Real-time Operation Information Research Articles

Optimizing Integrated All-Stop, Express, and Short-Turn Services on a Metro Line with Capacity Constraint Considering Passenger Spatio-Temporal Path Choice

Optimization of integrated train service is quite challenging under the situation of express trains overtaking local trains, because passenger path choice becomes more complicated and is largely unknown. To fill this gap, this study proposes a passenger spatio-temporal path choice model to replicate passenger behaviors given the real-time operation information in the metro system. Based on which, a non-linear programming model is developed to optimize integrated train services (i.e., all-stop, express, and short-turn) and the associated train timetables to minimize the total cost for a congested metro line, subject to a set of constraints (i.e., capacity, headway, service pattern, conditions of overtaking, etc.). The genetic algorithm with floating-point number coding is used to perform the optimization. It is applied to a real-world metro line in Chengdu, China, to assess its feasibility and effectiveness. The experimental results indicate that the integrated service is substantially improved in relation to total cost. The sensitivity analysis is conducted to explore the impacts of model parameters on various operation strategies. The proposed modeling approach is beneficial for transit agencies to improve their service planning.

A Design of 220 kV Line Protection Action Deduction System Based on Numerical Simulation

Accurate conditions monitoring and early wrong action warnings of relay protection in the Smart Substation is the basic guarantee to realize the normal operation of primary and secondary system of the power grid. At present, the traditional operation and maintenance monitoring methods of relay protections have poor timeliness, while some automatic monitoring methods have insufficient early warning performance, and lack the online action deduction function independent of the actual device. In this paper, a design method of integrated action deduction system including protection logic reasoning and software and hardware operation condition is proposed. The system can receive real-time operation information of protection online, simulate different types of faults, and output the recording information of sub modules belongs to the action deduction system. It can realize the online monitoring of device faults such as deducing the correctness of setting values at a certain time in the future, giving the wrong action warnings and predicting the probability of them as well, which effectively improves the operation and maintenance efficiency of the secondary system in the intelligent substation.

A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment

With the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based on sparse projection oblique randomer forests (SPORF) is proposed, which includes offline training, periodic update and online assessment. In the first stage, an improved adaptive synthetic sampling (ADASYN) method is developed to mitigate the class imbalance problem for the data-driven DSA approach. Then, the SPORF-based DSA model is trained using crucial features with low redundancy selected by a feature selection procedure based on the minimal-redundancy-maximal-relevance (MRMR) criterion. In the second stage, the periodic update of the DSA model for unseen system topologies is executed to enhance the robustness of the model. In the third stage, the trained model can provide the DSA result immediately when the real-time operation information of a system is received. The satisfactory performance of the proposed scheme is demonstrated through a series of tests and the comparisons on a 23-bus system and a practical 1648-bus system.

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understanding the real-time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on Curvelet transform to establish theory model of twin Curvelet support vector machine. In order to improve the prediction precision of the twin Curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

An integrated scheme for static voltage stability assessment based on correlation detection and random bits forest

To make static voltage stability assessment (VSA) more efficient for practical operation of power systems, an integrated scheme is proposed to rapidly predict the voltage stability margin (VSM) based on correlation detection (CD) and random bits forest (RBF). A feature selection framework is designed to select the representative features strongly related to the VSM with lower redundancy based on CD algorithms. By using the pivotal feature set and the corresponding VSM, the training of the RBF-based prediction model can be achieved. Once the real-time operation information of systems is received, the trained model will provide the corresponding result rapidly. The proposed scheme is examined on the IEEE 30-bus system and a practical 7917-bus system, and the encouraging prediction performance is verified. Moreover, the influences of the uncertainty of load increase direction (LID), variation of generation distribution (GD) and topology change on VSM prediction are analyzed.

Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015

The digital world we live in has transformed the way we communicate, network, seek help, access information, gain knowledge, and has shaped every aspect of our lives. A wide choice of communication platforms facilitates our indulgence from texting to posting on social media, which allows us to transcend geographic limitations. Natural disasters necessitate immediate communication to know the well-being of the people concerned and to seek rescue and relief measures. One such ‘black swan’ event was the Chennai floods of 2015, in south India, where social media such as Facebook and WhatsApp became disaster management tools for social activism. This research aims to analyse how Facebook and WhatsApp were used in the management of the Chennai floods of 2015, particularly by residents of Kotturpuram and Mudichur, two of the worst-affected areas in the city. The study used a quantitative approach, carrying out a survey with judgement sampling (n = 400). The satisfaction level of using Facebook and WhatsApp among the residents of Kotturpuram and Mudichur were analyzed. The factors of sense of empowerment – information, real-time operational information, emotional appeal, situational updates, and trustworthiness – were further analyzed. The implication of social media over traditional media and how those affected gained influence and power to set reverse agenda through social media during the Chennai floods of 2015 was also investigated. The use of both Facebook and WhatsApp increased and even surpassed the use of more conventional tools of communication such as radio and television during the Chennai floods. Apart from being mere tools for communication, social media facilitated disaster management during the floods. Our analysis reveals that the information sourced from Facebook and WhatsApp chats can be an eye-opener to specific areas of resource needs and gaps in resource distribution which will help in decision-making in real disasters.

Optimal Charging Scheduling for Catenary-Free Trams in Public Transportation Systems

Catenary-free tram with supercapacitor for short-distance travel is an emerging and energy-efficient way of urban transportation. However, a large number of decentralized and high-power charging loads from Catenary-free trams bring new challenges to the power system. In addition, frequent and high-voltage charging may result in a shorter lifetime of the supercapacitor. Thus, how to coordinate the charging processes of Catenary-free trams considering the power system constraint and the operation costs of the transportation system is an important open issue, especially as the charging requirements are time-varying. In this paper, we design a charging scheduling system to manage the charging processes of Catenary-free trams. Then, based on the historical and estimated operation data of Catenary-free trams, we propose a day-ahead optimal charging scheduling scheme to arrange their charging processes and report to the power system on the upper bound of the charging loads. Thereafter, we propose a real-time optimal charging scheduling scheme according to the real-time operation information to update the real-time charging loads of Catenary-free trams, such that a trade-off among the electricity cost, operation reliability, and battery lifetime is made to minimize the total operation costs. Finally, simulations have been conducted to evaluate the performance of the proposed charging scheduling schemes and show that the proposed algorithms can reduce the total operation cost by about 28%.

Impact of cooperative adaptive cruise control on multilane freeway merge capacity

ABSTRACTCooperative Adaptive Cruise Control (CACC) allows vehicles to exchange real-time operational information wirelessly, enabling vehicles to travel in strings with shorter than normal time gaps between adjacent vehicles and ultimately increasing the freeway capacity. This study is intended to investigate the impact of CACC vehicle string operation on the capacity of multilane freeway merge bottlenecks, commonly found at on-ramp merging areas on urban freeways. Simulation experiments were conducted using CACC car-following models derived from field test data, together with lane-changing models of CACC vehicles and manually driven vehicles, as well as a maximum CACC string length and inter-string time gap constraint. Simulation results reveal that the freeway capacity increases quadratically as the CACC market penetration increases, with a maximum value of 3080 veh/hr/lane at 100% market penetration. The disturbance from the on-ramp traffic causes the merge bottleneck and can reduce the freeway capacity by up to 13% but the bottleneck capacity still increases in a quadratic pattern as CACC market penetration becomes larger. The findings suggest that there is a need to implement advanced merging assistance systems with CACC at merge bottlenecks for achieving the capacity improvement comparable with the observations at homogeneous freeway segments.

Managing responsiveness in the emergency department: Comparing dynamic priority queue with fast track

Emergency Departments (EDs) commonly face capacity imbalances and long wait times in a service system handling patients with different priorities. These problems are particularly important for low-priority patients who often remain in the queue for extended periods. We investigate two distinct approaches to address these challenges: fast track (FT) and dynamic priority queue (DPQ). Traditionally, EDs have prioritized patients using an Emergency Severity Index (ESI), in conjunction with FT, to strictly or partially dedicate resources to different ESI patient classes. With our proposed DPQ, patients are prioritized using ESI and additional real-time operational information about the patient, specifically the amount of accumulated wait time and flow time. Using an empirical simulation, we compare the impact of different resource allocation and prioritization approaches on patient length of stay (LOS), including the existing system at the ED, FT with strict and partial dedication and the possibility of shorter and less variable service times, and versions of the proposed DPQ using simple dynamic prioritization. Our main results are that: (i) the DPQ approach dominates the other approaches tested; (ii) for various ED sizes, FT with strict and partial dedication do not reduce average LOS of low-priority patients without significantly increasing average LOS of high-priority patients, unless service time mean and variance are reduced; (iii) DPQ using accumulated wait time or accumulated flow time improves performance. The results are robust to changes in the proportion of patients in each priority level. Overall, expanding decision making about patient prioritization from only considering the patient's clinical condition to also including operational data can improve performance dramatically, even without improved service times.

