve communication quality compared with peak detection-based carrier sense. To improve the trade-off between communication quality and energy consumption in low-power wide-area networks (LPWANs), this study proposes a self-tuning method for the signal detection level of an energy detection-based carrier sense, that is, the carrier sense level in sub-GHz band LPWANs. In the proposed method, the carrier sense level of each end device is determined based on the reception success probability of the acknowledgment packet, such that they become low carrier sense levels for an end device with low probability and high carrier sense levels for an end device with high probability. The proposed method enables autonomous decentralized derivation of the carrier sense level using only existing protocols. Numerical examples show that the proposed method can improve the performance of end devices with a high path loss to a gateway.

Enhancing power quality in contemporary utility systems: A comprehensive analysis of active power filters and control strategies

In contemporary power distribution systems, a substantial proportion of the loads not only utilize active power but also extract reactive power and harmonic currents from the AC source. The increased extraction of power components is a result of the widespread usage of fast-switching devices in utility systems. These devices contribute to an escalation in harmonic issues and problems related to reactive power. Furthermore, power quality disturbances have a range of negative repercussions on the utility system, impacting its overall performance and reliability.In order to address these difficulties and enhance the quality of electricity, Active electricity Filters (APFs) have been created and refined throughout the years. These filters are specifically engineered to identify sudden shifts in the reference current and counterbalance the harmonic currents produced by the voltage source inverter (VSI). The efficacy of these filters mostly relies on the selected current control procedures, which are essential for attaining adequate performance levels.This study provides a comprehensive examination of control mechanisms designed to improve power quality (PQ) in utility systems. Additionally, it examines the choice of filter components, taking into account both technical and economic factors. The paper explores the factors involved in selecting filters that are customized for individual applications, in order to achieve the best possible performance and cost efficiency.Based on an extensive analysis of more than 200 research publications, this study presents unique insights and discussions on the topic, providing readers with a full grasp of the difficulties and remedies associated with enhancing power quality in modern power distribution networks.

Design and Performance Evaluation of a Hybrid Active Power Filter Controller

This paper introduces a novel hybrid filter topology that combines passive and active components to enhance harmonic filtering and resonance damping in electrical power systems. The design integrates a three-phase two-level voltage-source converter with a double-tuned passive filter in parallel, significantly reducing the power rating and operational costs while maintaining good harmonic filtering performance and reactive current compensation. Double-tuned passive filters, compared to single-tuned ones, offer improved harmonic attenuation at multiple frequencies, enhancing overall system efficiency. Moreover, when used with the proposed hybrid filter topology, the double-tuned version allows for even lower dimensions of the active part, thereby further reducing system cost. A state-feedback controller is designed to enhance the performance of the hybrid filter, proving particularly effective in environments with complex impedance conditions. This paper also examines the impact of variations in passive component parameters, demonstrating the design’s robustness against potential deviations expected over the operational lifespan. The results indicate that the hybrid filter effectively mitigates harmonics and maintains operational stability under various transient conditions, as confirmed by analytical and simulation studies on a real industrial network model. These findings underline the hybrid filter’s potential to significantly improve power quality in modern electrical networks.

Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress

We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.