Network Engineering Research Articles

Review: the application of artificial intelligence in distribution network engineering field

The use of deep detection networks can help to enhance the management, reduce workload, and improve the efficiency and quality of dynamic defect detection in distribution network engineering. This involves classifying defects, researching and analyzing advanced deep detection networks and their applications in dynamic defect detection, reviewing existing research, and identifying key issues and their solutions. The paper also explores future research directions to provide useful references for future studies. Overall, the aim is to address potential safety and quality issues and mitigate hazards in the operation of distribution networks.

Gamification Design of Computer Network Virtual Laboratory Using SAGD-VL Framework

The Vocational High School curriculum has an orientation to the industrial world that improving the skill of students is needed to achieve curriculum goals. Learning activities in Vocational High Schools (in Indonesia called SMK) are mostly conducting practical activities in the laboratory to support skill improvement. Computer Networking and Telecommunications Engineering (in Indonesia called TJKT) studies network subjects from the basic level. In collecting data at SMKN 1 Cipanas it was found that laboratory facilities were inadequate so there were still many students unable to carry out practicums even though computer network practicum activities were very important activities at TJKT. Making gamification in a computer network virtual laboratory will help students to be able to do practicums anywhere and be able to meet the needs of practicum tools and materials. However, researchers have not found a gamification design in a computer network virtual laboratory that can help developers. From this problem, the researcher proposes a gamification design for a computer network virtual laboratory by using components from the SAGD-VL framework. SAGD-VL framework is a framework for building games in virtual laboratories. Testing is carried out by implementing the proposed design into a virtual computer network laboratory for wiring material then a pre-test and post-test are carried out to test the functionality of the proposed design for increasing ability and testing a questionnaire to test the increase in student motivation. After testing using the pre-test and post-test, it was found that the average increase in student scores was 32,353 with the highest increase being 60. So it can be said that the proposed design of a computer network virtual laboratory gamification using SAGD-VL can help increase students' understanding of practical learning. In addition, this proposed design can produce a virtual laboratory that can motivate students in practical learning.

Tunable single-photon nonreciprocal scattering and targeted router in a giant atom-waveguide system with chiral couplings

We investigate the single-photon scattering properties of a driven three-level giant atom chirally coupled to two waveguides simultaneously in both the Markovian and the non-Markovian regimes. It is shown that under the Markovian limit, the chiral photon-atom interactions enable nonreciprocal scattering in a single waveguide and targeted photon routing with a probability of 100% in two waveguides, while the presence of the driving field and the giant atom structure introduce a more tunable parameter to manipulate the single-photon scattering behaviors. We also examine how the non-reciprocity and routing capability are influenced by the imperfect chirality and the atomic dissipation. In the non-Markovian regime, we show that the scattering behaviors are more complicated. The non-Markovicity induced non-reciprocity and photon routing are demonstrated in this paper. We believe that those results have potential applications in quantum network engineering.

Pengenalan Bioinformatika, Aplikasi dan Database pada Siswa-Siswi SMK Laniang Makassar

Bioinformatics is one of the fields of science that is currently developing rapidly in society. This is due to the increase in molecular biology research, which has resulted in an unprecedented amount of biological data/information explosion. In its application, bioinformatics uses online software and databases. Community service activities have provided education about Introduction to Bioinformatics, Applications, and Databases to students of SMK Laniang Makassar, Tamalanrea District, Makassar City. This activity was attended by 23 participants from the Computer and Network Engineering (TKJ) department and nursing assistants. This activity aimed to provide knowledge about bioinformatics, databases, and the use of bioinformatics-based applications in various aspects. The activity was carried out for one day, on Wednesday, January 11, 2023, from 09:00 to 13:00 local time. The method used in this activity is by presenting bioinformatics introduction material, databases, and applications related to bioinformatics as well as direct practice using mobile phones, closed with a discussion/question and answer between participants and speakers at the end of the activity. It is known that the result of this service community activity was an increase in student knowledge related to bioinformatics, databases, and bioinformatics applications.

Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control

The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads. We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic (''opportunistic'') links between its nodes. In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks. We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of Duo's control and data plane.

