Switching Algorithm Research Articles

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Seismic performance of FRP-repaired RC piers after blast loading

Aiming to evaluate the effectiveness of fiber-reinforced polymer (FRP) repair on the seismic performance of blast-damaged reinforced concrete (RC) piers, the field test and numerical simulation were conducted. Firstly, a successive field explosion test, carbon FRP (CFRP) repair, and lateral cyclic loading test were performed on two 1/2-scale RC pier specimens. The test data including the incident overpressure-time histories of blast wave, damage profiles and lateral force-displacement relationships of pier specimens, etc. were comprehensively recorded and discussed. By comparing with the previous blast-damaged pier specimen, the negative maximum force and ductility of CFRP-repaired pier were increased by 97 % and 44.9 % after 1.0 kg TNT explosion, respectively. Then, an integrated numerical simulation approach based on explicit-implicit switching and full restart algorithms was proposed and validated in predicting the dynamic and quasi-static behaviors of RC piers under successive blast loading, CFRP repair, and lateral cyclic loading. Finally, a prototype RC pier was designed and the suicide vest bomb (TNT equivalence of 9 kg) specified by the Federal Emergency Management Agency was selected as the threat of contact explosion. The influences of FRP type, repair height, and number of FRP layer on restoring the seismic performance of the blast-damaged pier were further discussed. It indicates that, (i) CFRP is more effective than glass FRP and aramid FRP; (ii) increasing the repair height could not significantly improve the seismic performance of pier; (iii) the number of FRP layer has significant effect, e.g., the positive and negative maximum forces after 4-layer CFRP repair are improved by 9.4 % and 45.3 % compared to the unrepaired pier, respectively. The present study could provide a helpful reference for assessing the FRP repair effectiveness on the seismic performance of blast-damaged RC bridge piers.

Base Station Energy Saving based on Imitation Learning in 5G Network

Abstract With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Multi-Agent Collaborative Path Planning Algorithm with Multiple Meeting Points

Traditional multi-agent path planning algorithms often lead to path overlap and excessive energy consumption when dealing with cooperative tasks due to the single-agent-single-task configuration. For this reason, the “many-to-one” cooperative planning method has been proposed, which, although improved, still faces challenges in the vast search space for meeting points and unreasonable task handover locations. This paper proposes the Cooperative Dynamic Priority Safe Interval Path Planning with a multi-meeting-point and single-meeting-point solving mode switching (Co-DPSIPPms) algorithm to achieve multi-agent path planning with task handovers at multiple or single meeting points. First, the initial priority is set based on the positional relationships among agents within the cooperative group, and the improved Fermat point method is used to locate multiple meeting points quickly. Second, considering that agents must pick up sub-tasks or conduct task handovers midway, a segmented path planning strategy is proposed to ensure that cooperative agents can efficiently and accurately complete task handovers. Finally, an automatic switching strategy between multi-meeting-point and single-meeting-point solving modes is designed to ensure the algorithm’s success rate. Tests show that Co-DPSIPPms outperforms existing algorithms in 1-to-1 and m-to-1 cooperative tasks, demonstrating its efficiency and practicality.

An efficient static solver for the lattice discrete particle model

AbstractThe lattice discrete particle model (LDPM) has been proven to be one of the most appealing computational tools to simulate fracture in quasi‐brittle materials. Despite tremendous advancements in the definition and implementation of the method, solution strategies are still limited to dynamic algorithms, resulting in prohibitive computational costs and challenges related to solution accuracy for quasi‐static conditions. This study presents a novel static solver for LDPM, introducing fundamental innovation: (1) LDPM constitutive laws are modified to provide continuous response through all possible strain/stress states; (2) an adaptive arc‐length method is proposed in combination with a criterion to select the sign of the iterative load factor; (3) an adaptive limit‐unloading–reloading path switch algorithm is proposed to restrict oscillations in the global stiffness matrix. Extensive validation of the proposed approach is presented. Numerical results demonstrate that the static solver exhibits satisfactory convergence rates, significantly outperforming available dynamic solutions in computational efficiency.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

