Real-Time Bus Tracking and Management System with IOT, Chatbot Integration and Accident Zone Prediction

The tax big data supervision system and method, including the distributed real-time management and control system deployed in the superior unit and each subordinate unit, the superior distributed real-time management and control system is used to receive the management and control rules, and issue the management and control rules to the subordinate distributed real-time management and control System: The distributed real-time management and control system writes the massive data in the data source at the same level into the distributed message queue Kafka in real time, and uses the distributed stream computing engine Flink to compare and calculate based on the management and control rules, and write the data that conforms to the management and control rules. Enter the alarm information into Kafka; the lower-level distributed real-time management and control system pushes the alarm information in the Kafka subscription at this level to Kafka in the upper-level distributed real-time management and control system; the upper-level distributed real-time management and control system pushes the alarm information in the Kafka subscription to users , at the same time push the alarm information to the distributed search engine ElasticSearch to save, and provide the historical alarm information query function.

Stockouts constitute a major challenge in the retail industry. Stockouts are caused by errors related to manual stockkeeping and by the misplacement of items on shelves. Such errors account for up to 4% of lost sales. Real-time inventory management systems for misplaced items or missing stock detection in retail stores are limited. Accordingly, a conductive polymer-based interactive shelving system for real-time inventory management is developed. The system comprises an 80 × 48 sensor array fabricated by screen-printing a piezoresistive carbon-based conductive polymer layer onto gold interdigitated electrodes deposited on a flexible substrate. Each sensing pixel has dimensions of 5 mm × 5 mm and a sensing area of 4 mm × 4 mm. The sensor mat can detect the shape and weight features of stockkeeping units (SKUs), which can then be analyzed by a TensorFlow model for SKU identification. The developed system is characterized for functional resistance range, uniformity, repeatability, and durability. The accuracy of SKU identification achieved using shape features only and the accuracy of SKU identification achieved using both shape and weight features is 95% and 99.2%, respectively. The key novelty of the work is the development of a deep learning-embedded interactive smart shelving system for retail inventory management by using the shape and weight features of SKU. Also, the developed system helps to detect the SKU if they are stacked one over the other. Furthermore, multiple sensor mats implemented on various shelves in a retail store can be modularized and integrated for monitoring under the control of a single PC. Accordingly, the proposed retail inventory tracking system can facilitate the development of automated "humanless" shops.

This paper is based on NDRC project "National Large and medium-sized agricultural products market informatization construction projects," developing the logistics process of real-time tracking management system for real-time Management of agricultural products in China. The real-time monitoring system of the logistics is designed with four subsystems: testing the temperature and humidity of agricultural products, collecting the speed and line of vehicles, wireless data transmission, and data processing in the control center. The system use GPS, ZigBee technology, GPRS network and Internet to monitor and management the fresh agricultural products and the logistics process. In this paper, the data transmission and process in the real-time monitoring of the logistics are discribed in detail.

Smart homes are an integral component in developing smart cities. This paper studies the problem of real-time energy management of controllable loads for Internet of Things (IoT) connected homes with renewable energy generators and energy storage devices. By exploiting the delay tolerance of elastic loads, we develop a joint real-time energy storage control and load management system, aiming to minimize the long term time-averaged energy consumption cost without reducing energy consumption. This residential energy management problem is formulated as a constrained stochastic programming problem. A Lyapunov-based control algorithm is designed to decompose the long-term optimization problem into per-slot sub-problems and provide an asymptotically online optimal solution that is able to quickly adapt to the system dynamics without requiring any statistics of time-varying load demand and stochastic renewable generation. The proposed online control algorithm jointly optimizes energy consumption, load scheduling, and energy charging/discharging actions while satisfying the time-varying energy consumption preference of the user in each time slot. It is demonstrated through numerical simulations that the low-complexity online control algorithm ensures the load demand of the user served with a lower delay at a relatively low cost.

Purpose: This paper presents a real-time automatic equipments management system of the environmental reliability laboratory based on IoT (Internet of Things) to manage test equipments efficiently and to improve laboratory productivity.BR Methods: An internet-based web system composed of application program, database, and agent server is developed, and an integrated management software is also developed as web-based system, and hardware configuration is connected ethernet with test equipments via convertor, DAQ, electricity power meter, thermometer sensor, where an HTML5-based interface is configured for management on PCs and tablets.BR Results: The real-time and automatic equipments management system is developed to present equipment operation, test data collection and utilization, equipment management, and sample management in the environmental reliability laboratory.BR Conclusion: By performing integrated management of the environmental reliability laboratories, the practitioners have efficient time distribution and data management, ensured transparency by sharing all information, and increased work efficiency due to reduction of the working hours.

Unexpected incidents, such as adverse traffic, unavailability of parking space and vehicle breakdowns, that occur during urban deliveries, lead to delays, higher costs, and inferior customer service. This paper presents the architecture of a real-time fleet management system that handles unforeseen events by monitoring distribution vehicles and by recommending intelligent re-routing interventions in order to improve urban delivery performance.

