Modern Algorithms Research Articles

COCO Annotator

COCO Annotator is an image annotation tool that allows the labelling of images to create training data for object detection and localization. It provides many features, including the ability to label an image segment by drawing, label objects with disconnected visible parts, efficiently store and export annotations in the well-known COCO format as well as importing existing publicly available datasets in COCO format. Once installed, or started with Docker, the interface is web-based and customizable, and provides different tools for creating datasets. The exported annotations can be used for the training of modern deep learning algorithms (Mask R-CNN, YOLO, etc.). Import of the COCO format is supported by several machine learning frameworks. COCO Annotator is developed by Justin Brooks and is supported by a lively community on Github [1].

Supporting Social Interactions In Human-Ai Teams: Profiling Human Teammates From Sparse Data

Artificial intelligence has been developed to perform all manner of tasks but has not gained capabilities to support social cognition. We suggest that teams comprised of both humans and artificially intelligent agents cannot achieve optimal team performance unless all teammates have the capacity to employ social-cognitive mechanisms. These form the foundation for generating inferences about their counterparts and enable execution of informed, appropriate behaviors. Social intelligence and its utilization are known to be vital components of human-human teaming processes due to their importance in guiding the recognition, interpretation, and use of the signals that humans naturally use to shape their exchanges. Although modern sensors and algorithms could allow AI to observe most social cues, signals, and other indicators, the approximation of human-to-human social interaction -based upon aggregation and modeling of such cues is currently beyond the capacity of potential AI teammates. Partially, this is because humans are notoriously variable. We describe an approach for measuring social-cognitive features to produce the raw information needed to create human agent profiles that can be operated upon by artificial intelligences.

Gradient-based optimization of a 15 MW wind turbine spar floater

We discuss the optimization-based conceptual design of support structures and mooring system for floating wind turbines. A four degree-of-freedom frequency-domain model is used for the dynamic response of a spar floating wind turbine subjected to wind and wave loads. The framework allows for design optimization involving the geometrical properties of the floater and the mooring system and inclusion of long realizations of multiple load cases in the analysis. The adopted optimization approach adopted relies on analytical design sensitivities of the governing frequency-domain equations and of the design requirements. This ensures that modern gradient-based optimization algorithms can effectively be used to solve the design problem at hand. The optimization approach is applied to the design of the spar-buoy floater and its mooring system for the IEA 15 MW reference wind turbine. A post-processing approach for identifying discrete designs from predefined catalogues is also presented. The post-processing allows to transform continuous design solutions into practical ones that can be used in subsequent analyses with time domain response models for the full validation of the design solutions obtained. The numerical results highlight the capability of the approach discussed herein to provide discrete optimized designs for given design constraints and loads in few minutes requiring modest computational resources and time.

Pantograph Spark Fault Detection using YOLO

Pantograph-catenary is now the dominant form of current collection for modern electric trains because they can be used for higher voltages. Faults in pantograph-catenary systems threaten the operation and safety of railway transportation. They need to be continuously monitored and controlled to maintain safe transport. Pantograph may be damaged as a result of extreme weather conditions which can affect its normal operation, leading to failure of pantograph and overhead contact line systems. Poor contact between pantograph and overhead contact line causes thermal erosion to the wire. When the pantographs are exposed to air, they could deteriorate due to electrochemical reaction with the environment since they are made of metals. Movement of catenary lines and pantograph in high crosswinds has been found to cause the wire to be trapped in the pantograph. There is a serious issue regarding the quality of images generated by pantograph video monitoring system on high-speed railway trains which often shows inconsistencies of catenary faults. The application of traditional image processing and deep learning techniques have been unable to meet the requirements of spark detection. In this paper, a modern deep learning algorithm is proposed to detect sparks in the pantograph. Specifically, the YOLOv3 model is used to counter this problem that traditional image processing algorithms have been unable to. The results on a very large sample of data show the efficiency and real-time performance of the proposed method, which meets the requirements of pantograph spark detection in high-speed railway. Keywords : High-speed railway pantograph; Spark detection; Deep learning; YOLOv3; DOI: 10.7176/ISDE/12-3-02 Publication date: September 30 th 2021

Customizable Route Planning in Road Networks (Extended Abstract)

