Trip Time Research Articles

A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocol.

In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.

Theoretical model of passive mode-locking in terahertz quantum cascade lasers with distributed saturable absorbers

Abstract In research and engineering, short laser pulses are fundamental for metrology and communication. The generation of pulses by passive mode-locking is especially desirable due to the compact setup dimensions, without the need for active modulation requiring dedicated external circuitry. However, well-established models do not cover regular self-pulsing in gain media that recover faster than the cavity round trip time. For quantum cascade lasers (QCLs), this marked a significant limitation in their operation, as they exhibit picosecond gain dynamics associated with intersubband transitions. We present a model that gives detailed insights into the pulse dynamics of the first passively mode-locked QCL that was recently demonstrated. The presence of an incoherent saturable absorber, exemplarily realized by multilayer graphene distributed along the cavity, drives the laser into a pulsed state by exhibiting a similarly fast recovery time as the gain medium. This previously unstudied state of laser operation reveals a remarkable response of the gain medium on unevenly distributed intracavity intensity. We show that in presence of strong spatial hole burning in the laser gain medium, the pulse stabilizes itself by suppressing counter-propagating light and getting shortened again at the cavity facets. Finally, we study the robustness of passive mode-locking with respect to the saturable absorber properties and identify strategies for generating even shorter pulses. The obtained results may also have implications for other nanostructured mode-locked laser sources, for example, based on quantum dots.

A math-heuristic approach for scheduling the production and delivery of a mobile additive manufacturing hub

This paper investigates an innovative printing-while-delivering paradigm within a mobile additive manufacturing (MAM) hub. Different from the conventional production-first-delivery-second (PFDS) mode, the MAM hub, comprising a 3D printer and a truck, can seamlessly integrate the production and delivery processes. Throughout the delivery process of the MAM hub, the embedded 3D printer within the truck concurrently prints products for customers in batches. This capability significantly diminishes customer dissatisfaction caused by time delays between order placement and product receipt, which requires coordinating the delivery routing of truck and the production scheduling of 3D printer. To tackle this problem, a mixed integer programming model is firstly established to minimize the required time of the MAM hub returning to depot after completing all deliveries. Then, a math-heuristic approach combining the genetic algorithm and branch-and-bound is devised to solve the mathematical model efficiently. The delivery sequence of truck is represented as a chromosome in the genetic algorithm; while, for each delivery sequence, the optimal production scheduling of 3D printer is obtained by branch-and-bound based on the theoretical analysis about the relationship between production and delivery sequences. In the numerical experiments, the proposed math-heuristic algorithm produces results identical to CPLEX solver outcomes in small-scale cases. For larger instances, it outperforms traditional genetic algorithms by 3.39% to 19.54%. The proposed batch printing mode consistently surpasses the non-batch mode by 10% to 25%. Moreover, compared to PFDS, the printing-while-delivering paradigm reduces trip time by over 40%. Finally, we analyze the effects of customer quantity, distribution radius, and printing time on the objective, providing management insights about optimal production and delivery strategy. The research finding not only aids in scheduling the MAM hub but also holds the potential to address other applications characterized by simultaneous production and delivery requirements.

Voltage Dip Measurements along MV lines vs Primary Substations Measurements

The Italian MV networks voltage quality monitoring system has been collecting power quality data since 6 years by 600 instruments, 400 of which installed on HV/MV substations MV bus-bars and statistical representative of the network, the other 200 installed at some nodes of the MV lines [1]. The paper is focused on the analysis of voltage dips monitored at some of the 200 measuring points considered more significant. The aims were to confirm, on a wider scale, the representativeness of the statistics gathered on the HV/MV stations MV bus-bars with respect to that expected along the MV lines and to investigate the events way of propagation along different voltage levels. The analysis has been performed by both statistical methods on real data and DIgSILENT1 simulations. The possibility to get the MV network voltage dip performance along the lines by measurements at HV/MV stations level has been widely confirmed. Measurements at MV lines have also confirmed the percentages of probable HV origin voltage dips, evaluated at national and macro area levels. The voltage dips duration distributions have shown a good accordance with the typical tripping times of the HV and MV protection system.

The rich heterogeneous dial‐a‐ride problem with trip time prediction

AbstractThe dial‐a‐ride problem (DARP) involves designing vehicle routes to fulfill the door‐to‐door transportation requests of users where the goal is to minimize costs while satisfying transportation requests. In this paper, we introduce the rich heterogeneous DARP, which extends the generalized heterogeneous DARP to consider a fleet of buses and taxis, multiple depots, time windows at pickup and delivery locations, maximum ride and waiting times, and the possibility of an accompanying person. Our approach is based on a real service in Barcelona, and we also consider the variation in trip duration based on the time of day and day of the week. A predictive model is developed using machine learning techniques to estimate trip durations accurately. We apply our proposal to the daily door‐to‐door transportation of people with reduced mobility in Barcelona and demonstrate its superiority in terms of costs and quality of service by using the Gurobi optimizer. Additionally, we provide an analysis of the consequences of varying certain features on the costs and quality of service.

