Real-time Problem Research Articles

Applications Of Operations Research in Various Fields

Operations research uses techniques from a variety of disciplines, including mathematics, statistics, economics, engineering etc. A mathematical model of the real time problem can be formulated by Operations research techniques. These models help to predict future behaviour of the systems, changes in the system or in the methods of operations. This Paper discusses about the applications of Operations research in different field.

Real-Time EEG-Based Emotion Recognition.

Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

Cross-Modal Transformer for RGB-D semantic segmentation of production workshop objects

Scene understanding in a production workshop is an important technology to improve its intelligence level, semantic segmentation of production workshop objects is an effective method for realizing scene understanding. Since the varieties of information of production workshop, making full use of the complementary information of RGB image and depth image can effectively improve the semantic segmentation accuracy of production workshop objects. Aiming at solving the multi-scale and real-time problems of segmenting the production workshop objects, this paper proposes Cross-Modal Transformer (CMFormer), a Transformer-based cross-modal semantic segmentation model. Its key feature correction and feature fusion parts are composed of the Multi-Scale Channel Attention Correction(MS-CAC) module and the Global Feature Aggregation(GFA) module. By improving Multi-Head Self-Attention(MHSA) in Transformer, we design Cross-Modal Multi-Head Self-Attention(CM-MHSA) to build long-range interaction between RGB image and depth image, and further design the MS-CAC module and the GFA module on the basis of the CM-MHSA module, to achieve cross-modal information interaction in the channel and spatial dimensions. Among them, the MS-CAC module enriches the multi-scale features of each channel and achieve more accurate channel attention correction between the two modals; the GFA module interacts with RGB feature and depth feature in the spatial dimension and fuses global and local features at the same time. In the experiments on the NYU Depth v2 dataset, the CMFormer reached 68.00% MPA(Mean Pixel Accuracy) and 55.75% mIoU(Mean Intersection over Union), achieves the state-of-the-art results. While in the experiments on the Scene Objects for Production workshop dataset(SOP), the CMFormer achieves 96.74% MPA, 92.98% mIoU and 43 FPS(Frames Per Second), which has high precision and good real-time performance. Code is available at: https://github.com/FutureIAI/CMFormer

Multi-Objective Adaptive Optimization Model Predictive Control: Decreasing Carbon Emissions from a Zinc Oxide Rotary Kiln

The zinc oxide rotary kiln, as an essential piece of equipment in the zinc smelting industrial process, is presenting new challenges in process control. China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry, including green production and the use of fewer resources; thus, traditional stability control is no longer suitable for multi-objective control tasks. Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics (CFD) simulation models to study its dynamics, these models cannot be directly applied to process control due to their high computational complexity. To address these issues, this paper proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification. More specifically, with a large amount of data collected from a CFD model, a sparse regression problem is first formulated and solved to obtain a reduction model. Then, a two-layered control framework including real-time optimization (RTO) and model predictive control (MPC) is designed. In the RTO layer, an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up. By solving the optimization problem in real time, a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state. Our experiments show the strength and reliability of the proposed method, which reduces the usage of coal while maintaining high profits.

Strategi Efektif Komunikasi Bisnis Dalam Menangani Keluhan Pelanggan Pada Aplikasi Tiktokshop

It is hoped that this research can identify and examine appropriate procedures in business correspondence that can be used to determine client complaints in the Tiktokshop application. The Tiktokshop application is now one of the most widely used e-commerce platforms between buyers and sellers in today's digital era. However, the problem of customer complaints is also growing as a result of the rapid growth of platform users and transactions. This study uses a subjective examination strategy by leading meetings from top to bottom with various Tiktokshop clients and dissecting information using substance investigation techniques. The results of the examination show that an effective business communication strategy for dealing with customer complaints on the Tiktokshop application consists of several important steps. First, it is important to have a well-trained and responsive customer support team. This team must respond quickly to customer complaints and provide the right solutions. Second, effective communication through the Tiktokshop platform itself is also important. The use of private messages or comments under posts can be used to interact directly with customers and provide real-time problem solutions. When dealing with customer complaints on the Tiktokshop application, an effective business communication strategy includes quick responses, effective cross-platform communication, an empathetic approach, and a long-term relationship building system. This study contributes to understanding the importance of effective business communication in handling customer complaints in the fast-growing e-commerce era.