Computing driving directions in road networks is a fundamental problem. Although it can be solved in essentially linear time by Dijkstra's algorithm, this is not fast enough to enable interactive queries on large-scale inputs. Instead, modern algorithms typically work in two stages: first an offline preprocessing routine computes some auxiliary data, which is then used to answer exact queries in real time. The past decade has seen a surprisingly diverse set of techniques that follow this approach, mostly relying on the fact that road networks tend to have a strong hierarchy. These methods work very well when minimizing driving times, but are much less efficient with other cost functions. We present a practical algorithm that has no such drawbacks, and can compute shortest paths on continental road networks with arbitrary metrics (cost functions). Our customizable route planning approach works in three stages. The first, metric-independent preprocessing, uses graph partitioning to define the topology of a multilevel overlay graph, which is the same regardless of the cost function. The second stage, customization, uses the metric to compute the actual costs of the overlay arcs. Finally, the query stage uses the output of the first two stages to compute shortest paths in real time (milliseconds). The first stage uses a recent partitioning algorithm based on the notion of natural cuts, which are sparse regions separating much denser areas. It may take a few minutes (or even hours), but only needs to be run (or updated) when new road segments are built. Metric changes (which are much more frequent) require running only customization, which takes a second or less even on continental road networks. Since it does not rely on strong hierarchies, CRP is robust to metric changes. Unlike most other methods, it can also handle turn costs (and restrictions) quite naturally, with little effect on performance and space usage. It is thus ideal for a real-world routing engine, and is indeed in use by Bing Maps. This extended abstract includes results first published at SEA 2011 and SEA 2013.

Dynamic Model of TRMS Based on Homogeneous Transformation and Euler-Lagrange Equation

The aerodynamic experimental set TRMS (Twin Rotor MIMO System) is a strong nonlinear system, which has been taken by many scientists as an object to test modern control algorithms. The paper built a complete and explicit dynamic model for the TRMS based on dissociating TRMS into 3 subsystems, using homogeneous transformations in orthogonal coordinate systems to calculate the mass point’s position and velocity in component sub-systems; Euler - Lagrange equation was applied to model the dynamics for the object. Keywords— TRMS, dynamics, homogeneous transformation, mechanical system dissociation, Euler - Lagrange.

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

An optimal power management solution is a potential tool to produce cost-effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi- and single-objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods.

A Learning-Based Hybrid Framework for Dynamic Balancing of Exploration-Exploitation: Combining Regression Analysis and Metaheuristics

The idea of hybrid approaches have become a powerful strategy for tackling several complex optimisation problems. In this regard, the present work is concerned with contributing with a novel optimisation framework, named learning-based linear balancer (LB2). A regression model is designed, with the objective to predict better movements for the approach and improve the performance. The main idea is to balance the intensification and diversification performed by the hybrid model in an online-fashion. In this paper, we employ movement operators of a spotted hyena optimiser, a modern algorithm which has proved to yield good results in the literature. In order to test the performance of our hybrid approach, we solve 15 benchmark functions, composed of unimodal, multimodal, and mutimodal functions with fixed dimension. Additionally, regarding the competitiveness, we carry out a comparison against state-of-the-art algorithms, and the sequential parameter optimisation procedure, which is part of multiple successful tuning methods proposed in the literature. Finally, we compare against the traditional implementation of a spotted hyena optimiser and a neural network approach, the respective statistical analysis is carried out. We illustrate experimental results, where we obtain interesting performance and robustness, which allows us to conclude that our hybrid approach is a competitive alternative in the optimisation field.

Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic

The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.