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

Engineering amorphous SnO2 nanoparticles integrated into porous N-doped carbon matrix as high-performance anode for lithium-ion batteries

A facile in-situ preparation strategy is proposed to anchor amorphous SnO2 nanoparticles into the porous N-doped carbon (NC) matrix to fabricate amorphous composite powders (am-SnO2@p-NC), which feature the hierarchically interconnected and well interlaced porous configurations by employing polyvinylpyrrolidone as the soft template. The morphology regulation of the porous structure is precisely realized by adjusting the content of the template and the relationship between structural evolution and electrochemical performance of composite powders is accurately described to explore the optimal template dosage. The results indicate that the am-SnO2@p-NC-50 % composite electrode can deliver the improved lithium storage capacity and cycling performance when the content of the template is controlled at 0.500 g, in which the initial discharge specific capacity is about 1557.6 mAh/g and the reversible value retains at 841.5 mAh/g after 100 cycles at 100 mA/g. Meanwhile, the discharge specific capacity of 869.8 mAh/g is exhibited for the am-SnO2@p-NC-50 % composite electrode after 60 cycles when the current density is recovered from 2000 to 100 mA/g. Moreover, the Li+ ions diffusion coefficient up to about 5.5 × 10−12 cm2/s is calculated from galvanostatic intermittent titration technique tests, which can be partly ascribed to the conductive NC substrate that provides the high electronic conductivity, and partly to the highly porous structure that shortens Li+ ions transfer pathways and guarantees the fast reaction kinetics. Therefore, the hierarchically porous engineering of carbon networks to confine amorphous transition metal oxide nanoparticles is of great significance in the development of high-performance anode materials for lithium-ion batteries.

An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping

This study aims to develop an integrated approach of deep learning convolutional neural network (DL-CNN) and Google Earth Engine (GEE) platform for salt storm modeling and monitoring. First, we selected several ST's predisposing factors, including Land Surface Temperature (LST), soil salinity, AOD, NDWI and NDVI to train models. We then collected 957 Ground Control Points (GCPs) from the study area, which were randomly divided into training (70%) and validation (30%) datasets. Finally, ReLu, Cross-Entropy, and Adam employed as activation function, loss function and optimizer, respectively. Our findings demonstrate the efficiency of an integrated DL-CNN and GEE for monitoring salt storms (Overall Accuracy (OA) = 0.93.02, 0.92.99, 0.93.88, and 0.92.01 for years 2002, 2010, 2015 and 2021, respectively). The results also show an increase in the frequency of salt storm in the study area from 2002 to 2021. Such approach is a promising step toward understanding, controlling, and managing salt storms and recommend salt storm spatial monitoring in other favored areas with similar environmental conditions. In addition, the results of this study provide critical insights into the environmental impacts of the Lake Urmia drought and its intensive environmental impacts on the human health and wellbeing of the residents.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic. As an important part of this field, intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers. In particular, it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks. For this purpose, we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks. First, individual interactions are described by a modified activity-driven network (ADN) model. Then, we establish a novel node-based susceptible–infected–recovered–susceptible (SIRS) model to characterize the information diffusion dynamics. On these bases, three synergetic intervention strategies are formulated. Second, we derive the critical threshold of the controlled-SIRS system via stability analysis. Accordingly, we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget. Third, we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense, in which the optimal intervention inputs are obtained through optimal control theory and a forward–backward sweep algorithm. Finally, extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

A Data-Driven Model to Predict Constitutive and Failure Behavior of Elastomers Considering the Strain Rate, Temperature, and Filler Ratio

Abstract This new machine-learned (ML) constitutive model for elastomers has been developed to capture the dependence of elastomer behavior on loading conditions such as strain rate and temperature, as well as compound morphology factors such as filler percentage and crosslink density. It is based on our recent new generation of machine-learning algorithms known as conditional neural networks (CondNNs) Ghaderi et al. (2020, “A Physics-Informed Assembly of Feed-Forward Neural Network Engines to Predict Inelasticity in Cross-Linked Polymers,” Polymers, 12(11), p. 2628), and uses data-infused knowledge-driven machine-learned surrogate functions to describe the quasi-static response of polymer batches in cross-linked elastomers. The model reduces the 3D stress-strain mapping space into a 1D space, and this order reduction significantly reduces the training cost by minimizing the search space. It is capable of considering the effects of loading conditions such as strain rate, temperature, and filler percentage in different deformation states, as well as enjoying a high training speed and accuracy even in complicated loading scenarios. It can be used for advanced implementations in finite element programs due to its computing efficiency, simplicity, correctness, and interpretability. It is applicable to a variety of soft materials, including soft robotics, soft digital materials (DMs), hydrogels, and adhesives. This model has a distinct advantage over existing phenomenological models as it can capture strain rate and temperature dependency in a much more comprehensive way.

