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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1138 条记录，以下是1091-1100 订阅

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Design and FPGA implementation of orthonormal discrete wavelet transforms

Design and FPGA implementation of orthonormal discrete wavel...

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IEEE international conference on Electronics, Circuits and Systems (ICECS)

作者： M. Nibouche A. Bouridane O. Nibouche D. Crookes S. Boussekta Image and Vision Systems Group The School of Computer Science Queen''s University Belfast Belfast UK School of Electronic and Electrical Engineering University of Leeds UK

ISBN: (纸本)0780365429

FPGA technology offers the potential for low cost and high performance for certain applications, including image processing. However, the programming model which FPGAs typically present to application developers is prohibitively low level. the purpose of this paper is to present a novel bit-serial architecture based on a time-interleaved structure. To overcome the problem of wait cycles within the structure, a second line of bit adders is provided. this allows the structure to use additional "dummy" cycles to deal with additional bits. the proposed architecture is modular and scalable, which allows a bit-level parameterisation. To assess the effectiveness of the approach the design has been implemented efficiently on the Xilinx 4000 series FPGAs.

关键词： Discrete wavelet transforms Field programmable gate arrays Filter bank Signal processing Signal resolution Application software Computer architecture Costs Image processing Arithmetic

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Java Session Language: A Session-Based Language for Cloud Service Composition 2019

Java Session Language: A Session-Based Language for Cloud Se...

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Proceedings of the 2019 7th international conference on Information Technology: IoT and Smart City

作者： Dongming Jiang Yuan Jiang School of Mathematics and Computer Science Jiangxi Science & Technology Normal University Nanchang China School of Information Engineering Nanchang University Nanchang China

ISBN: (纸本)9781450376631

Since the limited capability of single one, cloud services need to be assembled to form business process, which crate for users demands. However, to realize service composition is a time-consuming task which requires users know both the business logic and techniques of service composition. Its main reason is that current solution lacks a suitable language which can implement cloud service and easy to use. Regarding this issue, we put forward JSL, a new dialect language for integrating services. Because interaction is the basis of service composition, JSL apply session to depict the operation of interaction, then express complex composition further. JSL also use functions operators to shield the prone-to-error concurrent programming. Moreover, we designed model-driven framework to support JSL Simulation experiences demonstrates the efficiency of JSL since JSL and its framework can speed up the development and decrease the concurrent mistakes.

关键词： session type JSL MDA Cloud services function combinator

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Collaborative Optimization Analysis of Wind-AACAES-Heat

Collaborative Optimization Analysis of Wind-AACAES-Heat

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China international Electrical and Energy conference (CIEEC)

作者： Xueru Han Junrui Feng Dechang Yang China Agricultural University College of Information and Electrical Engineering Beijing China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Advanced adiabatic compressed air energy storage (AA-CAES) devices are getting increasing attention, but the operating modes of compression and turbine units in them are complicated, which are affected by many factors such as heat transfer medium temperature, gas mass flow rate, heat exchanger heat transfer efficiency and so on. this paper establishes an AA-CAES dual-charge energy model for combined heat and power storage, considering the efficiency optimization in the energy release stage, coupling wind turbines (WTs) and unit combined heat and power (CHP) to analyze the role of different operating modes of AA-CAES, and constructs a quadratically constrained quadratic programming (QCQP) about the economic optimization of the system. the optimization results show that in the integrated energy system the AA-CAES combined heat and power storage is more advantageous than the traditional AA-CAES in participating in the wind power consumption, the results are more economical, and the operation of the CHP is more flexible.

关键词： Economics Analytical models Cogeneration Wind power generation Wind turbines thermal analysis Optimization

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Fuzzy Mixed Integer Linear programming for Daily Unit Commitment with Pumped Storage Hydropower, EV Charging Demand, and Solar PV Supply

Fuzzy Mixed Integer Linear Programming for Daily Unit Commit...

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IEEE PES Asia-Pacific Power and Energy engineering conference (APPEEC)

