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POWER-SYSTEM VOLTAGE CONTROL BY DISTRIBUTED EXPERT systems

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1990年第3期37卷 236-240页

作者： MATSUDA, S OGI, H NISHIMURA, K OKATAKU, Y TAMURA, S Artificial Intelligence Technology Department Computer and Communication Research Center Tokyo Electric Power Company Limited Japan Systems and Software Engineering Laboratory Toshiba Corporation Japan

A case study of the application of a distributed control scheme to a power system control is presented. In addition, an investigation has been conducted into voltage control. Combined injection of VAr-compensating devices controlled by distributed expert systems has been proposed as a measure to maintain voltage stability in a power system under heavy loading conditions. A simulation study has been carried out by using five workstations that represent a power system and four VAr-compensating devices. The results demonstrate the effectiveness of the proposed system for voltage recovery.< >

关键词： Power system control Power systems Voltage control Expert systems Power system stability Power system simulation Distributed control Power system measurements Control systems Power measurement

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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling

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Environment International 2025年 199卷 109496页

作者： Ahmed, Maqsood Zhang, Xiang Shen, Yonglin Ahmed, Tanveer Ali, Shahid Ali, Ayaz Gulakhmadov, Aminjon Nam, Won-Ho Chen, Nengcheng National Engineering Research Center of Geographic Information System School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Software College Northeastern University Shenyang110169 China Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Research Center of Ecology and Environment in Central Asia Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi830011 China Institute of Water Problems Hydropower and Ecology of the National Academy of Sciences of Tajikistan Dushanbe734042 Tajikistan School of Social Safety and Systems Engineering Institute of Agricultural Environmental Science National Agricultural Water Research Center Hankyong National University Anseong Korea Republic of

Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on single static image analysis, which does not account for the dynamic and temporal nature of air pollution. Meanwhile, research on video-based air quality estimation remains limited, particularly in achieving accurate multi-pollutant outputs. This study proposes Air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model that integrates a structured Selective State Space Model (SSM) with a selective scan mechanism and a hybrid predictor (HP) to estimate air quality. The spatiotemporal forward and backward SSM dynamically adjusts parameters based on input, ensures linear complexity, and effectively captures long-range dependencies by bidirectional processing of spatiotemporal features through four scanning techniques (row-wise, column-wise, and their vertical reversals), which allows the model to accurately track pollutant concentrations and air quality variations over time. Thus, the model efficiently extracts spatiotemporal features from video and simultaneously performs regression (PM2.5, PM10, and AQI), and classification (AQI) tasks, respectively. A high-quality outdoor hourly air quality dataset (LMSAQV) with 13,176 videos collected from six monitoring stations in Lahore, Pakistan, was utilized as the case study. The experimental results demonstrate that the AQP-Mamba significantly outperforms several state-of-the-art models, including VideoSwin-T, VideoMAE, I3D, VTHCL, and TimeSformer. The proposed model achieves strong regression performance (PM2.5: R2 = 0.91, PM10: R2 = 0.90, AQI: R2 = 0.92) and excellent classification metrics: accuracy (94.57 %), precision (93.86 %), recall (94.20 %), and F1-score (93.44 %), respectively. The proposed model delivers consistent, real-time performance with a latency

关键词： Air quality

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Leveraging Geographic Information systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： Information management

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Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach

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WATER SCIENCE AND TECHNOLOGY 2009年第12期60卷 3051-3059页

作者： Zaqoot, Hossam Adel Ansari, Abdul Khalique Unar, Mukhtiar Ali Khan, Shaukat Hyat Department of Chemical Engineering Department of Computer Systems & Software Engineering Environment Quality Authority (Palestinian Authority)-PhD Scholar at the Institute of Environmental Engineering & Management Mehran university of Engineering and Technology and National Institute of Oceanography Oceanography Pakistan E-mail: hanreen2@***

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

关键词： ANNs dissolved oxygen Gaza Mediterranean Sea MLP prediction RBF

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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A framework to achieve reverse channel multicast in IEEE 802.16 based wireless broadband networks for interoperability with IP networks

A framework to achieve reverse channel multicast in IEEE 802...

