Search results for: computing network control systems

Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: C. Pacheco, A. Cipriano

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A new concept of response system is proposed for filling the gap that exists in reducing vulnerability during immediate response to natural disasters. Real Time Early Response Systems (RTERSs) incorporate real time information as feedback data for closing control loop and for generating real time situation assessment. A review of the state of the art works that fit the concept of RTERS is presented, and it is found that they are mainly focused on manmade disasters. At the same time, in response phase of natural disaster management many works are involved in creating early warning systems, but just few efforts have been put on deciding what to do once an alarm is activated. In this context a RTERS arises as a useful tool for supporting people in their decision making process during natural disasters after an event is detected, and also as an innovative context for applying well-known automation technologies and automatic control concepts and tools.

Keywords: disaster management, emergency response system, natural disasters, real time

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Authors: Pichamon Chansuchai

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The objective of this research were to investigate electronic payment system on the internet and offer the guidelines for proper internal control of the payment system based on international standard security control (ISO/IEC 17799:2005),in a case study of payment of the internet, Thailand. The guidelines covered five important areas: (1) business requirement for access control, (2) information systems acquisition, development and maintenance, (3) information security incident management, (4) business continuity management, and (5) compliance with legal requirement. The findings from this qualitative study revealed the guidelines for proper internet control that were more reliable and allow the same line of business to implement the same system of control.

Keywords: audit, best practice, internet, payment

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Ahmed N. Shmroukh, I. M. S. Taha, A. M. Abdel-Ghany, M. Attalla

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Fog systems application for cooling and humidification is still limited, although these systems require less initial cost compared with that of other cooling systems such as pad-and-fan systems. The undesirable relative humidity and air temperature inside the space which have been cooled or humidified are the main reasons for its limited use, which results from the poor control of fog systems. Any accurate control system essentially needs air dry bulb temperature as an input parameter. Therefore, the air dry-bulb temperature in the space needs to be measured accurately. The Scope of the present work is the separation of the fog droplets from the air in a fogged space to measure the air dry bulb temperature accurately. The separation is to be done in a small device inside which the sensor of the temperature measuring instrument is positioned. Vortex separator will be designed and used. Another reference device will be used for measuring the air temperature without separation. A comparative study will be performed to reach at the best device which leads to the most accurate measurement of air dry bulb temperature. The results showed that the proposed devices improved the measured air dry bulb temperature toward the correct direction over that of the free junction. Vortex device was the best. It respectively increased the temperature measured by the free junction in the range from around 2 to around 6°C for different fog on-off duration.

Keywords: fog systems, measuring air dry bulb temperature, temperature measurement, vortex separator

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: transportation networks, freight delivery, data flow, monitoring, e-services

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Authors: Rami A. Maher, Ibraheem K. Ibraheem

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This work presents a comparison study between the state-space and polynomial methods for the design of the robust governor for load frequency control of steam turbine power systems. The robust governor is synthesized using the two approaches and the comparison is extended to include time and frequency domains performance, controller order, and uncertainty representation, weighting filters, optimality and sub-optimality. The obtained results are represented through tables and curves with reasons of similarities and dissimilarities.

Keywords: robust control, load frequency control, steam turbine, H∞-norm, system uncertainty, load disturbance

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Authors: Zhaocheng Shang

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In order to study the topic of cities crossing rivers, a Four-Dimensional Analysis Method consisting of timeline, X-axis, Y-axis, and Z-axis is proposed. Policies, plans, and their implications are summarized and researched along with the timeline. The X-axis is the direction which is parallel to the river. The research area was chosen because of its important connection function. It is proposed that more surface water network should be built because of the ecological orientation of the research area. And the analysis of groundwater makes it for sure that the proposal is feasible. After the blue water network is settled, the green landscape network which is surrounded by it could be planned. The direction which is transversal to the river (Y-axis) should run through the transportation axis so that the urban texture could stretch in an ecological way. Therefore, it is suggested that the work of the planning bureau and river bureau should be coordinated. The Z-axis research is on the section view of the river, especially on the Yellow River’s special feature of being a perched river. Based on water control safety demands, river parks could be constructed on the embankment buffer zone, whereas many kinds of ornamental trees could be used to build the buffer zone. City Crossing River is a typical case where we make use of landscaping to build a symbiotic relationship between the urban landscape architecture and the environment. The local environment should be respected in the process of city expansion. The planning order of "Benefit- Flood Control Safety" should be replaced by "Flood Control Safety - Landscape Architecture- People - Benefit".