Segmentation of dental X-ray in endodontic treatment

Background: The basis of successful endodontic treatment is the correct determination of the working length of the root canal (the distance between the external landmark on the crown of the tooth to the apical border). An apical constriction zone is recommended as a border for root canal treatment and filling. Intraoral radiograph allows you to obtain information about the direction of bending of the root canals, as well as to determine the working length. However, the radiograph is a two-dimensional total image and does not reproduce the entire anatomy of the apical part of the root therefore there are often layers and distortions of the image. When interpreting radiographs, there is a probability of error associated with the subjectivity of the evaluation result of the specialist. Thus, it is impractical to be guided exclusively by this method of determining the working length. The method of apexlocation is based on the difference of electrical resistance of tissues. The hard tissues of the tooth have a higher resistance than the mucous membrane of the mouth and periodontal tissue. Devices for electrometric determination of the working length of the root canal determine the impedance using alternating currents of different frequencies and apply the method of ratio. This measurement is stable and accurate even when working in wet channels and provides smooth visualization of all process of penetration of a top of the channel tool and high accuracy of definition of a place of physiological top of a root (over 80%). Modern algorithms for electrometric determination of the working length of the root canal do not combine the data obtained from the radiograph. In this regard, it is important to develop new methods and means of displaying electrometric data on the radiograph to more accurately determine the location of the physiological apex of the root. Objectives: Development of a method of segmentation of the dental radiograph to determine the area of apical narrowing. Materials and methods: principles of endodontic tooth preparation; methods for determining the working length of the root canal (radiological, electrometric); threshold segmentation method; method of segmentation of bone structures on tomographic images. Results: As a result of the performed work, the structures of the root canals of the tooth were segmented and their length was determined. Comparison of electronic determination of working length with radiological led to the fact that in some cases, X-ray and electronic length do not match. With lateral curvature of the canal, the X-ray may show a shorter working length than apexlocation devices, so the electronic working length is usually more accurate than the length determined by X-ray. Conclusions: The advantage of measuring the length of the root canal with an apex locator is much greater accuracy (about 0.5 mm) compared to the method of radiography, but the combination of these two methods is more reliable, which requires further statistical studies. Particular attention should be to the peculiarities of processing and segmentation methodsof the obtained diagnostic images to ensure the maximum quality of visualization of the contours of the root canals.

Multi-objective optimization of a chip-attached micro pin fin liquid cooling system

This study explores heat transfer from a chip-attached micro pin fin cooling device undergoing water jet impingement where the silicon chip is in direct contact with the cooling liquid. Directly attaching the micro pin fins onto the chip surface is a new packaging technique that eliminates the contact and sequential conduction resistance of thermal interfaces, which are still used in conventional electronic packages. A multi-objective optimization is solved to minimize the cooling device’s thermal resistance and pressure drop as response parameters. The influence of design parameters, such as pin fin cross section, fin spacing and the fin height profile, on the response parameters is investigated using computational fluid dynamics (CFD) and full factorial design of experiment (FFD). The optimization study is accomplished in two parts. In the first part, the shape optimization is performed by a traditional weighted sum approach using an iterative JAYA algorithm. The shape optimization problem is also solved using modern optimization algorithm of NSGA-II. The results of both approaches are in a close agreement. In the second part, an optimization of the fin height profile is solved using an Artificial Neural Network (ANN) combined with an elitist NSGA-II. The overall analysis demonstrates that the optimized cooling device can surpass its initial working design in thermal and hydraulic performance. Hence, there is excellent potential for the use of lightweight and compact designs in Heterogeneous Integration (HI) applications.

A review of seismic attribute taxonomies, discussion of their historical use, and presentation of a seismic attribute communication framework using data analysis concepts

Beginning in the 1970s, seismic attributes have grown from a few simple measurements of wavelet amplitude, frequency, and phase to an expanded attribute toolbox that measures not only wavelet properties but also their context within the 3D seismic volume. When using multiple seismic attributes, the interpreter must understand not only each individual attribute but also the relationships between them. Researchers communicate these relationships via seismic attribute taxonomies, which group attributes by their signal property, mathematical formulation, or their interpretive value. The first attempts to organize seismic attributes began in the 1990s, and with new attributes and their increasing breadth of applications, continues to this day. Most scientific papers that use seismic attributes focus on a specific application, new algorithms, or a novel interpretation workflow, rather than how a specific attribute fits within the greater whole, leading to confusion for the less experienced interpreter. We have analyzed more than 2100 citing works, identified the 231 papers that discuss the taxonomies specifically, and found out how the authors use those citations. The result is a list of more than a dozen seismic attribute classification systems, which we reduce to a smaller subset by including only those that apply to general use. An optimal seismic attribute taxonomy should not only be useful to the interpretation community today, but it should also adapt to the ever-changing needs of the profession, including changes appropriate for their use in modern machine-learning algorithms. The adaptability of prior work to modern workflows remains a shortcoming. However, as we develop our work in two parts — the first covering the evolution of seismic attribute taxonomies and their use through time and the second proposing a new seismic attribute communication framework for the larger community — we link attributes together via data analysis principles and provide an extensible model as the profession and research expand.

