End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments,such as low intelligence and poor comfort perfor-mance in the driving process.The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions.In this paper,in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are pro-posed.Especially,the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition,and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve.By using low-order interpolation curve splicing,the planning computation is reduced,and the real-time performance of planning is improved,com-pared with one-segment curve fitting method.Furthermore,the comfort performance of the planned path is reflected intuitively by the curvature information of the path.Finally,the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim.The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Intelligent PID guidance control for AUV path tracking

Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as planning level during the control system using transverse deviation is came up with. Continuous tracking of path expressed by a point sequence can be realized by the law. Firstly, a path tracking control system based on rational behavior model of three layers is designed, mainly satisfying the needs of underactuated AUV. Since there is no need to perform spatially coupled maneuvers, the 3D path tracking control is decoupled into planar 2D path tracking and depth or height tracking separately. Secondly, planar path tracking controller is introduced. For the reason that more attention is paid to comparing with vertical position control, transverse deviation in analytical form is derived. According to the Lyapunov direct theory, control law is designed using discrete PID algorithm whose parameters obey adaptive fuzzy adjustment. Reference heading angle is given as an output of the guidance controller conducted by lateral deviation together with its derivative. For the purpose of improving control quality and facilitating parameter modifying, data normalize modules based on Sigmoid function are applied to input-output data manipulation. Lastly, a sequence of experiments was carried out successfully, including tests in Longfeng lake and at the Yellow sea. In most challenging sea conditions, tracking errors of straight line are below 2 m in general. The results show that AUV is able to compensate the disturbance brought by sea current. The provided test results demonstrate that the designed guidance controller guarantees stably and accurately straight route tracking. Besides, the proposed control system is accessible for continuous comb-shaped path tracking in region searching.

Global Inverse Optimal Tracking Control of Underactuated Omni-directional Intelligent Navigators （ODINs）

This paper presents a design of optimal controllers with respect to a meaningful cost function to force an underactuated omni-directional intelligent navigator （ODIN） under unknown constant environmental loads to track a reference trajectory in two-dimensional space. Motivated by the vehicle＇s steering practice, the yaw angle regarded as a virtual control plus the surge thrust force are used to force the position of the vehicle to globally track its reference trajectory. The control design is based on several recent results developed for inverse optimal control and stability analysis of nonlinear systems, a new design of bounded disturbance observers, and backstepping and Lyapunov＇s direct methods. Both state- and output-feedback control designs are addressed. Simulations are included to illustrate the effectiveness of the proposed results.

Intelligent control over chaos in the rolling process and its comparison with tracking control

Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.

Study on video intelligent early warning and tracking system based on ARM

The protrusion of the planning of numerical intelligent early-warning and tracking system in this study which can ease triggerman's work strength,lay the next generation intelligence supervision system foundation and expand effectively the video resources use etc. In the numerical intelligent early-warning matrix sub-system,the authors have designed a kind dual-core system which includes both ARM and DSP,and designed detailedly traffic dynamics affairs early-warning arithmetic which bases on that system. And then,this system will carry quickly on fixing the right position of license plate,correcting the inclination degree of license plate,and thinning it to get the number of this license and severity grade. Secondly,in the rotated dome camera sub-system,the authors have designed three-dimensional trajectory mathematical model which makes use of a fuzzy PID controller to achieve the high- speed track. At last,Simulation shows that the proposed control method has high profile tracking precision,accuracy and robustness of the disturbance.

Towards Harmonization IEC61850 and Intelligent Agents: Characteristic Analysis of Service Tracking

IEC61850 is the most promising standard for design of power utility communication networks and automation systems. On the other hand multi-agent systems are attracting growing interest for different applications of power engineering. This paper presents the possibility that IEC61850 could be applied to build intelligent agents for power engineering applications. The paper investigates the correlation between IEC61850 and multi-agents and presents IEC61850 service tracking as a scenario. First, briefly introduces reporting and service tracking;then, investigates agents, intelligent agents and the characteristics of intelligent agents;last, analyzes the characteristics of service tracking and compares it with intelligent agents. And analysis shows that IEC61850 could be applied to build intelligent agents, which represents a significant step forward for MAS to apply in power engineering.