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作者： Ansari, Abdul Qadir Rajput, Abdul Qadeer Khan Hashmani, Manzoor Department of Computer Systems and Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (纸本)3540898522

The purpose of this paper is to present a framework design to achieve reverse channel multicast support in IEEE-802.16 networks. The unavailability of reverse channel multicast in IEEE 802.16 networks is one of the issues to be resolved in order to achieve its interoperability with IP networks. The interoperability of IEEE 802.16 networks and IP networks is not yet addressed. Forward channel multicast support is inherent in the IEEE-802.16 standard. Achieving reverse channel multicast support, we can enable IP services requiring native multicast support over IEEE-802.16 networks for seamless interoperation. IP network rudiments are mature enough and not likely to be redefined for any new network design;rather the reverse of this is more likely to be worked on. A framework design to achieve reverse channel multicast support is presented introducing a concentrator layer (Multi Node Access Layer) before Base Station (BS) and MARS (Multicast Address Resolution Server) to handle multicast requests. © 2008 Springer-Verlag.

关键词： Interoperability

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Model- and constraint-based engineering of complex software ecosystems 2

Model- and constraint-based engineering of complex software ...

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2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： Rausch, Andreas Bergner, Sandra Wang, Daning Software Systems Engineering Department of Computer Science TU Clausthal Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9781479952984

No software is an island. It is executed by hardware and interacts with its environment. So-called software systems are complicated hierarchical systems. Competent engineers carefully engineer them. In contrast, complex systems, like biological ecosystems, railway systems and the Internet itself, have never been developed and tested as a whole by a team of engineers. Nevertheless, those complex systems have the ability to evolve without explicit control by anyone, and they are more robust to deal with problems at the level of its constituent elements compared to classical engineered systems. Consequently in this article we introduce the concept of complex software ecosystems comprising of interacting adaptive software systems and human beings. Ecosystems achieve the demanded flexibility and dependability by means of a kind of higher-level regulatory system. Thereby equilibrium is continuously preserved through the appropriate balance between self-adaption and self-control capabilities of ecosystem's participants. We will outline a methodology to support engineering of ecosystems by integrating a model- and constraint-based engineering approach and applying it during design and run time. The open-world semantics of constraints set up a frame for the behavior of participants and the ecosystem itself. Violations of constraints can be identified during design time, but also provide a knowledge transfer to run time. Constraints are additionally monitored and enforced during run time. Thus, we propose an evolutionary engineering approach covering the whole life-cycle for forever active complex software ecosystems. © 2014 IEEE.

关键词： Ecosystems

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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DEPCAS: an RFID middleware generalization. An example with tracking systems

DEPCAS: an RFID middleware generalization. An example with t...

引用

2008 International Conference on Wireless Networks, ICWN 2008

作者： Abad, I. Cerrada, C. Cerrada, J.A. Heradio, R. Software Engineering and Computer Systems Department UNED: Distance Learning University of Spain Madrid Spain

ISBN: (纸本)1601320914

Introduction of new standards that impose the use of labels and identification by radio frequency in supply management area has promoted a necessity to research alternatives to solve this question. The different solutions proposed from the software point of view to incorporate the RFID ubiquitous information have been characterized by two extreme positions: of a side the solutions ad-hoc for every specific system, and by another one the solutions based on software packages that give generic support to the established standards. In this article we present our development: DEPCAS (Data EPC Acquisition System) based in the architecture for control systems (SCADAs) applied to the collection of RFID information. An application of DEPCAS is the resolution of heterogeneous situations of items tracking like for example, the luggage tracking, the sport events with massive participation management, the track of goods or merchandising, etc.

关键词： Radio frequency identification (RFID)

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