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Journal articles on the topic 'Detector logic network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Sarath kumar, A., M. Durga Kaveri, K. B.V Bhargavi, N. Naga Swetha, and K. Priyanka. "Efficient Routing In Wsn Using Enhanced Fuzzy Logic." International Journal of Engineering & Technology 7, no. 2.17 (2018): 108. http://dx.doi.org/10.14419/ijet.v7i2.17.11719.

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In order to gather info additional precisely, wireless detector networks (WSNs) square measure divided into clusters. The cluster provides a good merit to make longer the period of WSNs. Topical clump comes close to usually use 2 methods: choosing cluster heads with additional enduring energy, and turning cluster heads sporadically, to distribute the energy consumption among nodes in every cluster and extend the network period. However, most of the previous algorithms haven't thought of the expected residual energy, that is, that the predicated left behind energy for being hand-picked as a clu

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Tang, Xiaoyu, Sijia Xu, and Hui Ye. "Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM." Applied Sciences 13, no. 1 (2022): 581. http://dx.doi.org/10.3390/app13010581.

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In network edge computing scenarios, close monitoring of network data and anomaly detection is critical for Internet services. Although a variety of anomaly detectors have been proposed by many scholars, few of these take into account the anomalies of the data in business logic. Expert labeling of business logic exceptions is also very important for detection. Most exception detection algorithms focus on problems, such as numerical exceptions, missed exceptions and false exceptions, but they ignore the existence of business logic exceptions, which brings a whole new challenge to exception dete

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Erdinç, Gizem, Chiara Colombaroni, and Gaetano Fusco. "Two-Stage Fuzzy Traffic Congestion Detector." Future Transportation 3, no. 3 (2023): 840–57. http://dx.doi.org/10.3390/futuretransp3030047.

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This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague noti

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Villarreal Campos, Carlos Alberto, and Eduardo Caicedo Bravo. "Computational Intelligence Techniques Applied to Coagulant Estimation Models in Water Purification Process." Revista Facultad de Ingeniería Universidad de Antioquia, no. 69 (January 20, 2014): 205–15. http://dx.doi.org/10.17533/udea.redin.18150.

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Four coagulant estimation models in water purification process are presented. For its developing computational intelligence techniques are used, which include neural networks, fuzzy logic (Sugeno and Mamdani type) and ANFIS structures. The methodology described is based on the operator's experience extraction from operational historical data (in neural network, fuzzy logic Sugeno type and ANFIS case), and the linguistic information given by an experimented plant operator (in fuzzy logic Mamdani type case). According to the reached results, by applying some of this models in a control system st

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Chen, Xiyue, and Jianmin Pang. "Temporal Logic-Based Artificial Immune System for Intrusion Detection." Wireless Communications and Mobile Computing 2022 (March 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/4685754.

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Artificial immune system has made many contributions to network security areas, but there are still problems of insufficient detection range and high time cost. This paper presents a Hybrid Detector (HD) mechanism in which temporal logic antigens are proposed. The HD mechanism is constructed by using the advantage of temporal logic to describe time-varying behaviors in system. Finally, simulation experiments were carried out on KDD99 and NSL-KDD datasets. Experimental results show that the proposed method can extend the detection range and improve the detection rate. This work proves the possi

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Li, Jishuai, Tengfei Tu, Yongsheng Li, Sujuan Qin, Yijie Shi, and Qiaoyan Wen. "DoSGuard: Mitigating Denial-of-Service Attacks in Software-Defined Networks." Sensors 22, no. 3 (2022): 1061. http://dx.doi.org/10.3390/s22031061.

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Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical threats. Due to the lack of an effective message-verification mechanism in SDN, attackers can easily launch a DoS attack by faking the source address information. This paper presents DoSGuard, an efficient and protocol-independent defense framework for SDN networks

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Bai, Li Fang, and Jin Xue Xu. "Research on Urban Traffic Signal Control Method at Single Intersection." Advanced Materials Research 361-363 (October 2011): 1799–802. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1799.