Intelligent Tracking Telescope Using Embedded Microcontrollers

This paper presents the design and implementation of the embedded microcontrollers within a telescope control system. The design objectives of the overall system are to automatically find light emitting objects at night within a user specified area, to track a light emitting object for a user specified time given the initial position of the object, and to allow the user access to these functions through a graphical user interface. The embedded system is used to provide a communication link between the graphical user interface and system hardware, to provide angle data processing for a dual axis accelerometer, to provide data processing for an electronic compass, and to provide pulse outputs for the step and direction inputs of three stepper motor drives. This paper will describe the design, implementation, and results of each of these objectives.

Target Path Tracking Method of Intelligent Vehicle Based on Competitive Cooperative Game

To improve intelligent vehicle drive performance and avoid vehicle side-slip during target path tracking,a linearized four-wheel vehicle model is adopted as a predictive control model,and an intelligent ve-hicle target path tracking method based on a competitive cooperative game is proposed.The design variables are divided into different strategic spaces owned by each player by calculating the affecting factors of the design variables with objective functions and fuzzy clustering.Based on the competitive cooperative game model,each game player takes its payoff as a mono-objective to optimize its own strategic space and obtain the best strategy to deal with others.The best strategies were combined into the game strategy set.Considering the front wheel angle and side slip angle increment constraint,tire side-slip angle,and tire side slip deflection dynamics,it took the path tracking state model was used as the objective,function and the calculation was validated by competitive cooperative game theory.The results demonstrated the effectiveness of the proposed algorithm.The experimental results show that this method can track an intelligent vehicle quickly and steadily and has good real-time per-formance.

Rollover Prevention and Motion Planning for an Intelligent Heavy Truck

It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently.However,it was rarely considered in intelligent vehicle motion planning.To improve rollover stability,a motion planning strategy with autonomous anti rollover ability for an intelligent heavy truck is put forward in this paper.Considering the influence of unsprung mass in the front axle and the rear axle and the body roll stiffness on vehicle rollover stability,a rollover dynamics model is built for the intelligent heavy truck.From the model,a novel rollover index is derived to evaluate vehicle rollover risk accurately,and a model predictive control algorithm is applicated to design the motion planning strategy for the intelligent heavy truck,which integrates the vehicle rollover stability,the artificial potential field for the obstacle avoidance,the path tracking and vehicle dynamics constrains.Then,the optimal path is obtained to meet the requirements that the intelligent heavy truck can avoid obstacles and drive stably without rollover.In addition,three typical scenarios are designed to numerically simulate the dynamic performance of the intelligent heavy truck.The results show that the proposed motion planning strategy can avoid collisions and improve vehicle rollover stability effectively even under the worst driving scenarios.

Intelligent outdoor video advertisement recommendation system based on analysis of audiences' characteristics

An integrated implementation framework of an intelligent recommendation system for outdoor video advertising is proposed, which is based on the analysis of audiences＇ characteristics. Firstly, the images of the scene and the people who view the video advertisements are captured by the net- work camera deployed on the video advertising terminal side. Then audiences＇ characteristics can be obtained by applying computer vision technologies ： face detection, face tracking, gender recogni- tion and age estimation. Finally, an intelligent recommendation algorithm is designed to decide the most fitting video ads for each terminal according to multi-dimensional statistical information of its reover, a novel face detection method and a new face tracking method have been proposed to meet the practical requirements of the system, of which the average Fl-score is O. 988 and 0. 951 respec- tively.

Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective

The development of single-cell subclones,which can rapidly switch from dormant to dominant subclones,occur in the natural pathophysiology of multiple myeloma(MM)but is often"pressed"by the standard treatment of MM.These emerging subclones present a challenge,providing reservoirs for chemoresistant mutations.Technological advancement is required to track MM subclonal changes,as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease.Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells,which include immunohistochemistry,flow cytometry,or cytogenetics.Still,all of these technologies may be limited by the sensitivity for picking up rare events.In contrast,more incisive methods such as RNA sequencing,comparative genomic hybridization,or whole-genome sequencing are not yet commonly used in clinical practice.Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution,such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing,and their respective advantages and disadvantages.In addition,we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease,which has a broad prospect.