作者： Dino Ny Chawalit Jeenanunta Jun-Ichi Takada Sapa Chanyachatchawan Sukita Kaewpasuk Boonyarit Intiyot Aussadavut Dumrongsiri School of Integrated Science and Innovation Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand School of Management Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Department of Transdisciplinary Science and Engineering Major in Global Engineering for Development Environment and Society Tokyo Institute of Technology Tokyo Japan National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

there is increasing penetration of renewable energy sources such as photovoltaic (PV) systems and the growing popularity of electric vehicles (EVs) in many countries. the uncertainty of PV generation and EV charging demand has become a significant challenge in unit commitment (UC) planning. Pumped Storage Hydropower (PSH) is introduced into the power system to respond to these uncertainties. this paper presents a Fuzzy Mixed-integer Linear programming (FMILP) model for the schedule of the usage of PSHs with the uncertainties of demand and supply. the FMILP model considers uncertainties and imprecisions associated with forecasted demand, EV charging demand, and PV supply in the daily UC problem. the objective is to minimize the total production cost while ensuring the power system’s reliable and sustainable operation by determining the optimal schedule for power generators to meet the power demand over 48 periods. A small system of 33 generators, including thermal and hydro generators, and 7 PSHs are studied. Numerical simulations have demonstrated the effectiveness of the FMILP model by identifying the uncertainty range between the lower and upper bounds, including the worst-case scenario. the numerical results show that the FMILP model can provide a more reliable and robust solution than deterministic MILP unit commitment.

关键词： Schedules Uncertainty Costs Upper bound Hydroelectric power generation Electric vehicle charging Generators

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Extractive Single Document Summarization Using an Archive-Based Micro Genetic-2

Extractive Single Document Summarization Using an Archive-Ba...

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international conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Dipanwita Debnath Ranjita Das Partha Pakray Computer Science & Engineering National Institute of Technology Mizoram Mizoram India Computer Science & Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9781728175591

ISBN: (纸本)9781728175607

Research on automatic text summarization is growing with the rapid growth of online information services. In the current study, Extractive Single Document Summarization (ESDS) is formulated as a multi-objective optimization problem, and an Archive-based Micro Genetic-2 Algorithm (AMGA2) is employed to solve this problem. In this approach, each summary is represented in a binary form. the initial population is created using the clustering and Latin Hypercube (LH) sampling, resulting in a good overall unbiased population distribution in the genotypic space. Two objective functions were formed considering content coverage and informativeness as objective function 1, and anti-redundancy as objective function 2. the summary length limit is considered as the constraint. For generating offspring, an archive-based mating pool and modified mutation operator of differential evaluation are used. Finally, from the optimal set of solutions, the best solution is selected to generate the summary. For evaluation, DUC-2001 and DUC-2002 datasets are utilized, and the obtained results are compared with various existing methods using ROUGE measures. the obtained result illustrates the superiority of our approach in terms of convergence rate and ROUGE scores.

关键词： Statistics Sociology Optimization Linear programming Feature extraction Electrostatic discharges Genetic algorithms

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Optimal scheduling of battery energy storage in microgrid to minimize electricity and fuel costs

Optimal scheduling of battery energy storage in microgrid to...

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international Multidisciplinary conference on Computer and Energy science (SpliTech)

作者： Zvonimir Š imi7 Danijel Topi7 Marina Dubravac Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781665488280

Optimal scheduling of the battery energy storage (BES) in a microgrid is presented in this paper. the microgrid model in this paper consists of a photovoltaic (PV) power plant, a wind generator (WG), battery energy storage (BES), load, and diesel generator (DG). BES is required to store the generation excess from renewable energy sources (RES). BES also can store energy from the main grid but optimization is required to decide when to charge and discharge the battery. the optimization goal is to minimize electricity costs when the main grid is available and fuel costs when it is not available. the optimization problem in this paper is a linear programming (LP) problem that is modeled and solved with Python Gurobi. Optimization results are presented for different cases. A DG is used only when the main grid is not available due to the high fuel price. Electricity cost from DG is 5.28 times higher than the cost from the grid for the first case and 4.36 times higher for the second case. there is no generation excess in the second case so the battery is not used when the main grid is not available.

关键词： Wind energy generation Costs Wind speed Simulation Microgrids Optimal scheduling Wind power generation

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artificial Intelligence Enabled IOT System for Football Identification in a Football Match 14th

Artificial Intelligence Enabled IOT System for Football Iden...

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12th international Advanced Computing conference, IACC 2022

作者： Singh, Mahesh Kumar Singh, Akhilesh Kumar Singh, Pushpendra Kalpana Rishi, Om Prkash Department of Computer Science ABES Engineering College Ghaziabad201009 India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Information Technology GL Bajaj Institute of Technology and Management Uttar Pradesh Gr Noida201306 India Department of Information Technology Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad201003 India Department of Computer Science Informatics University of Kota Rajasthan Kota324010 India

ISBN: (纸本)9783031356438

Due to growth of AI enabled IOT devices, their use in sports feature identification can be beneficial in terms of recognition quality. Computer Vision is a powerful tool for moving object recognition and tracking. Football identification and recognition problem can be implemented using OpenCV libraries with the help of a programming language like Java, Python etc. OpenCV has special modules that contain specific algorithms and features for moving object recognition and tracking. Internet of things provides a way to connect hardware components through software implementation. In the proposed model the automatic camera module has been implemented through IoT with the help of a servo motor and raspberry pi. the main aim of this paper is to automate the work of a cameraman in a football match by automatically tracking and recognizing the football and broadcasting it with the help of an automatic camera. the initial loss was about 1749, and after 1600+ epochs iterations, the loss reduced to 0.09 in the final model. © 2023, Springer Nature Switzerland AG.