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A fuzzy logic controller is presented for a four-phase isolated signalized intersection on normal and abnormal conditions. It controls the traffic light timings to ensure smooth flow of traffic with minimal delay, according to the real-time traffic flow information detected by the vehicle detector. A new controller is proposed, in which the fuzzy membership functions are optimized by neural network and the control rules are optimized by genetic algorithm. Results show that the traditional fuzzy controller achieves good control effect and the performance of the controller optimized is better th

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Ammendola, R., A. Biagioni, A. Ciardiello, et al. "Progress report on the online processing upgrade at the NA62 experiment." Journal of Instrumentation 17, no. 04 (2022): C04002. http://dx.doi.org/10.1088/1748-0221/17/04/c04002.

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Abstract A new FPGA-based low-level trigger processor has been installed at the NA62 experiment. It is intended to extend the features of its predecessor due to a faster interconnection technology and additional logic resources available on the new platform. With the aim of improving trigger selectivity and exploring new architectures for complex trigger computation, a GPU system has been developed and a neural network on FPGA is in progress. They both process data streams from the ring imaging Cherenkov detector of the experiment to extract in real time high level features for the trigger log

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Pappachen James, Alex, Anusha Pachentavida, and Sherin Sugathan. "Edge detection using resistive threshold logic networks with CMOS flash memories." International Journal of Intelligent Computing and Cybernetics 7, no. 1 (2014): 79–94. http://dx.doi.org/10.1108/ijicc-06-2013-0032.

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Purpose – The purpose of this paper is to present a new approach to edge detection using semiconductor flash memory networks having scalable and parallel hardware architecture. Design/methodology/approach – A flash cell can store multiple states by controlling its voltage threshold. The equivalent resistance of the operation states controlled by threshold voltage of flash cell gives out different combinations of logic 0 and 1 states. The paper explores this basic feature of flash memory in designing a resistance change memory network for implementing novel edge detector hardware. This approach

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Ribeiro, Bendito Freitas, and Yasuhiro Takahashi. "Optimizing Energy/Current Fluctuation of RF-Powered Secure Adiabatic Logic for IoT Devices." Sensors 25, no. 14 (2025): 4419. https://doi.org/10.3390/s25144419.

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The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar s

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Giagu, Stefano. "Fast and resource-efficient Deep Neural Network on FPGA for the Phase-II Level-0 muon barrel trigger of the ATLAS experiment." EPJ Web of Conferences 245 (2020): 01021. http://dx.doi.org/10.1051/epjconf/202024501021.

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The Level-0 muon trigger system of the ATLAS experiment will undergo a full upgrade for the High Luminosity LHC to stand the challenging requirements imposed by the increase in instantaneous luminosity. The upgraded trigger system will send raw hit data to off-detector processors, where trigger algorithms run on a new generation of FPGAs. To exploit the flexibility provided by the FPGA systems, ATLAS is developing novel precision deep neural network architectures based on trained ternary quantisation, optimised to run on FPGAs for efficient reconstruction and identification of muons in the ATL

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Nagarajan, Manikandan, Rajappa Muthaiah, Yuvaraja Teekaraman, Ramya Kuppusamy, and Arun Radhakrishnan. "Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model." Computational Intelligence and Neuroscience 2022 (March 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/3505439.

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Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (G

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Hasan, Moh Khalid, Mostafa Zaman Chowdhury, Md Shahjalal, and Yeong Min Jang. "Fuzzy Based Network Assignment and Link-Switching Analysis in Hybrid OCC/LiFi System." Wireless Communications and Mobile Computing 2018 (November 19, 2018): 1–15. http://dx.doi.org/10.1155/2018/2870518.

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In recent times, optical wireless communications (OWC) have become attractive research interest in mobile communication for its inexpensiveness and high-speed data transmission capability and it is already recognized as complementary to radio-frequency (RF) based technologies. Light fidelity (LiFi) and optical camera communication (OCC) are two promising OWC technologies that use a photo detector (PD) and a camera, respectively, to receive optical pulses. These communication systems can be implemented in all kinds of environments using existing light-emitting diode (LED) infrastructures to tra

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Salamah, Irma, Syafa Naura Afifa, and Emilia Hesti. "Rancang Bangun Pendeteksi Penyakit Jantung menggunakan Teknik Algoritma Fuzzy Logic berbasis IoT." Edumatic: Jurnal Pendidikan Informatika 6, no. 2 (2022): 176–85. http://dx.doi.org/10.29408/edumatic.v6i2.6164.