Many of the distribution management systems or outage management systems available in the market have substantial drawbacks when trying to meet regulatory pressures and increasing customer expectations regarding quality of supply. Typically the procedure of outage modelling in medium voltage network is based on offline data. Determining of affected zones due to switching changes is often supported by tracing functionality but the accuracy of results can be poor due to out-of-date electrical network connectivity model. Another common approximation is that the tracing does not dive into connectivity model of low voltage network, or if that is enabled then the model does not have dynamic switching state. Thus, outages are typically recorded on distribution substation level and the evaluation of interrupted consumption sites is based on either the normal switching state of low voltage network or even some rougher allocation methods. On this basis, it is very difficult to e.g. automate direct compensation or provide timely information for customer service about running interruptions. This paper presents recent development of a real-time distribution management system to improve network topology supervision and restoration processes with synchronized outage data generation for miscellaneous needs like outage reporting and customer service.

This paper introduces the structure of the integrated information system of the power plant. This system can be classified by two components, one is the integration of the control and diagnostic system, the other is integration of the management information and real-time information. Multi-goal optimization and coordination can be used to realize the integration of the control systems, and failure trend-based forecasting methods can be used to realize the integration between the control and diagnostic systems. Through the inter-link between the distributed control system (DCS) networks, the information can be shared by real-time system and management system.

Real-time management and optimisation of industrial utilities systems

Microgrid systems show great promise for integrating large numbers of distributed energy resource (DER) systems into future power networks. This paper provides a brief overview of current microgrid technologies and issues associated with their implementation. The case is made for a real-time power management and control system that attempts to optimize microgrid systems based on multiple objectives, such as power demands, fuel consumption, environmental emissions, costs, dispatchable loads, etc. A multi-agent based control architecture that can ensure robust, stable, and optimal microgrid operation is also addressed. In addition to the discussion of power management and control topics, a qualitative classification tool has been proposed for assisting system planners in assessing the impact of microgrid systems on broader grid operations.

Optimization of a Linac-based 4D Cone-beam CT Acquisition Protocol for Image-guided Radiation Therapy

TOADS (Total On-line Access Data System) is a new generation of real-time monitoring and information management system developed to support unattended environmental monitoring and long-term stewardship of U.S. Department of Energy facilities and sites. TOADS enables project managers, regulators, and stakeholders to view environmental monitoring information in realtime over the Internet. Deployment of TOADS at government facilities and sites will reduce the cost of monitoring while increasing confidence and trust in cleanup and long term stewardship activities. TOADS: Reliably interfaces with and acquires data from a wide variety of external databases, remote systems, and sensors such as contaminant monitors, area monitors, atmospheric condition monitors, visual surveillance systems, intrusion devices, motion detectors, fire/heat detection devices, and gas/vapor detectors; Provides notification and triggers alarms as appropriate; Performs QA/QC on data inputs and logs the status of instruments/devices; Provides a fully functional data management system capable of storing, analyzing, and reporting on data; Provides an easy-to-use Internet-based user interface that provides visualization of the site, data, and events; and Enables the community to monitor local environmental conditions in real time. During this Phase II STTR project, TOADS has been developed and successfully deployed for unattended facility, environmental, and radiological monitoring at a Department of Energy facility.

International audience

The issue of human error, inaccurate and time-consuming of manual attendance system gradually magnified the inefficiency and obsolescence of the system. An automatic attendance management system with face detection will increase the efficiency, accuracy, and security of a system in attendance taking task. Real-Time Face Detection Attendance Management System is concerned in finding human faces through real-time with the attendance taken and saved as reference for future. Face detection system is implemented by using Haar Cascades Classifiers for the object detection. Attendance system is managed with a database management system called MySQL using SQL programming language. Real-Time Face Detection Attendance Management System able to detect front face and manage the attendance in real-time. A text box will display for the attendee to enter their name for the purpose of attendance taken when face is detected. The attendance list and image collection in the database will auto increment and update in MySQL. The accuracy of Real-Time Face Detection Attendance Management System is shown with the comparison between different front face condition in different illumination condition. The result is tested with 4 sets of the attendee’s face in different illumination and different front face condition. 94.44% accuracy of the face detection system is shown from the result taken by the system. The image acquisition for every attendee is collected and saved in the right folder. The attendance is taken with the attendee’s name, date and time in attendance database and image is save into face database. Some technology such as anti-spoofing measures, mask detection and deep learning concept are recommended to be added into the system for future improvement

Real-time intelligent system technology can be an effective means of optimising industrial operations. A large, intelligent Utilities Management System (UMS) has been developed at Foster's brewery in Yatala, Australia. The system is based on the G2 real-time intelligent system platform and it contains a number of modules that have been designed to improve the energy and utilities consumption of brewery operations. Of particular interest was the management and optimisation of the brewery's extensive Clean-In-Place (CIP) systems. The UMS monitors every step of every cycle of each CIP set and its real-time mass balance estimates chemical use and loss. An operator decision support system has also been developed that helps to minimise any redundant cleaning cycles. This system has been instrumental in lowering the bulk caustic soda consumption (per hectolitre of beer) of Yatala brewery by over 57% between the financial years F05 and F08.