The photophysics of distorted nanographenes: Ultra-slow relaxation dynamics, memory effects, and delayed fluorescence

The controlled deformation and engineering of the sp2 carbon network in atomically-precise nanographenes, and their substantially larger size as compared to typical optical dyes, opens new opportunities for the modulation of optical and electronic properties, but the peculiar photophysics of these systems is still poorly understood. Here, through a detailed comparative study of two well-defined distorted nanographenes, we show that they can exhibit interesting photophysical features, such as triplet-triplet annihilation delayed fluorescence, ultra-slow excited state dynamics, and excitation-wavelength memory effects on the nanosecond and sub-nanosecond relaxation cascades. Some of these behaviours are highly unexpected and strongly deviate from the archetypal behaviour of typical optical dyes. The distortion of the sp2 network was obtained by means of a non-hexagonal ring and a helicene unit, introduced by taking advantage of the high control provided by specific bottom-up organic synthesis. We believe the rich conformational landscape provided by the combination of the saddle-shaped curvature due to the heptagon ring and the helicene edge is at the heart of the uncommon photophysics.

Network regulation meets substrate modification chemistry.

Biochemical networks are at the heart of cellular information processing. These networks contain distinct facets: (i) processing of information from the environment via cascades/pathways along with network regulation and (ii) modification of substrates in different ways, to confer protein functionality, stability and processing. While many studies focus on these factors individually, how they interact and the consequences for cellular systems behaviour are poorly understood. We develop a systems framework for this purpose by examining the interplay of network regulation (canonical feedback and feed-forward circuits) and multisite modification, as an exemplar of substrate modification. Using computational, analytical and semi-analytical approaches, we reveal distinct and unexpected ways in which the substrate modification and network levels combine and the emergent behaviour arising therefrom. This has important consequences for dissecting the behaviour of specific signalling networks, tracing the origins of systems behaviour, inference of networks from data, robustness/evolvability and multi-level engineering of biomolecular networks. Overall, we repeatedly demonstrate how focusing on only one level (say network regulation) can lead to profoundly misleading conclusions about all these aspects, and reveal a number of important consequences for experimental/theoretical/data-driven interrogations of cellular signalling systems.

Distributed Multicast Traffic Engineering for Multi-Domain Software-Defined Networks

Previous research on SDN multicast traffic engineering mainly focused on intra-domain optimization. However, the main traffic on the Internet is inter-domain, and the selection of border nodes and sharing of network information between domains are usually distributed but ignored in previous works. In this article, we explore multi-domain online distributed multicast traffic engineering (MODMTE). To effectively solve MODMTE, we first prove that MODMTE is inapproximable within <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> , which indicates that it is impossible to find any algorithm with a ratio better than <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> for MODMTE, and <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> is the maximum number of destinations for a multicast tree. Then, we design a <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> -competitive distributed algorithm with the ideas of Domain Tree, Dual Candidate Forest Construction, and Forest Rerouting to achieve the tightest performance bound for MODMTE. Experiments on a real SDN with YouTube traffic manifest that the proposed distributed algorithm can reduce more than 30% of the total cost of bandwidth consumption and rule updates for multicast tree rerouting compared with the state-of-the-art algorithms.

PRE-NAS: Evolutionary Neural Architecture Search With Predictor

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space. Prediction of the performance of a network can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, predictor-assisted evolutionary NAS (PRE-NAS) which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous. This circumvents bias and leads to more accurate predictions. Extensive experiments on the NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, a competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet, respectively.

Study on sound source localization inside a structure using a domain transfer model for real-world adaption of a trained model

Sound source localization (SSL) is important to reduce noise of products such as machinery and electrical appliances. Currently, SSL methods have been proposed that use the correlation of time-frequency signals of sound waves observed by multiple microphones. However, the application of these SSL methods are limited to the case where the acoustic signals can be directly observed. In the case of estimating the location of a sound source inside a structure from outside the structure, these methods are not applicable because the acoustic signal is observed as indirect sound. An SSL method using deep neural network (DNN) and computer-aided engineering (CAE) was proposed to estimate sound sources inside structures. This method successfully estimates the location of sound sources inside a structure from signals observed by accelerometers installed on the outer surface of the structure in both CAE and real domains. Nevertheless, the proposed method still has the challenge of adapting DNNs trained in CAE domain to real domain. In this research, deep convolution auto-encoder (DCAE) was used as a domain transformation model built from CAE and actual experimental data. DCAE contributed to the improvement of the SSL performance of the trained DNN over the baseline.

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is a recorded from lost-cost yet effective uncooled LWIR thermal camera, mounted stand-alone and on an electric vehicle to minimize mechanical vibrations. State-of-the-art YOLO-V5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.