Sentimental Analysis Using Machine Learning for Mental Healthcare Management System

Abstract: Mental health issues are becoming more and more common around the world, with more and more people suffering from stress, anxiety, depression, and other related disorders. It has led to the development of innovative approaches to identify and treat the problem. Sentiment analysis, is a computer technique that analyzes text and identifies the emotions behind it, detects and monitors mental health problems in real time. It can be used for analyzing social media posts, chat messages, and other forms of online communication, sentiment analysis can provide valuable insight into the emotional state of individuals and groups. This technology helps mental health professionals identify those in need of support and provide timely interventions, ultimately improving mental health outcomes for individuals and society as a whole.

Population Dynamics on Fractional Tumor System Using Laplace Transform and Stability Analysis

Modeling is an effective way of using mathematical concepts and tools to represent natural systems and phenomena. Fractional calculus is an essential part of modeling a biological system. Recently, many researchers have been interested in modeling real-time problems mathematically and analyzing them. In this paper, the tumor system under fractional order is considered, and it comprises normal cells, tumor cells and effector-immune cells. By taking chemotherapy drugs into account, the toxicity of the drug and concentration of the drug is also studied in the model. The main objective of this work is to establish the solution for the model using Laplace transform and analyze the stability of the model. Laplace transform, a simple and efficient method, is used in solving the system that proves the existence and uniqueness of the solution. The boundedness of the system is also verified using the Lipschitz condition. Further, the system is solved for numerical values, and the population dynamics of cells are provided for different values of $\alpha$ as a graphical representation. Also, after analyzing the effect of chemotherapy drugs on tumor cells for different $\alpha$'s, which signifies that $\alpha$ = 0.9 provides a sufficient decrease in the dynamics of tumor cells. The main and significant part of this work is presenting that the usage of chemotherapy drugs reduces the number of tumor cells. The importance of the work is that apart from the immune system, chemotherapy drugs play a significant role in destroying tumor cells. The Hyers Ulam stability has a significant application that one need not find the exact solution to when analyzing a Hyers Ulam stable system. Thus, the stability of this tumor model under Caputo fractional order is presented using Hyers-Ulam stability and Hyers-Ulam-Rassias stability.

DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images

Diabetic retinopathy (DR) is a complication of diabetic Mellitus, developing retinal lesions that impair vision. The DR detection in the early stages avoids permanent vision loss. The treatments provide relief, and the vision loss due to DR is irreversible. The manual grading of DR is time-consuming and prone to human errors. The other real-time problem is exchanging patient fundus image information with hospitals worldwide while upholding the organisations' privacy concerns. When training a deep learning (DL) network, two critical factors to keep in mind are creating a collaborative platform and protecting patient data privacy. Therefore, an automated DR detection technique is required while protecting patient data and privacy. In this work, we propose a novel DR severity grading technique based on Federated Learning (FL), a recent advancement in DL called DRFL. FL is a new research paradigm that allows DL models to be trained collectively without disclosing clinical information. In DRFL, we combined the Federated averaging (FedAvg) technique and the median of the categorical cross-entropy loss. Since in comparison to FedAvg, the median cross-entropy is better suited for either under-fitted or over-fitted clients. Also, we propose a novel central server that extracts multi-scale features from the fundus images to identify small lesions present in the fundus image. In this work, we consider five clients holding different preprocessed fundus images collected from publicly available databases such as MESSIDOR-2, IDRiD, Kaggle, and a local database collected from Silchar Medical College and Hospital. The proposed model obtained an accuracy of 98.6%, specificity of 99.3%, precision of 97.25%, and an F1 score of 97.5%, which are better results than other techniques.