Keywords: blue-green landscape network, city crossing river, four-dimensional analysis method, planning order

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Authors: Malinwo Estone Ayikpa

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Electric power systems are likely to operate with minimum losses and voltage meeting international standards. This is made possible generally by control actions provide by automatic voltage regulators, capacitors and transformers with on-load tap changer (OLTC). With the development of photovoltaic (PV) systems technology, their integration on distribution networks has increased over the last years to the extent of replacing the above mentioned techniques. The conventional analysis and simulation tools used for electrical networks are no longer able to take into account control actions necessary for studying distributed PV generation impact. This paper presents an unbalanced optimal power flow (OPF) model that minimizes losses with association of active power generation and reactive power control of single-phase and three-phase PV systems. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. The unbalance OPF is formulated by current balance equations and solved by primal-dual interior point method. Several simulation cases have been carried out varying the size and location of PV systems and the results show a detailed view of the impact of PV distributed generation on distribution systems.

Keywords: distribution system, loss, photovoltaic generation, primal-dual interior point method

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Shashank Gupta, Iteash Agarwal, Vijayalaxmi Mogiligidda, Rajesh Kumar Krishnan, Sruthi Chennuri, Deepika Aggarwal, Anwesha Hoodati, Sheroy Cooper, Ranjan, Mohammad Bilal Sheik, Bhavya K. M., Manasa Hegde, M. Naveen Krishna, Amit Kumar Chauhan, Mallikarjun Korrapati, Sumit Singh, J. B. Singh, Sunil Sud, Sunil Gupta, Sidhartha Pant, Sankar, Neha Agrawal, Ashish Ranjan, Piyush Mohapatra, Roopak T., Arsh Ahmad, Nanjunda M., Dilip Singh

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Major research interests on quantum key distribution (QKD) are primarily focussed on increasing 1. point-to-point transmission distance (1000 Km), 2. secure key rate (Mbps), 3. security of quantum layer (device-independence). It is great to push the boundaries on these fronts, but these isolated approaches are neither scalable nor cost-effective due to the requirements of specialised hardware and different infrastructure. Current and future QKD network requires addressing different sets of challenges apart from distance, key rate, and quantum security. In this regard, we present ChaQra -a sub-quantum network with core features as 1) Crypto agility (integration in the already deployed telecommunication fibres), 2) Software defined networking (SDN paradigm for routing different nodes), 3) reliability (addressing denial-of-service with hybrid quantum safe cryptography), 4) upgradability (modules upgradation based on scientific and technological advancements), 5) Beyond QKD (using QKD network for distributed computing, multi-party computation etc). Our results demonstrate a clear path to create and accelerate quantum secure Indian subcontinent under the national quantum mission.

Keywords: quantum network, quantum key distribution, quantum security, quantum information

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Gagandeep Kaur, Sonal Chawla

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In a Cloud Computing environment, resource provisioning, resource allocation and resource scheduling is the most complex issues these days. Cloud User expects the best resource utilization and Cloud Provider expects revenue maximization by considering budget and time constraints. In this research paper, Resource Management Framework has been proposed to allocate the resources to Cloud Users and Cloud Providers in Cloud environment. The main aim of the proposed work is to provide the resources and services to Cloud Providers and Cloud Users in an efficient and effective manner. The proposed framework has been simulated and tested using the CloudSim simulator tool.

