ATM Transaction Status Analysis and Anomaly Detection

This article mainly studies ATM transaction featureanalysis and anomaly detection. Select the trading successrate, transaction response time and other characteristicparameters, analyze the relationship between transactionresponse time and transaction success rate. After comparingthe fuzzy C-means clustering algorithm and the K-meansclustering algorithm, K-means clustering algorithm was usedto classify the transaction response time. Thendesign tradinganomaly detection program. The use of naive Bayesianclassifier for data classification can determine the alarm level.And using Gaussian distribution and Laplace smoothingcalibration to increase model accuracy to reduce false alarm.The use of MATLAB programming to get the followingresult: When the transaction response time is between 0 ~85.57, the system predicts to be successful. When thetransaction response time between 85.57 ~ 212.31, the systempredicts a warning. When the transaction response timebetween 212.31 ~ 1007.8, the system predicts that the alarm

Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem

A PSO-GRNN model for railway freight volume prediction: empirical study from China

Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably. Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper. Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easilyPeer Reviewe

A Time-Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion

This study explores an operational-level container routing problem in the road-rail multimodal service network. In response to the demand for an environmentally friendly transportation, we extend the problem into a green version by using both emission charging method and bi-objective optimization to optimize the CO2 emissions in the routing. Two uncertain factors, including capacity uncertainty of rail services and travel time uncertainty of road services, are formulated in order to improve the reliability of the routes. By using the triangular fuzzy numbers and time-dependent travel time to separately model the capacity uncertainty and travel time uncertainty, we establish a fuzzy chance-constrained mixed integer nonlinear programming model. A linearization-based exact solution strategy is designed, so that the problem can be effectively solved by any exact solution algorithm on any mathematical programming software. An empirical case is presented to demonstrate the feasibility of the proposed methods. In the case discussion, sensitivity analysis and bi-objective optimization analysis are used to find that the bi-objective optimization method is more effective than the emission charging method in lowering the CO2 emissions for the given case. Then, we combine sensitivity analysis and fuzzy simulation to identify the best confidence value in the fuzzy chance constraint. All the discussion will help decision makers to better organize the green multimodal transportation

A 39-GHz Doherty-Like Power Amplifier with 22-dBm Output Power and 21% Power-Added Efficiency at 6-dB Power Back-Off

© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/JETCAS.2024.3351075The design of a Doherty-like power amplifier for millimetre-wave (mm-wave) applications is presented in this work. The designed power amplifier employs a novel symmetrical loadmodulated balanced amplifier (S-LMBA) architecture. This design is advantageous in minimizing the undesired impedance interaction often encountered in the classic LMBA approach. Such interactions are typically due to the use of a non-50 Ω load at the isolation port of the output quadrature coupler. Moreover, magnitude and phase control networks are carefully designed to generate the specific magnitude and phase information for the designed S-LMBA. To demonstrate the proposed ideas, the SLMBA is fabricated in a 45-nm CMOS SOI technology. At 39 GHz, a 22.1 dBm saturated output power (Psat) with a maximum poweradded efficiency (PAE) of 25.7% is achieved. In addition, 1.68 times drain efficiency enhancement is obtained over an ideal Class-B operation, when the designed S-LMBA is operated at 6 dB power back-off. An average output power of 13.1 dBm with a PAE of 14.4% at an error vector magnitude (EVMrms) above -22.5 dB and adjacent channel power ratio (ACPR) of -23 dBc is also achieved, when a 200 MHz single carrier 64-quadratureamplitude- modulation (QAM) signal is used. Including all testing pads, the footprint of the designed S-LMBA is only 1.56 mm2.Peer reviewe