Design and development of brain emotional learning based adaptive membership functions for type-2 fuzzy systems

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600.

Anomaly-based intrusion detection system for IoT application

Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, and many more. Those applications are increasingly popular because they address real-time problems. In contrast, the use of transmission and communication protocols has raised serious security concerns for IoT devices, and traditional methods such as signature and rule-based methods are inefficient for securing these devices. Hence, identifying network traffic behavior and mitigating cyber attacks are important in IoT to provide guaranteed network security. Therefore, we develop an Intrusion Detection System (IDS) based on a deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) to detect network anomalies. The PCC-CNN model combines the important features obtained from the linear-based extractions followed by the Convolutional Neural Network. It performs a binary classification for anomaly detection and also a multiclass classification for various types of attacks. The model is evaluated on three publicly available datasets: NSL-KDD, CICIDS-2017, and IOTID20. We first train and test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification and Regression Tree,& Support Vector Machine) PCC-based Machine Learning models to evaluate the model performance. We achieve the best similar accuracy from the KNN and CART model of 98%, 99%, and 98%, respectively, on the three datasets. On the other hand, we achieve a promising performance with a better detection accuracy of 99.89% and with a low misclassification rate of 0.001 with our proposed PCC-CNN model. The integrated model is promising, with a misclassification rate (or False alarm rate) of 0.02, 0.02, and 0.00 with Binary and Multiclass intrusion detection classifiers. Finally, we compare and discuss our PCC-CNN model in comparison to five traditional PCC-ML models. Our proposed Deep Learning (DL)-based IDS outperforms traditional methods.

QED as a many-body theory of worldlines. II. All-order S -matrix formalism

In arXiv:2206.04188, we developed a first-quantized worldline formalism for all-order computations of amplitudes in QED. In particular, we demonstrated in this framework an all-order proof of the infrared safety of the Faddeev-Kulish (FK) S-matrix for virtual exchanges in the scattering of charged fermions. In this work, we extend the worldline formalism for both the Dyson and FK S-matrix to consider further the emission and absorption of arbitrary numbers of photons. We show how Low's theorem follows in this framework and derive Weinberg's theorem for the exponentiation of IR divergences. In particular, we extend our all-order proof of the IR safety of the FK S-matrix to both virtual exchanges and real photon emissions. We argue that the worldline approach leads to a modern Wilsonian interpretation of the IR safety of the FK S-matrix and provides a novel template for the treatment of IR divergences in real-time problems. Using Grassmannian integration methods, we derive a simple and powerful result for N-th rank vacuum polarization tensors. Applications of these methods will be discussed in follow-up work.

Implementation of fuzzy logic controller for TV viewing distance detector tool

This study describes the design of a safe distance detection device for watching TV using Arduino Uno. This system is expected to help reduce the risk of eye damage, especially those caused by viewing TV that is too close. This system functions to provide a warning in the form of an alarm where at a close distance the buzzer will activate and at a safe distance the buzzer will turn off. The system also provides warnings in the form of LEDs and LCDs as visual notification features, so that they can provide information to the audience to stay at a safe distance. In this study using the fuzzy logic method because this method is suitable for use in most real-time problems such as in making decisions to find variable and uncertain distances. With this fuzzy logic method, a mathematical framework is obtained that is used to represent uncertainty, ambiguity, inaccuracy, lack of information and partial truth.

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects.

With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.