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Heart rate is a health indicator that has the advantage of assessing or knowing a person's health quickly. It is useful as the first diagnosis of the presence or absence of heart disorders. By building an IoT (Internet of Things) based heart rate monitoring system, it is hoped that it can be monitored, and through Android can check the heart rate. An IoT system is an idea that can expand and utilize a relationship that is connected continuously. Where IoT can monitor a person's condition so that a person's condition remains monitored 24 hours. This study aims to design a heart disease detector

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Yoddumnern, Anekwong, Roungsan Chaisricharoen, and Thongchai Yooyativong. "A Smart WiFi Multi-Sensor Node for Fire Detection Mechanism Based on Social Network." International Journal of Online Engineering (iJOE) 14, no. 10 (2018): 4. http://dx.doi.org/10.3991/ijoe.v14i10.8488.

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<p class="0abstract">A small device with WiFi multi-sensing element is very important under a social digital century<strong>.</strong> This study aims to implement the hardware and the power of the algorithm with WiFi technologies. Especially, the multi-sensors have to reinforce around a home area and support to any requirement in the term of digital society. This study focus to care the home security— on going to the fire detection with applying several technologies based on a Cloud. Firstly, the multi-sensor calibration has used calibration time and self-calibration as the

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J., A. Enokela, and Ibanga E.J. "AN AUTOMATIC CAR ANTI-THEFT ALARM SYSTEM." Continental J. Engineering Sciences 1 (July 22, 2007): 15–19. https://doi.org/10.5281/zenodo.833559.

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The theft of cars and other automobiles by criminals has become so frequent in our society as to be classified as alarming. Most of the thefts are organized by gangs of robbers but sometimes individuals engage in this activity. The result usually, however, is that the persons from whom the vehicles have been stolen are left to grieve as many of these vehicles are never recovered. This paper describes a simple alarm system that can be easily installed in all kinds of vehicles. The system described will effectively defeat intended car thieves.

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Zhai, Huishan, and Bingo Wing-Kuen Ling. "Implementation and Performance Evaluation of the Frequency-Domain-Based Bit Flipping Controller for Stabilizing the Single-Bit High-Order Interpolative Sigma Delta Modulators." Applied Sciences 10, no. 17 (2020): 5785. http://dx.doi.org/10.3390/app10175785.

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This paper is an extension of the existing works on the frequency-domain-based bit flipping control strategy for stabilizing the single-bit high-order interpolative sigma delta modulator. In particular, this paper proposes the implementation and performs the performance evaluation of the control strategy. For the implementation, a frequency detector is used to detect the resonance frequencies of the input sequence of the sigma delta modulator. Then, a neural-network-based controller is used for finding the solution of the integer programming problem. Finally, the buffers and the combinational

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Wongsathan, Rati, and Pornchai Supnithi. "Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (2021): 190–99. http://dx.doi.org/10.37936/ecti-eec.2021192.241449.

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Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processin

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Ansar, Hira, Amel Ksibi, Ahmad Jalal, et al. "Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier." Applied Sciences 12, no. 13 (2022): 6481. http://dx.doi.org/10.3390/app12136481.

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convo

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Dang, Dinh-Thuan, and Jing-Wein Wang. "Developing a Deep Learning-Based Defect Detection System for Ski Goggles Lenses." Axioms 12, no. 4 (2023): 386. http://dx.doi.org/10.3390/axioms12040386.

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Ski goggles help protect the eyes and enhance eyesight. The most important part of ski goggles is their lenses. The quality of the lenses has leaped with technological advances, but there are still defects on their surface during manufacturing. This study develops a deep learning-based defect detection system for ski goggles lenses. The first step is to design the image acquisition model that combines cameras and light sources. This step aims to capture clear and high-resolution images on the entire surface of the lenses. Next, defect categories are identified, including scratches, watermarks,

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Ardi, Syahril, Muhammad Abdul Rahman Nurdin, and Agus Ponco. "Design of pokayoke system on the process of mounting actuator bracket based on programmable logic controller in automotive manufacturing industry." MATEC Web of Conferences 197 (2018): 14014. http://dx.doi.org/10.1051/matecconf/201819714014.