Smart Control of Traffic Light Using Image detection

Abstract: Urban areas are increasingly grappling with the issue of traffic congestion, a problem exacerbated by growing populations and the proliferation of motor vehicles. This not only leads to delays and increased stress for commuters, but also contributes to greater fuel usage and environmental pollution. This issue is particularly pronounced in large metropolitan areas. The escalating nature of this problem underscores the necessity for real-time assessments of road traffic density, which can lead to more effective traffic management strategies and signal control. The role of the traffic man controller is pivotal in influencing the flow of traffic, necessitating the optimization of traffic management system to cater to this burgeoning demand. Our proposed solution leverages live footage from intersection cameras to estimate traffic density through image processing and AI techniques. The system also incorporates a traffic signal switching algorithm based on vehicle density, aiming to alleviate traffic congestion. This, in turn, facilitates smoother movement for commuters and helps to mitigate pollution

Optimization of heating temperature uniformity for multi-microwave source group based on dynamic switching topology

Aiming at the mutual coupling of multiple physical fields in the microwave heating process and the generation of thermal runaway phenomenon, in this paper, a cooperative variable power heating strategy with multi-microwave source clusters under dynamic switching topology is proposed in order to improve the temperature uniformity. Firstly, an algebraic graph theory is used to establish a communication topology for complete information transfer, which is used to construct a distributed structure among multiple microwave source intelligences. Secondly, the dynamic topology switching algorithm under event triggering is introduced, and the real-time temperature distribution of the heated materials is considered to achieve the dynamic switching of the topology connection mode between microwave source clusters in order to synchronously coordinate the power consistency output between different clusters. On this basis, a numerical calculation model is constructed to solve the mixed optimization of integer and continuum variables by means of the finite element method. Finally, the effectiveness of the heating strategy to improve temperature uniformity is verified by microwave heating SiC media simulation calculation. The numerical calculation results show that the temperature uniformity indexes are improved by 34.1%–50.4% and 28.1%–55.2% in each horizontal and vertical section for single materials, and by 59.1%–59.7% and 46.5%–63.4% in each horizontal and vertical section for multi-materials, respectively, in comparison with the general heating method. This strategy effectively avoids the residence of hot and cold spots in the material, which in turn optimizes the temperature distribution.

Pictorial depiction on controlling crowd in smart conurbations using Internet of Things with switching algorithms

The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models.

Research on indoor and outdoor positioning switching algorithm based on improved PSO-BP

Aiming at the problems of poor positioning accuracy in the indoor and outdoor junction areas and the loss of positioning signals and discontinuous positioning results during the transition from the indoor area to the outdoor area, this paper proposes a machine learning method to solve the positioning problem in the indoor and outdoor junction areas and the switching problem of positioning methods. An indoor and outdoor positioning switching algorithm based on Particle Swarm Optimization-Back Propagation (PSO-BP) and BP neural network is designed. Through this algorithm, the position of the positioning tag can be judged independently and the coordinates of the positioning tag in the indoor and outdoor junction area can be predicted independently. The experimental results show that the accuracy of positioning area judgment based on PSO-BP neural network can reach 99.91%. The indoor and outdoor boundary area coordinates obtained by BP neural network prediction method are lower than the root mean square error of ultra wide band indoor positioning method and BDS positioning method. The algorithm model proposed in this paper effectively improves the positioning problem of the indoor and outdoor junction area and the positioning problem of the transition from the indoor area to the outdoor area, improves the positioning accuracy and positioning stability of the indoor and outdoor junction area, reduces the positioning cost, and has strong practicability.

The Performance of a Passive Autoranging Method for a Photonic Current Transducer.

This paper reports on the testing and evaluation of a passive autoranging (AR) method designed to dynamically extend the measurement range of a photonic current transducer (PCT) to pave the way toward a realization of a combined metering- and protection-class current sensor. The PCT utilizes a current transformer (CT), a piezoelectric transducer (PZT), and a fiber Bragg grating (FBG) to enable current measurement at multiple points in an electrical power network whereby multiple sensors are deployed and interrogated serially using a single optical fiber. The autoranging technique relies on incorporating static MOSFET switches to instantaneously short individual serially connected CT burdens in response to a measured current magnitude exceeding pre-set thresholds. The AR circuit switching events produce distinctive signal features that are used by the proposed switching algorithm to apply appropriate scaling factors to reconstruct the measured current from the optical signal. It is shown through laboratory experiments that the AR circuit correctly reacts to pre-set burden current thresholds of 130% of the nominal value and 22 times the nominal value, signifying its "metering" and "protection" range boundaries. The circuit reaction time is below 4 ms, rendering it suitable for standard power system protection purposes. Moreover, the operation of the AR circuit is demonstrated for burden currents of up to 100 A for over 1 s, satisfying a test procedure for the secondary CT circuit, as required by some power system operators. It is demonstrated that the proposed switching algorithm allows for a correct reconstruction of the burden currents from the optical signal acquired by the FBG interrogator, offering the potential to realize a dual-class optical current sensor.