Impact of Non Scheduled Public Mini Bus Stops on Transit Trip Time: A Case Study Amman-Jordan

Public transport services face many challenges around the world. Due to the rise in demand for public transport, authorities desperately need to develop the systems, and it provides a service to be the same level for private cars in terms of service reliability measures. This research aims to study the influence of the non-scheduled random public mini bus stops on travel time for a specific mini bus route. These non-scheduled stops became a habitual behavior for Public mini buses, which affects the daily users of this transportation service in Jordan. In this study, a comprehensive plan was designed to collect data regarding the mini bus behavior along a selected bus route, using a transit on- board survey, GPS and questionnaires. The data collection involved locations of non-scheduled stops, number of stops, distance and speed. The data was then used to develop a trip time model. The developed model detects the delay time due to the non- scheduled mini bus stops. The analysis of the data indicated that passengers mainly depend on random mini bus stops because there aren’t scheduled stops, thus a large impact on traffic jam. Furthermore, it was found that the main variables that affect the trip travel time are the total delay time due to stopping randomly to board and alight passengers. The study concluded that eliminating the non-scheduled mini bus stops will reduce the trip time, thus get improve he service reliability.

Utilizing Machine Learning as a Prediction Scheme for Network Performance Metrics of Self-Clocked Congestion Control Algorithm

Congestion Control (CC) is a fundamental mechanism to achieve effective and equitable sharing of network facilities. As future networks evolve towards more complex paradigms, traditional CC methods are required to become more powerful and reliable. On the other hand, Machine Learning (ML) has become increasingly popular for solving challenging and sophisticated problems, and scientists have started to turn their interest from rule-based approaches to ML-based methods. This paper employs machine learning models to construct a performance evaluation scheme to predict network metrics for the Self-Clocked Rate Adaptation for Multimedia (SCReAM) algorithm. It uses a rigorous data preprocessing pipeline and a systematic application of ML methods to enhance the performance of the regression model for SCReAM’s performance metrics. Also, we constructed a dataset that provides SCReAM’s input parameters and output metrics, such as network queue delay, smoothed Round Trip Time (sRTT), and network throughput. Each prediction process has several phases: choosing the best initial regressor model, hyperparameter tuning, ensemble learning, stacking regressors, and utilizing the holdout data. Each model’s performance was evaluated through various regression metrics; this study will mainly focus on the coefficient of determination (R2) score. The improvement between the initial best-selected model and the final improved model determined that we were able to increase R2 up to 96.64% for network throughput, 99.4% for network queue delay, and 100% for sRTT.

Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time

The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.

A Round Trip Time Weighting Model for One-Way Delay Estimation

A Round Trip Time Weighting Model for One-Way Delay Estimation

A Private SDR 5G SA Network: An Evaluation of the Quality of Service (QoS) and Computational Resource Allocation for eMBB Slice

This paper assesses the network performance in terms of Quality of Service (QoS) for the enhanced Mobile Broadband (eMBB) slicing of a private Stand-Alone (SA) network and a base station, identified as Next-Generation Node B (gNB), which is operating at 3.75GHz and using a Software Defined Radio (SDR) device. The contributions of this work are the evaluation of the computational resource allocation of a private 5G SA and the comparison of the QoS performance of the private SA network with a controlled laboratory testbed and a simulation tool, which is known as UERANSIM (User Equipment and Radio Access Network). Additionally, open-source monitoring systems were used in order to collect memory and CPU usage of the network during the experimentation. For the deployment of the SDR 5G SA gNB, the Universal Radio Software Peripheral (USRP) version B210 equipment was utilized. The results also consider the evaluation of QoS metrics i.e. throughput, jitter, round trip time and propagation delay to assess the performance of the private SDR 5G SA network.

Evaluating the Impact of Cyberattacks on PLC Performance: A Systematic Implementation and Empirical Investigation

Programmable Logic Controller plays a pivotal role in the operation of Industrial Control Systems, which are widely used to monitor and control critical infrastructure and manufacturing processes. However, the rise of Industry 4.0, coupled with the increasing sophistication of cyber threats poses a significant risk to the security of PLC. This study implements a wide range of attacks on a PLC, including Code Injection, Man in the Middle, and a Denial-of-Service attack. The study further scrutinizes the impact of these attacks on the performance of the PLC with a view to developing effective security measures to mitigate cyber risks in Operational Technology systems. The study employs throughput and round trip time as the key metrics to evaluate PLC performance under normal conditions and during cyberattacks. The results indicate a significant degradation in the PLC performance metrics during the attacks, with DoS having the highest impact followed by Man in the Middle attack. These results highlight the need for robust cybersecurity measures to secure PLC from potential cyber threats.