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This research was conducted in automotive manufacturing industry company in Indonesia. There is one Key Performance Index that needs to be fixed, that is the process mounting actuator bracket. The type of trouble that can occur on the actuator bracket is on the quality of the mounted bracket. The bracket mounting process is carried out using three bolts on each side which must be mounted with a predetermined torque value of 5.4 Nm. Manually process can cause bolt on one side or more. Based on data from the Quality Assurance network, the process diagnostic table or tightening has shown less opt

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Shepelev, Vladimir, Sultan Zhankaziev, Sergey Aliukov, et al. "Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems." Mathematics 10, no. 2 (2022): 282. http://dx.doi.org/10.3390/math10020282.

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This study addresses the problem of non-stop passage by vehicles at intersections based on special processing of data from a road camera or video detector. The basic task in this article is formulated as a forecast for the release time of a controlled intersection by non-group vehicles, taking into account their classification and determining their number in the queue. To solve the problem posed, the YOLOv3 neural network and the modified SORT object tracker were used. The work uses a heuristic region-based algorithm in classifying and measuring the parameters of the queue of vehicles. On the

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Mecocci, Alessandro, and Claudio Grassi. "RTAIAED: A Real-Time Ambulance in an Emergency Detector with a Pyramidal Part-Based Model Composed of MFCCs and YOLOv8." Sensors 24, no. 7 (2024): 2321. http://dx.doi.org/10.3390/s24072321.

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In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LSs) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The foundation of the proposed system is built on the simultaneous processing o

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Kumar, A. Naresh, K. Lingaswamy, M. Ramesha, Bharathi Gururaj, M. Suresh Kumar, and K. Viswanath Allamraju. "A novel reverse and forward directional relaying scheme in six phase overhead transmission lines using adaptive neuro-fuzzy inference system." International Journal of Applied Power Engineering (IJAPE) 13, no. 4 (2025): 783–89. https://doi.org/10.11591/ijape.v13.i4.pp783-789.

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Recent power system is structurally difficult and is vulnerable to undesirable conditions like transmission faults. In this event of transmission line faults, exact fault zone detection enhances the restoration process, thus improving reliability of the complete power system. In order to solve the above problem, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based fault zone detector, which combines artificial neural network (ANN) and fuzzy logic technique (FLT) in six phase overhead transmission lines (SPOTL). To overcome the limitation of ANN and fuzzy expert system (FE

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Kumar, A. Naresh, K. Lingaswamy, M. Ramesha, Bharathi Gururaj, M. Suresh Kumar, and K. Viswanath Allamraju. "A novel reverse and forward directional relaying scheme in six phase overhead transmission lines using adaptive neuro-fuzzy inference system." International Journal of Applied Power Engineering (IJAPE) 13, no. 4 (2024): 783. http://dx.doi.org/10.11591/ijape.v13.i4.pp783-789.

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Recent power system is structurally difficult and is vulnerable to undesirable conditions like transmission faults. In this event of transmission line faults, exact fault zone detection enhances the restoration process, thus improving reliability of the complete power system. In order to solve the above problem, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based fault zone detector, which combines artificial neural network (ANN) and fuzzy logic technique (FLT) in six phase overhead transmission lines (SPOTL). To overcome the limitation of ANN and fuzzy expert system (FE

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Yang, Gang, Chaojing Tang, and Xingtong Liu. "DualAC2NN: Revisiting and Alleviating Alert Fatigue from the Detection Perspective." Symmetry 14, no. 10 (2022): 2138. http://dx.doi.org/10.3390/sym14102138.

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The exponential expansion of Internet interconnectivity has led to a dramatic increase in cyber-attack alerts, which contain a considerable proportion of false positives. The overwhelming number of false positives cause tremendous resource consumption and delay responses to the really severe incidents, namely, alert fatigue. To cope with the challenge from alert fatigue, we focus on enhancing the capability of detectors to reduce the generation of false alerts from the detection perspective. The core idea of our work is to train a machine-learning-based detector to grasp the empirical intellig

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Liu, Chengcheng, Weihua Zhang, Weijin Xu, Bo Lu, Weijie Li, and Xuefeng Zhao. "Substation Inspection Safety Risk Identification Based on Synthetic Data and Spatiotemporal Action Detection." Sensors 25, no. 9 (2025): 2720. https://doi.org/10.3390/s25092720.