Detection of Drowsy Drivers in Video Sequences via LSTM with CNN Features

Fatigued driving is one of the main causes of traffic accidents. This paper developed a method for sleep drive detection that makes use of the convolutional neural network (CNN), recurrent neural network (RNN), short-term neural network (LSTM), cascade, and cam switching algorithms. Using CNN, the algorithm initially extracts the driver's face traits. The RNN that predicts the driver's degree of tiredness is then fed these features. The technology tracks the driver's eyes and eyelids using algorithms called Cascades and Cam shifts. The accuracy of the sleepiness prediction is increased further with the usage of this data. Using datasets of video recordings of attentive and somnolent drivers, the system was assessed. The outcomes demonstrated how well the algorithm could identify sleepy drivers. This system may be able to stop sleepy driving-related accidents. Ten thousand videos of awake and sleepy drivers were used to train the algorithm. Drivers can be observed in real time by the system. If the system detects that the driver is sleepy, it has the ability to sound an alarm. The technology can be used to stop driving when sleepy from causing accidents. Keywords: Convolution neural network, Recurrent neural networks, long short-term memory, eye tracking, face detection, drowsy driver detection, deep learning, computer vision, machine learning, and deep learning.

Optimization of Switching Algorithms for a Thermal Switch-PCM-Switch Stack in a Building Wall for Energy Savings

Thermally switchable building envelopes can provide energy savings compared to their traditional static counterparts. For example, in the summertime cooling season the envelope can switch into a thermally conductive state at night to exploit the free cooling of the nighttime air, while switching back into a highly insulating state during the warm daytime. Further benefits can be realized by incorporating phase change materials (PCMs) into the same building envelope between two thermal switches, because the PCM can store that nighttime cooling for later use during the day. Here we use analytical and numerical modelling to investigate the advantages of a switch-PCM-switch building envelope and specifically look to optimize the switching algorithms for the two switches. We frame the problem using dimensionless groups in order to identify the key parameter groupings that determine the response. We then build a lightweight lumped element model with runtime speeds of <0.5 seconds per scenario, which enables a brute force search of over 300,000 potential switching algorithms to identify the one with the highest total and net energy savings. We compare the results of the brute force approach with an algorithm that was proposed previously based on physical intuition [1]. We then conduct a similar brute force investigation of switching algorithms optimizing for average daily cost of energy, focusing on scenarios where time-of-day energy pricing is important. Using the resulting optimal switching algorithms, we present cost and energy savings for several real cities based on their realistic temperature data and time-of-day pricing.

The time-varying Multivariate Autoregressive Index model

Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model.

Tuning the Poisson’s ratio of two-dimensional model network materials with application to 2D-silica bilayer structures

A fundamental understanding of the elastic properties of 2D network structures is highly beneficial for research and applications of 2D materials. In this context, Poisson’s ratio has been studied elaborately for different crystallographic structures of crystalline phases of bilayer silica structures. This paper focuses on the elastic structure–property relationship of crystalline and vitreous polymorphs of plane network materials. We generate defective crystalline network states and seven sets of disordered states, each with a different level of network heterogeneity, using a dual Monte Carlo bond switching algorithm. Firstly, uniaxial tensile deformation is applied to a model material in a two-dimensional framework, concentrating only on the in-plane network topologies. Secondly, uniaxial tensile deformation is applied to bilayer silica structures, considering the out-of-plane morphology of the material. Mechanical testing is performed using the athermal quasistatic deformation protocol, which allows one to study the influence of the network topology exclusively without any thermal disturbances. We show that manipulating the network heterogeneity allows one to directly tune the Poisson’s ratio of the material, creating possibilities for novel mechanical applications.