Smart Energy Monitoring and Controlling System

In recent times internet of thing based applications are more efficient to provide solution regarding the real time problems. The labor involved in gathering energy utility meter readings. The Internet of Things (IoT) provides an efficient and cost-effective way to wirelessly send energy consumer information as well as monitor power use. In this paper we presenting smart energy monitoring system. the main objective of our project is reduced manual efforts for measuring electricity by Internet and make the electrical appliance intelligence and provide consolation to consumer and also get reduction in electric bill

A combined adaptive entropy‐TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace

SummaryIn this paper, a combined adaptive entropy‐TOPSIS and model predictive control strategy is proposed to deal with the mixed loading and delay operations in the reheating furnace. Firstly, the mathematical models consistent with the behaviour of the real reheating furnace are built to describe the complicated heat exchange process. Secondly, a dynamical optimization problem for the mixed loading operation and delay operation in the walking beam reheating furnace is obtained. To adjust the weighting factors of the optimization problem in real time, the adaptive entropy‐TOPSIS method is proposed. Then, the rolling horizon approach is applied to solve the proposed optimization problem. Finally, numerical experiments and simulation analysis are undertaken to verify the reliability and accuracy of the proposed strategy. The simulation results demonstrate that the proposed strategy can deal with three typical cases of delays effectively and the control accuracy is successfully improved from 74.79% to 99.17%.

Traffic Prediction for Intelligent Transportation System using Deep Learning

The most important challenge to sustainable mobility is persistent congestions of differing strength and duration in the dense transport networks. The standard Adaptive Traffic Signal Control cannot properly address this kind of congestion. Deep learning-based mechanisms have proved their significance to anticipate in adjective outcomes to improve the decision making on the predictions of traffic length. The deep learning models have long been used in many application domains which needed the identification and prioritization of adverse factors for a simplifying human life. Several methods are being popularly used to handle real time problems occurring from traffic congestion. This study demonstrates the capability of DL models to overcome the traffic congestion by simply allowing the vehicles through a signal depending on the length of vehicles. Our proposed method integrates a numeral of approach, intended to advance the cooperativeness of the explore operation. In this work, we implement the application to detect the number of vehicles in the images from the user and gives vehicles counts. To detect the vehicles count here we are using the YOLO pretrained weights.

An athlete–referee dual learning system for real-time optimization with large-scale complex constraints

Constrained optimization (CO) has made a profound impact in solving many real-world problems. Due to the high computation burden in exact solvers, data-driven CO based on machine learning techniques is recently receiving extensive research interests for its capability to solve CO problems in real time. The existing data-driven CO approaches only serve for optimization problems with rather simple constraints that can be directly incorporated into model training. However, constraints that are computationally infeasible or burdensome to evaluate are commonly experienced in realistic optimization applications, especially in the engineering sector. This paper proposes an athlete–referee dual learning system (ARDLS) for end-to-end CO with large-scale complex constraints, where an athlete model is trained as the main optimizer while a referee model is trained as a probabilistic constraint classifier to guide the athlete training. A risk-based constrained loss function is designed to fine-tune the athlete model for constraint satisfaction. A case study on electric power system emergency control application is conducted to validate the proposed ARDLS, where the testing results demonstrate the excellent capability of ARDLS to improve the likelihood of satisfying large-scale complex constraints in CO.

Smart Utilities IoT-Based Data Collection Scheduling

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements.

A Measuring Tool for Interrupt Latency Based on Linux

Linux has been used more and more in the field of industrial control and robot control, the non-real-time nature of the final human-computer interaction program may cause system performance impact in industrial production. Interrupt response time is an important indicator to measure the real-time performance of real-time operating systems. Accurate test data can be used as an evaluation index for selecting the real-time performance of the operating system. In order to improve the real-time performance of Linux, analyze real-time problems, and solve possible real-time bottlenecks, this paper designs a probe-type tool that can measure the interrupt response time, output the interrupt call stack, and analyze the reasons for high interrupts. By optimizing specific limited and high-latency paths, Linux can still maintain an acceptable range of latency for a long time under various high-load scenarios. After testing, the tool has good measurement accuracy, which provides a basis for developing real-time tasks and solving real-time problems.