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During substation inspection, operators are often exposed to hazardous working environments. It is necessary to use visual sensors to determine work status and perform action detection to distinguish between normal and dangerous actions in order to ensure the safety of operators. However, due to information security, privacy protection, and the rarity of dangerous scenarios, there is a scarcity of related visual action datasets. To address this issue, this study first introduces a virtual work platform, which includes a controller for the parameterized control of scenarios and human resources.

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KATSIRI, ELEFTHERIA, JEAN BACON, and ALAN MYCROFT. "LINKING TEMPORAL FIRST ORDER LOGIC AND HIDDEN MARKOV MODELS WITH ABSTRACT EVENTS." International Journal on Artificial Intelligence Tools 19, no. 06 (2010): 857–93. http://dx.doi.org/10.1142/s0218213010000443.

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In previous work, we introduced a novel concept of a generalised event, an abstract event, which we define as a change of state of abstract predicates that represent knowledge about the surrounding world. Abstract predicates are defined by formulae in temporal first-order logic (Abstract Event Specification Language (AESL)) whose leaf predicates represent low-level sensor-derived knowledge. Abstract events are detected by Rete Networks structured as a deductive knowledge-base. Current Abstract Event detectors cannot express sufficiently well certain high-level situations, such activity derived

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Holenko, Maksym Yu. "Adaptive super-resolution integration to enhance object detection on low-quality unmanned aerial vehicleimagery." Herald of Advanced Information Technology 8, no. 2 (2025): 164–78. https://doi.org/10.15276/hait.08.2025.10.

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The article addresses the problem of improving the accuracy of object detection in images captured by unmanned aerial vehicles under conditions of reduced spatial resolution and the presence of noise artifacts. The relevance of this research is driven by the practical need to maintain the reliability of computer vision systems in challenging field environments, where conventional detection algorithms tend to lose effectiveness.The aim of the study is to enhance the robustness of object detection in low-quality unmanned aerial vehiclesimagery through the development of an adaptive preprocessing

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Rubenzahl, Ryan A., Samuel Halverson, Josh Walawender, et al. "Staring at the Sun with the Keck Planet Finder: An Autonomous Solar Calibrator for High Signal-to-noise Sun-as-a-star Spectra." Publications of the Astronomical Society of the Pacific 135, no. 1054 (2023): 125002. http://dx.doi.org/10.1088/1538-3873/ad0b30.

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Abstract Extreme precision radial velocity (EPRV) measurements contend with internal noise (instrumental systematics) and external noise (intrinsic stellar variability) on the road to 10 cm s−1 “exo-Earth” sensitivity. Both of these noise sources are well-probed using “Sun-as-a-star” RVs and cross-instrument comparisons. We built the Solar Calibrator (SoCal), an autonomous system that feeds stable, disk-integrated sunlight to the recently commissioned Keck Planet Finder (KPF) at the W. M. Keck Observatory. With SoCal, KPF acquires signal-to-noise ratio (S/N) ∼ 1200, R = 98,000 optical (445–870

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N., Priya, and Pankajavalli P.B. "Fuzzy Logic Based Hardware Faulty Node Detection And Redundancy Mechanism For Wireless Sensor Networks." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 45–52. https://doi.org/10.35940/ijitee.A8074.1110120.

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In recent years, applications of wireless sensor network (WSN) is emerged as the revolutionary phase in many functional areas such as industrial, environmental, business, military and many need based self-intelligent real time systems. Some of the applications require data communication from harsh physical environment which poses great challenges to wireless sensor networks. The deployment of these sensor nodes in the hostile environment cause sensor nodes failure. This demands fast, redundant fault tolerant, energy saving approaches which meet the requirements of most recurring failures and p

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San, Santoso, Ratna Hartayu, Niken Basyarach, Kurnia Riyanti, Ahmad Ridho'i, and Kukuh Setyadjit. "Analisis Jarak Deteksi dan Respon Robot Pendeteksi Api Berbasis Sensor Cahaya." ALINIER: Journal of Artificial Intelligence & Applications 6, no. 1 (2025): 65–72. https://doi.org/10.36040/alinier.v6i1.14469.

