Optimal Speed Research Articles

Development and Optimization of a Real-Time Monitoring System of Small-Scale Multi-Purpose Juice Extractor

According to the concept of smart postharvest management, an information and communication technology sensor–based monitoring system is required in the juicing process to reduce losses and improve process efficiency. Such technologies are considered economically burdensome and technically challenging for small-scale enterprises to adopt. From this perspective, this study aimed to develop a smart monitoring system for the juicing processes in small-scale enterprises and to identify the optimal operating conditions based on the monitoring data. The system developed is equipped with two weight sensors attached to the twin-screw juice extractor, allowing for the automatic measurement of the weight of the raw material and the resulting juice product. The measured data are automatically transmitted and stored on a computer. Additionally, the system was designed to remotely control the speeds of the juicing and feeding screws, which are the primary controlling factors of the twin-screw juicer. Juice yield and processing time were optimized using carrots and pears. The optimal juicing and feeding speeds for pear yield were found to be 167.4 rpm and 1557 rpm, respectively; carrots achieved an optimal yield at a juicing speed of 502.2 rpm and feeding speed of 1211 rpm. In contrast, the processing time was minimized at juicing–feeding speeds of 6–6 and 7–5 for pears and carrots, respectively. Consequently, it was challenging to determine the optimal conditions for simultaneously optimizing the yield and processing time. This also suggests that the juicing process is affected by the properties of the fruits and vegetables being processed. By developing a system capable of accumulating the data necessary for the digitization of postharvest management and food processing, this research offers a valuable platform for the smart monitoring and optimization of the juicing process.

Cooperative Eco-Driving for an All-Electric and Mixed Two-Vehicle Platoon Based on Pontryagin's Minimum Principle

Abstract Cooperative eco-driving (Co-ED) is a promising technology for improving vehicle efficiency through appropriate coordination. Additionally, platooning can significantly improve the vehicle's energy efficiency by reducing aerodynamic resistance. The optimal trajectory of the Co-ED vehicles in a platoon will be challenging to derive due to the high nonlinearity of the aerodynamic drag coefficient. Furthermore, although the electrification of vehicles has made rapid progress, the traffic on the road will still tend to be a mix of conventional vehicles (CVs) and electric vehicles (EVs) for a long time. It is critical to take the energy consumption characteristics of different vehicle types into account for a mixed platoon during Co-ED. This paper considers the platooning effects and heterogeneity of leading vehicles in two-vehicle platoons and utilizes Pontryagin's Minimum Principle (PMP) to derive the optimal speed trajectories for both homogeneous (all-electric) platoon and heterogeneous platoons (with different fuel types of vehicle). Simulation results from the proposed PMP-based Co-ED strategy show that the platooning effect has a noticeable impact on the Co-ED driving behaviors (particularly the inter-vehicle space and the transient performance). Simulation results also demonstrated that the same following EV will result in less energy consumption by 4.8% in an EV-led platoon under urban/suburban scenario and approximately same energy consumption under interstate scenario compared in a CV-led platoon.

Speed Advisor for Fuel Consumption Minimisation Under Real Driving Conditions

This paper deals with minimisation of fuel consumption under real driving conditions using a vehicle speed advisor. The aim is to explore the potential of speed profile optimisation in real driving conditions while assessing the suitability of an application which recommends the driver the optimal vehicle speed sequence that minimises the fuel consumption on a particular route. The speed advisor is based on solving the Optimal Control problem of covering a particular route with minimum fuel consumption with a defined time constraint. The approach presented was applied to and implemented on a real passenger vehicle to obtain a trade-off between fuel consumption and travel time for several trips on the route. Experimental results are presented with and without advisory. With speed advisor, the results approach the pareto front with lesser dispersion. On the other hand, without advisory, the dispersion is higher and largely above the pareto front.

Weak dependence and optimal quantitative self-normalized central limit theorems

Consider a stationary, weakly dependent sequence of random variables. Given only mild conditions, allowing for polynomial decay of the autocovariance function, we show a Berry–Esseen bound of optimal order n^{-1/2} for studentized (self-normalized) partial sums, both for the Kolmogorov and Wasserstein (and L^{p} ) distances. The results show that in general, (minimax) optimal estimators of the long-run variance lead to suboptimal bounds in the central limit theorem, that is, the rate n^{-1/2} cannot be reached, refuting a popular belief in the literature. This can be salvaged by simple methods. We reveal that in order to maintain the optimal speed of convergence n^{-1/2} , simple over-smoothing within a certain range is necessary and sufficient. The setup contains many prominent dynamical systems and time series models, including random walks on the general linear group, products of positive random matrices, functionals of GARCH models of any order, functionals of dynamical systems arising from stochastic differential equations, iterated random functions and many more.

