In the context of carbon neutralization, the electric vehicle and energy storage market is growing rapidly. As a result, battery recycling is an important work with the consideration of the advent of battery retirement and resource constraints, environmental factors, resource regional constraints, and price factors. Based on the theoretical research of intelligent algorithm and mathematical models, an integer programming model of urban power battery reverse supply chain scheduling was established with the goal of the highest customer satisfaction and the least total cost of logistics and distribution, to study the influence of the resources and operation status of a built city recycling center and dismantling center on the power battery reverse supply chain. The model includes vehicle load, customer demand point satisfaction range, and service capacity constraints. This study collected regional image data, conducted image analysis, and further designed an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization algorithm suitable to solve the global optimization problem by introducing the improvement strategy of convergence rate, particle search, and the traditional elite individual retention. The results verified the practicability of the model, the global optimization ability of the algorithm to solve the problem, and the operation speed through comparing the results obtained from the basic algorithm. A reasonable comprehensive solution for the location and path optimization of the urban recycling center was also obtained. Multi-objective optimization was carried out in vehicle scheduling, facility construction, and customer satisfaction construction. The basic algorithm and integrated optimization software were compared. We found that the model and the scheme provided by the algorithm can significantly reduce the operation cost of the enterprise. This research provided new insights for enterprises to effectively utilize resources and optimize the reverse supply chain scheduling of an urban power battery.

/pdf/research-on-multi-objective-optimal-scheduling-for-power-fr89v1mt.pdf

Research on Multi-Objective Optimal Scheduling for Power Battery Reverse Supply Chain

As electric vehicles (EVs) continue to grow in popularity, the demand for efficient and effective EV charging infrastructure is rapidly increasing. However, the design and deployment of such infrastructure faces numerous challenges, some of which include demand uncertainty, EV network layout, maintenance cost, and quality of service (QoS), and have proven to be particularly challenging in cases where all objectives need to be addressed simultaneously. To address these challenges, this study proposes a hierarchical iterative optimization approach that aims to minimize the demand–supply mismatch at a higher level and considers both cost and QoS under uncertainty at a lower level in a dynamic EV charging network. Specifically, the proposed approach uses a time-series linear regression ensemble to accurately predict future demand at demand points, which is then used to match demand at demand points with supply at supply points. We then model the infrastructure allocation problem as a knapsack problem and use an allocation algorithm to determine the optimal number of charging stations at each supply point. The infrastructure is allocated to minimize total cost and maximize QoS. A framework for EV infrastructure optimization is designed and can be applied to any type of network. The effectiveness of our proposed approach is demonstrated through extensive experimentation on various datasets by comparing with existing state-of-the-art approaches. Simulation results show that the proposed approach is robust to different EV networks, handles uncertainty well, can effectively address the challenges associated with EV infrastructure optimization, and can provide policy makers and infrastructure planners with a practical and efficient tool for designing and deploying charging infrastructure. This study contributes to the field of EV infrastructure network optimization by modeling QoS and proposing a hierarchical optimization framework that effectively addresses the challenges associated with the design and deployment of charging infrastructure. 

Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration

Every day, hundreds of domain names, websites, and logos are being cloned by cyber criminals who want to gain our trust to steal our data. As a result, faking logos is becoming a big issue in the online world and needs to be addressed. As a result, fake logos on the internet have become a significant source of worry for businesses and consumers. The algorithm can detect differences in logo design, color, and positioning and assess the possibility of a fake logo. The system's accuracy was evaluated on a massive dataset of actual and false logos, and it obtained a high level of accuracy in recognizing fake logos. The fake logo identification technology has the potential to dramatically increase the credibility and dependability of online material, thereby protecting brand identity integrity. This research proposes a method for detecting fake logos using a Context-dependent similarity algorithm. Our approach involves extracting features from the logos and training a machine-learning classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy.

Online Fake Logo Detection System

Abstract With the increasing prevalence of online fraud and the use of fake logos to deceive consumers, there is a need for effective methods to detect and prevent the use of fake logos on the internet. In this paper, we propose a method for detecting fake logos using machine learning techniques. Our approach involves extracting features from the logos and training a classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy. Every day, hundreds of domain names, websites and logos are being cloned by cyber criminals who want to gain your trust so they can steal your data. It is becoming a big issue in the online world and needs to be addressed. This article will discuss the initial project background of our new Online Fake Logo Detection System 

/pdf/online-fake-logo-detection-system-80kc8qs4.pdf

The impact of green transformation in data centers on corporate carbon emission reduction: empirical evidence from China

Capping carbon emission from green data centers

Electric vehicles (EV) are moderately defeating more roads and replacing pollutants of classical vehicles. Due to rising EV, it is imperative to provide charging stations. However, unscheduled EVs are left because of the unavailability of adequate energy or charging slots. This paper develops an optimisation aware technique for charge scheduling in EV. The first step is the simulation of EV in the Vehicular Ad-hoc Network (VANET) model. Here, the discovery of the changing request from EVs and accessible charging stations is performed. Then, the charge scheduling algorithm is called for scheduling the EV, wherein the charge scheduling algorithm is newly modelled using Jaya-based Multi-Verse Optimizer (JMVO). The proposed JMVO is devised by combining Multi-Verse Optimiser (MVO) and Jaya optimisation algorithm. Here, a multiobjective fitness function is newly devised with certain parameters, including charging cost, user preference, remaining power and distance parameter. As per the scheduling method, the EVs are allocated to the charging station. Then, the EV charging scheduling model attributes are updated to expose the method’s efficiency. The proposed JMVO outperformed with the smallest charging cost of 62.721, smallest fitness of 0.010, highest power of 2.605J and highest user convenience of 0.769. durgamahato379@gmail.com 

Multi-objective optimisation model and hybrid optimization algorithm for Electric Vehicle Charge Scheduling

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Capping carbon emission from green data centers

Multi-objective optimisation model and hybrid optimization algorithm for Electric Vehicle Charge Scheduling