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Polynomial-time Combinatorial Algorithm for General Max–Min Fair Allocation

Algorithmica 2024年第2期86卷 485-504页

作者： Ko, Sheng-Yen Chen, Ho-Lin Cheng, Siu-Wing Hon, Wing-Kai Liao, Chung-Shou Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu30013 Taiwan Department of Electrical Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan

In the general max–min fair allocation problem, there are m players and n indivisible resources, each player has his/her own utilities for the resources, and the goal is to find an assignment that maximizes the minimum total utility of resources assigned to a player. The problem finds many natural applications such as bandwidth distribution in telecom networks, processor allocation in computational grids, and even public-sector decision making. We introduce an over-estimation strategy to design approximation algorithms for this problem. When all utilities are positive, we obtain an approximation ratio of c1-ϵ, where c is the maximum ratio of the largest utility to the smallest utility of any resource. When some utilities are zero, we obtain an approximation ratio of (1 + 3 c^ + O(δc^ 2)) , where c^ is the maximum ratio of the largest utility to the smallest positive utility of any resource. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Approximation algorithms

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TE-waves in waveguide structures filled with graded dielectric media 25

TE-waves in waveguide structures filled with graded dielectr...

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25th International Conference on Electromagnetics in Advanced Applications, ICEAA 2024

作者： Dalarsson, Mariana School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE 100-44 Sweden

ISBN: (纸本)9798350360974

Graded dielectric media composites, with negative-index materials (MM), are of interest for various applications such as microwave graded-index (GRIN) lenses [ 1,2], wideband THz filtering [3], and enhanced energy harvesting [4]. Graded dielectric media with regular positive-index materials are also of importance in e.g. the field of biosensing. One example is mimicking of dielectric phantoms for the characterization of biological tissue's properties and response to electromagnetic radiation [5,6]. Our research is therefore of interest for applications within both general electromagnetics and biosensing due to its generality. In line with our previous work [7], in the present abstract we review TE-wave propagation in waveguide structures filled with dielectric composites graded along the direction of the waveguide (conveniently chosen to be z-direction). Using Maxwell's equations in a source-free (p = 0 and 7 = 0) inhomogeneous medium{Formula Method} for TE-waves (Ez = 0) in the z-direction along the waveguide structure filled with a non-magnetic (ii(r, a») = ii0) dielectric composite with graded permittivity along the z-direction (e(r, a») = e(z, a»)), the wave equation for the electric field reduces to a simple form V2E(r, a>) + fc2£r(z, a>)E(r, a>) = 0 where k2 = o»2£0lu0 and £r(z,a>) is the relative permittivity function. The longintudinal component of the magnetic field Hz(r,a>) satisfies the same differential equation, i.e. V2Hz(r,a>) + k2Er(z,a>)Hz(r,a>) = 0 . These wave equations have the form of the Klein-Gordon (or Schrôdinger) equation of quantum physics, and they offer readily available exact analytical solutions for a number of permittivity profiles of interest, such as the hyperbolic tangent profile or periodic (sinusoidal) profile and similar. As an example, for a circular waveguide, of radius r0 , filled with graded dielectric composite with the periodic permittivity function{Formula Method} where C {...} and S {...} are the even and odd Mathieu func

关键词： Maxwell equations

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WITH BALANCING DATA TECHNIQUE

FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WIT...

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2023 IET International Conference on engineering Technologies and Applications, ICETA 2023

作者： Damanik, Nurafni Liu, Chuan Ming College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Science National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781839539848

In today's world, online payment has become the most popular transaction method, making payments convenient for people. However, this ease of use comes with the risk of an increasing number of online fraud incidents. Conventional rule-based systems and static fraud detection approaches often struggle to keep up with the ever-evolving tactics of fraudsters. Consequently, there is a growing need for advanced fraud detection methods that can adapt to dynamic fraud patterns, which presents both importance and challenges. This study proposes a fraud online payment detection system based on Logistic Regression and Decision Tree algorithms, enhanced through the use of oversampled data techniques. The experimental evaluation reveals that the Decision Tree, when utilizing the random over-sampling (ROS) technique, outperforms other techniques in terms of accuracy, achieving a remarkable 100% accuracy rate. © The Institution of engineering & technology 2023.

关键词： Decision trees

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Relative Power Loss Analysis of Poly-Si PV Panels: An Overview in Eastern Indian Climatic Condition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1775-1780页

作者： Pal, Sudipta Basu Das Bhattacharya, Konika Mukherjee, Dipankar Department of Computer Science Technology University of Engineering Management Kolkata India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Department of Electronics and Telecommunication Engineering Indian Institute of Engineering Science and Technology Shibpur India

Renewable energies are generated from natural processes that are continuously renewed. Therefore, it is required to combine the PV modules either in series combinations or parallel combinations for developing the appropriate Photovoltaic (PV) panel/PV arrays. Several PV modules are connected in a series–parallel combination to constitute PV arrays. These PV arrays currently act as a decentralized source of D.C power for onward transmission. When identical wattages PV modules are coupled in a parallel or series connection, there must be a probability to the PV module's manufacturing process that gives rise to power loss. Mainly these types of losses are called Mismatch losses. Mismatch losses (power losses) in PV panels principally occur in terms of Relative Power Loss (RPL). Therefore, the authors carried out I–V characterizations of different technology of PV modules under varying insolation and temperatures. The main aim of the work is to develop the right kind of photovoltaic (PV) panel/PV arrays in a particular Eastern Indian climatic zone. From the authors' characterization of RPL, analysis was conducted to judge which technology is most suitable to build a large size array/BIPV. The experimental results reveal lower mismatch losses of poly-Si PV modules for crystalline silicon and amorphous silicon counterparts. © 2022, The Institution of Engineers (India).