Keywords: cloud computing, resource allocation, auction, provisioning

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Authors: Osama Abdallah Mohammed Enan, Amin Babiker A/Nabi Mustafa

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The aim of this study is to evaluate and analyze different parameters of WCDMA (wideband code division multiplication). Moreover, this study also incorporates brief yet throughout analysis of WCDMA’s components as well as its internal architecture. This study also examines different power controls. These power controls may include open loop power control, closed or inner group loop power control and outer loop power control. Different handover techniques or methods of WCDMA are also illustrated in this study. These handovers may include hard handover, inter system handover and soft and softer handover. Different duplexing techniques are also described in the paper. This study has also presented an idea about different parameters of WCDMA that leads the system towards QoS issues. This may help the operator in designing and developing adequate network configuration. In addition to this, the study has also investigated various parameters including Bit Energy per Noise Spectral Density (Eb/No), Noise rise, and Bit Error Rate (BER). After simulating these parameters, using MATLAB environment, it was investigated that, for a given Eb/No value the system capacity increase by increasing the reuse factor. Besides that, it was also analyzed that, noise rise is decreasing for lower data rates and for lower interference levels. Finally, it was examined that, BER increase by using one type of modulation technique than using other type of modulation technique.

Keywords: duplexing, handover, loop power control, WCDMA

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Authors: Muhammad Muhammad Suleiman

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Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

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Authors: Aliasghar Arab

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Autonomous robots interacting with humans, as safety-critical nonlinear control systems, are complex closed-loop cyber-physical dynamical machines. Keeping these intelligent yet complicated systems safe and smooth during their operations is challenging. The aim of the safe predictive output feedback linearization control synthesis is to design a novel controller for smooth trajectory following while unsafe situations must be avoided. The controller design should obtain a linearized output for smoothness and invariance to a safety subset. Inspired by finite-horizon nonlinear model predictive control, the problem is formulated as constrained nonlinear dynamic programming. The safety constraints can be defined as control barrier functions. Avoiding unsafe maneuvers and performing smooth motions increases the predictability of the robot’s movement for humans when robots and people are working together. Our results demonstrate the proposed output linearization method obeys the safety constraints and, compared to existing safety-guaranteed methods, is smoother and performs better.

Keywords: robotics, collaborative robots, safety, autonomous robots

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Authors: Anand Kishore Kola, G. Uday Bhaskar Babu, Kotturi Ajay Kumar

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The modern dynamic systems used in industries are complex in nature and hence the fractional order controllers have been contemplated as a fresh approach to control system design that takes the complexity into account. Traditional integer order controllers use integer derivatives and integrals to control systems, whereas fractional order controllers use fractional derivatives and integrals to regulate memory and non-local behavior. This study provides a method based on the maximumsensitivity (Ms) methodology to discover all resilient fractional filter Internal Model Control - proportional integral derivative (IMC-PID) controllers that stabilize the closed-loop system and deliver the highest performance for a time delay system with a Smith predictor configuration. Additionally, it helps to enhance the range of PID controllers that are used to stabilize the system. This study also evaluates the effectiveness of the suggested controller approach for minimum phase system in comparison to those currently in use which are based on Integral of Absolute Error (IAE) and Total Variation (TV).

Keywords: modern dynamic systems, fractional order controllers, maximum-sensitivity, IMC-PID controllers, Smith predictor, IAE and TV

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Authors: Hossein Abbasi

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The stability is an important topic to plan and manage the energy in the microgrids as the same as the conventional power systems. The voltage and frequency stability is one of the most important issues recently studied in microgrids. The objectives of this paper are the modelling and designing of the components and optimal controllers for the voltage and frequency control of the AC/DC hybrid microgrid under the different disturbances. Since the PI controllers have the advantages of simple structure and easy implementation, so they are designed and modeled in this paper. The harmony search (HS) algorithm is used to optimize the controllers’ parameters. According to the achieved results, the PI controllers have a good performance in voltage and frequency control of the microgrid.