System Structure and Calibration Models of Intelligent Photogrammetron

This paper describes the structure, geometric model and geometric calibrationof Photogrammetron Ⅰ - the first type of photogrammetron which is designed to be a coherent stereophotogrammetric system in which two cameras are mounted on a physical base but driven by anintelligent agent architecture. The system calibration is divided into two parts: the in-labcalibration determines the fixed parameters in advance of system operation, and the in-situcalibration keeps tracking the free parameters in real-time during the system operation. In a videosurveillance set-up, prepared control points are tracked in stereo image sequences, so that the freeparameters of the system can be continuously updated through iterative bundle adjustment and Kalmanfiltering.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

Design and manufacture of intelligent Cu-based wet friction materials

The friction sheets working process was analyzed. It is found that its characteristic is microregion instantaneous high temperature and the current cooling method, making the sheets cooled by the lubricating oil flowing through the friction surface, is not very efficient. Then, intelligent materials concept was introduced, the component and microstructure of intelligent Cu-based friction materials were designed, and the intelligent Cu-based wet friction materials as well as sheets were manufactured. And the intelligent friction materials working principle, i.e. the materials cooling the friction microregion in real time or the friction sheets cutting the peak value of microregion instantaneous high temperature during friction process, was given depending on the characteristics of the materials’ and friction sheets’ working process. Finally, it is indicated that the intelligent friction sheets excell the currently used friction sheets in properties, including anti-heating property, anti-wearing property as well as friction characteristic.

Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep- Learning Techniques

As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.

Parametric Tracking of Legs by Exploiting Intelligent Edge

In this paper the idea of Intelligent Scissors is adopted for contourtracking in dynamic image sequence. Tracking contour of human can therefore be converted to trackingseed points in images by making use of the properties of the optimal path (Intelligent Edge). Themain advantage of the approach is that it can handle correctly occlusions that occur frequently whenhuman is moving. Non-Uniform Rational B-Spline (NURBS) is used to represent parametrically thecontour that one wants to track. In order to track robustly the contour in images, similarity andcompatibility measurements of the edge are computed as the weighting functions of optimal estimator.To reduce dramatically the computational load, an efficient method for extracting the regioninterested is proposed. Experiments show that the approach works robustly for sequences withfrequent occlusions.

Beam tracking method based on reconfigurable intelligent surface for obstructed communication

This paper proposes a new reconfigurable intelligent surface based three-dimensional beam tracking method to solve the beam tracking problems for the unmanned aerial vehicle with obstacles in communication channels.The proposed beam tracking method can not only regulate the reconfigurable intelligent surfaces to achieve the beam tracking of the obstructed communications,but also optimize the transmission efficiency of the communication.Firstly,a reconfigurable intelligent surface is proposed,which can correlate the transmission signals by adjusting the phaseshift matrix.Meanwhile,a new communication channel is constructed according to the reconfigurable intelligent surface,which consists of two parts.The first one is the channel between the unmanned aerial vehicle and the reconfigurable intelligent surface,the other is the channel between the reconfigurable intelligent surface and the ground base station.Note that the transmission performance of the communications can be optimized by adjusting the phase shift of each uniform linear array on the reconfigurable intelligent surface.Then,a new beam tracking method for the unmanned aerial vehicle with obstructed communications is proposed on the basis of the reconfigurable intelligent surface.By proposing the mixed genetic algorithm,the estimation accuracy of the azimuth and elevation angles is improved to enhance the performance of the beam tracking.Finally,the simulations are provided to verify the effectiveness of the proposed three-dimensional beam tracking method with reconfigurable intelligent surface.

Sustainable transit vehicle tracking service,using intelligent transportation system services and emerging communication technologies:A review

The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.