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Amri, H.S. (2010) ‘Sensor UVtron sebagai pendeteksi api pada robot pemadam api berbasis mikrokontroler atmega8535’. Available at: https://digilib.uns.ac.id/dokumen/15413/Sensor-UVtron-sebagai-pendeteksi-api-pada-robot-pemadam-api-berbasis-mikrokontroler-atmega8535 (Accessed: 18 May 2025). Arias, L. et al. (2008) ‘Photodiode-based sensor for flame sensing and combustion-process monitoring’, Applied Optics, 47(29), pp. 5541–5549. Available at: https://doi.org/10.1364/AO.47.005541. Cahyadi, H.D., Mirza, Y. and Laila, E. (2022) ‘Rancang Bangun Alat Pendeteksi Kebakaran Menggunakan Flame Sensor dan

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Iman, Subhi Mohammed, and Mohammed Alhamdani Israa. "A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4277–86. https://doi.org/10.11591/ijece.v9i5.pp4277-4286.

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The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile factory. The p

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Mang, Qiuyang, Jinsheng Ba, Pinjia He, and Manuel Rigger. "Finding Logic Bugs in Graph-processing Systems via Graph-cutting." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725300.

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Graph-processing systems, including Graph Database Management Systems (GDBMSes) and graph libraries, are designed to analyze and manage graph data efficiently. They are widely used in applications such as social networks, recommendation systems, and fraud detection. However, logic bugs in these systems can lead to incorrect results, compromising the reliability of applications. While recent research has explored testing techniques specialized for GDBMSes, it is unclear how to adapt them to graph-processing systems in general. This paper proposes G raph - cutting , a universal approach for dete

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Strizhko, Mikhail. "NEURAL NETWORK FORECASTING OF TRANSPORT FLOW PARAMETERS IN INTELLIGENT TRAFFIC LIGHT CONTROL SYSTEMS." Automation and modeling in design and management 2024, no. 2 (2024): 45–53. http://dx.doi.org/10.30987/2658-6436-2024-2-45-53.

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A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural

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Sowah, Robert A., Kwaku Apeadu, Francis Gatsi, Kwame O. Ampadu, and Baffour S. Mensah. "Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)." Journal of Engineering 2020 (February 1, 2020): 1–16. http://dx.doi.org/10.1155/2020/3645729.

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This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are main

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Hawas, Yaser E., and Hani S. Mahmassani. "Comparative Analysis of Robustness of Centralized and Distributed Network Route Control Systems in Incident Situations." Transportation Research Record: Journal of the Transportation Research Board 1537, no. 1 (1996): 83–90. http://dx.doi.org/10.1177/0361198196153700112.

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A procedure for providing real-time route guidance in congested vehicular traffic networks is described. The control procedure, implementable in a decentralized scheme, envisions a set of distributed local controllers in the network in which every controller can extract only limited information from detectors. The assignment logic is driven by informed local search procedures and heuristics. A simulation-assignment model was developed and used to assess the effectiveness and robustness of the procedure in dealing with normal as well as incident traffic conditions. A comparative study was under

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Bengag, Asmae, Amina Bengag, and Omar Moussaoui. "Intrusion detection based on fuzzy logic for wireless body area networks: review and proposition." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1091–102. https://doi.org/10.11591/ijeecs.v26.i2.pp1091-1102.

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Wireless body area networks (WBANs) are very helpful for monitoring the patient’s case, due to the medical sensors. However, this technology faces several problems such as loss communication, security issues and energy consumption. Our work focused on the security and specifically the intrusion detection system (IDS), which is one of the most effective techniques used to identify the presence of intrusions in a network. To make the IDS more efficient, the fuzzy logic (FL) is one of the well-known techniques that is known for its powerful mechanism used to differentiate network traffic le

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Baghdasaryan, Ashot, and Hovhannes Bolibekyan. "On Recurrent Neural Network Based Theorem Prover For First Order Minimal Logic." JUCS - Journal of Universal Computer Science 27, no. (11) (2021): 1193–202. https://doi.org/10.3897/jucs.76563.