Real-Time Traffic Light Optimization Using AI and IOT

Abstract: Urban traffic congestion is a significant challenge globally, impacting transportation efficiency, environmental sustainability, and urban liveability. Traditional traffic control systems often struggle to adapt to changing traffic dynamics, leading to increased congestion and delays. This paper presents a novel traffic management system developed by our team, leveraging cutting-edge technologies such as computer vision, artificial intelligence (AI), and the Internet of Things (IoT). Deployed at intersections, our system utilizes real-time CCTV feeds for traffic analysis, employing advanced image processing and machine learning algorithms, including YOLO V7, to dynamically assess traffic density. These insights inform adaptive adjustments to traffic light timings, with the aim of reducing congestion, enhancing transit efficiency, and mitigating pollution. Our signal switching algorithm, incorporating dynamic logic developed by our team, iteratively adjusts signal timings based on real-time traffic conditions, ensuring responsive and efficient traffic flow management. Through the integration of our innovative technologies, our system seeks to revolutionize urban transportation networks, promoting smarter, more adaptive, and sustainable urban environments.

Traffic Flow Optimization for Metropolitan Cities

This research paper presents the design and implementation of an adaptive traffic signal timer system that utilizes real-time traffic density calculation and an intelligent signal switching algorithm to optimize traffic flow at intersections. The proposed system leverages computer vision techniques like object detection to count and classify vehicles, and then dynamically adjusts the green signal durations based on the detected traffic density. A simulation module is also developed to visualize the system's performance and compare it to a static traffic signal implementation. The results demonstrate the effectiveness of the adaptive approach in reducing vehicle waiting times and improving overall intersection throughput

A newly developed algorithm for switching outpatient medications to medications listed in the hospital formulary: a prospective real-word evaluation in patients admitted electively to hospital

PurposeIn many countries, outpatient and inpatient care are separated. During hospitalization, therefore, switching the outpatient medication to medication of the hospital formulary is required.MethodsWe newly designed a switching algorithm in six switching steps (S0–S5) and conducted a study at Bundeswehr Hospital Hamburg (300 beds, 80% civilians). We performed (i) a medication reconciliation to obtain information on outpatient medications and (ii) a medication review to solve drug-related-problems, e.g., drug-drug interactions. We applied (iii) the algorithm to switch medications to the hospital formulary.Results(i) We identified 475 outpatient medications (median per patient: 4; Q25/Q75 2/7) in 100 patients consecutively admitted to hospital (median age: 71; Q25/Q75: 64/80 years). Of 475 medications, the switching algorithm could not be used since product names were missing in 23.9% and strength in 1.7%. In 3.2%, switching was not required since medication was not prescribed during the hospital stay. (ii) Drug-drug interactions were identified in 31 of 79 patients with more than one medication. (iii) Of 475 medications, 18.5% were on the hospital formulary and therefore did not need to be switched (S0), 0.2% were on a substitution-exclusion list not allowing switching (S1), 42.0% were switched to a generic medication of the hospital formulary (S2), 1.7% to a therapeutically equivalent medication (S3), 0.4% were patient-individually switched (S4), and for 8.2% a standardized/patient-individual switching was not possible (S5).ConclusionsDespite comprehensive medication reconciliation, patient- and medication-related information for switching medications to the hospital formulary was often missing. Once all the necessary information was available, standardized switching could be easily carried out according to a newly developed switching algorithm.

REAL-TIME TRAFFIC LIGHT OPTIMIZATION USING AI AND IOT

In modern cities, the escalating number of vehicles has led to significant traffic issues, affecting road capacity and service quality. Traditional traffic control systems, reliant on fixed signal timers, struggle to adapt to changing traffic dynamics, exacerbating congestion. This paper proposes an innovative approach to traffic management utilizing advanced Computer Vision technology, specifically the YOLOv7 object detection algorithm. By analysing live CCTV footage at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real-time. The system architecture ensures swift processing and response times, with YOLOv7 enabling rapid and accurate vehicle detection. This enables the system to make timely decisions, thereby enhancing overall traffic management efficacy. Leveraging Artificial Intelligence (AI) and Machine Learning techniques, the proposed solution addresses the urgent need for innovative traffic management strategies in urban areas, aiming to alleviate congestion, enhance traffic flow, and reduce environmental impact. Hence, the integration of YOLOv7 technology with adaptive traffic signal switching algorithms represents a promising step towards addressing the complex challenges of urban traffic congestion. Key Words: Computer Vision, Artificial Intelligence, Machine Learning, Traffic Prediction, Adaptive Control, YOLO.