关键词： Cameras

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Analytical Comparison for the Diagnosis of Chronic Kidney Disease Applying Intelligent ML-based Systems

Analytical Comparison for the Diagnosis of Chronic Kidney Di...

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international conference on ent Computing and Control Systems (ICICCS)

作者： P Durga T Sudhakar School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

Diseases like Chronic Kidney Disease (CKD) develop gradually over time and then stick around for a very long time. At its most advanced stage, it is fatal, and the only treatment option is a transplanted kidney or ongoing dialysis, which is an artificial filtering mechanism. Even though there have been improvements in surgical care and treatment, CKD remains a Serious threat to Health. As CKD has become more common in recent years, scientists throughout the world have been increasingly invested in finding effective ways to diagnose, treat, and prevent the disease. By studying the features that cause the problem, it is possible to improve performance. Healthcare providers can learn more about a patient’s condition before a visit to the clinic by analyzing the patient’s medical records. Although an early diagnosis of CKD is crucial for providing the curative or at least manageable treatments it requires, the effectiveness of current systems remains to be improved. A clever categorization and prediction model is proposed in this paper. Classification techniques such as artificial Neural Networks (ANNs), XGBoost, and K-NN are used to provide predictions about kidney illness. Based on the results of the evaluation, the suggested model outperforms the state-of-the-art methods for predicting CKD by 98.33 percent. Analyzing these outcomes demonstrates that cutting-edge ML learning approaches are useful for clinical decision-making and can aid in the early prediction of CKD and its related phases, which can help to slow the worsening of kidney damage. Algorithms for the dataset obtained from the default UCI repository have been compared using the alternative metric. this study also emphasizes the significance of statistical analysis and domain understanding of the features when extrapolating from clinical data to predict CKD.

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A Novel Adaptive Graph Transformer For Point Cloud Object Detection

A Novel Adaptive Graph Transformer For Point Cloud Object De...

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Shaoyu Sun Chunhao Shi Chunyang Wang Xuelian Liu School of Electronic and Information Engineering Changchun University of Science and Technology Changchun China Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology Xi'an Technological University Xi'an China

With the advancement of autonomous driving technology, Lidar-driven 3D object detection has become a focal point of research. However, many existing point-driven approaches face challenges due to rigid local dependencies and entangled foreground-background points. the direct utilization of point clouds for 3D object detection is also complicated by the inherent sparsity and irregularities of Lidar data. To counter these challenges, we propose AGT-SSD a single-stage graph-based 3D detector to improve object performance especially for small objects. Which comprises two critical modules adaptive graph feature convolution module (AGF) and a point cloud transformer layer with self-attention mechanism (TSA). AGF module utilizes an adaptive graph kernel to process graph features, facilitating feature interactions within the feature domain to help detect small objects. Non-local transformer layer incorporates self-attention module, designed to capture long-range dependencies to encode global sematic and spatial feature to improve detection performance. this enhancement is attributed to the geometric structure refinement through graph convolution methods and the use of a self-attention mechanism to grasp long-range dependencies. Both modules employ non-local operations, adapt at discerning long-range correlations. Extensive experiments on the KITTI dataset demonstrated that our detector is not flexible and plug and play for other detectors but also highlight the superior accuracy of our method compared to other state-of-the-art detectors.

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Towards Fast and Energy-Efficient Offloading for Vehicular Edge Computing

Towards Fast and Energy-Efficient Offloading for Vehicular E...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Meijia Su Chenhong Cao Miaoling Dai Jiangtao Li Yufeng Li School of Computer Engineering and Science Shanghai University China

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU) aiming to reduce the processing delay as well as the resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications resulting in suboptimal offloading policies. In this paper, we present FEVEC, a Fast and Energy-efficient VEC framework with the objective of making the optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirement. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decision and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC, where an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Furthermore, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on realistic and simulated vehicle trajectories verify that our proposed algorithm has a better performance compared with the state-of-the-art VEC mechanism.

关键词： Energy consumption Quality of service Energy efficiency Delays Trajectory Complexity theory Task analysis

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