Measuring breathing induced oesophageal motion and its dosimetric impact.

Stereotactic body radiation therapy allows for a precise dose delivery. Organ motion bears the risk of undetected high dose healthy tissue exposure. An organ very susceptible to high dose is the oesophagus. Its low contrast on CT and the oblong shape render motion estimation difficult. We tackle this issue by modern algorithms to measure oesophageal motion voxel-wise and estimate motion related dosimetric impacts. Oesophageal motion was measured using deformable image registration and 4DCT of 11 internal and 5 public datasets. Current clinical practice of contouring the organ on 3DCT was compared to timely resolved 4DCT contours. Dosimetric impacts of the motion were estimated by analysing the trajectory of each voxel in the 4D dose distribution. Finally an organ motion model for patient-wise comparisons was built. Motion analysis showed mean absolute maximal motion amplitudes of 4.55±1.81mm left-right, 5.29±2.67mm anterior-posterior and 10.78±5.30mm superior-inferior. Motion between cohorts differed significantly. In around 50% of the cases the dosimetric passing criteria was violated. Contours created on 3DCT did not cover 14% of the organ for 50% of the respiratory cycle and were around 38% smaller than the union of all 4D contours. The motion model revealed that the maximal motion is not limited to the lower part of the organ. Our results showed motion amplitudes higher than most reported values in the literature and that motion is very heterogeneous across patients. Individual motion information should be considered in contouring and planning.

Finite control set model predictive direct current control strategy with constraints applying to drive three-phase induction motor

In this, work the finite control set (FCS) model predictive direct current control strategy with constraints, is applied to drive three-phase induction motor (IM) using the well-known field-oriented control. As a modern algorithm approach of control, this kind of algorithm decides the suitable switching combination that brings the error between the desired command currents and the predicated currents, as low as possible, according to the process of optimization. The suggested algorithm simulates the constraints of maximum allowable current and the accepted deviation, between the desired command and actual currents. The new constraints produce an improvement in system performance, with the predefined error threshold. This can be applied by avoiding the switching combination that exceeds the limited values. The additional constraints are more suitable for loads that require minimum distortion in harmonic and offer protection from maximum allowable currents. This approach is valuable especially in electrical vehicle (EV) applications since its result offers more reliable system performance with low total harmonics distortion (THD), low motor torque ripple, and better speed tracking.

СОВЕРШЕНСТВОВАНИЕ ПРОЦЕССНОГО ПОДХОДА В МАРШРУТИЗАЦИИ ПАЦИЕНТОВ СО ЗЛОКАЧЕСТВЕННЫМИ НОВООБРАЗОВАНИЯМИ ЧЕЛЮСТНО-ЛИЦЕВОЙ ОБЛАСТИ НА ТЕРРИТОРИИ ПЕНЗЕНСКОЙ ОБЛАСТИ В 2021 ГОДУ

The worldwide concern about oncological morbidity is due to the steady increase in the detection rate of malignant neoplasms, which has a tendency to increase. More than 7 million people die from oncology every year in the world. The system of organizing the provision of medical care to the population with oncological diseases in the Russian Federation is currently undergoing a stage of improvement. New programs are aimed at early diagnosis and prevention of malignant neoplasms. Each region has the right to create and implement routing taking into account the territorial characteristics and the existing material and technical base and personnel. The routing system, created for a specific region, determines the order of treatment of patients and the functions of all links in the system of providing medical care to patients with oncopathology. An optimized system for routing patients according to the "Oncology" profile for patients with malignant neoplasms of the maxillofacial region should be structured in accordance with the regional healthcare system. A complex system of interaction, an information system capable of containing and processing a set of information, a trained interested staff dictate the need for making management decisions in the direction of optimizing the oncological service. Management decisions and technologies should be focused on improving the quality and speed of patient admission, providing highly qualified medical care using modern equipment and algorithms. The staff, in turn, determines the basis of the health care system, in connection with which it is necessary to continue education, identify and search for specialists that a specific organization needs, on which the functioning of the entire service for providing medical care to patients with oncopathology of the maxillofacial region depends. In general, the optimization of the system of providing medical care to patients with malignant neoplasms of the maxillofacial region dictates the need to combine organizational measures and a clinical approach. Unambiguously, the multi-component system of integration of various directions and departments in the process of organizing an oncological service predetermines the need to use modern management technologies and the use of all available resources in order to improve the quality of medical care.