Design of Intelligent City Communication Network Based on Internet of Things

Abstract In response to the advancements in Internet of Things (IoT) technology and the growing demands for data transmission, there is a crucial need for further integration of communication network technology with IoT to enhance efficiency. This paper presents the design of a multi-node communication network model utilizing the Orthogonal Frequency-Division Multiplexing (OFDM) algorithm. In the realm of physical layer parameter design, uplink, and downlink carriers are allocated based on OFDM symbols, employing the Zadoff-Chu (ZC) sequence as the pilot frequency sequence. Additionally, the Cyclic Redundancy Check (CRC) error-checking code is implemented to ensure the accuracy of network transmissions. The model proposed in this study is applied to the operational dynamics of traffic systems within a smart city, focusing on the security and operational efficiency of the traffic communication network. Empirical testing and analysis demonstrate that the communication network, as designed, is susceptible to malware attacks by 25.68%, representing the least vulnerability among the three algorithms assessed. Moreover, in examining the bus trip time distribution with the implementation of this model, the average running time is predominantly around 11.76 minutes, which is a reduction of 0.74 minutes compared to previous configurations, thereby indicating an improvement in operational efficiency.

SCALABILITY IMPROVEMENT OF ACTIVE PROBING FOR FAILURE DETECTION IN LARGE-SCALE SOFTWARE DEFINED NETWORKS USING COMMUNITY DETECTION ALGORITHMS

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

Optimizing Vertical Transportation in Buildings: Strategies for Efficiency, Safety, and User Experience

The design and planning of vertical transportation systems within buildings are critical components that profoundly impact the overall functionality, safety, and user experience. As urban landscapes evolve, with the construction of taller and more complex structures, the demand for efficient vertical mobility solutions becomes increasingly imperative. This abstract delves into key strategies for optimizing vertical transportation within buildings, encompassing considerations such as traffic analysis, smart systems, energy efficiency, and user experience. Key Words: vertical transportation, lifts, escalator, Round trip time, Waiting time, Rated speed.

Vehicle-to-grid bidding for regulation and spinning reserve markets: A robust optimal coordinated charging approach

This paper handles the problem of optimal charging control for electric vehicle (EV) aggregators in energy and ancillary service markets under uncertainties. We propose a robust optimal coordinated charging (OCC) model that formulates a robust linear programming (RLP) for the EV aggregator formed on unidirectional vehicle-to-grid (V2G) and coordinates the provision of regulation and spinning reserves. Our model considers many uncertainties in the electricity markets, like ancillary service prices and the placement signals, as well as accurate parameters associated with EV behavior, like EV availability, time of trips, and trip duration. The objective of the optimization is to maximize the aggregator's income from V2G by contributing to the ancillary services markets. The proposed robust OCC model, a robust linear problem (RLP) model, is simulated using the CPLEX solver in GAMS software. We compare our model with the existing deterministic models and show that our model increases the aggregator's revenues and then reduces the deviation between predicted and realized revenues. We propose a new and effective method for EV aggregators to bid into energy and ancillary service markets under uncertainties. Our model shifts the charging time of EVs from peak to off-peak hours, resulting in an improved final state of charge.

Coordinated Overcurrent Relay Development with Arduino and ACS712

This study presents the design and evaluation of an overcurrent relay system using Arduino Uno, ACS712 current sensors, and electromagnetic relays. Conducted at the University of Negros Occidental–Recoletos, Philippines, from March 2023, the research focuses on developing a relay with accurate trip times and reliable performance. The designed relay features multiple characteristics including Standard Inverse, Very Inverse, and Extremely Inverse, with time delays ranging from 20 ms to 100 ms and time multiplier settings from 0.1 to 0.23. Testing results showed that the Extremely Inverse characteristic achieved the fastest trip time of 0.11 seconds at 9.3 A, while the Normal Inverse characteristic had a trip time of 1.04 seconds at 1.7 A. The relay's error margins were well within the 5% tolerance limit set by IEC 60255 standards, with errors ranging from 0.027 to 0.028. Effective coordination was achieved by adjusting the Time Multiplier Setting (TMS) values to ensure fault isolation without disrupting other system components. The results confirm that the relay design is both accurate and reliable, suitable for practical implementation in electrical protection systems. Future work could involve integrating additional functionalities and testing under diverse environmental conditions.