Optimal Speed and Range Estimation using Dual Linear Frequency Modulated Signals

Optimal Speed and Range Estimation using Dual Linear Frequency Modulated Signals

Recent innovations in interfacial strategies for DLP 3D printing process optimization.

Three-dimensional (3D) printing, also known as additive manufacturing, is capable of transforming computer-aided designs into intricate structures directly and on demand. This technology has garnered significant attention in recent years. Among the various approaches, digital light processing (DLP) 3D printing, which utilizes polymers or prepolymers as the ink, has emerged as the leading new technology, driven by high demand across diverse fields such as customized production, healthcare, education, and art design. DLP 3D printing technology employs cured slices as molding units and is recognized for its potential to achieve both high printing speed and resolution. Recent insights into the DLP printing process highlight its inherent interface transformations between liquid and solid states. This review summarizes key aspects of the printing process, speed, precision, and material diversity optimization, from the view of interfacial interactions between solid and liquid phases which are influenced by resin formation, curing surfaces and light source properties. These interactions include those at the liquid resin-UV pattern interface, the cured structure-curing surface interface, the liquid resin-curing surface interface, and the liquid resin-cured structure interface, each contributing to the unique characteristics of the printed results. Finally, this review addresses the current challenges and limitations of DLP 3D printing, providing valuable insights for future improvements and guiding potential innovations in the field.

Strategi Optimalisasi Search Engine Optimization (SEO) untuk Meningkatkan Visibilitas Produk Lembaga Keuangan Syariahdi Internet

This research aims to optimize Search Engine Optimization (SEO) strategies to increase the visibility of Islamic financial institution products on the internet. With a quantitative descriptive approach, this research analyzes the effectiveness of SEO implementation using secondary data obtained from SEO analysis tools such as Google Keyword Planner and SEMrush. The data collected includes keyword search volume, difficulty level, as well as backlinks and technical SEO performance (page speed and mobile optimization). Descriptive statistical techniques are used to describe SEO trends, while correlation analysis evaluates the relationship between SEO variables and the digital visibility of Islamic financial institutions. The research results show that comprehensive SEO implementation including on-page SEO, off-page SEO, and technical SEO is able to increase website rankings in search engines, increase organic traffic, brand awareness, and customer conversions. The case example of Dubai Islamic Bank (DIB) illustrates how keyword optimization and quality backlinks contribute significantly to increasing digital visibility. These findings indicate that Islamic financial institutions in Indonesia can take advantage of search trends that are still low in competition to strengthen their position in search engines, through an integrated and well-targeted SEO strategy.

Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Modeling and Simulation of Reel Motion in a Foxtail Millet Combine Harvester

Due to the high plant height, heavy ear, and easy forward tilt of millet during harvesting, the reel of a traditional combine harvester is often difficult to adapt to the special growth characteristics of millet, resulting in serious grain loss. Therefore, optimizing the design of the reel is important to improve the harvesting efficiency of millet and reduce the grain header loss. In order to determine the optimal reel speed ratio(λ), kinematics simulation experiments and analysis were carried out under different combinations of forward speed and reel revolution speed. The results showed that the supporting effect of the reel is insufficient when λ ≤ 1, and the trochoidal trajectory of the reel can provide a backward driving force when λ > 1, the optimum speed ratio of the reel should be controlled between 1.5 and 1.6. Field experiments results showed that the grain header loss rate was the lowest (0.9%) when λ = 1.6. This study provides key guidance for the adjustment of the combine harvester, effectively reducing the grain header loss rate in harvesting millet, and improving the harvesting efficiency.