RETRACTED ARTICLE: News propaganda of rural rainfall slope stability and environmental protection based on image reconstruction

Image resolution restoration technology is one of the main researches in the field of image processing. In recent years, image resolution reconstruction algorithms based on convolutional neural networks have attracted widespread attention. However, many modern models and algorithms are not enough to use the image function, and the reconstruction effect is not ideal. In this article, the image resolution restoration algorithm is explored and improved to achieve high-resolution image restoration. In this paper, the typical representative landslide of loess mudstone and typical landslide of accumulation layer are used as the research object. The SEEP/W module, SLOPE module, and FLAC-3D software in GeoStudio are used to analyze the landslide based on the step-by-step coupling path of rainfall infiltration and landslide deformation. Stability trend and deformation failure mechanism under different rainfall conditions. China attaches great importance to the ecological environment, and protecting the environment has become an important task for the country and every citizen. Human behavior will more or less affect the environment. In order to make a good contribution to environmental protection, human cognition must be improved. In addition, in order to raise people's awareness of environmental protection, it is necessary for the country to increase mainstream media propaganda. News media has a wide range of influence and high transparency, which can help promote environmental work. As we all know, the establishment of ecological civilization is inseparable from environmental protection, and environmental protection is inseparable from the support of news and propaganda. Therefore, based on image reconstruction, this article studies the stability of rural rainfall slopes, discusses the importance of news propaganda to environmental protection, and analyzes how to use news propaganda to carry out environmental work.

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors.

The early detection of skin cancer, especially through the examination of lesions with malignant characteristics, has been reported to significantly decrease the potential fatalities. Segmentation of the regions that contain the actual lesions is one of the most widely used steps for achieving an automated diagnostic process of skin lesions. However, accurate segmentation of skin lesions has proven to be a challenging task in medical imaging because of the intrinsic factors such as the existence of undesirable artifacts and the complexity surrounding the seamless acquisition of lesion images. In this paper, we have introduced a novel algorithm based on gamma correction with clustering of keypoint descriptors for accurate segmentation of lesion areas in dermoscopy images. The algorithm was tested on dermoscopy images acquired from the publicly available dataset of Pedro Hispano hospital to achieve compelling equidistant sensitivity, specificity, and accuracy scores of 87.29%, 99.54%, and 96.02%, respectively. Moreover, the validation of the algorithm on a subset of heavily noised skin lesion images collected from the public dataset of International Skin Imaging Collaboration has yielded the equidistant sensitivity, specificity, and accuracy scores of 80.59%, 100.00%, and 94.98%, respectively. The performance results are propitious when compared to those obtained with existing modern algorithms using the same standard benchmark datasets and performance evaluation indices.

Detection and localization of cotton based on deep neural networks

Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

AbstractA multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F1‐score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state‐of‐the‐art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differential equations (PDEs) as well as identification of the equation parameters. The performance of existing PINN approaches, however, may degrade in the presence of sharp gradients, as a result of the inability of the network to capture the solution behavior globally. We posit that this shortcoming may be remedied by introducing long-range (nonlocal) interactions into the network’s input, in addition to the short-range (local) space and time variables. Following this ansatz, here we develop a nonlocal PINN approach using the Peridynamic Differential Operator (PDDO)—a numerical method which incorporates long-range interactions and removes spatial derivatives in the governing equations. Because the PDDO functions can be readily incorporated in the neural network architecture, the nonlocality does not degrade the performance of modern deep-learning algorithms. We apply nonlocal PDDO-PINN to the solution and identification of material parameters in solid mechanics and, specifically, to elastoplastic deformation in a domain subjected to indentation by a rigid punch, for which the mixed displacement–traction boundary condition leads to localized deformation and sharp gradients in the solution. We document the superior behavior of nonlocal PINN with respect to local PINN in both solution accuracy and parameter inference, illustrating its potential for simulation and discovery of partial differential equations whose solution develops sharp gradients.