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There are three main problems for theorem proving with a standard cut-free system for the first order minimal logic. The first problem is the possibility of looping. Secondly, it might generate proofs which are permutations of each other. Finally, during the proof some choice should be made to decide which rules to apply and where to use them. New systems with history mechanisms were introduced for solving the looping problems of automated theorem provers in the first order minimal logic. In order to solve the rule selection problem, recurrent neural networks are deployed and they are used to

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CHATTOPADHYAY, TANAY, GOUTAM KUMAR MAITY, and JITENDRA NATH ROY. "DESIGNING OF ALL-OPTICAL TRI-STATE LOGIC SYSTEM WITH THE HELP OF OPTICAL NONLINEAR MATERIAL." Journal of Nonlinear Optical Physics & Materials 17, no. 03 (2008): 315–28. http://dx.doi.org/10.1142/s0218863508004159.

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Nonlinear optics has been of increased interest for all-optical signal, data and image processing in high speed photonic networks. The application of multi-valued (nonbinary) digital signals can provide considerable relief in transmission, storage and processing of a large amount of information in digital signal processing. Here, we propose the design of an all-optical system for some basic tri-state logic operations (trinary OR, trinary AND, trinary XOR, Inverter, Truth detector, False detector) which exploits the polarization properties of light. Nonlinear material based optical switch can p

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Shepelev, Vladimir, Alexander Glushkov, Ivan Slobodin, Irina Alferova, and Olga Fadina. "MODELING THE TRAFFIC CAPACITY OF THE NODES OF URBAN TRANSPORT NETWORK BASED ON THE FUZZY LOGIC METHODS." Bulletin of the South Ural State University series "Economics and Management" 15, no. 4 (2021): 181–87. http://dx.doi.org/10.14529/em210419.

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Traffic state prediction is a key component of intelligent transport systems (ITS) which has attracted a lot of attention over the past few decades. The improvement in the accuracy of mod-eling and predicting the traffic capacity of intersections, depending on such uncertain factors as the intensity of pedestrian flow and its discontinuity, is possible only with the development and use of new methods. In order to form a number of typical control algorithms for each regulated node of a city transport network, the need to cluster them arises. The traffic flow parameters of each separate regulate

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Chandrakala, Chandrakala, and Mungamuri Sasikala. "An efficient novel dual deep network architecture for video forgery detection." International Journal of Reconfigurable and Embedded Systems (IJRES) 13, no. 2 (2024): 458. http://dx.doi.org/10.11591/ijres.v13.i2.pp458-471.

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The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible

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S., Venkatesan, and Ramakrishnan M. "Energy Efficient of IDS Using Fuzzy Logic for Lifetime Improvement in Wireless Sensor Network." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 233–38. https://doi.org/10.35940/ijeat.C2214.0210321.

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The Wireless Sensor Network (W.S.N.) comprises little batteries fueled sensor gadgets with restricted energy assets. The Sensor hubs used to monitor the physical screen or conditionsbased on normal, theinformation must be private organization to primary area. The Most significant obstacles in a sensing the remote in the particular network which used to make an efficient energy framework. Clustering is the one of the major process in the sensor network based on wireless which used to drag out the life time of an organization lifetime which in turn reduce the energy utilization of the network. I

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G. Srinivasan. "Fuzzy based Congestion Control and Congestion Aware Routing Technique for IoT Networks." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 160–65. https://doi.org/10.52783/jisem.v10i13s.2016.

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Introduction: In Internet of Things (IoT) networks, the existing congestion control mechanisms did not often scale efficiently or maintain the heterogeneity of devices, causing performance bottlenecks and uneven network performance. Existing congestion control protocols may not adjust well to these dynamics, causing increased packet loss, suboptimal performance, and higher latency. Objectives: The main objectives of this work are to detect congestion at node level or link level and to determine congestion aware routing paths. Methods: A Fuzzy-based congestion control and congestion aware routi

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Perháč, Ján, Daniel Mihályi, and Lukáš Maťaš. "Elimination of network intrusions via a resource oriented BDI architecture." Open Computer Science 8, no. 1 (2018): 173–81. http://dx.doi.org/10.1515/comp-2018-0016.