Incremental sheet forming of dissimilar friction stir welded AA1100 and AA6061 blanks using single point tool

Recently, single point incremental forming (SPIF) gained popularity among researchers as well as in industry as a die - less flexible forming technique. In this work, two different metal sheets of Aluminium alloy, AA1100 and AA6061, each of thickness 1.2 mm is welded together to produce a tailor welded blank utilizing Friction Stir Welding (FSW) which is done by vertical milling machine and the forming operation is performed on the tailor welded blanks as well as base sheets. The main objective of this study is to identify the input factors affecting FSW process, compare the formability of the tailor welded sheets and to find out optimum step depth, feed and speed of SPIF. The single point incremental sheet forming is done using CNC Milling machine and optimized the experiment results using Taguchi method. After conducting the experiments, the spring back, surface roughness, wall angle, and formability is measured and Signal to Noise ratio, means were calculated. With the help of graphs, optimum parameter values were obtained using Minitab software. From analysing the results revealed that the step depth and spindle speed are the critical parameters affecting the incremental sheet forming process.

Research on Speed Planning and Energy Management Strategy for Fuel Cell Hybrid Bus in Green Wave Scenarios at Traffic Light Intersections Based on Deep Reinforcement Learning

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy.

Online reinforcement learning control for maintaining an optimum beam collision on an electron-positron collider

For double-ring colliders, it is a vital task to make the two beams meeting each other properly at the interaction point. BEPCII is a double-ring collider that operates in the decay mode, as the beam currents decrease over time, the beam orbits need to be continuously adjusted to maintain the optimum collision conditions. Originally, this task was carried out manually, with operators adjusting three offset control knobs (x,y,y′) according to the luminosity. There is a crucial necessity to implement advanced automated control approaches. However, the feedback methods are ineffective due to the constraints imposed by machine characteristics. Nevertheless, the optimization methods are also limited by the slow response speed of BEPCII and have intrinsic limitations. Reinforcement learning (RL), which learns from past experiences, provides a new approach for handling such problems. In this paper, we implemented two automated control methods: the dither method and the deep Q-network (DQN) RL method. The dither method is a numerical optimization method used to provide more online data for DQN training. With the help of the historical data from the dither method, we successfully trained a DQN agent to control the offset knobs to optimize the luminosity. The DQN method exhibited significantly better performance than the dither method, achieving faster optimization speeds and yielding higher integral luminosity. Furthermore, the DQN method has been integrated into the daily operations of BEPCII, achieving long-term stable deployment and effectively substituting manual labor. Published by the American Physical Society 2024

Efficiency of Two Laterites in Cyanide Removal from Aqueous Solutions: Equilibrium and Kinetic Studies

In the dynamic of drinking water supply in rural populations, water pollution by cyanide is one of challenges that impacts the process in the mining areas of Burkina Faso. The objective of this work was to assess the efficiency of laterite soils to remove cyanide from water. To do this, two laterites were prepared and characterized by spectroscopic and analytical techniques to serve as adsorbent. The cyanide removal was carried out using batch experiments with cyanide aqueous solutions. The characterization of laterites using analytical techniques showed a specific surface area of 42.39 and 24.55 m<sup>2</sup>.g<sup>-1</sup>. The crystalline phases were mainly kaolinite, goethite, hematite, quartz, and alumina. The optimization of the operating parameters indicated a strong influence of operating conditions on the adsorption process. Indeed, the optimum stirring speed was 150 rpm corresponding to an adsorption capacity of 0.14 and 0.34 mg/g using raw (LB) and treated (LT) laterites, respectively. By assessing the influence of the contact time, the adsorption capacities were 0.35 and 0.19 mg.g<sup>-1</sup> at 40 and 75 min respectively using LT and LB corresponding to a treatment rate of 53% and 28%. The optimal doses were 28 and 45 g.L<sup>-1</sup> at the optimal temperature of 30°C using LB and LT. Results concluded the efficiency of treated laterite comparatively to the raw laterite. The isotherm modelling concluded on Freundlich isotherm indicating a multilayer adsorption following a pseudo-second order kinetic. Therefore, these laterites would be good filters for the treatment of cyanide enriched waters and other heavy metals in dynamic experiments.

Dynamic open time‐dependent traveling salesman problem with speed optimization

AbstractIncreased awareness of people of the problems caused by CO2 emissions brings companies to consider environmental issues in their distribution systems. The rapid advance in technology allows logistics companies to tackle with dynamic nature of distribution networks (e.g., a change in the vehicle speed due to unexpected events). The planned routes at the beginning of the time horizon could be subject to modification at any point in time to account for the recent traffic information. This study addresses a dynamic open time‐dependent traveling salesman problem. The problem also involves speed optimization that aims to find optimal vehicle speed in a dynamic setting by respecting real‐time traffic conditions. We develop a mixed integer linear programming (MILP) formulation for the addressed problem to determine routing and vehicle speed decisions. Furthermore, a MILP‐based myopic‐clustering decomposition heuristic algorithm has been introduced to solve large‐sized instances within reasonable solution times. The use of the heuristic algorithm provides decision‐makers with a responsiveness capacity by enabling fast incorporation of dynamically observed data during operations. The numerical analyses demonstrate the potential benefits of employing the proposed tools.