Energy Efficiency In Optical-Wireless Access Network Using Dynamic Bandwidth Allocation

High capacity networks are needed for enormous quantity of internet traffic, which is driven by both the exponential growth of the number of people using the internet and the ongoing introduction of new high-bandwidth requiring applications. Combining optical and wireless technologies is a new and promising access network solution. The energy efficiency of telecommunication networks is therefore attracting increasing attention today than in the past. This study is aimed at evaluating energy efficiency of optical-wireless access network which combines Passive Optical Network (PON) and Long Term Evolution - Advanced (LTE-A) networks into one. The network was designed using Optimum Network Engineering Tool (OPNET) modeler software. Traffic was generated using inbuilt tools provided in the OPNET modeler software. Dynamic Bandwidth Allocation (DBA) algorithm was applied on the network and simulation was carried out using the simulation tool provided by OPNET to evaluate the effect of the DBA algorithm on the energy consumption and efficiency in the network. The findings showed energy utilization of 430 watt-second when DBA algorithm was applied on the access network as against 700 watt-second when it was not activated. This shows a 38.6% energy conservation. It was concluded that with the application of energy saving techniques to combined optical-wireless access network, substantial energy could be conserved even in the presence of heavy traffic without compromising the quality of service of the access network.

Study on the Calculation Method of Typical Distribution Network Engineering Works in Xinjiang

At present, the investment focus of the power grid is from the main network to the distribution network, and the distribution network engineering projects, as an important means to improve the security and stability level of the power grid, the power grid transmission capacity, the equipment health level and reduce the power supply energy consumption, is also a necessary guarantee for the power grid companies to improve the economic benefits, social benefits, and achieve sustainable development. With the continuous improvement of the investment scale of the distribution network, there are still prominent problems, such as insufficient precision of the distribution network cost control and extensive management. Based on the current situation of management and control and the actual situation of typical distribution network project construction, this paper puts forward a calculation method based on the reasonable range of engineering quantity of Chebyshev inequality, which can provide a reference for the subsequent construction of similar projects, and further improve the standardization and standardization level of the company’s distribution network project cost management.

WORKSHOP PENGGUNAAN MIKROKONTROLER BAGI GURU DI SMK NEGERI 1 KLATEN

Vocational High School is one of the educational institutions that is responsible for creating human resources who have the ability, skills and expertise, so that graduates are ready to enter the world of work. SMK Negeri 1 Klaten, Central Java, especially lecturers in the department of computer and telecommunications network engineering, has the hope that it is necessary to explore the material and develop learning in the practical use of the Arduino microcontroller. The purpose of this activity is to implement the use of microcontrollers on devices that require automatic controllers such as machine controllers, IoT relay control, home security systems, and also other electronic equipment. The PKM activity method is carried out in stages: observation &amp; requirements analysis, communication &amp; dialogue, workshop, and evaluation. As a result of this activity, the workshop participants participated actively and looked enthusiastic in the activity by diligently asking questions. In the evaluation of the workshop, the results of the practicum were obtained: 75% of the participants succeeded well, and 25% of the participants with good enough results and were able to carry out the practicum independently.

An Analysis of Students’ Difficulties on Reading Text in Finding Main Idea at the Tenth Graders of SMK Darul A’mal Metro

Reading is about looking through the text for details. The context of this research is explained about the students’ difficulties on reading text in finding main idea. The objective of this research is to know the difficulties that faced by the students in reading text, the factors of the students’ difficulties in finding the main idea in text, the students overcome the difficulties in finding main idea in the text at the tenth graders of SMK Darul A’mal Metro. The research design of this research was qualitative research. The data collecting technique of this research was documentation, observation, and interview. The sample of this research was ten students of Computer and Network Engineering in e class at SMK Darul A’mal Metro. The result of this research shows that the students’ difficulties in reading text at the tenth graders of SMK Darul A’mal Metro is the students still difficult to find the main idea in English text. The factors that caused the students difficult to find the main idea in text are students need a long time in answering question of main idea question, students do not focus while answering question, and students also do not know the meaning of the sentence. Also most of students at the tenth graders of SMK Darul A’mal Metro are do not know what is main idea, and the students do not know how to find the main idea in the text. Another reason of the factor that make the students difficult to find the main idea in English text because of the long sentence, limit vocabulary of students, and the students’ lack of reading strategy. The students’ solving of the difficulties in finding main idea in the text are read again the text, ask the teacher, and ask friends for a help.&#x0D; Keywords: Students’ Difficulties Reading, Finding Main Idea, Reading Text