Keywords: frequency control, HS algorithm, microgrid, PI controller, voltage control

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Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Luca Garbarino, Urbano Tancredi, Domenico Accardo, Michele Grassi, Giancarmine Fasano, Anna Elena Tirri

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The development of guidance, navigation and control algorithms and avionic procedures requires the disposability of suitable analysis and verification tools, such as simulation environments, which support the design process and allow detecting potential problems prior to the flight test, in order to make new technologies available at reduced cost, time and risk. This paper presents a simulation environment for avionic software development and qualification, especially aimed at equipment for general aviation aircrafts and unmanned aerial systems. The simulation environment includes models for short and medium-range radio-navigation aids, flight assistance systems, and ground control stations. All the software modules are able to simulate the modeled systems both in fast-time and real-time tests, and were implemented following component oriented modeling techniques and requirement based approach. The paper describes the specific models features, the architectures of the implemented software systems and its validation process. Performed validation tests highlighted the capability of the simulation environment to guarantee in real-time the required functionalities and performance of the simulated avionics systems, as well as to reproduce the interaction between these systems, thus permitting a realistic and reliable simulation of a complete mission scenario.

Keywords: ADS-B, avionics, NAVAIDs, real-time simulation, TCAS, UAS ground control station

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Authors: Joaquin Navarro Perales, Ana Lidia Franzoni Velázquez, Francisco Cervantes Pérez

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The Mexican educational system faces diverse challenges related with the quality and coverage of education. The development of Intelligent Tutoring Systems (ITS) may help to solve some of them by helping teachers to customize their classes according to the performance of the students in online courses. In this work, we propose the adaptation of a functional ITS based on Bloom’s taxonomy called Sistema de Apoyo Generalizado para la Enseñanza Individualizada (SAGE), to measure student’s metacognition and their emotional response based on Marzano’s taxonomy. The students and the system will share the control over the advance in the course, so they can improve their metacognitive skills. The system will not allow students to get access to subjects not mastered yet. The interaction between the system and the student will be implemented through Natural Language Processing techniques, thus avoiding the use of sensors to evaluate student’s response. The teacher will evaluate student’s knowledge utilization, which is equivalent to the last cognitive level in Marzano’s taxonomy.

Keywords: intelligent tutoring systems, student modelling, metacognition, affective computing, natural language processing

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Authors: Byung Cheol Kim

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This study explores a method for the signal-noise separation challenge in project control, focusing on the limitations of traditional deterministic approaches that use single-point performance metrics to predict project outcomes. We detail how traditional methods often overlook future uncertainties, resulting in tracking biases when reliance is placed solely on immediate data without adjustments for predictive accuracy. Our investigation led to the development of the Predictive Tracking Project Control (PTPC) framework, which incorporates network simulation and Bayesian control models to adapt more effectively to project dynamics. The PTPC introduces controlled disturbances to better identify and separate tracking biases from useful predictive signals. We will demonstrate the efficacy of the PTPC with examples, highlighting its potential to enhance real-time project monitoring and decision-making, marking a significant shift towards more accurate project management practices.

Keywords: predictive tracking, project control, signal-noise separation, Bayesian inference

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Authors: Aamir Shahzad, Hubert Roth

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This paper is presenting the bilateral telecontrol of AutoMerlin Mobile Robot having communication delay. Passivity Observers has been designed to monitor the net energy at both ports of a two port network and if any or both ports become active making net energy negative, then the passivity controllers dissipate the proper energy to make the overall system passive in the presence of time delay. The environment force is modeled and sent back to human operator so that s/he can feel it and has additional information about the environment in the vicinity of mobile robot. The experimental results have been presented to show the performance and stability of bilateral controller. The results show the whenever the passivity observers observe active behavior then the passivity controller come into action to neutralize the active behavior to make overall system passive.