A high-precision calibration method for nonlinear error coefficients of accelerometer components

Abstract With the continuous improvement of measurement accuracy requirements for inertial devices, how to accurately calibrate nonlinear error coefficients of accelerometer components has become an important factor affecting the accuracy of the inertial navigation system. Centrifuge calibration method can continuously provide a specific force greater than 1g, which can fully excite the nonlinear errors of accelerometer components and is a commonly used method for calibrating nonlinear error coefficients. However, on the one hand, traditional centrifuge speeds are often selected based on empirical experience, lacking a scientific determination method. This can lead to a decrease in the calibration accuracy of nonlinear error coefficients. On the other hand, the inability to accurately model the highly complex and time-varying test errors during actual calibration further reduces the calibration accuracy. Therefore, a high-precision calibration method for nonlinear error coefficients is proposed. Firstly, by introducing G-optimal experimental design criterion to minimize the maximum scaled prediction variance of output prediction values, the optimal speed combination is designed to achieve the highest accuracy in estimating nonlinear error coefficients. Based on the idea of semi-parametric regression, system errors caused by calibration test errors are treated as parameters to be estimated, and a high-precision nonlinear error coefficient calibration model is established. Then the influence of calibration test errors is eliminated by estimating and compensating the system errors. Centrifuge calibration test results show compared with the traditional method, the ranges and standard deviations of the repeated calibration results of the proposed method are reduced by more than 80.37% and 63.01%. This indicates that the proposed method can effectively eliminate the influence of calibration test errors and achieve high-precision calibration of nonlinear error coefficients.

Prevention and Control of Spontaneous Combustion of Coal in the Goaf Under Z+I Ventilation Based on 2GN00 mining Process

ABSTRACT Traditional coal pillar mining technology will cause the waste of resources, and the roadway excavation. The N00 process eliminates the need for roadway excavation and coal pillar storage. The ventilation mode under the N00 construction method differs from conventional U-shaped ventilation. It is difficult to determine the danger area of spontaneous combustion. Therefore, this paper takes 21607 goaf with Z+I ventilation under the 2GN00 construction method in a mine in Guizhou province as the engineering background and adopts experimental research and numerical simulation methods to prevent and control coal spontaneous combustion hazards. Firstly, the spontaneous combustion tendency experiment determines the grade of spontaneous combustion tendency of coal samples. Secondly, the Fluent numerical simulation method is used to screen the optimal wind speed ratio of the two air inlets. Finally, numerical simulation is used to select the optimal spraying distance. The results show that the spontaneous combustion grade of coal samples makes it easy to produce spontaneous combustion. By analyzing the numerical simulation results of 6 groups of wind speed ratios, the best wind speed ratio of the two inlets is 1:1. Currently, the risk area of spontaneous combustion in the goaf is 5440.8 m2. Then the numerical simulation of the best slurry spraying distance is carried out with the wind speed ratio 1:1. The optimum distance of 90 m was finally determined. The area of the spontaneous combustion hazards area was reduced to 2223.8 m2. The results provide theoretical guidance and technical support for engineering applications.

A speed optimization model for connected and autonomous vehicles at expressway tunnel entrance under mixed traffic environment.

Rear-end collisions frequently occurred in the entrance zone of expressway tunnel, necessitating enhanced traffic safety through speed guidance. However, existing speed optimization models mainly focus on urban signal-controlled intersections or expressway weaving zones, neglecting research on speed optimization in expressway tunnel entrances. This paper addresses this gap by proposing a framework for a speed guidance model in the entrance zone of expressway tunnels under a mixed traffic environment, comprising both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). Firstly, a CAV speed optimization model is established based on a shooting heuristic algorithm. The model targets the minimization of the weighted sum of the speed difference between adjacent vehicles and the time taken to reach the tunnel entrance. The model's constraints incorporate safe following distances, speed, and acceleration limits. For HVs, speed trajectories are determined using the Intelligent Driver Model (IDM). The CAV speed optimization model, represented as a mixed-integer nonlinear optimization problem, is solved using A Mathematical Programming Language (AMPL) and the BONMIN solver. Safety performance is evaluated using Time-to-Collision (TTC) and speed standard deviation (SD) metrics. Case study results show a significant decrease in SD as the CAV penetration rate increases, with a 58.38% reduction from 0% to 100%. The impact on SD and mean TTC is most pronounced when the CAV penetration rate is between 0% and 40%, compared to rates above 40%. The minimum TTC values at different CAV penetration rates consistently exceed the safety threshold TTC*, confirming the effectiveness of the proposed control method in enhanced safety. Sensitivity analysis further supports these findings.