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Abstract We propose a resource-oriented architecture of a rational agent for a network intrusion detection system. This architecture describes the behavior of a rational agent after detection of unwanted network activities. We describe the creation of countermeasures to ward off detected threats. Examples are created based on the proposed architecture, describing the process during a rational agent detection. We have described these examples by linear BDI logic behavioral formulæ, that have been proven by Gentzen sequent calculus.

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Sharma, Sharad, Shakti Kumar, and Brahmjit Singh. "AntMeshNet." International Journal of Applied Metaheuristic Computing 5, no. 1 (2014): 20–45. http://dx.doi.org/10.4018/ijamc.2014010102.

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Wireless Mesh Networks (WMNs) are emerging as evolutionary self organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self organizing and fault tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest path between a source-terminal node pair perform poorly under the constraints imposed by dynamic network conditi

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Kyrkou, Christos, Eftychios Christoforou, Stelios Timotheou, Theocharis Theocharides, Christos Panayiotou, and Marios Polycarpou. "Optimizing the Detection Performance of Smart Camera Networks Through a Probabilistic Image-Based Model." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 5 (2017): 1197–211. https://doi.org/10.1109/TCSVT.2017.2651362.

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Networks of smart cameras, equipped with on-board processing and communication infrastructure, are increasingly being deployed in a variety of different application fields, such as security and surveillance, traffic monitoring, industrial monitoring, and critical infrastructure protection. The task(s) that a network of smart cameras executes in these applications, e.g., activity monitoring, object identification, can be severely degraded because of errors in the detection module. However, in most cases higher-level tasks and decision making processes in smart camera networks (SCNs) assume idea

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Bastian, Andreas. "Modeling Fuel Injection Control Maps Using Fuzzy Logic and Neural Networks." Journal of Robotics and Mechatronics 6, no. 4 (1994): 340–44. http://dx.doi.org/10.20965/jrm.1994.p0340.

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Determining the correct ignition point of the air-fuel mixture is critical in order to achieve maximum output torque and to reduce exhaust emissions. In some fuel injection control systems the amount of air cannot be detected, thus, look-up tables are utilized, which contain the amount of air for given engine speed and inlet manifold pressure. In this paper, we model the look-up table using fuzzy logic. A neural network approach is used to identify the inputs of the fuzzy model.

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Muhammad, K. Shahzad1 and Tae Ho Cho1. "AN ENHANCED DETECTION AND ENERGYEFFICIENT EN-ROUTE FILTERING SCHEME IN WIRELESS SENSOR NETWORKS." Informatics Engineering, an International Journal (IEIJ) 03, sep (2015): 01–16. https://doi.org/10.5121/ieij.2015.3302.

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Wireless sensor networks (WSNs), due to their small size, low cost, and untethered communication over a short-range, have great potential for applications and services. Due to hostile environments and an unattended nature, they are prone to many types of attacks by adversaries. False data injection attacks compromise data accuracy at the sink node and cause undesirable energy depletion at the sink and intermediate nodes. In order to detect and counter false data attacks, a number of en-route filtering schemes have been proposed. However, they lack a strong false report detection capacity or ca

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Swapnil, Dalve, Ramdasi Ishwar, Kothawade Ganesh, Khadke Yash, and Wete Manasi. "Real Time Prevention of Driver Fatigue Using Deep Learning and MediaPipe." International Journal of Innovative Research in Computer Science and Technology (IJIRCST) 11, no. 03 (2023): 7–11. https://doi.org/10.5281/zenodo.8109918.

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This paper describes the development of a system for detecting driver drowsiness whose goal is to alert drivers of their sleepy state to prevent traffic accidents. It is essential that drowsiness detection in a driving environment be conducted in a non-intrusive manner and that the driver not be troubled by alerts when they are not sleepy. We make use of the MediaPipe Facemesh framework to extract facial features and the Binary Classification Neural Network to precisely detect drowsy states in our solution to this open problem. The solution that minimize false positives is created to determine

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