Keywords: bilateral control, human operator, haptic device, communication network, time domain passivity control, passivity observer, passivity controller, time delay, mobile robot, environment force

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Authors: Muhammad Mohsin, Fawad Ahmad, Fatima Batool, Muhammad Kaab Zarrar

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The integration of the Internet of Things (IoT) and edge computing devices offers a transformative approach to electronic waste management, particularly in the dismantling of printed circuit boards (PCBs). This paper explores how these technologies optimize operational efficiency and improve environmental sustainability by addressing challenges such as data security, interoperability, scalability, and real-time data processing. Proposed solutions include advanced machine learning algorithms for predictive maintenance, robust encryption protocols, and scalable architectures that incorporate edge computing. Case studies from leading e-waste management facilities illustrate benefits such as improved material recovery efficiency, reduced environmental impact, improved worker safety, and optimized resource utilization. The findings highlight the potential of IoT and edge computing to revolutionize e-waste dismantling and make the case for a collaborative approach between policymakers, waste management professionals, and technology developers. This research provides important insights into the use of IoT and edge computing to make significant progress in the sustainable management of electronic waste

Keywords: internet of Things, edge computing, waste PCB disassembly, electronic waste management, data security, interoperability, machine learning, predictive maintenance, sustainable development

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Mehdi Naserian, Mohammad Jooshaki, Mahmud Fotuhi-Firuzabad, Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee

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In recent years, the development of communication infrastructure and smart meters have facilitated the utilization of demand-side resources which can enhance stability and economic efficiency of power systems. Direct load control programs can play an important role in the utilization of demand-side resources in the residential sector. However, investments required for installing control equipment can be a limiting factor in the development of such demand response programs. Thus, selection of consumers with higher potentials is crucial to the success of a direct load control program. Heating, ventilation, and air conditioning (HVAC) systems, which due to the heat capacity of buildings feature relatively high flexibility, make up a major part of household consumption. Considering that the consumption of HVAC systems depends highly on the ambient temperature and bearing in mind the high investments required for control systems enabling direct load control demand response programs, in this paper, a recent solution is presented to uncover consumers with high air conditioner demand among large number of consumers and to measure the demand response potential of such consumers. This can pave the way for estimating the investments needed for the implementation of direct load control programs for residential HVAC systems and for estimating the demand response potentials in a distribution system. In doing so, we first cluster consumers into several groups based on the correlation coefficients between hourly consumption data and hourly temperature data using K-means algorithm. Then, by applying a recent algorithm to the hourly consumption and temperature data, consumers with high air conditioner consumption are identified. Finally, demand response potential of such consumers is estimated based on the equivalent desired temperature setpoint changes.

Keywords: communication infrastructure, smart meters, power systems, HVAC system, residential HVAC systems

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Authors: Wiseborn Manfe Danquah, D. Turgay Altilar

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Vehicular Ad-hoc Network (VANET) is an integral component of Intelligent Transport Systems (ITS) that has enjoyed a lot of attention from the research community and the automotive industry. This is mainly due to the opportunities and challenges it presents. Vehicular Ad-hoc Network being a class of Mobile Ad-hoc Networks (MANET) has all the security concerns existing in traditional MANET as well as new security and privacy concerns introduced by the unique vehicular communication environment. This paper provides a survey of the possible attacks in vehicular environment, as well as security and privacy concerns in VANET. It also provides an insight into the development of a comprehensive cloud framework to provide a more robust and secured communication among vehicular nodes and road side units. Our proposal, a Metropolitan Based Public Interconnected Vehicular Cloud (MIVC) infrastructure seeks to provide a more reliable and secured vehicular communication network.

Keywords: mobile Ad-hoc networks, vehicular ad hoc network, cloud, ITS, road side units (RSU), metropolitan interconnected vehicular cloud (MIVC)

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

Abstract:

In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Farnaz Fotovvatikhah, Javad Akbari

Abstract:

Test time of a test architecture is an important factor which depends on the architecture's delay and test patterns. Here a new architecture to store the test results based on network on chip is presented. In addition, simple analytical model is proposed to calculate link test time for built in self-tester (BIST) and external tester (Ext) in multiprocessor systems. The results extracted from the model are verified using FPGA implementation and experimental measurements. Systems consisting 16, 25, and 36 processors are implemented and simulated and test time is calculated. In addition, BIST and Ext are compared in terms of test time at different conditions such as at different number of test patterns and nodes. Using the model the maximum frequency of testing could be calculated and the test structure could be optimized for high speed testing.

Keywords: test, nano on-chip network, JTAG, modelling

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