关键词： Solar panels

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Saturation-tolerant prescribed control of MIMO nonlinear systems with actuator faults

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Journal of Automation and Intelligence 2025年第1期4卷 10-20页

作者： Hao Lei Ruihang Ji Dongyu Li Shuzhi Sam Ge Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Cyber Science and Technology Beihang UniversityBeijing100191China

This paper addresses the tracking control problem of a class of multiple-input–multiple-output nonlinear systems subject to actuator *** a balance between input saturation and performance constraints,rather than conducting isolated analyses,especially in the presence of frequently encountered unknown actuator faults,becomes an interesting yet challenging ***,to enhance the tracking performance,Tunnel Prescribed Performance(TPP)is proposed to provide narrow tunnel-shape constraints instead of the common over-relaxed trumpet-shape performance constraints.A pair of non-negative signals produced by an auxiliary system is then integrated into TPP,resulting in Saturation-tolerant Prescribed Performance(SPP)with flexible performance boundaries that account for input saturation ***,SPP can appropriately relax TPP when needed and decrease the conservatism of control *** the help of SPP,our developed Saturation-tolerant Prescribed Control(SPC)guarantees finite-time convergence while satisfying both input saturation and performance constraints,even under serious actuator *** are conducted to illustrate the effectiveness of the proposed SPC.

关键词： Prescribed performance control Input saturation Auxiliary system Actuator faults MIMO nonlinear systems Finite-time stability

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Multi‐feature fusion and memory‐based mobile robot target tracking system

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IET Cyber-Systems and Robotics 2024年第3期6卷 62-73页

作者： Hanqing Sun Shijie Zhang Qingle Quan College of Information Engineering Henan University of Animal Husbandry and EconomyZhengzhouHenanChina College of Information Technology and Computer Science University of the CordillerasBaguioPhilippines College of Electrical Engineering Henan University of TechnologyZhengzhouHenanChina

In crowded settings,mobile robots face challenges like target disappearance and occlusion,impacting tracking *** existing optimisations,tracking in complex environments remains *** address this issue,the authors propose a tailored visual navigation tracking system for crowded *** target disappearance,an autonomous navigation strategy based on target coordinates,utilising a path memory bank for intelligent search and re‐tracking is *** significantly enhances tracking *** handle target occlusion,the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced *** control technology ensures robust target tracking by fully utilising appearance information and motion characteristics,even in occluded *** testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments,affirming algorithm robustness.

关键词： autonomous navigation Kalman filtering memory bank mobile robots proportional‐integral‐derivative

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Impact of Skin Lesion Segmentation Labeling Errors on Convolutional Neural Networks 66

Impact of Skin Lesion Segmentation Labeling Errors on Convol...

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66th International Symposium ELMAR, ELMAR 2024

作者： Sojo, Robert Bencevic, Marin Habijan, Marija Galic, Irena Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9798350375411

Accurate segmentation of skin lesions is critical for dermatological diagnosis and treatment. This study investigates the impact of label errors on the performance of convolutional neural networks in skin lesion segmentation. We conducted experiments using ground truth and simulated erroneous labels to train models and analyzed the effects of the errors using regression analysis. To simulate errors, we assume that errors are more likely to occur in more complex border regions. In addition, we add parameters to bias the errors towards either false positives or false negative regions. The overall effect of the false positive and false negative errors on the Dice coefficient is deceptively low. However, when we observe the false negative or false positive rate of the predictions, we see that they are significantly influenced by the bias of the label errors. In addition, label false positives have a more severe impact on prediction results than label false negatives. We recommend focusing on minimizing label false positives and suggest moving beyond mean Dice Score Coefficients for model evaluation. © 2024 IEEE.

关键词： Convolutional neural networks

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A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

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Digital Communications and Networks 2023年第5期9卷 1180-1189页

作者： Xin Liu Liang Zheng Sumi Helal Weishan Zhang Chunfu Jia Jiehan Zhou College of Computer Science and Technology China University of Petroleum(East China)QingdaoChina School of Computing Lancaster University UK Nankai University TianjinChina Information Technology and Electrical Engineering University of OuluFinland

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection *** DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing *** paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to *** defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection *** also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS *** evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence *** show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

关键词： Denial-of-service DRDoS SSDP reflection Attack Broad learning Traffic detection

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer Systems science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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