Study of time indicators of public transport operation depending on the season of the year

Mobility problems in large cities of Ukraine and Eastern Europe are complicated by the fact that the increase of private transport volume significantly exceeds street and road network`s capacity. This is most noticeable during peak periods in terms of daylight hours and throughout the year. From the point of sustainable mobility view, this negative phenomenon significantly affects urban public transport, which does not have separate dedicated traffic lines. This article analyzes the issue regarding the deterioration of the transport situation in large cities. The reason for this is the increase in traffic on main streets during the day peaks, as well as the presence of seasonal traffic factors. If the issue of the occurrence and traffic jams duration and the increase in the correspondence time of private transport is sufficiently studied, then the problems of changing the schedules of public transport and taking into account the increase in the trip duration depending on the time of year need to be clarified. The routes of public transport, which do not have a separate infrastructure and move in the general flow together with private cars, were chosen for the study. According to the results of remote monitoring of public transport, a change in the trip duration and time lost due to the boarding and disembarking of passengers on similar trolleybus routes in different seasons was established. Based on the obtained data, a matrix of trip duration unevenness coefficients for public transport routes was formed, and a measure of the seasonality effect on these indicators was established. The obtained results make it possible to quantitatively determine the influence of the season and time of the day on the change in the trip duration, which can be applied in further studies using simulation tools and for practical use in drawing up seasonal traffic schedules. The results of the research complement the currently relevant scientific works, which concern the problems of seasonal mobility, as well as the influence of the social infrastructure objects functioning (schools, kindergartens, and other educational institutions) of cities on the peak load of the street and road network, which extends the duration of traffic not only for private but also public transport.

Vliyanie nagreva na generatsiyu i svoystva platikonov v vysokodobrotnykh opticheskikh mikrorezonatorakh

Pumping a high-Q optical microresonator by an external laser is inevitably associated with thermal effects. They have a significant impact on the dynamics of nonlinear processes in such structures, including the generation of optical frequency combs and dissipative solitons. The generation process and the properties of bright solitons in such heated microresonators with anomalous group velocity dispersion (GVD) have been well studied, and a number of methods have been developed to minimize the effect of thermal processes. However, for dark solitons or platicons excited at normal GVD, these issues have been studied significantly less. In this work, the properties of platicons in heated microresonators are analyzed, and it is shown that in the case of “positive” thermal effects, when the direction of the thermal shift of the resonance frequencies of a microresonator coincides with the direction of the nonlinear shift, the widest high-energy platicons with the duration close to the round trip time in the resonator are stable. In the case of “negative” thermal effects, narrow low-energy platicons remain stable. Moreover, in microresonators with “negative” thermal effects, the interaction between cubic nonlinear and thermal processes can ensure the generation of platicons without special techniques required in other cases.

Optimal deployment of dynamic wireless charging facilities for electric bus route considering stochastic travel times

Installing dynamic wireless charging (DWC) facilities on dedicated bus lanes endows electric buses (EBs) with the ability to charge while in motion, effectively overcoming the limitations of short driving ranges. However, if the facilities are deployed along the entire EB route, it would incur significant economic costs and result in resource wastage. In this study, we divide the EB route into segments including station segments, intersection segments, and elementary segments of road section. The fluctuations in travel times of different trips on these segments are described. To optimize the DWC deployment plan, a chance constraint programming model is established with the objective of minimizing the costs of purchasing power transmitters and inverters. A combined algorithm using stochastic simulation and particle swarm optimization is designed to solve the model. A real EB route is used to conduct the case study, and results indicate that it is more suitable to deploy DWC facilities in adjacent elementary segments of road section compared to individually distributed station segments or intersection segments. Additionally, as wireless charging power or battery capacity increases, the number of required inverters and the length of power transmitters decrease, resulting in a nonlinear decrease in the deployment costs of charging facilities.

RANCANG BANGUN SISTEM MONITORING THERMAL OVERLOAD RELAY PADA MOTOR INDUKSI 3 PHASA

Thermal Overload Relay (TOR) is a tool that works based on heat (temperature) where the flowing current will be converted into heat to affect the bimetal. The length of trip time on the Thermal Overload Relay (TOR) varies depending on the setting value and the magnitude of the load current flowing. This prompted researchers to conduct trials and analyze Thermal Overload Relay (TOR). The research to be carried out here is to conduct a Thermal Overload Relay (TOR) test by adjusting the setting value and giving it more load, then analyzing whether the setting value works according to the setting value. When the motor is loaded, by holding the motor shaft, nothing happens or does not work on thermal overload, because the setting on the thermal overload relay is 2.5 A while the current obtained when the motor shaft is held is 0.40 A