Mixed Label Assignment Realizes End-to-End Object Detection

Currently, detectors have made significant progress in inference speed and accuracy. However, these detectors require Non-Maximum Suppression (NMS) during the post-processing stage to eliminate redundant boxes, which limits the optimization of model inference speed. We first analyzed the reason for the dependence on NMS in the post-processing stage. The result showed that a score loss in a one-to-many label assignment leads to the presence of high-quality redundant boxes, making them difficult to remove. To realize end-to-end object detection and simplify the detection pipeline, we propose herein a mixed label assignment (MLA) training method, which uses one-to-many label assignment to provide rich supervision signals, alleviating the performance degradation, and we eliminate the need for NMS in the post-processing stage by using one-to-one label assignment. Additionally, a window feature propagation block (WFPB) is introduced, utilizing the inductive bias of images to enable feature sharing in local regions. Through these methods, we conducted experiments on the VOC and DUO datasets; our end-to-end detector MA-YOLOX achieved 66.0 mAP and 52.6 mAP, respectively, outperforming the YOLOX by 1.7 and 1.6. Additionally, our model performed faster than other real-time detectors without NMS.

Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones

The rapid development of autonomous driving technology is widely regarded as a potential solution to current traffic congestion challenges and the future evolution of intelligent vehicles. Effective driving strategies for autonomous vehicles should balance traffic efficiency with safety and comfort. However, the complex driving environment at highway entrance ramp merging areas presents a significant challenge. This study constructed a typical highway ramp merging scenario and utilized deep reinforcement learning (DRL) to develop and regulate autonomous vehicles with diverse driving strategies. The SUMO platform was employed as a simulation tool to conduct a series of simulations, evaluating the efficacy of various driving strategies and different autonomous vehicle penetration rates. The quantitative results and findings indicated that DRL-regulated autonomous vehicles maintain optimal speed stability during ramp merging, ensuring safe and comfortable driving. Additionally, DRL-controlled autonomous vehicles did not compromise speed during lane changes, effectively balancing efficiency, safety, and comfort. Ultimately, this study provides a comprehensive analysis of the potential applications of autonomous driving technology in highway ramp merging zones under complex traffic scenarios, offering valuable insights for addressing these challenges effectively.

ADDITIVE TECHNOLOGIES IN CONSTRUCTION: TECHNICAL, ECONOMIC AND MANAGEMENT ANALYSIS

The study is devoted to a comprehensive analysis of the introduction of additive technologies into modern construction production, revealing the technical, economic, and managerial aspects of concrete 3D-printing of architectural structures. The work systematically analyzes the evolution of additive manufacturing technologies and identifies the main structural types of 3D-printers (an additive machine for layer-by-layer construction of objects), including portal, robotic, mobile, and hybrid systems. A detailed study of the technological parameters of concrete 3D-printing with concrete mixtures is presented, in particular, optimal printing speed modes, layer parameters, criteria for shaping and quality of building structures. A comparative analysis of the technological capabilities of leading world equipment manufacturers, such as ICON, COBOD International, Apis Cor, and WinSun, is conducted. The economic analysis demonstrates significant advantages of additive technologies: reduction of construction time by 2-6 times, reduction of construction cost by 20-35%, and increase in the load-bearing capacity of structures. The study comprehensively explores the structure of capital and operational expenses associated with technology implementation. Special emphasis is placed on the management aspects of introducing additive technologies, highlighting the critical need for an interdisciplinary approach and knowledge integration across architecture, engineering, management, and computer modeling. The study determines the prospects for the development of concrete 3D-printing technology in the construction industry and outlines the main directions of further scientific research and practical implementation of innovative solutions. This study was developed between the Department of "Integrated Technologies of Mechanical Engineering" named after M. Semko of NTU "KhPI" and "Geopolimer" LTD to implement innovative technologies in the construction industry.