Complex method to calculate objective assessments of information systems protection to improve expert assessments reliability

The paper considers the questions of filling the relevant SIEM nodes based on calculations of objective assessments in order to improve the reliability of subjective expert assessments. The proposed methodology is necessary for the most accurate security risk assessment of information systems. This technique is also intended for the purpose of establishing real-time operational information protection in the enterprise information systems. Risk calculations are based on objective estimates of the adverse events implementation probabilities, predictions of the damage magnitude from information security violations. Calculations of objective assessments are necessary to increase the reliability of the proposed expert assessments.

A Feasibility Study on a Future Zero-Carbon Electricity System Based on 100% Nuclear Power in Japan

The realization of a zero-carbon electricity system is of vital importance to CO2 reduction in an energy system, and nuclear power is expected to contribute to this much more than the intermittent, complicated, and costly renewable energy in the future in Japan. Therefore, in the present study, a future zero-carbon electricity system based on 100% nuclear power was proposed in Japan. The feasibility of the system was studied in terms of the reliability of fluctuations of both the daily and seasonal electricity demand and supply using an hour-by-hour computer simulation. The simulation provided a detailed system design and real-time operation information for the electricity system based on hourly data series of electrical load, nuclear power output, and electricity storage. The obtained results show that the proposed nuclear-powerbased zero-carbon electricity system is technologically feasible with the help of EV batteries and hydrogen for daily and seasonal electricity storage, respectively, and smart control technologies are necessary to operate the whole system. Finally, key issues relating to the implementation of such a system were discussed to highlight the need for continued research.

Modeling an e-based real-time quality control information system in distributed manufacturing shops

In a distributed manufacturing shop (DMS), factories of various levels of manufacturing capabilities are equipped with diverse types of machines and tools and are scattered around many geographically different locations. Because of such geographical diversity, the quality control of sub-assemble products (SAP) is of great importance to ensure a high level of production quality. To address the challenges of DMS management, manufacturers need to access real-time operational information captured from multiple locations. A real-time quality control information system (RTQCIS) can help control the flow of quality-related information among shop floors in the production network. This paper suggests a unique design of RTQCIS based on a new mathematical model. This model concentrates on converting multi-attribute quality measurements into a quality value corresponding to each entity in the quality space. To validate the proposed approach a prototype e-RTQCIS–DMS is developed and applied to subcontractors of an automobile company.

Effect of machining processes on the fatigue strength of hardened AISI 4340 steel: Matsumoto, Y., Magda, D., Hoeppner, D.W. and Kim, T.Y. J. Eng. Ind. (Trans. ASME) May 1991 113, (2), 154–159

Emergency Departments (EDs) commonly face capacity imbalances and long wait times in a service system handling patients with different priorities. These problems are particularly important for low-priority patients who often remain in the queue for extended periods. We investigate two distinct approaches to address these challenges: fast track (FT) and dynamic priority queue (DPQ). Traditionally, EDs have prioritized patients using an Emergency Severity Index (ESI), in conjunction with FT, to strictly or partially dedicate resources to different ESI patient classes. With our proposed DPQ, patients are prioritized using ESI and additional real-time operational information about the patient, specifically the amount of accumulated wait time and flow time. Using an empirical simulation, we compare the impact of different resource allocation and prioritization approaches on patient length of stay (LOS), including the existing system at the ED, FT with strict and partial dedication and the possibility of shorter and less variable service times, and versions of the proposed DPQ using simple dynamic prioritization. Our main results are that: (i) the DPQ approach dominates the other approaches tested; (ii) for various ED sizes, FT with strict and partial dedication do not reduce average LOS of low-priority patients without significantly increasing average LOS of high-priority patients, unless service time mean and variance are reduced; (iii) DPQ using accumulated wait time or accumulated flow time improves performance. The results are robust to changes in the proportion of patients in each priority level. Overall, expanding decision making about patient prioritization from only considering the patient's clinical condition to also including operational data can improve performance dramatically, even without improved service times.